record_id,study_id,model_id,table,column,page,var_name_printed,taxonomy_id,taxonomy_group,x_transform,x_unit,beta,se,t,p,p_stars,ci_lo,ci_hi,confidence,notes,dep_var,dep_transform,estimator,n_obs,r2,paren,sample_subset,family,es,es_se,es_formula,se_source,partial_r,partial_r_se,partial_r_formula,t_used,df_used,x_unit_norm,harm_flags,year,country,continent,income_group,sample_start,sample_end,property_type,price_type,document_type,time_fe,location_fe,se_type,ai_methods,data_mid_year,estimator_class,pub_type,usable,usable_partial_r,extreme
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,No. of rooms,bedrooms,A_structural,level,count,0.0499,0.0029,,,***,,,medium,Total number of rooms (jian) of the estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",semi_elasticity,4.99,0.29,100*b/1 (per unit count),reported,0.6145017307898943,0.035712525436687244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.20689655172414,488.0,count,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,No. of rooms,bedrooms,A_structural,level,count,0.0502,0.0035,,,***,,,medium,Total number of rooms (jian) of the estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",semi_elasticity,5.0200000000000005,0.35000000000000003,100*b/1 (per unit count),reported,0.6249521663145041,0.04357236219324231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.342857142857143,321.0,count,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,No. of rooms,bedrooms,A_structural,level,count,0.0626,0.0039,,,***,,,medium,Total number of rooms (jian) of the estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",semi_elasticity,6.260000000000001,0.38999999999999996,100*b/1 (per unit count),reported,0.5918013878394601,0.036869415536324196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.05128205128205,478.0,count,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,No. of rooms,bedrooms,A_structural,level,count,0.0682,0.0052,,,***,,,medium,Total number of rooms (jian) of the estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",semi_elasticity,6.819999999999999,0.52,100*b/1 (per unit count),reported,0.5943066832462751,0.04531370605396819,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.115384615384615,315.0,count,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,No. of Courtyards,other,other,level,count,0.32,0.0454,,,***,,,medium,Number of courtyards (size measure specific to Beijing courtyard houses); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",semi_elasticity,32.0,4.54,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.3039708069512409,0.04312585823620731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.048458149779735,488.0,count,unit_not_harmonisable,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,No. of Courtyards,other,other,level,count,0.33,0.0471,,,***,,,medium,Number of courtyards (size measure specific to Beijing courtyard houses); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",semi_elasticity,33.0,4.71,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.3641998977863258,0.05198125813859377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.006369426751593,321.0,count,unit_not_harmonisable,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,No. of Courtyards,other,other,level,count,0.269,0.0459,,,***,,,medium,Number of courtyards (size measure specific to Beijing courtyard houses); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",semi_elasticity,26.900000000000002,4.590000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.25891536161271456,0.04417923828261561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.860566448801743,478.0,count,unit_not_harmonisable,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,No. of Courtyards,other,other,level,count,0.254,0.0484,,,***,,,medium,Number of courtyards (size measure specific to Beijing courtyard houses); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",semi_elasticity,25.4,4.84,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.2835516604155076,0.0540311037957109,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.247933884297521,315.0,count,unit_not_harmonisable,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Red contract,other,other,binary,binary,0.079,0.0948,,,,,,medium,Officially registered (red) vs unregistered (white) contract; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,7.735232474270015,10.259296973226583,100*(exp(b - se^2/2) - 1),reported,0.037696415263971265,0.045235698316765514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Red contract,other,other,binary,binary,0.0958,0.121,,,,,,medium,Officially registered (red) vs unregistered (white) contract; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,9.251185588786814,13.316521103758213,100*(exp(b - se^2/2) - 1),reported,0.04414728430763382,0.055760139887512446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7917355371900826,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Red contract,other,other,binary,binary,0.0242,0.094,,,,,,medium,Officially registered (red) vs unregistered (white) contract; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,1.9978960374892774,9.630254846548896,100*(exp(b - se^2/2) - 1),reported,0.0117745266448832,0.04573576465367854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2574468085106383,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Red contract,other,other,binary,binary,0.105,0.117,,,,,,medium,Officially registered (red) vs unregistered (white) contract; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,10.313430915724119,12.995314141161751,100*(exp(b - se^2/2) - 1),reported,0.05050026620924781,0.05627172520459042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8974358974358974,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Close to commercial center,commercial_distance,D_location,binary,binary,-0.261,0.0851,,,***,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,-23.25032276985106,6.555090625924323,100*(exp(b - se^2/2) - 1),reported,-0.137516647889639,0.04483780358393976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.066980023501763,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Close to commercial center,commercial_distance,D_location,binary,binary,-0.264,0.0944,,,***,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,-23.54406926725341,7.2496702143593055,100*(exp(b - se^2/2) - 1),reported,-0.15422406320285761,0.05514678623617333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7966101694915255,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Close to commercial center,commercial_distance,D_location,binary,binary,-0.245,0.0842,,,***,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-22.006509663062722,6.59037221199653,100*(exp(b - se^2/2) - 1),reported,-0.1319251170632093,0.045339162680498875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9097387173396676,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Close to commercial center,commercial_distance,D_location,binary,binary,-0.216,0.0917,,,**,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-19.764525622257057,7.388592718179809,100*(exp(b - se^2/2) - 1),reported,-0.13156416069053833,0.05585385895982576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3555070883315157,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Far from commercial center,commercial_distance,D_location,binary,binary,-0.318,0.127,,,**,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,-27.824136750146078,9.240555412865364,100*(exp(b - se^2/2) - 1),reported,-0.1126267118021554,0.044979850310923705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5039370078740157,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Far from commercial center,commercial_distance,D_location,binary,binary,-0.853,0.215,,,***,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,-58.360148144807766,9.161894084860197,100*(exp(b - se^2/2) - 1),reported,-0.21620355655828638,0.05449444860496082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.967441860465116,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,True
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Far from commercial center,commercial_distance,D_location,binary,binary,-0.278,0.128,,,**,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-24.888161262898944,9.693399315383495,100*(exp(b - se^2/2) - 1),reported,-0.09885269432673126,0.045514909618063315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.171875,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Far from commercial center,commercial_distance,D_location,binary,binary,-0.817,0.216,,,***,,,medium,Reference: inside commercial district; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-56.84310544025713,9.541906561340475,100*(exp(b - se^2/2) - 1),reported,-0.20843377012776135,0.0551061130325538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.782407407407407,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Forbidden city,cbd_distance,D_location,binary,binary,-0.0142,0.156,,,,,,medium,Adjacent to Forbidden City (political centre); reference: elsewhere in Inner City; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,-2.602339973814116,15.38004537380447,100*(exp(b - se^2/2) - 1),reported,-0.0041205021791301645,0.04526748872847222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09102564102564104,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Forbidden city,cbd_distance,D_location,binary,binary,-0.0428,0.154,,,,,,medium,Adjacent to Forbidden City (political centre); reference: elsewhere in Inner City; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-5.319109876726092,14.754786069170056,100*(exp(b - se^2/2) - 1),reported,-0.012710833022198737,0.045735240313518825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2779220779220779,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Outer city,cbd_distance,D_location,binary,binary,-0.302,0.107,,,***,,,medium,Located in Outer City (vs Inner City); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,-26.48821849281302,7.910917294317992,100*(exp(b - se^2/2) - 1),reported,-0.12673517364624606,0.04490285953691499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.822429906542056,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,True
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Outer city,cbd_distance,D_location,binary,binary,-0.22,0.108,,,**,,,medium,Located in Outer City (vs Inner City); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-20.214787043521763,8.667203017994767,100*(exp(b - se^2/2) - 1),reported,-0.09277010668104858,0.04554168873433294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.037037037037037,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Clay material,roof_materials,A_structural,binary,binary,-0.675,0.204,,,***,,,medium,Built with clay (vs brick and tile); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,-50.132864129883515,10.386790980394004,100*(exp(b - se^2/2) - 1),reported,-0.14813095613116642,0.04476846674186362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.308823529411765,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Clay material,roof_materials,A_structural,binary,binary,-0.679,0.257,,,***,,,medium,Built with clay (vs brick and tile); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,-50.93501293698479,13.03308327269835,100*(exp(b - se^2/2) - 1),reported,-0.14588571892443797,0.055217422332224673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.642023346303502,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,True
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Clay material,roof_materials,A_structural,binary,binary,-0.473,0.21,,,**,,,medium,Built with clay (vs brick and tile); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-39.045957048710456,13.085731494730814,100*(exp(b - se^2/2) - 1),reported,-0.10247911468237811,0.04549812702600296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2523809523809524,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Clay material,roof_materials,A_structural,binary,binary,-0.466,0.258,,,*,,,medium,Built with clay (vs brick and tile); parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-39.30336948957128,16.18968792842139,100*(exp(b - se^2/2) - 1),reported,-0.1012449962285544,0.05605409662439278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.806201550387597,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Commercial estate,other,other,binary,binary,0.235,0.111,,,**,,,medium,Commercial vs residential estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,25.71402518064343,14.040487332935097,100*(exp(b - se^2/2) - 1),reported,0.0954002737782933,0.04506140591230025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.117117117117117,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Commercial estate,other,other,binary,binary,0.151,0.15,,,,,,medium,Commercial vs residential estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,14.998626635057377,17.444949871227305,100*(exp(b - se^2/2) - 1),reported,0.05609817475361911,0.05572666366253555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0066666666666666,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Commercial estate,other,other,binary,binary,0.276,0.11,,,**,,,medium,Commercial vs residential estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,30.989895414813894,14.496326504300336,100*(exp(b - se^2/2) - 1),reported,0.11401478257614933,0.045440674215131975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5090909090909093,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Commercial estate,other,other,binary,binary,0.206,0.147,,,,,,medium,Commercial vs residential estate; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,21.554860301384803,18.062672098731085,100*(exp(b - se^2/2) - 1),reported,0.0787127426984169,0.05616880182848196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.401360544217687,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Broken,condition,A_structural,binary,binary,-0.333,0.143,,,**,,,medium,Property in poor repair; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,-29.052108433665314,10.249813776426494,100*(exp(b - se^2/2) - 1),reported,-0.10483314838351987,0.04501843909562564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.328671328671329,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Broken,condition,A_structural,binary,binary,-0.291,0.186,,,,,,medium,Property in poor repair; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,-26.530365412722922,13.903791611136493,100*(exp(b - se^2/2) - 1),reported,-0.08699173725448243,0.055602966080184654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.564516129032258,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Broken,condition,A_structural,binary,binary,-0.391,0.14,,,***,,,medium,Property in poor repair; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-33.02159955559637,9.46932218455005,100*(exp(b - se^2/2) - 1),reported,-0.12671264126600423,0.045370255184758554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7928571428571427,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Broken,condition,A_structural,binary,binary,-0.325,0.18,,,*,,,medium,Property in poor repair; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,-28.908328935500137,13.0054923655573,100*(exp(b - se^2/2) - 1),reported,-0.101209156553874,0.056054302091376365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8055555555555556,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,True,True,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c1,7,1,35,Having a well,other,other,binary,binary,0.756,0.271,,,***,,,medium,Presence of a water well; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,498.0,0.522,se,"All available contracts (Inner and Outer City); 8 period dummies (25-year periods) included, not reported",pct_premium,105.29535246365143,57.71595939395977,100*(exp(b - se^2/2) - 1),reported,0.12528729100382371,0.04491118500269342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7896678966789668,488.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c2,7,2,35,Having a well,other,other,binary,binary,0.64,0.335,,,*,,,medium,Presence of a water well; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,331.0,0.583,se,"Outer City contracts only; 8 period dummies (25-year periods) included, not reported",pct_premium,79.2995168547478,63.53210945671587,100*(exp(b - se^2/2) - 1),reported,0.10602971400782528,0.05549992842597105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9104477611940298,321.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c3,7,3,35,Having a well,other,other,binary,binary,0.507,0.269,,,*,,,medium,Presence of a water well; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,488.0,0.513,se,"All contracts, outliers (room number > 100 or = 1) dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,60.130592586774426,44.662145526678955,100*(exp(b - se^2/2) - 1),reported,0.08588828723668138,0.04556991965812089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8847583643122676,478.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d,008c3229443d60b36b8e1525b5af0589f63eb75d:T7-c4,7,4,35,Having a well,other,other,binary,binary,0.354,0.328,,,,,,medium,Presence of a water well; parentheses assumed standard errors (no explicit legend; consistent with star pattern),log price of transacted estate,log,OLS,325.0,0.583,se,"Outer City only, outliers dropped; 8 period dummies (25-year periods) included, not reported",pct_premium,35.01396074101493,46.731970116540346,100*(exp(b - se^2/2) - 1),reported,0.06069775751918053,0.05623973013076615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0792682926829267,315.0,binary,,2012.0,China,Asia,Upper middle income,,,mixed,transaction,working_paper,True,False,unknown,,2009.0,ols,other,False,False,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,mk,living_area,A_structural,level,m2,0.002,,7.75,,***,,,high,"dwelling size in m2 (mean 163.9); robust SE printed as 0.000 (rounded) -> se null; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",semi_elasticity,0.2,0.025806451612903226,100*b/1 (X per m2->m2),|b/t|,0.2391630745985124,0.030859751561098374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.75,990.0,m2,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,yas,age,A_structural,level,years,-0.019,0.002,-11.65,,***,,,high,"age of dwelling in years; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",semi_elasticity,-1.9,0.2,100*b/1 (per year),reported,-0.3472244127837892,0.029804670625217956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.65,990.0,years,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,oda,bedrooms,A_structural,level,count,0.096,0.02,4.9,,***,,,high,"number of rooms (oda sayisi), all rooms; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",semi_elasticity,9.6,2.0,100*b/1 (per unit count),reported,0.15387744302758405,0.03140355980154776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9,990.0,count,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,kbk,floor_level,A_structural,level,count,0.024,0.005,4.69,,***,,,high,"floor on which the dwelling is located; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",semi_elasticity,2.4,0.5,100*b/1 (per unit count),reported,0.1474291709350446,0.031434791244145964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.69,990.0,count,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,bks,storeys,A_structural,level,count,0.023,0.004,5.35,,***,,,high,"number of floors in the building; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",semi_elasticity,2.3,0.4,100*b/1 (per unit count),reported,0.16762822624471183,0.03133237873732932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.35,990.0,count,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,bs,bathrooms,A_structural,level,count,0.184,0.028,6.63,,***,,,high,"number of bathrooms; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",semi_elasticity,18.4,2.8000000000000003,100*b/1 (per unit count),reported,0.206187489378034,0.031099168835299243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.63,990.0,count,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,kombi,heating,A_structural,dummy,binary,0.05,0.051,0.97,,,,,high,"1 if heating by individual combi boiler (kombi); reference = other heating types; columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,4.990480693746169,5.361482591517722,100*(exp(b - se^2/2) - 1),reported,0.03081398432884509,0.031766994153448545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.97,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,soba,heating,A_structural,dummy,binary,0.007,0.091,0.08,,,,,high,"1 if heating by stove (soba); columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,0.28635922698105887,9.163923471128323,100*(exp(b - se^2/2) - 1),reported,0.002542558686296684,0.031781983578708546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.08,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,kalorifer,heating,A_structural,dummy,binary,0.065,0.113,0.56,,,,,high,"1 if central heating (kalorifer); columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,6.036745106268393,12.058896975541376,100*(exp(b - se^2/2) - 1),reported,0.017795150091572516,0.03177705373495092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,yer,heating,A_structural,dummy,binary,-0.032,0.132,-0.25,,,,,high,"1 if underfloor heating (yerden isitma); columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,-3.9894399452920837,12.784286883445409,100*(exp(b - se^2/2) - 1),reported,-0.007945270783315908,0.03178108313326363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,site,other,other,dummy,binary,-0.027,0.036,-0.76,,,,,high,"1 if dwelling is inside a gated housing complex (site); columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,-2.7269292245166055,3.504100469487612,100*(exp(b - se^2/2) - 1),reported,-0.02414734242694102,0.03177281898281713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.76,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,False,False,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,emlk,other,other,dummy,binary,-0.047,0.026,-1.88,,*,,,high,"1 if listed by a real-estate agent (reference: sold by owner, shbn); columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,-4.623502945677382,2.4806272337349644,100*(exp(b - se^2/2) - 1),reported,-0.05964394965259199,0.03172550513435744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.88,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,False,False,False
00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78,00a2fd8a3af8f6e0e127c590fbea4879d95d7c78:T4-c1,4,1,11,daire,dwelling_type,A_structural,dummy,binary,-0.34,0.07,-5.91,,***,,,high,"1 if apartment flat (daire), reference = detached house (mustakil); columns: Katsayi (coef+stars), t istatistik, Direncli Standart Hata (White robust SE); table note says *,**,*** = 1%,5%,10% but t-values show the conventional reading (*** = 1%, * = 10%) is intended","lnp (log of listing price, TL)",log,OLS,1000.0,0.6664,se,"Full sample of 1,000 listings, five provinces; stepwise variable selection (AIC/R2), VIF>5 variables dropped; robust SE in separate column",pct_premium,-28.997138007097334,4.982392259338267,100*(exp(b - se^2/2) - 1),reported,-0.18460386130862488,0.03123584793716157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.91,990.0,binary,,2021.0,Turkey,Asia,Upper middle income,2021.0,2021.0,mixed,listing,journal,False,True,robust,,2021.0,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(SQM),living_area,A_structural,log,m2,0.777,0.005,,,***,,,high,"floor area in m2, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,0.777,0.005,b (log-log),reported,0.8589548098449802,0.005527379728732177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),155.4,8582.0,m2,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,LIFT,accessibility,B_interior,binary,binary,0.026,0.004,,,***,,,high,"building has a lift; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,2.6332737778697046,0.4105363793893768,100*(exp(b - se^2/2) - 1),reported,0.06999269049417632,0.010768106229873281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,FIREPLACE,fireplace,B_interior,binary,binary,0.108,0.01,,,***,,,high,"stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,11.399204439173483,1.1140477453864677,100*(exp(b - se^2/2) - 1),reported,0.11579720493223722,0.010721963419651596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.799999999999999,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,BALCONY,terrace_balcony,C_exterior,binary,binary,0.033,0.004,,,***,,,high,"one or more balconies; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,3.3542270870065005,0.41342021569652226,100*(exp(b - se^2/2) - 1),reported,0.08870422908145113,0.010752027767448621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.25,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,GTERRACE,terrace_balcony,C_exterior,binary,binary,0.105,0.015,,,***,,,high,"garden terrace; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,11.058566244049217,1.6660659155335578,100*(exp(b - se^2/2) - 1),reported,0.07534726310073978,0.01076389472867711,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,ISHOUSE,dwelling_type,A_structural,binary,binary,0.133,0.013,,,***,,,high,"single family house (vs apartment); stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,14.21534822838957,1.4849249978301624,100*(exp(b - se^2/2) - 1),reported,0.10976949368794675,0.01072934900709254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.230769230769232,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,BUILTBEF21,age,A_structural,dummy,years,0.077,0.006,,,***,,,high,"built before 1921; reference 1931-1980; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,8.002263581019097,0.6480252458355603,100*(exp(b - se^2/2) - 1),reported,0.13722002596834962,0.010692469555975297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.833333333333332,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,BUILT21TO30,age,A_structural,dummy,years,0.086,0.011,,,***,,,high,"built 1921-1930; reference 1931-1980; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,8.974039701670765,1.1987869611356416,100*(exp(b - se^2/2) - 1),reported,0.0840950426286279,0.010756342661801243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.818181818181818,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,BUILT81TO90,age,A_structural,dummy,years,0.018,0.005,,,***,,,high,"built 1981-1990; reference 1931-1980; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,1.8150249432132615,0.5090814881948968,100*(exp(b - se^2/2) - 1),reported,0.038831178459139214,0.010786438460872004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5999999999999996,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,BUILT91TO05,age,A_structural,dummy,years,0.067,0.005,,,***,,,high,"built 1991-2005; reference 1931-1980; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,6.928211206466139,0.5346477390873001,100*(exp(b - se^2/2) - 1),reported,0.1431574911297339,0.010683394860427901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.4,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(CARTT_CBD),cbd_distance,D_location,log,minutes,-0.294,0.007,,,***,,,high,"average car travel time to Zürich CBD in minutes, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,-0.294,0.007,b (log-log),reported,-0.4129172757223733,0.009831363707675555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.0,8582.0,minutes,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,PTACC,employment_access,D_location,level,index,0.006,0.001,,,***,,,high,"regional public transport accessibility to employment (log-sum index); stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,semi_elasticity,0.6,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.06463206107370717,0.010772010178951196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.012,0.002,,,***,,,high,"Euclidean distance to next rail station in km, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,-0.012,0.002,b (log-log),reported,-0.06463206107370717,0.010772010178951196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,AUTOBAHN,highway_distance,D_location,binary,binary,-0.06,0.013,,,***,,,high,"Autobahn within 100 m; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,-5.8315042156716785,1.2242938936595231,100*(exp(b - se^2/2) - 1),reported,-0.04975942116321067,0.010781207918695645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.615384615384616,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,AIRNOISE,noise,E_environment,binary,binary,-0.039,0.006,,,***,,,high,"daily average aircraft noise above 52 dB; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,pct_premium,-3.826660221059519,0.57705042548782,100*(exp(b - se^2/2) - 1),reported,-0.06999269049417632,0.010768106229873281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.5,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.02,0.002,,,***,,,high,"jobs in hotel and restaurant industry within 1 km, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,0.02,0.002,b (log-log),reported,0.10732233407321959,0.010732233407321958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.028,0.002,,,***,,,high,"inhabitants per hectare, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,-0.028,0.002,b (log-log),reported,-0.14942740258847478,0.01067338589917677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.018,0.002,,,***,,,high,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,other,-0.018,0.002,not harmonised,reported,-0.09669596507497084,0.010743996119441207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.13,0.014,,,***,,,high,"local income tax level for individuals (index), log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,-0.13,0.014,b (log-log),reported,-0.0997356084648765,0.010740757834679007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.285714285714286,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,SLOPE (logit),other,other,other,share,0.016,0.002,,,***,,,high,"terrain slope (25 m raster), logit transform; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,other,0.016,0.002,not harmonised,reported,0.08603642800727886,0.010754553500909857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.008,0.001,,,***,,,high,"visibility of lake surface (>1 km2) in hectares, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,0.008,0.001,b (log-log),reported,0.08603642800727886,0.010754553500909857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(VIEW_ALL),view,E_environment,log,hectare,0.005,0.002,,,**,,,high,"total visibility of terrain surface in hectares, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,0.005,0.002,b (log-log),reported,0.026976628401263626,0.01079065136050545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c1,2,Model 1,14,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.037,0.003,,,***,,,high,"evening solar exposure index, log; stars: *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.854,se,Model 1: with Ln(CARTT_CBD); adj R2; quarterly time dummies,elasticity,0.037,0.003,b (log-log),reported,0.13196875303582847,0.010700169165067174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.333333333333332,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(SQM),living_area,A_structural,log,m2,0.776,0.005,,,***,,,high,"floor area in m2, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,0.776,0.005,b (log-log),reported,0.8586645444503753,0.005532632374035926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),155.2,8582.0,m2,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,LIFT,accessibility,B_interior,binary,binary,0.024,0.005,,,***,,,high,building has a lift,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,2.4277514341670248,0.5121451589453108,100*(exp(b - se^2/2) - 1),reported,0.05174457099579829,0.010780118957457977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,FIREPLACE,fireplace,B_interior,binary,binary,0.118,0.011,,,***,,,high,,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,12.51760361579004,1.2377685225040767,100*(exp(b - se^2/2) - 1),reported,0.11502777784526268,0.010722928443202454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.727272727272727,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,BALCONY,terrace_balcony,C_exterior,binary,binary,0.033,0.004,,,***,,,high,one or more balconies,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,3.3542270870065005,0.41342021569652226,100*(exp(b - se^2/2) - 1),reported,0.08870422908145113,0.010752027767448621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.25,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,GTERRACE,terrace_balcony,C_exterior,binary,binary,0.107,0.016,,,***,,,high,garden terrace,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,11.279180801152066,1.7806948071669213,100*(exp(b - se^2/2) - 1),reported,0.07200138940313028,0.010766562901402658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6875,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,ISHOUSE,dwelling_type,A_structural,binary,binary,0.125,0.013,,,***,,,high,single family house (vs apartment),"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,13.305270606790987,1.473092988986874,100*(exp(b - se^2/2) - 1),reported,0.10323941760862675,0.010736899431297181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.615384615384615,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,BUILTBEF21,age,A_structural,dummy,years,0.109,0.006,,,***,,,high,built before 1921; reference 1931-1980,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,11.51422775997968,0.6690974102048687,100*(exp(b - se^2/2) - 1),reported,0.1924362927989667,0.010592823456823856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.166666666666668,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,BUILT21TO30,age,A_structural,dummy,years,0.094,0.011,,,***,,,high,built 1921-1930; reference 1931-1980,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,9.849328506300692,1.208415720508891,100*(exp(b - se^2/2) - 1),reported,0.09185462115161774,0.010748945028380797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454547,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,BUILT81TO90,age,A_structural,dummy,years,0.018,0.005,,,***,,,high,built 1981-1990; reference 1931-1980,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,1.8150249432132615,0.5090814881948968,100*(exp(b - se^2/2) - 1),reported,0.038831178459139214,0.010786438460872004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5999999999999996,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,BUILT91TO05,age,A_structural,dummy,years,0.067,0.005,,,***,,,high,built 1991-2005; reference 1931-1980,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,6.928211206466139,0.5346477390873001,100*(exp(b - se^2/2) - 1),reported,0.1431574911297339,0.010683394860427901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.4,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,CARACC,employment_access,D_location,level,index,0.119,0.005,,,***,,,high,regional car accessibility to employment (log-sum index),"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,semi_elasticity,11.899999999999999,0.5,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.24883039845677182,0.010455058758687893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.799999999999997,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,PTACC,employment_access,D_location,level,index,0.011,0.001,,,***,,,high,regional public transport accessibility to employment (log-sum index),"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,semi_elasticity,1.0999999999999999,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.11791205092046077,0.010719277356405524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.012,0.003,,,***,,,high,"Euclidean distance to next rail station in km, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,-0.012,0.003,b (log-log),reported,-0.043138125634160994,0.010784531408540248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,AUTOBAHN,highway_distance,D_location,binary,binary,-0.067,0.013,,,***,,,high,Autobahn within 100 m,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,-6.488381931906673,1.2157537617378096,100*(exp(b - se^2/2) - 1),reported,-0.05554770766976833,0.010777913428462512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.153846153846154,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,AIRNOISE,noise,E_environment,binary,binary,-0.096,0.006,,,***,,,high,daily average aircraft noise above 52 dB,"Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,pct_premium,-9.155233613641279,0.5450784096412237,100*(exp(b - se^2/2) - 1),reported,-0.17019350567269406,0.010637094104543379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.032,0.002,,,***,,,high,"jobs in hotel and restaurant industry within 1 km, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,0.032,0.002,b (log-log),reported,0.17019350567269406,0.010637094104543379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.026,0.002,,,***,,,high,"inhabitants per hectare, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,-0.026,0.002,b (log-log),reported,-0.13896790499819134,0.010689838846014718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.023,0.002,,,***,,,high,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,other,-0.023,0.002,not harmonised,reported,-0.12319209270936812,0.010712355887771141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.5,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.223,0.015,,,***,,,high,"local income tax level for individuals (index), log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,-0.223,0.015,b (log-log),reported,-0.1584520325647806,0.010658208468482998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.866666666666667,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,True
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,SLOPE (logit),other,other,other,share,0.026,0.002,,,***,,,high,"terrain slope (25 m raster), logit transform","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,other,0.026,0.002,not harmonised,reported,0.13896790499819134,0.010689838846014718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.012,0.001,,,***,,,high,"visibility of lake surface (>1 km2) in hectares, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,0.012,0.001,b (log-log),reported,0.1284616931266385,0.01070514109388654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(VIEW_ALL),view,E_environment,log,hectare,-0.001,0.002,,,,,,high,"total visibility of terrain surface in hectares, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,-0.001,0.002,b (log-log),reported,-0.005397211333671679,0.010794422667343357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,True
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T2-c2,2,Model 2,14,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.018,0.003,,,***,,,high,"evening solar exposure index, log","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.836,se,Model 2: with CARACC instead of CARTT_CBD; adj R2,elasticity,0.018,0.003,b (log-log),reported,0.06463206107370716,0.010772010178951196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.999999999999999,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,True,True,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(SQM),living_area,A_structural,log,m2,0.773,,,,,,,medium,"floor area in m2, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,0.773,,b (log-log),,,,,,8582.0,m2,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,LIFT,accessibility,B_interior,binary,binary,0.026,,,,,,,medium,building has a lift; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,2.634094847344204,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,FIREPLACE,fireplace,B_interior,binary,binary,0.085,,,,,,,medium,no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,8.871706669839874,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,BALCONY,terrace_balcony,C_exterior,binary,binary,0.03,,,,,,,medium,one or more balconies; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,GTERRACE,terrace_balcony,C_exterior,binary,binary,0.094,,,,,,,medium,garden terrace; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,9.855974591717365,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,ISHOUSE,dwelling_type,A_structural,binary,binary,0.137,,,,,,,medium,single family house (vs apartment); no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,14.682814852039815,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,BUILTBEF21,age,A_structural,dummy,years,0.082,,,,,,,medium,built before 1921; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,8.545580982954903,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,BUILT21TO30,age,A_structural,dummy,years,0.075,,,,,,,medium,built 1921-1930; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,7.788415088463152,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,BUILT81TO90,age,A_structural,dummy,years,0.019,,,,,,,medium,built 1981-1990; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,1.918164861740812,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,BUILT91TO05,age,A_structural,dummy,years,0.08,,,,,,,medium,built 1991-2005; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,8.328706767495863,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,CARACC,employment_access,D_location,level,index,0.113,,,,,,,medium,regional car accessibility to employment (log-sum index); no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",semi_elasticity,11.3,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,PTACC,employment_access,D_location,level,index,0.006,,,,,,,medium,regional public transport accessibility to employment (log-sum index); no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",semi_elasticity,0.6,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.013,,,,,,,medium,"Euclidean distance to next rail station in km, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,-0.013,,b (log-log),,,,,,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,AUTOBAHN,highway_distance,D_location,binary,binary,-0.076,,,,,,,medium,Autobahn within 100 m; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,-7.318379344061777,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,AIRNOISE,noise,E_environment,binary,binary,-0.083,,,,,,,medium,daily average aircraft noise above 52 dB; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.029,,,,,,,medium,"jobs in hotel and restaurant industry within 1 km, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,0.029,,b (log-log),,,,,,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.028,,,,,,,medium,"inhabitants per hectare, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,-0.028,,b (log-log),,,,,,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.014,,,,,,,medium,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",other,-0.014,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.201,,,,,,,medium,"local income tax level for individuals (index), log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,-0.201,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,SLOPE (logit),other,other,other,share,0.016,,,,,,,medium,"terrain slope (25 m raster), logit transform; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",other,0.016,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.01,,,,,,,medium,"visibility of lake surface (>1 km2) in hectares, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,0.01,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(VIEW_ALL),view,E_environment,log,hectare,0.008,,,,,,,medium,"total visibility of terrain surface in hectares, log; printed * = NOT significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,0.008,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c2,4,SARerr,16,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.012,,,,,,,medium,"evening solar exposure index, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_error,8592.0,,none,"spatial error model (k=9 nearest neighbours), Model 2 variables; no SE printed; * marks NOT significant at 0.05",elasticity,0.012,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(SQM),living_area,A_structural,log,m2,0.773,,,,,,,medium,"floor area in m2, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,0.773,,b (log-log),,,,,,8582.0,m2,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,LIFT,accessibility,B_interior,binary,binary,0.025,,,,,,,medium,building has a lift; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,2.531512052442886,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,FIREPLACE,fireplace,B_interior,binary,binary,0.088,,,,,,,medium,no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,9.19881220281975,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,BALCONY,terrace_balcony,C_exterior,binary,binary,0.03,,,,,,,medium,one or more balconies; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,GTERRACE,terrace_balcony,C_exterior,binary,binary,0.097,,,,,,,medium,garden terrace; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,10.186037362101064,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,ISHOUSE,dwelling_type,A_structural,binary,binary,0.141,,,,,,,medium,single family house (vs apartment); no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,15.142464798474409,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,BUILTBEF21,age,A_structural,dummy,years,0.075,,,,,,,medium,built before 1921; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,7.788415088463152,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,BUILT21TO30,age,A_structural,dummy,years,0.07,,,,,,,medium,built 1921-1930; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,7.250818125421654,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,BUILT81TO90,age,A_structural,dummy,years,0.02,,,,,,,medium,built 1981-1990; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,BUILT91TO05,age,A_structural,dummy,years,0.085,,,,,,,medium,built 1991-2005; reference 1931-1980; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,8.871706669839874,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,CARACC,employment_access,D_location,level,index,0.068,,,,,,,medium,regional car accessibility to employment (log-sum index); no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",semi_elasticity,6.800000000000001,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,PTACC,employment_access,D_location,level,index,0.002,,,,,,,medium,regional public transport accessibility to employment (log-sum index); printed * = NOT significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",semi_elasticity,0.2,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.016,,,,,,,medium,"Euclidean distance to next rail station in km, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,-0.016,,b (log-log),,,,,,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,AUTOBAHN,highway_distance,D_location,binary,binary,-0.079,,,,,,,medium,Autobahn within 100 m; no SE printed; unmarked = significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,-7.596007555491324,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,AIRNOISE,noise,E_environment,binary,binary,0.032,,,,,,,medium,daily average aircraft noise above 52 dB; printed * = NOT significant at 0.05,"Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",pct_premium,3.251750530511832,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.014,,,,,,,medium,"jobs in hotel and restaurant industry within 1 km, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,0.014,,b (log-log),,,,,,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.028,,,,,,,medium,"inhabitants per hectare, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,-0.028,,b (log-log),,,,,,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.013,,,,,,,medium,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",other,-0.013,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.002,,,,,,,medium,"local income tax level for individuals (index), log; printed * = NOT significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,-0.002,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,SLOPE (logit),other,other,other,share,0.007,,,,,,,medium,"terrain slope (25 m raster), logit transform; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",other,0.007,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.003,,,,,,,medium,"visibility of lake surface (>1 km2) in hectares, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,0.003,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(VIEW_ALL),view,E_environment,log,hectare,0.017,,,,,,,medium,"total visibility of terrain surface in hectares, log; no SE printed; unmarked = significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,0.017,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c3,4,SARmix,16,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.006,,,,,,,medium,"evening solar exposure index, log; printed * = NOT significant at 0.05","Ln(RENT), monthly net asking rent in CHF",log,spatial_lag,8592.0,,none,"SARmix (spatial lag with lagged regressors, spdep 'mixed' = spatial Durbin), rho 0.599; no SE; * marks NOT significant at 0.05",elasticity,0.006,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(SQM),living_area,A_structural,log,m2,0.772,,,,,,,medium,"floor area in m2, log; GWR mean of local coefficients; std. dev. across study area = 0.055 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,0.772,,b (log-log),,,,,,8582.0,m2,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,LIFT,accessibility,B_interior,binary,binary,0.022,,,,,,,medium,building has a lift; GWR mean of local coefficients; std. dev. across study area = 0.017 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,2.2243784470438177,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,FIREPLACE,fireplace,B_interior,binary,binary,0.096,,,,,,,medium,GWR mean of local coefficients; std. dev. across study area = 0.038 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,10.075906399397883,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,BALCONY,terrace_balcony,C_exterior,binary,binary,0.031,,,,,,,medium,one or more balconies; GWR mean of local coefficients; std. dev. across study area = 0.011 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,3.1485503886522714,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,GTERRACE,terrace_balcony,C_exterior,binary,binary,0.098,,,,,,,medium,garden terrace; GWR mean of local coefficients; std. dev. across study area = 0.073 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,10.296278510850776,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,ISHOUSE,dwelling_type,A_structural,binary,binary,0.138,,,,,,,medium,single family house (vs apartment); GWR mean of local coefficients; std. dev. across study area = 0.047 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,14.797555027417864,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,BUILTBEF21,age,A_structural,dummy,years,0.091,,,,,,,medium,built before 1921; reference 1931-1980; GWR mean of local coefficients; std. dev. across study area = 0.026 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,9.52690052584655,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,BUILT21TO30,age,A_structural,dummy,years,0.084,,,,,,,medium,built 1921-1930; reference 1931-1980; GWR mean of local coefficients; std. dev. across study area = 0.042 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,8.76288938088261,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,BUILT81TO90,age,A_structural,dummy,years,0.026,,,,,,,medium,built 1981-1990; reference 1931-1980; GWR mean of local coefficients; std. dev. across study area = 0.024 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,2.634094847344204,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,BUILT91TO05,age,A_structural,dummy,years,0.078,,,,,,,medium,built 1991-2005; reference 1931-1980; GWR mean of local coefficients; std. dev. across study area = 0.023 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,8.112265867008306,,100*(exp(b) - 1),,,,,,8582.0,years,year_built_not_age,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,CARACC,employment_access,D_location,level,index,0.011,,,,,,,medium,regional car accessibility to employment (log-sum index); GWR mean of local coefficients; std. dev. across study area = 0.098 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),semi_elasticity,1.0999999999999999,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,PTACC,employment_access,D_location,level,index,0.039,,,,,,,medium,regional public transport accessibility to employment (log-sum index); GWR mean of local coefficients; std. dev. across study area = 0.034 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),semi_elasticity,3.9,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.009,,,,,,,medium,"Euclidean distance to next rail station in km, log; GWR mean of local coefficients; std. dev. across study area = 0.016 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,-0.009,,b (log-log),,,,,,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,AUTOBAHN,highway_distance,D_location,binary,binary,-0.071,,,,,,,medium,Autobahn within 100 m; GWR mean of local coefficients; std. dev. across study area = 0.066 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,-6.853810787240788,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,AIRNOISE,noise,E_environment,binary,binary,-0.014,,,,,,,medium,daily average aircraft noise above 52 dB; GWR mean of local coefficients; std. dev. across study area = 0.032 (not a SE),"Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),pct_premium,-1.3902455737138109,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.025,,,,,,,medium,"jobs in hotel and restaurant industry within 1 km, log; GWR mean of local coefficients; std. dev. across study area = 0.017 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,0.025,,b (log-log),,,,,,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.029,,,,,,,medium,"inhabitants per hectare, log; GWR mean of local coefficients; std. dev. across study area = 0.014 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,-0.029,,b (log-log),,,,,,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.018,,,,,,,medium,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform; GWR mean of local coefficients; std. dev. across study area = 0.007 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),other,-0.018,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.16,,,,,,,medium,"local income tax level for individuals (index), log; GWR mean of local coefficients; std. dev. across study area = 0.268 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,-0.16,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,SLOPE (logit),other,other,other,share,0.01,,,,,,,medium,"terrain slope (25 m raster), logit transform; GWR mean of local coefficients; std. dev. across study area = 0.009 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),other,0.01,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.008,,,,,,,medium,"visibility of lake surface (>1 km2) in hectares, log; GWR mean of local coefficients; std. dev. across study area = 0.007 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,0.008,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(VIEW_ALL),view,E_environment,log,hectare,0.006,,,,,,,medium,"total visibility of terrain surface in hectares, log; GWR mean of local coefficients; std. dev. across study area = 0.024 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,0.006,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T4-c4,4,GWR,16,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.024,,,,,,,medium,"evening solar exposure index, log; GWR mean of local coefficients; std. dev. across study area = 0.027 (not a SE)","Ln(RENT), monthly net asking rent in CHF",log,GWR,8592.0,,none,GWR (adaptive kernel): mean of local coefficients; second column is std. dev. across study area (not SE),elasticity,0.024,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,spatial,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(CARTT_CBD),cbd_distance,D_location,log,minutes,-0.258,,,,***,,,medium,"average car travel time to Zürich CBD in minutes, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,-0.258,,b (log-log),,,,,,8582.0,minutes,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,PTACC05,employment_access,D_location,level,index,-0.012,,,,***,,,medium,"regional public transport accessibility to employment (2005 version); stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",semi_elasticity,-1.2,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.019,,,,***,,,medium,"Euclidean distance to next rail station in km, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,-0.019,,b (log-log),,,,,,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,AUTOBAHN,highway_distance,D_location,binary,binary,-0.061,,,,**,,,medium,"Autobahn within 100 m; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",pct_premium,-5.917676022399032,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,AIRNOISE,noise,E_environment,binary,binary,-0.009,,,,,,,medium,"daily average aircraft noise above 52 dB; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.0,,,,,,,medium,"jobs in hotel and restaurant industry within 1 km, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,0.0,,b (log-log),,,,,,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.093,,,,***,,,medium,"inhabitants per hectare, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,-0.093,,b (log-log),,,,,,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.087,,,,***,,,medium,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",other,-0.087,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.289,,,,***,,,medium,"local income tax level for individuals (index), log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,-0.289,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,SLOPE (logit),other,other,other,share,0.018,,,,***,,,medium,"terrain slope (25 m raster), logit transform; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",other,0.018,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.015,,,,***,,,medium,"visibility of lake surface (>1 km2) in hectares, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,0.015,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(VIEW_ALL),view,E_environment,log,hectare,0.007,,,,,,,medium,"total visibility of terrain surface in hectares, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,0.007,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c1,6,Model 3,25,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.071,,,,***,,,medium,"evening solar exposure index, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.235,none,"appendix: spatial variables only, with Ln(CARTT_CBD); stars only; adj R2",elasticity,0.071,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,CARACC05,employment_access,D_location,level,index,0.064,,,,***,,,medium,"regional car accessibility to employment (2005 version); stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",semi_elasticity,6.4,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,PTACC05,employment_access,D_location,level,index,-0.004,,,,,,,medium,"regional public transport accessibility to employment (2005 version); stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",semi_elasticity,-0.4,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,8582.0,index,unit_not_harmonisable,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(RAILSTATION),transit_distance,D_location,log,km,-0.015,,,,**,,,medium,"Euclidean distance to next rail station in km, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,-0.015,,b (log-log),,,,,,8582.0,km,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,AUTOBAHN,highway_distance,D_location,binary,binary,-0.064,,,,**,,,medium,"Autobahn within 100 m; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",pct_premium,-6.199500046927053,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,AIRNOISE,noise,E_environment,binary,binary,-0.053,,,,***,,,medium,"daily average aircraft noise above 52 dB; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",pct_premium,-5.161998751770181,,100*(exp(b) - 1),,,,,,8582.0,binary,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(HOTREST_JOBS),neighbourhood_amenities,F_neighbourhood,log,count,0.02,,,,***,,,medium,"jobs in hotel and restaurant industry within 1 km, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,0.02,,b (log-log),,,,,,8582.0,count,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(POP_DENS),density,F_neighbourhood,log,per_hectare,-0.091,,,,***,,,medium,"inhabitants per hectare, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,-0.091,,b (log-log),,,,,,8582.0,per_hectare,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,FOREIGNERS (logit),demographics,F_neighbourhood,other,share,-0.091,,,,***,,,medium,"fraction of foreigners (non NW-Europe/N-America/Australia) in hectare, logit transform; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",other,-0.091,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(TAXLEVEL),public_services,F_neighbourhood,log,index,-0.368,,,,***,,,medium,"local income tax level for individuals (index), log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,-0.368,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,SLOPE (logit),other,other,other,share,0.027,,,,***,,,medium,"terrain slope (25 m raster), logit transform; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",other,0.027,,not harmonised,,,,,,8582.0,share,nonstandard_x_transform,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(VIEW_LAKE),view,E_environment,log,hectare,0.018,,,,***,,,medium,"visibility of lake surface (>1 km2) in hectares, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,0.018,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(VIEW_ALL),view,E_environment,log,hectare,0.005,,,,,,,medium,"total visibility of terrain surface in hectares, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,0.005,,b (log-log),,,,,,8582.0,ha,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3,03ae226b461f39ce77988b8e944f5ba8259156d3:T6-c2,6,Model 4,25,Ln(SOLAR_EVE),sunlight,E_environment,log,index,0.056,,,,***,,,medium,"evening solar exposure index, log; stars only, no SE; *** p<0.01, ** p<0.05, * p<0.1","Ln(RENT), monthly net asking rent in CHF",log,OLS,8592.0,0.215,none,"appendix: spatial variables only, with CARACC05; stars only; adj R2",elasticity,0.056,,b (log-log),,,,,,8582.0,index,,2010.0,Switzerland,Europe,High income,2004.0,2005.0,rental,rent,journal,True,False,OLS,,2004.5,ols,journal,False,False,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T2-c1,2,1 (HMA),7,Effect of wood,roof_materials,A_structural,dummy,binary,0.017,0.025,,0.498,,,,high,wooden structure dummy (fully wooden or wooden-frame building); Std. err. printed in [brackets]; p-value from text (p.6-8); stars *p<0.05 **p<0.01 ***p<0.001; other hedonic coefficients not printed (Table 3 lists only which were significant),ln(transaction price of dwelling),log,OLS,27954.0,0.907,se,"Helsinki metropolitan area (Helsinki+Espoo+Vantaa), 137 postal-code dummies; housing, neighbourhood characteristics and distance to CBD included; apartment-building dwellings built 1990-2020, sold 1999-2018; stepwise OLS, SE clustered by postal code; only the wood coefficient is printed",pct_premium,1.6827514072139893,2.5428633058131016,100*(exp(b - se^2/2) - 1),reported,0.004067813499162229,0.005982078675238571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.68,27944.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T2-c2,2,2 (Helsinki),7,Effect of wood,roof_materials,A_structural,dummy,binary,0.085,0.02,,0.0,***,,,high,wooden structure dummy (fully wooden or wooden-frame building); Std. err. printed in [brackets]; p-value from text (p.6-8); stars *p<0.05 **p<0.01 ***p<0.001; other hedonic coefficients not printed (Table 3 lists only which were significant),ln(transaction price of dwelling),log,OLS,14867.0,0.91,se,"City of Helsinki, 79 postal codes; full controls; apartment-building dwellings built 1990-2020, sold 1999-2018; stepwise OLS, SE clustered by postal code; only the wood coefficient is printed",pct_premium,8.849934505794877,2.1774341333967975,100*(exp(b - se^2/2) - 1),reported,0.034846529414940805,0.008199183391750778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,14857.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T2-c3,2,3 (Espoo),7,Effect of wood,roof_materials,A_structural,dummy,binary,0.029,0.037,,0.443,,,,high,wooden structure dummy (fully wooden or wooden-frame building); Std. err. printed in [brackets]; p-value from text (p.6-8); stars *p<0.05 **p<0.01 ***p<0.001; other hedonic coefficients not printed (Table 3 lists only which were significant),ln(transaction price of dwelling),log,OLS,7422.0,0.891,se,"City of Espoo, 33 postal codes; full controls; apartment-building dwellings built 1990-2020, sold 1999-2018; stepwise OLS, SE clustered by postal code; only the wood coefficient is printed",pct_premium,2.872019444862506,3.8088709995579837,100*(exp(b - se^2/2) - 1),reported,0.009103545596555393,0.011614868519743086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7837837837837839,7412.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T2-c4,2,4 (Vantaa),7,Effect of wood,roof_materials,A_structural,dummy,binary,-0.011,0.022,,0.613,,,,high,wooden structure dummy (fully wooden or wooden-frame building); Std. err. printed in [brackets]; p-value from text (p.6-8); stars *p<0.05 **p<0.01 ***p<0.001; other hedonic coefficients not printed (Table 3 lists only which were significant),ln(transaction price of dwelling),log,OLS,5665.0,0.876,se,"City of Vantaa, 25 postal codes; distance to CBD excluded (multicollinearity); apartment-building dwellings built 1990-2020, sold 1999-2018; stepwise OLS, SE clustered by postal code; only the wood coefficient is printed",pct_premium,-1.1179044852767994,2.175932613305811,100*(exp(b - se^2/2) - 1),reported,-0.006648812719476,0.013297625438951998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,5655.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T4-c1,4,1 (Helsinki),8,Wood,roof_materials,A_structural,dummy,binary,-0.259,0.079,,,**,,,high,"wooden structure dummy; printed with decimal commas (e.g. '−0, 259', '[0, 079]'); stars *p<0.05 **p<0.01 ***p<0.001 (Table 2 legend); specification: wood only",ln(transaction price of dwelling),log,OLS,14888.0,0.003,se,"Helsinki only, gradually adding variable groups: wood only; SE clustered by 79 postal codes",pct_premium,-23.058169678425912,6.097401882045257,100*(exp(b - se^2/2) - 1),reported,-0.02686850960149904,0.008195414125553762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.278481012658228,14878.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T4-c2,4,2 (Helsinki),8,Wood,roof_materials,A_structural,dummy,binary,-0.167,0.07,,,*,,,high,"wooden structure dummy; printed with decimal commas (e.g. '−0, 259', '[0, 079]'); stars *p<0.05 **p<0.01 ***p<0.001 (Table 2 legend); specification: wood + age & size",ln(transaction price of dwelling),log,OLS,14888.0,0.574,se,"Helsinki only, gradually adding variable groups: wood + age & size; SE clustered by 79 postal codes",pct_premium,-15.587104012678743,5.923397279360318,100*(exp(b - se^2/2) - 1),reported,-0.01955523770297933,0.008196806222805707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3857142857142857,14878.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T4-c3,4,3 (Helsinki),8,Wood,roof_materials,A_structural,dummy,binary,0.043,0.024,,,,,,high,"wooden structure dummy; printed with decimal commas (e.g. '−0, 259', '[0, 079]'); stars *p<0.05 **p<0.01 ***p<0.001 (Table 2 legend); specification: + housing characteristics + CBD Euclidean distance",ln(transaction price of dwelling),log,OLS,14888.0,0.85,se,"Helsinki only, gradually adding variable groups: + housing characteristics + CBD Euclidean distance; SE clustered by 79 postal codes",pct_premium,4.363728402693212,2.5054509476414704,100*(exp(b - se^2/2) - 1),reported,0.014687168932021606,0.008197489636477175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7916666666666665,14878.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T4-c4,4,4 (Helsinki),8,Wood,roof_materials,A_structural,dummy,binary,0.041,0.02,,,*,,,high,"wooden structure dummy; printed with decimal commas (e.g. '−0, 259', '[0, 079]'); stars *p<0.05 **p<0.01 ***p<0.001 (Table 2 legend); specification: + neighbourhood characteristics",ln(transaction price of dwelling),log,OLS,14867.0,0.894,se,"Helsinki only, gradually adding variable groups: + neighbourhood characteristics; SE clustered by 79 postal codes",pct_premium,4.1643755960023565,2.083704211090959,100*(exp(b - se^2/2) - 1),reported,0.016816162099116762,0.008203005902008178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.05,14857.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f,03bfeba917085bc65a0ab641e5a03916b687b20f:T4-c5,4,5 (Helsinki),8,Wood,roof_materials,A_structural,dummy,binary,0.085,0.02,,,***,,,high,"wooden structure dummy; printed with decimal commas (e.g. '−0, 259', '[0, 079]'); stars *p<0.05 **p<0.01 ***p<0.001 (Table 2 legend); specification: + postal codes [79]; identical to Table 2 col 2",ln(transaction price of dwelling),log,OLS,14867.0,0.91,se,"Helsinki only, gradually adding variable groups: + postal codes [79]; identical to Table 2 col 2; SE clustered by 79 postal codes",pct_premium,8.849934505794877,2.1774341333967975,100*(exp(b - se^2/2) - 1),reported,0.034846529414940805,0.008199183391750778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,14857.0,binary,,2021.0,Finland,Europe,High income,1999.0,2018.0,condo_apartment,transaction,journal,False,True,clustered,,2008.5,ols,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,AREA,living_area,A_structural,level,m2,0.006,,,0.0,,,,medium,Area of apartment in square metres; p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,286.0,m2,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,AREA,living_area,A_structural,level,m2,0.006,,,0.0,,,,medium,Area of apartment in square metres; p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,286.0,m2,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,AGE,age,A_structural,level,years,-0.063,,,0.02,,,,medium,Age in years adjusted for major rehabilitation; magnitude per year seems large but recorded as printed; p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),semi_elasticity,-6.3,2.7081074461391577,100*b/1 (per year),|b/z(p)| (two-sided normal),-0.13627651569786828,0.05857959474528523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,286.0,years,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,True,True,True
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,AGE,age,A_structural,level,years,-0.084,,,0.0,,,,medium,Age in years adjusted for major rehabilitation; magnitude per year seems large but recorded as printed; p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",semi_elasticity,-8.4,,100*b/1 (per year),,,,,,286.0,years,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,BATH,bathrooms,A_structural,level,count,0.148,,,0.005,,,,medium,p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),semi_elasticity,14.799999999999999,5.272469525271241,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.16374311063194216,0.05833314599865084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,286.0,count,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,BATH,bathrooms,A_structural,level,count,0.128,,,0.002,,,,medium,p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",semi_elasticity,12.8,4.1420834202180865,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.17975293395632158,0.05816809745906533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,286.0,count,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,PENT,dwelling_type,A_structural,binary,binary,0.169,,,0.016,,,,medium,Penthouse; p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),pct_premium,18.12096781089658,8.307319192894656,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.14101872759968734,0.058540336893359184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.408915545815461,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,PENT,dwelling_type,A_structural,binary,binary,0.124,,,0.02,,,,medium,Penthouse; p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",pct_premium,13.04088996388506,6.033919929613821,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.13627651569786828,0.05857959474528523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3263478740408408,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,HEAT,heating,A_structural,binary,binary,0.112,,,0.029,,,,medium,Central heating; p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),pct_premium,11.704237644303994,5.73731226574661,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.12804938612267644,0.058644458978906876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1834865280065587,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,HEAT,heating,A_structural,binary,binary,0.084,,,0.03,,,,medium,Central heating; p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",pct_premium,8.681439396911417,4.210001022244586,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.12727654111504583,0.05865034121607055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1700903775845606,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,AIRCON,air_conditioning,A_structural,binary,binary,0.141,,,0.001,,,,medium,p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),pct_premium,15.03680407908925,4.933887143723051,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.19099117814265545,0.058042737144415116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,True,True,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,AIRCON,air_conditioning,A_structural,binary,binary,0.109,,,0.0,,,,medium,p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",pct_premium,11.516235034144785,,100*(exp(b) - 1),,,,,,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,ELEV,accessibility,B_interior,binary,binary,0.369,,,0.0,,,,medium,Elevator; p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),pct_premium,44.628760367473966,,100*(exp(b) - 1),,,,,,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,ELEV,accessibility,B_interior,binary,binary,0.295,,,0.0,,,,medium,Elevator; p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",pct_premium,34.31263586862767,,100*(exp(b) - 1),,,,,,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-OLS,2,OLS,14,FURNISH,other,other,binary,binary,0.124,,,0.003,,,,medium,Furnished apartment; p-value in parentheses (0.000 = <0.0005); no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,OLS,296.0,,p,Structural characteristics only; cross-validation R2 0.596 (not in-sample),pct_premium,13.102817052106097,4.729864008720778,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.17284479505582645,0.05824125964084937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,ols,journal,False,False,False
03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee,03c7319d6d03a3326e49bd775951ee9918110cee:T2-RK,2,RK,14,FURNISH,other,other,binary,binary,0.113,,,0.0,,,,medium,Furnished apartment; p-value in parentheses; no SE or t printed,"log(PRICE) (asking price, thousand EUR)",log,other,296.0,,p,"Regression-kriging (GLS regression + kriging of residuals, spherical variogram); cross-validation R2 0.733",pct_premium,11.963193294858598,,100*(exp(b) - 1),,,,,,286.0,binary,,2020.0,Spain,Europe,High income,2013.0,2013.0,condo_apartment,listing,journal,False,False,unknown,,2013.0,other,journal,False,False,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,log(Median Household Income),income,F_neighbourhood,log,currency,0.041,0.019,,,**,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,elasticity,0.041,0.019,b (log-log),reported,0.011134615671620387,0.005159943847824082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1578947368421053,37554.0,currency,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Frac Black,demographics,F_neighbourhood,level,share,-0.12,0.08,,,,,,high,Share Black residents,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-12.0,8.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.007740163744896053,0.0051601091632640355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Frac Hispanic,demographics,F_neighbourhood,level,share,-0.166,0.057,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-16.6,5.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.015026439784200927,0.005159681130719594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.912280701754386,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Frac Asian,demographics,F_neighbourhood,level,share,-0.069,0.088,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-6.9,8.799999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0040460827691377625,0.00516022150266845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7840909090909092,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2005,school_quality,F_neighbourhood,level,index,-0.156,0.027,,,***,,,high,"Standardized school test score, Illinois, year 2005 interaction",log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-15.6,2.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.029801614405556127,0.005157971724038561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.777777777777778,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2006,school_quality,F_neighbourhood,level,index,-0.175,0.027,,,***,,,high,year 2006 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-17.5,2.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.03342746237142695,0.005157379908734445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.481481481481481,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2007,school_quality,F_neighbourhood,level,index,-0.143,0.027,,,***,,,high,year 2007 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-14.299999999999999,2.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.027320084353675576,0.005158337605239446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.296296296296296,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2008,school_quality,F_neighbourhood,level,index,0.025,0.028,,,,,,high,year 2008 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,2.5,2.8000000000000003,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.0046073294389529845,0.005160208971627342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8928571428571429,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2009,school_quality,F_neighbourhood,level,index,0.128,0.027,,,***,,,high,year 2009 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,12.8,2.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.024456155613807165,0.005158720324787449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7407407407407405,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2010,school_quality,F_neighbourhood,level,index,0.114,0.027,,,***,,,high,year 2010 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,11.4,2.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.021782610672973202,0.005159039369914705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.222222222222222,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2011,school_quality,F_neighbourhood,level,index,0.081,0.026,,,***,,,high,year 2011 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,8.1,2.6,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.01607412927176913,0.005159597050197499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1153846153846154,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Average Test Score (IL) x2012,school_quality,F_neighbourhood,level,index,0.077,0.027,,,***,,,high,year 2012 interaction,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,7.7,2.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.014714714413129054,0.005159705053954343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8518518518518516,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Share of Schools with B,school_quality,F_neighbourhood,level,share,0.018,0.061,,,,,,high,Indiana school grades (ref. A),log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,1.7999999999999998,6.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0015226990109405549,0.005160257759298547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.29508196721311475,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Share of Schools with C,school_quality,F_neighbourhood,level,share,-0.205,0.053,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-20.5,5.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01995553613068818,0.005159236170373041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8679245283018866,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Share of Schools with D,school_quality,F_neighbourhood,level,share,-0.111,0.061,,,*,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-11.1,6.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009389574180945466,0.005160036261600661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.819672131147541,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Share of Schools with F,school_quality,F_neighbourhood,level,share,-0.104,0.145,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-10.4,14.499999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.003701129333691646,0.005160228397935469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7172413793103448,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Median Test Score (WI),school_quality,F_neighbourhood,level,index,-0.045,1.22,,,,,,high,Wisconsin,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,-4.5,122.0,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.0001903375935794287,0.0051602636481534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.036885245901639344,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Median Year Structure Built,age,A_structural,level,years,0.004,0.001,,,***,,,high,Median year built (higher = newer),log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,0.4,0.1,100*b/1 (per year),reported,0.020636659277812452,0.005159164819453113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,37554.0,years,year_built_not_age,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,False,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,Median Number of Rooms,bedrooms,A_structural,level,count,0.032,0.01,,,***,,,high,Median rooms in block group,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,semi_elasticity,3.2,1.0,100*b/1 (per unit count),reported,0.01651059312176734,0.0051595603505522935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2,37554.0,count,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T2-c1,2,1,44,log(Offenses Per Capita),crime,F_neighbourhood,log,rate,-0.376,0.686,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.793,se,block group x year; year-specific test-score coefficients,elasticity,-0.376,0.686,b (log-log),reported,-0.0028283548195284698,0.005160243101586517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5481049562682215,37554.0,rate,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,log(Median Household Income),income,F_neighbourhood,log,currency,0.044,0.022,,,**,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,elasticity,0.044,0.022,b (log-log),reported,0.01031997789050305,0.005159988945251525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,37554.0,currency,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Frac. Black,demographics,F_neighbourhood,level,share,-0.16,0.09,,,*,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-16.0,9.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009173416204311106,0.005160046614924998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.777777777777778,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Frac. Hispanic,demographics,F_neighbourhood,level,share,-0.201,0.06,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-20.1,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01728430113869993,0.0051594928772238586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3500000000000005,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Frac. Asian,demographics,F_neighbourhood,level,share,0.032,0.09,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,3.2,9.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0018347573532562105,0.005160255056033091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35555555555555557,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Mean Test Score (IL),school_quality,F_neighbourhood,level,index,-0.03,0.006,,,***,,,high,Illinois standardized test score,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-3.0,0.6,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.025792734920877136,0.005158546984175427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Share of Schools with B (IN),school_quality,F_neighbourhood,level,share,0.015,0.06,,,,,,high,Indiana school grades (ref. A),log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,1.5,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0012900648618991092,0.005160259447596437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Share of Schools with C (IN),school_quality,F_neighbourhood,level,share,-0.201,0.053,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-20.1,5.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.019566310367661273,0.005159275868089788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7924528301886795,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Share of Schools with D (IN),school_quality,F_neighbourhood,level,share,-0.108,0.061,,,*,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-10.8,6.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009135823380599047,0.005160048390893906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7704918032786885,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Share of Schools with F (IN),school_quality,F_neighbourhood,level,share,-0.093,0.144,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-9.3,14.399999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0033326518258178475,0.005160235085137312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6458333333333334,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Median Test Score (WI),school_quality,F_neighbourhood,level,index,-0.027,1.219,,,,,,high,Wisconsin,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,-2.7,121.9,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.00011429624291540775,0.005160263707921558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.022149302707136997,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Median Year Structure Built,age,A_structural,level,years,0.004,0.001,,,***,,,high,Median year built (higher = newer),log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,0.4,0.1,100*b/1 (per year),reported,0.020636659277812452,0.005159164819453113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,37554.0,years,year_built_not_age,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,False,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,Median Number of Rooms,bedrooms,A_structural,level,count,0.05,0.012,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,semi_elasticity,5.0,1.2,100*b/1 (per unit count),reported,0.021496130696474634,0.005159071367153912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.166666666666667,37554.0,count,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c1,3,1,45,log(Offenses per Capita),crime,F_neighbourhood,log,rate,-5.474,0.695,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.772,se,block group x year; amenities only,elasticity,-5.474,0.695,b (log-log),reported,-0.0406100456718569,0.005156006894764439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.876258992805756,37554.0,rate,implausible_magnitude,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,log(Median Household Income),income,F_neighbourhood,log,currency,0.001,0.019,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,elasticity,0.001,0.019,b (log-log),reported,0.00027159281849000307,0.005160263551310058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.052631578947368425,37554.0,currency,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Frac. Black,demographics,F_neighbourhood,level,share,-0.118,0.085,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,-11.799999999999999,8.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.007163476447697659,0.00516013133944323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3882352941176468,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Frac. Hispanic,demographics,F_neighbourhood,level,share,-0.23,0.058,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,-23.0,5.800000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.020458831829711817,0.005159183678796893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9655172413793105,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Frac. Asian,demographics,F_neighbourhood,level,share,-0.363,0.095,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,-36.3,9.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01971380750269521,0.005159260916683319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8210526315789473,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Mean Test Score (IL),school_quality,F_neighbourhood,level,index,0.016,0.006,,,***,,,high,Illinois standardized test score,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,1.6,0.6,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.013759400653561012,0.00515977524508538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6666666666666665,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Share of Schools with B (IN),school_quality,F_neighbourhood,level,share,0.004,0.063,,,,,,high,Indiana school grades (ref. A),log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,0.4,6.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0003276357755341375,0.005160263464662667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.06349206349206349,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Share of Schools with C (IN),school_quality,F_neighbourhood,level,share,-0.265,0.058,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,-26.5,5.800000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0235705168385529,0.005158830100513465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.568965517241379,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Share of Schools with D (IN),school_quality,F_neighbourhood,level,share,-0.061,0.067,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,-6.1,6.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.004698098721350504,0.005160206792303013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9104477611940298,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Share of Schools with F (IN),school_quality,F_neighbourhood,level,share,0.141,0.133,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,14.099999999999998,13.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.005470573684385772,0.005160186524987999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0601503759398494,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Median Test Score (WI),school_quality,F_neighbourhood,level,index,-0.557,1.239,,,,,,high,Wisconsin,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,-55.7,123.9,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.002319821767584708,0.00516024985644067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.44955609362389026,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Median Year Structure Built,age,A_structural,level,years,0.002,0.001,,,***,,,high,Median year built (higher = newer),log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,0.2,0.1,100*b/1 (per year),reported,0.01031997789050305,0.005159988945251525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,37554.0,years,year_built_not_age,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,False,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,Median Number of Rooms,bedrooms,A_structural,level,count,0.002,0.011,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,semi_elasticity,0.2,1.0999999999999999,100*b/1 (per unit count),reported,0.000938229358255109,0.005160261470403099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18181818181818182,37554.0,count,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c2,3,2,45,log(Offenses per Capita),crime,F_neighbourhood,log,rate,-3.338,0.664,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.788,se,adds Trading Opportunities r and E(V) controls,elasticity,-3.338,0.664,b (log-log),reported,-0.02593248126176429,0.0051585283276846874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.02710843373494,37554.0,rate,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,True
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,log(Median Household Income),income,F_neighbourhood,log,currency,0.005,0.015,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",elasticity,0.005,0.015,b (log-log),reported,0.0017200853692673167,0.005160256107801949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33333333333333337,37554.0,currency,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Frac. Black,demographics,F_neighbourhood,level,share,-0.106,0.057,,,*,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-10.6,5.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009595838117147043,0.005160026157333787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8596491228070173,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Frac. Hispanic,demographics,F_neighbourhood,level,share,-0.081,0.042,,,*,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-8.1,4.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009951444427422863,0.00516000822162667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9285714285714286,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Frac. Asian,demographics,F_neighbourhood,level,share,-0.18,0.066,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-18.0,6.6000000000000005,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.014072053068299372,0.005159752791709771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.727272727272727,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Mean Test Score (IL),school_quality,F_neighbourhood,level,index,0.004,0.005,,,,,,high,Illinois standardized test score,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,0.4,0.5,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.004128175817005606,0.005160219771257008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Share of Schools with B (IN),school_quality,F_neighbourhood,level,share,0.006,0.051,,,,,,high,Indiana school grades (ref. A),log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,0.6,5.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0006070897400822639,0.005160262790699243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11764705882352942,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Share of Schools with C (IN),school_quality,F_neighbourhood,level,share,-0.161,0.045,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-16.1,4.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0184591312605244,0.005159384513811168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.577777777777778,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Share of Schools with D (IN),school_quality,F_neighbourhood,level,share,-0.056,0.055,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-5.6000000000000005,5.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.005254014199595053,0.005160192517459426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0181818181818183,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Share of Schools with F (IN),school_quality,F_neighbourhood,level,share,-0.039,0.124,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-3.9,12.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0016229840392653387,0.005160256945356462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31451612903225806,37554.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Median Test Score (WI),school_quality,F_neighbourhood,level,index,-0.175,1.047,,,,,,high,Wisconsin,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,-17.5,104.69999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.0008625079454514931,0.005160261822215505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16714422158548234,37554.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Median Year Structure Built,age,A_structural,level,years,0.001,0.001,,,,,,high,Median year built (higher = newer),log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,0.1,0.1,100*b/1 (per year),reported,0.0051601950384175775,0.0051601950384175775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,37554.0,years,year_built_not_age,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,False,False,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,Median Number of Rooms,bedrooms,A_structural,level,count,0.011,0.008,,,,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",semi_elasticity,1.0999999999999999,0.8,100*b/1 (per unit count),reported,0.007095184046405893,0.005160133851931558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.375,37554.0,count,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6,05133875334147dc5ac7d37a0479720642327db6:T3-c3,3,3,45,log(Offenses per Capita),crime,F_neighbourhood,log,rate,-2.329,0.549,,,***,,,high,,log(Transaction Price_jt),log,OLS,37564.0,0.844,se,"adds credit-friction interactions and log balance, approval rate, interest cost controls",elasticity,-2.329,0.549,b (log-log),reported,-0.021885930007393826,0.005159027726088111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.242258652094717,37554.0,rate,,2019.0,United States,North America,High income,2005.0,2012.0,unknown,transaction,working_paper,False,False,unknown,,2008.5,ols,other,True,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.0991,0.0149,,,***,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0991,0.0149,"b (currency per unit, as reported)",reported,0.34945531249910794,0.05254171701550665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.651006711409395,318.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.12,0.0181,,,***,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.12,0.0181,"b (currency per unit, as reported)",reported,0.28374248356566273,0.04279782460448747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6298342541436455,502.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.075,0.0236,,,**,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.075,0.0236,"b (currency per unit, as reported)",reported,0.19858430523632423,0.06248786138103002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1779661016949152,246.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.05,0.0187,,,**,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.05,0.0187,"b (currency per unit, as reported)",reported,0.15281354156957597,0.057152264547021416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6737967914438503,299.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.136,0.0187,,,***,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.136,0.0187,"b (currency per unit, as reported)",reported,0.3043828011911366,0.04185263516378128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.2727272727272725,518.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.0541,0.018,,,**,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0541,0.018,"b (currency per unit, as reported)",reported,0.1424716566748728,0.04740276931881165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.005555555555556,436.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,CONDITION,condition,A_structural,level,"ordinal (0 = bad, 1 = average, 3 = good)",0.0758,0.0011,,,***,,,high,condition of dwelling,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0758,0.0011,"b (currency per unit, as reported)",reported,0.964781923988448,0.014000793092180623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.9090909090909,353.0,ordinal,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,ELEVATOR,accessibility,B_interior,binary,binary,0.0695,0.0401,,,,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0695,0.0401,"b (currency per unit, as reported)",reported,0.09673537044241708,0.05581422093152409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7331670822942646,318.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,ELEVATOR,accessibility,B_interior,binary,binary,0.0079,0.0422,,,,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0079,0.0422,"b (currency per unit, as reported)",reported,0.008355022484181445,0.044630626434488216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18720379146919433,502.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,ELEVATOR,accessibility,B_interior,binary,binary,0.1287,0.1104,,,,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.1287,0.1104,"b (currency per unit, as reported)",reported,0.07412174185514775,0.0635822867195673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1657608695652175,246.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,ELEVATOR,accessibility,B_interior,binary,binary,0.0161,0.0446,,,,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0161,0.0446,"b (currency per unit, as reported)",reported,0.0208718433152581,0.0578188951466156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3609865470852018,299.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,ELEVATOR,accessibility,B_interior,binary,binary,-0.1336,0.0492,,,**,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.1336,0.0492,"b (currency per unit, as reported)",reported,-0.11846967866993188,0.04362805531856773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7154471544715446,518.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,ELEVATOR,accessibility,B_interior,binary,binary,-0.0249,0.0436,,,,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0249,0.0436,"b (currency per unit, as reported)",reported,-0.027340549171851566,0.04787341140131439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5711009174311926,436.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,ELEVATOR,accessibility,B_interior,binary,binary,0.0253,0.0256,,,,,,high,,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0253,0.0256,"b (currency per unit, as reported)",reported,0.0525282845361733,0.05315114957019908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9882812499999999,353.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.2708,0.0161,,,***,,,medium,number of rooms excluding kitchen; dependent variable is price per m2; SE printed without brackets,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.2708,0.0161,"b (currency per unit, as reported)",reported,-0.6861494366762545,0.04079396576989548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.819875776397517,318.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.2565,0.0189,,,***,,,high,number of rooms excluding kitchen; dependent variable is price per m2,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.2565,0.0189,"b (currency per unit, as reported)",reported,-0.5180901971077745,0.038175067155309696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.571428571428571,502.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.2929,0.025,,,***,,,high,number of rooms excluding kitchen; dependent variable is price per m2,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.2929,0.025,"b (currency per unit, as reported)",reported,-0.5984529021540851,0.05107996774958049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.716,246.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.2427,0.0216,,,*,,,high,number of rooms excluding kitchen; dependent variable is price per m2,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.2427,0.0216,"b (currency per unit, as reported)",reported,-0.5448710900013114,0.048492853498262566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.23611111111111,299.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.091,0.0019,,,***,,,high,number of rooms excluding kitchen; dependent variable is price per m2,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.091,0.0019,"b (currency per unit, as reported)",reported,-0.9032072318582396,0.01885817297286435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-47.89473684210526,518.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.2384,0.0187,,,***,,,high,number of rooms excluding kitchen; dependent variable is price per m2,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.2384,0.0187,"b (currency per unit, as reported)",reported,-0.5211014233305398,0.040874985806548206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.748663101604278,436.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,ROOMS,bedrooms,A_structural,level,count (rooms excl. kitchen),-0.2798,0.0157,,,***,,,high,number of rooms excluding kitchen; dependent variable is price per m2,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.2798,0.0157,"b (currency per unit, as reported)",reported,-0.6881966969988406,0.03861575462073551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.821656050955415,353.0,count,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),-0.0617,0.05654,,,,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0617,0.05654,"b (currency per unit, as reported)",reported,-0.061080718932756906,0.05597250969948258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0912628227803325,318.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),0.1484,0.0633,,,*,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.1484,0.0633,"b (currency per unit, as reported)",reported,0.10406718453564227,0.044389843538451185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.344391785150079,502.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),0.0852,0.0709,,,,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0852,0.0709,"b (currency per unit, as reported)",reported,0.07639322400458229,0.06357135659536249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2016925246826515,246.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),0.0438,0.0657,,,,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0438,0.0657,"b (currency per unit, as reported)",reported,0.03852570642647098,0.057788559639706465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666667,299.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),0.2984,0.0074,,,***,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.2984,0.0074,"b (currency per unit, as reported)",reported,0.8708623796392968,0.02159645311437935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.32432432432432,518.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),-0.0107,0.0675,,,,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0107,0.0675,"b (currency per unit, as reported)",reported,-0.007591441429792183,0.0478899342533619,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1585185185185185,436.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,FLOOR,floor_level,A_structural,level,share (floor number / total number of floors),0.1189,0.0399,,,**,,,high,relative floor position,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.1189,0.0399,"b (currency per unit, as reported)",reported,0.1566486322797611,0.05256753934367088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9799498746867172,353.0,share,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,SEA,water_proximity,E_environment,level,m,-0.00031,0.00075,,,,,,high,distance to the sea coast,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.00031,0.00075,"b (currency per unit, as reported)",reported,-0.023172358568592805,0.056062157827240666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.41333333333333333,318.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,SEA,water_proximity,E_environment,level,m,-0.0007,0.00019,,,***,,,high,distance to the sea coast,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0007,0.00019,"b (currency per unit, as reported)",reported,-0.16225540944338238,0.04404075399177521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6842105263157894,502.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,SEA,water_proximity,E_environment,level,m,0.0017,0.005519,,,**,,,medium,distance to the sea coast; SE printed '(0.005519' (bracket missing),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0017,0.005519,"b (currency per unit, as reported)",reported,0.01963528628404645,0.06374537941273668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3080268164522558,246.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,SEA,water_proximity,E_environment,level,m,-0.00019,0.00021,,,,,,high,distance to the sea coast,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.00019,0.00021,"b (currency per unit, as reported)",reported,-0.05225225342701961,0.057752490629863776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9047619047619048,299.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,SEA,water_proximity,E_environment,level,m,-0.00076,0.00019,,,***,,,high,distance to the sea coast,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.00076,0.00019,"b (currency per unit, as reported)",reported,-0.17309692896316617,0.04327423224079155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,518.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,SEA,water_proximity,E_environment,level,m,-0.0003,0.0001,,,**,,,medium,distance to the sea coast; SE printed '(–0.00010)' with a minus sign,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0003,0.0001,"b (currency per unit, as reported)",reported,-0.14221363894199313,0.04740454631399772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,436.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,SEA,water_proximity,E_environment,level,m,4.677e-05,8.573e-05,,,,,,high,distance to the sea coast,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,4.677e-05,8.573e-05,"b (currency per unit, as reported)",reported,0.029024462599749046,0.053202206086732644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5455499825032077,353.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,CBD,cbd_distance,D_location,level,m,0.00051,0.00069,,,,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.00051,0.00069,"b (currency per unit, as reported)",reported,0.04141281892472525,0.05602910795698122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7391304347826088,318.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,CBD,cbd_distance,D_location,level,m,0.000132,0.000629,,,*,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.000132,0.000629,"b (currency per unit, as reported)",reported,0.009365961709225474,0.04463022662956683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2098569157392687,502.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,CBD,cbd_distance,D_location,level,m,-0.0016,0.0005349,,,**,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0016,0.0005349,"b (currency per unit, as reported)",reported,-0.18733638163732255,0.0626288940861274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9912133108992336,246.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,CBD,cbd_distance,D_location,level,m,0.00023,0.0002,,,,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.00023,0.0002,"b (currency per unit, as reported)",reported,0.06635962224055852,0.057704019339616115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.15,299.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,CBD,cbd_distance,D_location,level,m,0.00017,6.175e-05,,,**,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.00017,6.175e-05,"b (currency per unit, as reported)",reported,0.12008613597569696,0.04361952292058403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.753036437246964,518.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,CBD,cbd_distance,D_location,level,m,0.0001,9.64e-05,,,,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.0001,9.64e-05,"b (currency per unit, as reported)",reported,0.04961859295421793,0.04783232360786608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.037344398340249,436.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,CBD,cbd_distance,D_location,level,m,-0.0002,7.082e-05,,,**,,,high,distance to city centre (railway station),"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0002,7.082e-05,"b (currency per unit, as reported)",reported,-0.1486398703672708,0.05263337809705059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.824060999717594,353.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,GREEN,green_space,E_environment,level,m,0.00049,0.00012,,,***,,,high,distance to parks or green areas,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.00049,0.00012,"b (currency per unit, as reported)",reported,0.22320509257756996,0.05466247165164979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.083333333333333,318.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,GREEN,green_space,E_environment,level,m,-0.000495,0.000185,,,**,,,high,distance to parks or green areas,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.000495,0.000185,"b (currency per unit, as reported)",reported,-0.11857869203475954,0.044317288942283865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.675675675675676,502.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,GREEN,green_space,E_environment,level,m,1.089e-06,0.0006365,,,,,,medium,distance to parks or green areas; printed '1.089e–6 (0.6365e–3)',"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,1.089e-06,0.0006365,"b (currency per unit, as reported)",reported,0.00010908421624430248,0.06375767092699589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.001710919088766693,246.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,GREEN,green_space,E_environment,level,m,4e-05,0.00016,,,,,,high,distance to parks or green areas,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,4e-05,0.00016,"b (currency per unit, as reported)",reported,0.014456362470655182,0.05782544988262073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,299.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,GREEN,green_space,E_environment,level,m,-0.00018,0.00023,,,,,,high,distance to parks or green areas,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.00018,0.00023,"b (currency per unit, as reported)",reported,-0.034365541289865936,0.043911524981495365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.782608695652174,518.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,GREEN,green_space,E_environment,level,m,7.77e-05,0.0001,,,,,,high,distance to parks or green areas,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,7.77e-05,0.0001,"b (currency per unit, as reported)",reported,0.03718581449701508,0.04785819111584952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.777,436.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,GREEN,green_space,E_environment,level,m,-5.349e-05,0.00015,,,,,,high,distance to parks or green areas,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-5.349e-05,0.00015,"b (currency per unit, as reported)",reported,-0.018976485188839706,0.05321504539775576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35660000000000003,353.0,m,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,True,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),0.2943,0.1074,,,**,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.2943,0.1074,"b (currency per unit, as reported)",reported,0.1518814019563708,0.05542664821649412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.740223463687151,318.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),0.1873,0.1094,,,,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.1873,0.1094,"b (currency per unit, as reported)",reported,0.07619112130600438,0.04450244885679059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7120658135283364,502.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),0.7863,0.3022,,,*,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.7863,0.3022,"b (currency per unit, as reported)",reported,0.16365567335431466,0.06289805988512515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6019192587690267,246.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),-0.0244,0.1102,,,,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.0244,0.1102,"b (currency per unit, as reported)",reported,-0.012803745600995136,0.05782675267334689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22141560798548093,299.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),0.201,0.1,,,*,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.201,0.1,"b (currency per unit, as reported)",reported,0.0879719310824029,0.043767129891742744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,518.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),-0.7073,0.2201,,,**,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,-0.7073,0.2201,"b (currency per unit, as reported)",reported,-0.15210977222776656,0.0473340320476904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2135393003180375,436.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,TREAT,other,other,dummy,binary (treatment neighbourhood with infill development vs control),0.4244,0.1981,,,*,,,high,DiD treatment-group indicator,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",monetary,0.4244,0.1981,"b (currency per unit, as reported)",reported,0.11329178390077072,0.0528819566228621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1423523472993438,353.0,binary,,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case1,4,Case 1,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),-0.0953,0.0692,,,,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,328.0,0.6885,se,"Case 1 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,-0.0953,0.0692,not harmonised,reported,-0.07699845311952389,0.05591073405950737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3771676300578035,318.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case2,4,Case 2,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),-0.0298,0.0905,,,,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,512.0,0.6012,se,"Case 2 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,-0.0298,0.0905,not harmonised,reported,-0.014694977652262383,0.0446273650177767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3292817679558011,502.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case3,4,Case 3,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),0.0641,0.0852,,,,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,256.0,0.7302,se,"Case 3 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,0.0641,0.0852,not harmonised,reported,0.04791282922176443,0.06368444695310967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7523474178403756,246.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case4,4,Case 4,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),-0.1545,0.0857,,,,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,309.0,0.7499,se,"Case 4 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,-0.1545,0.0857,not harmonised,reported,-0.10369658227430407,0.05751972233597319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8028004667444575,299.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case5,4,Case 5,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),-0.2703,0.1018,,,**,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,528.0,0.7855,se,"Case 5 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,-0.2703,0.1018,not harmonised,reported,-0.11587717169234972,0.043641494925198684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.655206286836935,518.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case6,4,Case 6,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),0.1426,0.131,,,,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,446.0,0.664,se,"Case 6 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,0.1426,0.131,not harmonised,reported,0.052061374884670136,0.04782636823206022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0885496183206107,436.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056,07441798ac5873fe53746864fd3cf3231a87b056:T4-case7,4,Case 7,7,TREAT*AFTER,other,other,other,binary interaction (treatment x after infill development),-0.1597,0.077,,,*,,,high,DiD interaction: effect of infill development on existing apartment prices,"sale price (thousand EUR) per m2, CPI-deflated 2012",per_m2,OLS,363.0,0.8112,se,"Case 7 neighbourhood and its reference neighbourhood, before/after infill development; DiD specification; n = sum of Table 3 group counts; multiple R2",other,-0.1597,0.077,not harmonised,reported,-0.10972275921928173,0.0529032715083575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0740259740259743,353.0,binary interaction,nonstandard_x_transform,2018.0,Finland,Europe,High income,2000.0,2012.0,condo_apartment,transaction,journal,False,False,unknown,,2006.0,ols,journal,False,False,False
0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319:EQ2-c1,Equation 2,1,11,Landvalue,other,other,level,"currency (estimated land value, AUD)",1.4,,,,,,,medium,"Estimated land value of the parcel (AUD); coefficient read from Equation (2), no SE/t/p printed","Sale price (AUD, level)",level,OLS,4210.0,0.3694,none,"Stepwise multiple regression, all Bathurst sales; adjusted R2 0.3694 from Table 2; N inferred",monetary,1.4,,"b (currency per unit, as reported)",,,,,,4200.0,currency,,2012.0,Australia,Oceania,High income,1992.0,2004.0,single_family,transaction,journal,False,False,unknown,neural_network;ANFIS neuro-fuzzy,1998.0,ols,journal,False,False,False
0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319:EQ2-c1,Equation 2,1,11,Yearsold,transaction_timing,G_market,level,years (calendar year of sale),5491.0,,,,,,,medium,"Year of sale as a linear trend; coefficient read from Equation (2), no SE/t/p printed","Sale price (AUD, level)",level,OLS,4210.0,0.3694,none,"Stepwise multiple regression, all Bathurst sales; adjusted R2 0.3694 from Table 2; N inferred",monetary,5491.0,,"b (currency per unit, as reported)",,,,,,4200.0,years,,2012.0,Australia,Oceania,High income,1992.0,2004.0,single_family,transaction,journal,False,False,unknown,neural_network;ANFIS neuro-fuzzy,1998.0,ols,journal,False,False,False
0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319:EQ2-c1,Equation 2,1,11,Dcity,cbd_distance,D_location,level,"m (distance to CBD; unit not printed, metres implied)",7491.0,,,,,,,medium,"Positive sign as printed; coefficient read from Equation (2), no SE/t/p printed","Sale price (AUD, level)",level,OLS,4210.0,0.3694,none,"Stepwise multiple regression, all Bathurst sales; adjusted R2 0.3694 from Table 2; N inferred",monetary,7491.0,,"b (currency per unit, as reported)",,,,,,4200.0,m,,2012.0,Australia,Oceania,High income,1992.0,2004.0,single_family,transaction,journal,False,False,unknown,neural_network;ANFIS neuro-fuzzy,1998.0,ols,journal,False,False,False
0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319:EQ2-c1,Equation 2,1,11,Dculd,other,other,level,m (distance to cul-de-sac; unit not printed),-6305.0,,,,,,,medium,"Distance to the nearest cul-de-sac; coefficient read from Equation (2), no SE/t/p printed","Sale price (AUD, level)",level,OLS,4210.0,0.3694,none,"Stepwise multiple regression, all Bathurst sales; adjusted R2 0.3694 from Table 2; N inferred",monetary,-6305.0,,"b (currency per unit, as reported)",,,,,,4200.0,m,,2012.0,Australia,Oceania,High income,1992.0,2004.0,single_family,transaction,journal,False,False,unknown,neural_network;ANFIS neuro-fuzzy,1998.0,ols,journal,False,False,False
0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319:EQ2-c1,Equation 2,1,11,Slope,other,other,level,degrees (slope of parcel centroid),1613.0,,,,,,,medium,"Topographic slope (Slope_deg); coefficient read from Equation (2), no SE/t/p printed","Sale price (AUD, level)",level,OLS,4210.0,0.3694,none,"Stepwise multiple regression, all Bathurst sales; adjusted R2 0.3694 from Table 2; N inferred",monetary,1613.0,,"b (currency per unit, as reported)",,,,,,4200.0,degrees,,2012.0,Australia,Oceania,High income,1992.0,2004.0,single_family,transaction,journal,False,False,unknown,neural_network;ANFIS neuro-fuzzy,1998.0,ols,journal,False,False,False
0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319,0795e1f2569ab3c9b87c0451960aa54739093319:EQ2-c1,Equation 2,1,11,Dpark,green_space,E_environment,level,m (distance to closest park; unit not printed),-23052.0,,,,,,,medium,"Negative sign as printed; coefficient read from Equation (2), no SE/t/p printed","Sale price (AUD, level)",level,OLS,4210.0,0.3694,none,"Stepwise multiple regression, all Bathurst sales; adjusted R2 0.3694 from Table 2; N inferred",monetary,-23052.0,,"b (currency per unit, as reported)",,,,,,4200.0,m,,2012.0,Australia,Oceania,High income,1992.0,2004.0,single_family,transaction,journal,False,False,unknown,neural_network;ANFIS neuro-fuzzy,1998.0,ols,journal,False,False,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,TENURE,other,other,binary,binary,0.022,0.029,0.745,0.456,,,,high,Printed name TENURE; freehold (1) vs leasehold (0),LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,0.022,0.029,"b (currency per unit, as reported)",reported,0.024501343052612545,0.03288770879545308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.745,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,False,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,NUMBER OF ROOMS,bedrooms,A_structural,level,count,-0.003,0.014,-0.197,0.844,,,,high,"Number of rooms (bedrooms per data description, range 1-5)",LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,-0.003,0.014,"b (currency per unit, as reported)",reported,-0.006480688098825819,0.03289689390266913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.197,924.0,count,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,HILLVIEW,view,E_environment,binary,binary,-0.056,0.026,-2.131,0.033,,,,high,Hill view dummy (reference: town view),LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,-0.056,0.026,"b (currency per unit, as reported)",reported,-0.06993311441290263,0.032817041019663366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.131,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,SEAVIEW,view,E_environment,binary,binary,0.155,0.023,6.651,0.0,,,,high,Sea view dummy (reference: town view),LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,0.155,0.023,"b (currency per unit, as reported)",reported,0.2137452042785206,0.032137303304543764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.651,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,FACILITIES,recreation_private,C_exterior,binary,binary,-0.167,0.02,-8.374,0.0,,,,high,Full facilities vs semi-facilities of the scheme,LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,-0.167,0.02,"b (currency per unit, as reported)",reported,-0.2655906026166792,0.0317160977569476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.374,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,SECURITY,other,other,binary,binary,0.176,0.039,4.529,0.0,,,,high,Printed name SECURITY; double gated vs single gated,LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,0.176,0.039,"b (currency per unit, as reported)",reported,0.14736644850000685,0.032538407705896846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.529,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,False,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,OCCUPANCY,vacancy,F_neighbourhood,binary,binary,-0.077,0.018,-4.304,0.0,,,,high,Occupancy rate above 75% = 1 (inverse of vacancy),LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,-0.077,0.018,"b (currency per unit, as reported)",reported,-0.1401928801185601,0.03257269519483273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.304,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,DEVELOPER,other,other,binary,binary,0.18,0.017,10.535,0.0,,,,high,Printed name DEVELOPER; top-30 ranked developer = 1,LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,0.18,0.017,"b (currency per unit, as reported)",reported,0.32746678432462356,0.03108370045796142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.535,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,False,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,GREEN -GBI,energy_efficiency,A_structural,binary,binary,0.065,0.033,1.987,0.047,,,,high,Green Building Index certified condominium = 1,LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),monetary,0.065,0.033,"b (currency per unit, as reported)",reported,0.06522829212587838,0.0328275249752785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.987,924.0,binary,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,LOGSIZE,living_area,A_structural,log,m2,-0.08,0.02,-3.979,0.0,,,,high,Log of floor area in square metres; dependent variable is per m2 so coefficient is elasticity of unit price,LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),other,-0.08,0.02,not harmonised,reported,-0.1297922386589642,0.03261931104774169,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.979,924.0,m2,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,LOGLEVEL,floor_level,A_structural,log,count,0.016,0.01,1.564,0.118,,,,high,Log of floor level of unit,LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),other,0.016,0.01,not harmonised,reported,0.05138385351765355,0.032854126290059814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.564,924.0,count,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d,0893ce95f4ec80e891e9cf83dfb029d633e4a22d:T5-c1,5,1,8,LOGAGE,age,A_structural,log,years,0.007,0.019,0.393,0.694,,,,high,Log of age of building (years),LOGPRICE PSM (log of price per square metre),per_m2,OLS,934.0,0.36,se,All 934 condominium resale transactions 2016-2020; dep var is log of price per m2 (log + per_m2),other,0.007,0.019,not harmonised,reported,0.012927670404262343,0.032894835634255326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.393,924.0,years,,2022.0,Malaysia,Asia,Upper middle income,2016.0,2020.0,condo_apartment,transaction,journal,True,True,OLS,,2018.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,DIS,cbd_distance,D_location,level,km,-0.059,,,0.0,***,,,high,"Distance from project to city centre (HCMC post office), km",Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,-0.059,,"b (currency per unit, as reported)",,,,,,329.0,km,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,DIS,cbd_distance,D_location,level,km,-0.065,,,0.0,***,,,high,"Distance from project to city centre (HCMC post office), km",Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,-0.065,,"b (currency per unit, as reported)",,,,,,329.0,km,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,FLO,floor_level,A_structural,level,count,0.047,,,0.0,***,,,high,Floor of the apartment,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.047,,"b (currency per unit, as reported)",,,,,,329.0,count,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,FLO,floor_level,A_structural,level,count,0.031,,,0.0,***,,,high,Floor of the apartment,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.031,,"b (currency per unit, as reported)",,,,,,329.0,count,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,TIME-USED,age,A_structural,level,years,0.012,,,0.311,,,,high,Years the apartment has been used,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.012,0.01184452918610196,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.05576851582054381,0.055045984441835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0131259597958917,329.0,years,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,TIME-USED,age,A_structural,level,years,0.015,,,0.162,,,,high,Years the apartment has been used,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.015,0.010726723957559794,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.07686690258104024,0.05496866969729728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3983766207974953,329.0,years,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,RE-PRICE,other,other,level,currency,0.565,,,0.0,***,,,high,Reference price (billion VND); market variable,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.565,,"b (currency per unit, as reported)",,,,,,329.0,currency,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,RE-PRICE,other,other,level,currency,0.464,,,0.0,***,,,high,Reference price (billion VND); market variable,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.464,,"b (currency per unit, as reported)",,,,,,329.0,currency,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,FUR,luxury_finishes,B_interior,binary,binary,0.324,,,0.0,***,,,high,"Interior: 1 high-end, 0 budget",Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.324,,"b (currency per unit, as reported)",,,,,,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,FUR,luxury_finishes,B_interior,binary,binary,0.324,,,0.0,***,,,high,"Interior: 1 high-end, 0 budget",Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.324,,"b (currency per unit, as reported)",,,,,,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,VIEW,view,E_environment,binary,binary,0.056,,,0.397,,,,high,1 = river/lake/park view,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.056,0.06611638569574811,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.046645311007158276,0.055071774525846454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8469912474904283,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,VIEW,view,E_environment,binary,binary,0.045,,,0.443,,,,high,1 = river/lake/park view,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.045,0.05865965734947647,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.042255862187838715,0.05508254215434041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7671371097840485,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,IN-ENVI,recreation_private,C_exterior,binary,binary,-0.011,,,0.875,,,,high,"1 = project has gym, swimming pool, supermarket",Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,-0.011,0.06992532024928211,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.008672492629100149,0.05512971131357877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15731068461017067,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,IN-ENVI,recreation_private,C_exterior,binary,binary,-0.02,,,0.759,,,,high,"1 = project has gym, swimming pool, supermarket",Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,-0.02,0.06519027416202793,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.0169116945103876,0.05512390008383152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.30679423053645655,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,INVEST,other,other,binary,binary,0.009,,,0.899,,,,high,Investor reputation: 1 = at least 2 projects,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.009,0.07090818628277087,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.006997413698799755,0.05513043489467842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12692469617131924,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,INVEST,other,other,binary,binary,0.027,,,0.666,,,,high,Investor reputation: 1 = at least 2 projects,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.027,0.06255151241803779,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.023790581751506147,0.055116180365247396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.431644239383956,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,LEG,other,other,binary,binary,0.009,,,0.897,,,,high,Legal status: 1 = has pink book,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.009,0.06952383641718828,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.007136738290865752,0.05513038060960382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12945200471956325,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,LEG,other,other,binary,binary,-0.071,,,0.281,,,,high,Legal status: 1 = has pink book,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,-0.071,0.06585808742121638,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.05933152738416031,0.05503466080704681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0780756438597567,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,ULSTRUCTURE,other,other,binary,binary,-0.145,,,0.044,**,,,high,Planned supermarket/school/administrative works nearby next year,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,-0.145,0.07199278162373848,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.1103621242170064,0.05479500902266789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.014090812018139,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,ULSTRUCTURE,other,other,binary,binary,-0.167,,,0.009,***,,,high,Planned supermarket/school/administrative works nearby next year,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,-0.167,0.06393435624630925,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.14253682083546776,0.05456886156593503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6120541412292777,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,TREND,other,other,binary,binary,-0.083,,,0.133,,,,high,Expected price trend: 1 = uptrend; market expectation variable,Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,-0.083,0.05524581953714685,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.08254600356384728,0.05494363393254202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.502376119955854,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,TREND,other,other,binary,binary,-0.091,,,0.065,*,,,high,Expected price trend: 1 = uptrend; market expectation variable,Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,-0.091,0.04931559393978138,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.10120998608302668,0.05484868765188427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.845258116755501,329.0,binary,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c1,4,Model 1,6,WCBR,bedrooms,A_structural,level,count,0.379,,,0.0,***,,,high,Combined number of toilets and bedrooms (Model 1 only),Total apartment value (billion VND),level,OLS,339.0,0.829,p,"Model 1: with WCBR, without SQUA",monetary,0.379,,"b (currency per unit, as reported)",,,,,,329.0,count,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321,0a4917c7f444817b4974629ac2f7d58ef2f99321:T4-c2,4,Model 2,6,SQUA,living_area,A_structural,level,m2,0.016,,,0.0,***,,,high,Apartment area m2 (Model 2 only),Total apartment value (billion VND),level,OLS,339.0,0.863,p,"Model 2: with SQUA, without WCBR",monetary,0.016,,"b (currency per unit, as reported)",,,,,,329.0,m2,,2020.0,Vietnam,Asia,Lower middle income,,,condo_apartment,transaction,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Distance,hazard_site,E_environment,level,other,200.2874,,0.871,,,,,medium,"Straight-line distance from the landfill in 240-ft city blocks (unit ambiguous: 'blocks' in equation, 'feet' grouped into 240-ft blocks in variable definitions); positive = price rises away from landfill","sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,200.2874,229.95109070034442,"b (currency per unit, as reported)",|b/t|,0.12737588511537073,0.1462409702817115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.871,46.0,other,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Lot Size,lot_size,A_structural,level,sqft,0.5924,,1.654,,,,,high,Lot size in square feet,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,0.5924,0.35816203143893593,"b (currency per unit, as reported)",|b/t|,0.23692551307034937,0.14324396195305283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.654,46.0,sqft,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,House Size,living_area,A_structural,level,sqft,5.906,,2.783,,,,,high,Interior space in square feet,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,5.906,2.1221703197987782,"b (currency per unit, as reported)",|b/t|,0.37961539198904115,0.13640509952894042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.783,46.0,sqft,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Age,age,A_structural,level,years,-136.873,,-3.317,,,,,high,"Age of house at sale, years","sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,-136.873,41.264094060898394,"b (currency per unit, as reported)",|b/t|,-0.4393377907347567,0.13245034390556426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.317,46.0,years,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Bedroom,bedrooms,A_structural,level,count,888.9424,,1.145,,,,,high,Number of bedrooms,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,888.9424,776.3689082969432,"b (currency per unit, as reported)",|b/t|,0.1664655262576614,0.14538473908966063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.145,46.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Bathroom,bathrooms,A_structural,level,count,7131.2993,,5.226,,,,,high,Number of bathrooms,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,7131.2993,1364.5808075009568,"b (currency per unit, as reported)",|b/t|,0.6103579539512526,0.11679256677214937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.226,46.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Height,storeys,A_structural,level,count,8029.9949,,0.991,,,,,high,Height of house in stories,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,8029.9949,8102.921190716448,"b (currency per unit, as reported)",|b/t|,0.14457977010504658,0.1458928053532256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.991,46.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Basement,basement,A_structural,binary,binary,164.1152,,0.137,,,,,high,Basement dummy,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,164.1152,1197.9211678832114,"b (currency per unit, as reported)",|b/t|,0.02019542832651231,0.14741188559498036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.137,46.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c1,XI,1,98,Garage,garage,A_structural,binary,binary,1179.4304,,1.272,,,,,high,Garage dummy,"sale price of home, 1972 constant dollars (USD)",level,OLS,56.0,0.8779,t,"Homes sold in 1971 (Before) the Lavele landfill operation, within about 0.5 mile of the site",monetary,1179.4304,927.2251572327043,"b (currency per unit, as reported)",|b/t|,0.18433237084694276,0.1449153858859613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.272,46.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Distance,hazard_site,E_environment,level,other,675.8605,,2.323,,,,,medium,"Straight-line distance from the landfill in 240-ft city blocks (unit ambiguous: 'blocks' in equation, 'feet' grouped into 240-ft blocks in variable definitions); positive = price rises away from landfill","sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,675.8605,290.9429616874731,"b (currency per unit, as reported)",|b/t|,0.26932300818152893,0.11593758423656002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.323,69.0,other,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Lot Size,lot_size,A_structural,level,sqft,0.8929,,1.814,,,,,high,Lot size in square feet,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,0.8929,0.4922271223814774,"b (currency per unit, as reported)",|b/t|,0.21335183380803643,0.11761402084235745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.814,69.0,sqft,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,House Size,living_area,A_structural,level,sqft,14.6419,,3.709,,,,,high,Interior space in square feet,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,14.6419,3.9476678349959555,"b (currency per unit, as reported)",|b/t|,0.4077136963347682,0.10992550453889681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.709,69.0,sqft,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Age,age,A_structural,level,years,-281.8343,,-5.134,,,,,medium,"Age of house at sale, years; value read through OCR spacing","sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,-281.8343,54.89565640825866,"b (currency per unit, as reported)",|b/t|,-0.5257477196097967,0.10240508757495066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.134,69.0,years,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Bedroom,bedrooms,A_structural,level,count,-2906.6269,,-2.071,,,,,high,Number of bedrooms,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,-2906.6269,1403.489570255915,"b (currency per unit, as reported)",|b/t|,-0.24191376434578354,0.1168101228130292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.071,69.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Bathroom,bathrooms,A_structural,level,count,3454.0527,,1.541,,,,,high,Number of bathrooms,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,3454.0527,2241.4358857884495,"b (currency per unit, as reported)",|b/t|,0.18240240010298914,0.11836625574496373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.541,69.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Height,storeys,A_structural,level,count,12426.0,,2.01,,,,,high,Height of house in stories,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,12426.0,6182.089552238806,"b (currency per unit, as reported)",|b/t|,0.2351881185439784,0.11700901420098428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,69.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Basement,basement,A_structural,binary,binary,4770.998,,2.616,,,,,high,Basement dummy,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,4770.998,1823.775993883792,"b (currency per unit, as reported)",|b/t|,0.3003853046180448,0.11482618678059817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.616,69.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Fireplace,fireplace,B_interior,binary,binary,1091.5975,,0.561,,,,,high,Fireplace dummy (no fireplaces among 1971 sales),"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,1091.5975,1945.8065953654188,"b (currency per unit, as reported)",|b/t|,0.0673829657019408,0.12011223832788019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.561,69.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c2,XI,2,98,Garage,garage,A_structural,binary,binary,2484.6345,,1.224,,,,,high,Garage dummy,"sale price of home, 1972 constant dollars (USD)",level,OLS,79.0,0.7281,t,"Homes sold in 1979 (During) the Lavele landfill operation, within about 0.5 mile of the site",monetary,2484.6345,2029.9301470588236,"b (currency per unit, as reported)",|b/t|,0.14577816442948738,0.11909980754043085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.224,69.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Distance,hazard_site,E_environment,level,other,-165.0572,,-0.606,,,,,medium,"Straight-line distance from the landfill in 240-ft city blocks (unit ambiguous: 'blocks' in equation, 'feet' grouped into 240-ft blocks in variable definitions); positive = price rises away from landfill","sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,-165.0572,272.3716171617162,"b (currency per unit, as reported)",|b/t|,-0.07438677982937487,0.12275046176464502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.606,66.0,other,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Lot Size,lot_size,A_structural,level,sqft,0.2752,,0.572,,,,,high,Lot size in square feet,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,0.2752,0.4811188811188812,"b (currency per unit, as reported)",|b/t|,0.0702344602444236,0.12278751790983149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.572,66.0,sqft,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,House Size,living_area,A_structural,level,sqft,16.2432,,4.04,,,,,high,Interior space in square feet,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,16.2432,4.020594059405941,"b (currency per unit, as reported)",|b/t|,0.44527105411907714,0.11021560745521712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.04,66.0,sqft,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Age,age,A_structural,level,years,-150.0718,,-3.298,,,,,high,"Age of house at sale, years","sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,-150.0718,45.5038811400849,"b (currency per unit, as reported)",|b/t|,-0.37614300625326613,0.11405185150189999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.298,66.0,years,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Bedroom,bedrooms,A_structural,level,count,620.9696,,0.495,,,,,high,Number of bedrooms,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,620.9696,1254.4840404040403,"b (currency per unit, as reported)",|b/t|,0.06081750013443778,0.12286363663522783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.495,66.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Bathroom,bathrooms,A_structural,level,count,4395.0062,,2.262,,,,,high,Number of bathrooms,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,4395.0062,1942.9735632183906,"b (currency per unit, as reported)",|b/t|,0.26822976775056795,0.11858079918239078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.262,66.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Height,storeys,A_structural,level,count,-5098.5247,,-0.752,,,,,high,Height of house in stories,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,-5098.5247,6779.953058510639,"b (currency per unit, as reported)",|b/t|,-0.09217077268677878,0.12256751687071647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.752,66.0,count,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Basement,basement,A_structural,binary,binary,4885.4162,,3.05,,,,,high,Basement dummy,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,4885.4162,1601.7758032786885,"b (currency per unit, as reported)",|b/t|,0.3514755684207746,0.11523789128549987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,66.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Fireplace,fireplace,B_interior,binary,binary,1053.9359,,0.597,,,,,high,Fireplace dummy (no fireplaces among 1971 sales),"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,1053.9359,1765.3867671691792,"b (currency per unit, as reported)",|b/t|,0.07328800393649673,0.12276047560552215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.597,66.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc,0a56ff9c5aeeb80c811f28d082785dd37d837fcc:TXI-c3,XI,3,98,Garage,garage,A_structural,binary,binary,7811.6183,,4.205,,,,,high,Garage dummy,"sale price of home, 1972 constant dollars (USD)",level,OLS,76.0,0.764,t,"Homes sold in 1986 (After) the Lavele landfill operation, within about 0.5 mile of the site",monetary,7811.6183,1857.6975743162902,"b (currency per unit, as reported)",|b/t|,0.45967396788821296,0.10931604468209583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.205,66.0,binary,,1993.0,United States,North America,High income,1971.0,1986.0,single_family,transaction,thesis,False,False,OLS,,1978.5,ols,other,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Green (<500m),green_space,E_environment,binary,binary,0.006,0.014,,,,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,0.5919451117272434,1.408425250475691,100*(exp(b - se^2/2) - 1),reported,0.00517774203628168,0.012081398084657254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42857142857142855,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Green (<500m),green_space,E_environment,binary,binary,0.075,0.007,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,7.785774304643223,0.7545189056192421,100*(exp(b - se^2/2) - 1),reported,0.12837422954369088,0.01198159475741115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.714285714285714,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Green (<500m),green_space,E_environment,binary,binary,0.021,0.023,,,,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,2.0951974124196138,2.3488107187663156,100*(exp(b - se^2/2) - 1),reported,0.02164829965254311,0.023710042476594834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9130434782608696,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Green (<500m),green_space,E_environment,binary,binary,0.085,0.014,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,8.861037765371083,1.5242038933777584,100*(exp(b - se^2/2) - 1),reported,0.14251778273016852,0.023473517155557164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.071428571428572,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Transport stop (<500m),transit_distance,D_location,binary,binary,0.245,0.041,,,***,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,27.654792364629046,5.238247384140036,100*(exp(b - se^2/2) - 1),reported,0.07200727828890503,0.012050197591204515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.975609756097561,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Transport stop (<500m),transit_distance,D_location,binary,binary,0.11,0.041,,,***,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,11.534023292208605,4.5767400888813725,100*(exp(b - se^2/2) - 1),reported,0.032396926884166025,0.012075218202280066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6829268292682924,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Transport stop (<500m),transit_distance,D_location,binary,binary,-1.028,0.233,,,***,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,-65.18578828645508,8.334915353244584,100*(exp(b - se^2/2) - 1),reported,-0.10406552131137958,0.023586835083221253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.412017167381974,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Transport stop (<500m),transit_distance,D_location,binary,binary,0.05,0.079,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,4.799571798675273,8.305041661370591,100*(exp(b - se^2/2) - 1),reported,0.015008183041349188,0.023712929205331714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6329113924050633,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Distance train station (>1km),transit_distance,D_location,binary,binary,0.01,0.02,,,,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,0.9848177250407986,2.020100334168336,100*(exp(b - se^2/2) - 1),reported,0.006040669802439581,0.012081339604879164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Distance train station (>1km),transit_distance,D_location,binary,binary,-0.093,0.019,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,-8.897095528809995,1.731267650562667,100*(exp(b - se^2/2) - 1),reported,-0.05903292585708897,0.012060490228867638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.894736842105263,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Distance train station (>1km),transit_distance,D_location,binary,binary,0.02,0.024,,,,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,1.9907564346556716,2.448483216064214,100*(exp(b - se^2/2) - 1),reported,0.019759141878785764,0.023710970254542916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Distance train station (>1km),transit_distance,D_location,binary,binary,-0.155,0.037,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,-14.417083811865316,3.16873615668937,100*(exp(b - se^2/2) - 1),reported,-0.09886243577027844,0.023599420151614852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.1891891891891895,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Car park,parking,A_structural,binary,binary,0.077,0.027,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,7.964847279436893,2.916113606260021,100*(exp(b - se^2/2) - 1),reported,0.034434386290731415,0.012074395192853875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8518518518518516,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Car park,parking,A_structural,binary,binary,0.186,0.026,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,20.401523444487978,3.1314978769818373,100*(exp(b - se^2/2) - 1),reported,0.08610860112331188,0.012036686178527467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.153846153846154,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Car park,parking,A_structural,binary,binary,0.135,0.048,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,14.321903714386552,5.49377656488631,100*(exp(b - se^2/2) - 1),reported,0.06655224768512036,0.023663021399153907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8125,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Car park,parking,A_structural,binary,binary,0.159,0.044,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,17.12036726255437,5.158286965395157,100*(exp(b - se^2/2) - 1),reported,0.08538657198965882,0.02362898847512571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6136363636363638,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Commercial,commercial_distance,D_location,binary,binary,0.344,0.037,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,40.961342481272034,5.21914095137841,100*(exp(b - se^2/2) - 1),reported,0.11162387803853778,0.01200605664949389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.297297297297296,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Commercial,commercial_distance,D_location,binary,binary,0.374,0.084,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,44.84181066928754,12.209712063837562,100*(exp(b - se^2/2) - 1),reported,0.05371405181222328,0.012064118588841596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.452380952380952,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Commercial,commercial_distance,D_location,binary,binary,0.3,0.057,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,34.766774211786625,7.694195203183218,100*(exp(b - se^2/2) - 1),reported,0.12385783947578168,0.02353298950039852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.263157894736842,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Commercial,commercial_distance,D_location,binary,binary,0.406,0.028,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,50.021435315205245,4.202247146882456,100*(exp(b - se^2/2) - 1),reported,0.3251864992173418,0.022426655118437364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.5,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,View river/mountains,view,E_environment,binary,binary,0.144,0.021,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,15.462948465229754,2.4252566279023187,100*(exp(b - se^2/2) - 1),reported,0.08256214353922148,0.012040312599469801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.857142857142856,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,View river/mountains,view,E_environment,binary,binary,0.131,0.012,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,13.988570658646138,1.3679613375743884,100*(exp(b - se^2/2) - 1),reported,0.1307579947648053,0.01197783158150888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.916666666666666,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,View river/mountains,view,E_environment,binary,binary,0.117,0.031,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,12.357942005227596,3.484770232040664,100*(exp(b - se^2/2) - 1),reported,0.08915085965084166,0.023621167941675993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.774193548387097,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,View river/mountains,view,E_environment,binary,binary,0.188,0.027,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,20.639370469362195,3.2584504914439343,100*(exp(b - se^2/2) - 1),reported,0.16292445817469367,0.02339872537615282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.962962962962963,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Number of baths+,bathrooms,A_structural,log,count,0.449,0.036,,,***,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",elasticity,0.449,0.036,b (log-log),reported,0.14900180565220641,0.011946692658083366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.472222222222223,6851.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Number of baths+,bathrooms,A_structural,log,count,0.11,0.016,,,***,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",elasticity,0.11,0.016,b (log-log),reported,0.082775677843489,0.012040098595416581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.875,6851.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Number of baths+,bathrooms,A_structural,log,count,0.386,0.069,,,***,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),elasticity,0.386,0.069,b (log-log),reported,0.1315174893622056,0.023509603020705146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.594202898550725,1778.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Number of baths+,bathrooms,A_structural,log,count,0.06,0.028,,,**,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",elasticity,0.06,0.028,b (log-log),reported,0.05075364788740703,0.02368503568078995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.142857142857143,1778.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Economic building,condition,A_structural,binary,binary,-0.277,0.028,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,-24.224260201712553,2.122552591909423,100*(exp(b - se^2/2) - 1),reported,-0.11867648842781392,0.011996179335663498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.892857142857144,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Economic building,condition,A_structural,binary,binary,-0.327,0.009,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,-27.89454602736148,0.6489753687239463,100*(exp(b - se^2/2) - 1),reported,-0.4019431994693699,0.011062656866129445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-36.333333333333336,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Economic building,condition,A_structural,binary,binary,-0.364,0.051,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,-30.601192387568375,3.5439450931888437,100*(exp(b - se^2/2) - 1),reported,-0.16689042860836664,0.023382999612710707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.137254901960785,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Economic building,condition,A_structural,binary,binary,-0.389,0.016,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,-32.23524744208284,1.0843748320223539,100*(exp(b - se^2/2) - 1),reported,-0.4995030416328341,0.020545112252250246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.3125,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Historical building,other,other,binary,binary,0.35,0.023,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",pct_premium,41.869225486143826,3.263855361764491,100*(exp(b - se^2/2) - 1),reported,0.18081930734694693,0.011882411625656512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.217391304347826,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Historical building,other,other,binary,binary,0.31,0.021,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",pct_premium,36.31245120371105,2.8631927396775736,100*(exp(b - se^2/2) - 1),reported,0.1755763656997179,0.011893882837722827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.761904761904761,6851.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Historical building,other,other,binary,binary,0.356,0.032,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),pct_premium,42.68768002865795,4.568344154444303,100*(exp(b - se^2/2) - 1),reported,0.25510649924163264,0.022930921280146756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.125,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Historical building,other,other,binary,binary,0.231,0.031,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",pct_premium,25.925402249969267,3.905563642292617,100*(exp(b - se^2/2) - 1),reported,0.17402301406216628,0.023353737817866465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.451612903225807,1778.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Age+,age,A_structural,log,years,-0.047,0.015,,,***,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",elasticity,-0.047,0.015,b (log-log),reported,-0.03782845956891839,0.012072912628378209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1333333333333333,6851.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Age+,age,A_structural,log,years,-0.016,0.008,,,**,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",elasticity,-0.016,0.008,b (log-log),reported,-0.024156069257723893,0.012078034628861947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,6851.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Age+,age,A_structural,log,years,-0.064,0.02,,,***,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),elasticity,-0.064,0.02,b (log-log),reported,-0.07567232466537065,0.023647601457928328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2,1778.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Age+,age,A_structural,log,years,0.01,0.017,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",elasticity,0.01,0.017,b (log-log),reported,0.01394899584409664,0.023713292934964283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5882352941176471,1778.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Total surface+,living_area,A_structural,log,m2,1.028,0.029,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",elasticity,1.028,0.029,b (log-log),reported,0.3936855625607998,0.011105915675353302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.44827586206897,6851.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Total surface+,living_area,A_structural,log,m2,0.961,0.014,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",elasticity,0.961,0.014,b (log-log),reported,0.6383561685653841,0.00929967363154566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.64285714285714,6851.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Total surface+,living_area,A_structural,log,m2,1.093,0.04,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),elasticity,1.093,0.04,b (log-log),reported,0.5438249108041516,0.019902101035833546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.325,1778.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Total surface+,living_area,A_structural,log,m2,0.948,0.026,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",elasticity,0.948,0.026,b (log-log),reported,0.6540837336321846,0.017939005352781437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.46153846153846,1778.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Floor level+,floor_level,A_structural,log,count,-0.011,0.01,,,,,,high,Log of floor level (1-9),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",elasticity,-0.011,0.01,b (log-log),reported,-0.013288542599919302,0.012080493272653911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0999999999999999,6851.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Floor level+,floor_level,A_structural,log,count,0.02,0.005,,,***,,,high,Log of floor level (1-9),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",elasticity,0.02,0.005,b (log-log),reported,0.04826990761706643,0.01206747690426661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,6851.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Floor level+,floor_level,A_structural,log,count,0.001,0.014,,,,,,high,Log of floor level (1-9),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),elasticity,0.001,0.014,b (log-log),reported,0.0016939690175866119,0.023715566246212568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.07142857142857142,1778.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Floor level+,floor_level,A_structural,log,count,0.024,0.011,,,**,,,high,Log of floor level (1-9),"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",elasticity,0.024,0.011,b (log-log),reported,0.051673999382555645,0.023683916383671334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.181818181818182,1778.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Movida 50 (#),noise,E_environment,level,count,-0.008,0.012,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",semi_elasticity,-0.8,1.2,100*b (unit as reported: count; noise unit not dB),reported,-0.008054112112704062,0.012081168169056094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,6851.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Movida 50 (#),noise,E_environment,level,count,0.0,0.006,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",semi_elasticity,0.0,0.6,100*b (unit as reported: count; noise unit not dB),reported,0.0,0.012081560033090223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,6851.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Movida 50 (#),noise,E_environment,level,count,0.021,0.023,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),semi_elasticity,2.1,2.3,100*b (unit as reported: count; noise unit not dB),reported,0.02164829965254311,0.023710042476594834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9130434782608696,1778.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Movida 50 (#),noise,E_environment,level,count,0.008,0.013,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",semi_elasticity,0.8,1.3,100*b (unit as reported: count; noise unit not dB),reported,0.014592661581364859,0.023713075069717895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6153846153846154,1778.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Movida_51-75 (#),noise,E_environment,level,count,0.008,0.019,,,,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",semi_elasticity,0.8,1.9,100*b (unit as reported: count; noise unit not dB),reported,0.005086906828254094,0.012081403717103473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4210526315789474,6851.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Movida_51-75 (#),noise,E_environment,level,count,-0.002,0.005,,,,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",semi_elasticity,-0.2,0.5,100*b (unit as reported: count; noise unit not dB),reported,-0.004832567583058093,0.012081418957645232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,6851.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Movida_51-75 (#),noise,E_environment,level,count,-0.039,0.021,,,*,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),semi_elasticity,-3.9,2.1,100*b (unit as reported: count; noise unit not dB),reported,-0.04400060195318094,0.023692631820943583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.857142857142857,1778.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Movida_51-75 (#),noise,E_environment,level,count,-0.025,0.013,,,*,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",semi_elasticity,-2.5,1.3,100*b (unit as reported: count; noise unit not dB),reported,-0.045559566458861216,0.023690974558607827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9230769230769234,1778.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c1,3,1,34,Movida 76-100 (#),noise,E_environment,level,count,-0.0,0.013,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample (Turin, 2017-2018); coefficients for dwellings < 1 year on the market in a fully interacted specification; month-year and urban macro-area fixed effects; two-way clustered SE",semi_elasticity,-0.0,1.3,100*b (unit as reported: count; noise unit not dB),reported,-0.0,0.012081560033090223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,6851.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c2,3,2,34,Movida 76-100 (#),noise,E_environment,level,count,0.002,0.005,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,6861.0,0.865,se,"Full sample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 1)",semi_elasticity,0.2,0.5,100*b (unit as reported: count; noise unit not dB),reported,0.004832567583058093,0.012081418957645232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,6851.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c3,3,3,34,Movida 76-100 (#),noise,E_environment,level,count,0.006,0.011,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,Central subsample (OMI central districts); coefficients for dwellings < 1 year on the market (fully interacted specification),semi_elasticity,0.6,1.0999999999999999,100*b (unit as reported: count; noise unit not dB),reported,0.012934699801649678,0.023713616303024404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5454545454545455,1778.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T3-c4,3,4,34,Movida 76-100 (#),noise,E_environment,level,count,0.008,0.011,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,OLS,1788.0,0.873,se,"Central subsample; coefficients for dwellings >= 1 year on the market (fully interacted specification, same regression as column 3)",semi_elasticity,0.8,1.0999999999999999,100*b (unit as reported: count; noise unit not dB),reported,0.01724514440712586,0.02371207355979806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7272727272727273,1778.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,ols,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Green (<500m),green_space,E_environment,binary,binary,0.108,0.027,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,11.36417489806143,3.0079289125434627,100*(exp(b - se^2/2) - 1),reported,0.11634594543263002,0.029086486358157505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Green (<500m),green_space,E_environment,binary,binary,0.118,0.023,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,12.494652365759528,2.5880614561448874,100*(exp(b - se^2/2) - 1),reported,0.14857902666275702,0.0289603187563001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.130434782608695,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Green (<500m),green_space,E_environment,binary,binary,0.116,0.024,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,12.267249589049456,2.695190093119811,100*(exp(b - se^2/2) - 1),reported,0.1401489603533298,0.028996336624826854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.833333333333333,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Green (<500m),green_space,E_environment,binary,binary,0.105,0.021,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,11.046572566565116,2.332492281746981,100*(exp(b - se^2/2) - 1),reported,0.14488189694993614,0.028976379389987234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.999999999999999,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Green (<500m),green_space,E_environment,binary,binary,0.115,0.021,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,12.162609174983285,2.3559342189010706,100*(exp(b - se^2/2) - 1),reported,0.15834888669511268,0.028915883657368406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.476190476190476,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Green (<500m),green_space,E_environment,binary,binary,0.099,0.021,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,10.382287977784621,2.3185392290736524,100*(exp(b - se^2/2) - 1),reported,0.13676238010276626,0.02901020183998072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.714285714285714,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Green (<500m),green_space,E_environment,binary,binary,0.086,0.021,,,***,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,8.956605250111839,2.2885932894407706,100*(exp(b - se^2/2) - 1),reported,0.11907725846659677,0.029077004974401535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.095238095238095,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Green (<500m),green_space,E_environment,binary,binary,0.044,0.025,,,*,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,4.465584892175545,2.6124558872211097,100*(exp(b - se^2/2) - 1),reported,0.05147392475219968,0.029246548154658915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7599999999999998,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Green (<500m),green_space,E_environment,binary,binary,0.055,0.03,,,*,,,high,Dummy = 1 if a green area lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,5.606527835758324,3.1696218440264827,100*(exp(b - se^2/2) - 1),reported,0.05361262989388799,0.02924325266939345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8333333333333335,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Transport (<500m),transit_distance,D_location,binary,binary,0.381,0.203,,,*,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,43.389640845945166,29.714076389817492,100*(exp(b - se^2/2) - 1),reported,0.05488133115242958,0.029241234183577967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.876847290640394,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Transport (<500m),transit_distance,D_location,binary,binary,0.103,0.167,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,9.314132398626839,18.51180653156932,100*(exp(b - se^2/2) - 1),reported,0.018059288951974936,0.02928059470854189,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6167664670658682,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Transport (<500m),transit_distance,D_location,binary,binary,0.109,0.16,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,10.097923782342665,17.842597605463165,100*(exp(b - se^2/2) - 1),reported,0.019946689460298025,0.02927954416190536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.68125,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Transport (<500m),transit_distance,D_location,binary,binary,-0.08,0.15,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,-8.721051499903309,13.846745195799537,100*(exp(b - se^2/2) - 1),reported,-0.01561695958345289,0.02928179921897417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5333333333333333,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Transport (<500m),transit_distance,D_location,binary,binary,-0.057,0.125,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,-6.2756820000294695,11.807425867081541,100*(exp(b - se^2/2) - 1),reported,-0.013352938428474168,0.029282759711566155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.456,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Transport (<500m),transit_distance,D_location,binary,binary,-0.037,0.106,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,-4.172261788193254,10.214967034699967,100*(exp(b - se^2/2) - 1),reported,-0.010221717970983117,0.02928384067362731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3490566037735849,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Transport (<500m),transit_distance,D_location,binary,binary,-0.12,0.098,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,-11.732834572884677,8.691820279828145,100*(exp(b - se^2/2) - 1),reported,-0.035836603524221494,0.029266559544780895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2244897959183672,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Transport (<500m),transit_distance,D_location,binary,binary,-0.001,0.09,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,-0.5037270187532417,8.991004498500374,100*(exp(b - se^2/2) - 1),reported,-0.00032539298980549807,0.02928536908249482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.011111111111111112,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Transport (<500m),transit_distance,D_location,binary,binary,-0.025,0.102,,,,,,high,Dummy = 1 if a bus/metro stop lies within 500 m,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,-2.9750476651571933,9.948161102688992,100*(exp(b - se^2/2) - 1),reported,-0.007177602024931401,0.029284616261720114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2450980392156863,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.211,0.062,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,-19.17808241463338,5.020599214663913,100*(exp(b - se^2/2) - 1),reported,-0.0991733986066375,0.029140998642708648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.403225806451613,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.168,0.056,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,-15.597064146745787,4.733981474234089,100*(exp(b - se^2/2) - 1),reported,-0.08751899489873673,0.029172998299578912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.128,0.049,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,-12.120225106820282,4.311281557808755,100*(exp(b - se^2/2) - 1),reported,-0.0762776843237997,0.029200051030204576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6122448979591835,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.134,0.045,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,-12.629500969260288,3.935655290715003,100*(exp(b - se^2/2) - 1),reported,-0.08687561704349152,0.029174647514605363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.977777777777778,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.124,0.046,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,-11.755428075142337,4.063547268060654,100*(exp(b - se^2/2) - 1),reported,-0.07869832892690214,0.029194541376108857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6956521739130435,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.126,0.049,,,***,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,-11.944289680252496,4.319912749238739,100*(exp(b - se^2/2) - 1),reported,-0.07509261920093487,0.029202685244808006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.571428571428571,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.111,0.059,,,*,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,-10.66175372125886,5.280138618681283,100*(exp(b - se^2/2) - 1),reported,-0.055012770876958515,0.029241022358022995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.88135593220339,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.144,0.06,,,**,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,-13.56694479904139,5.19532648835523,100*(exp(b - se^2/2) - 1),reported,-0.07011192760364504,0.029213303168185437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Trainstation (>1km),transit_distance,D_location,binary,binary,-0.158,0.08,,,**,,,high,Dummy = 1 if the train station is more than 1 km away (reverse-coded proximity),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,-14.887817028236384,6.830798255747854,100*(exp(b - se^2/2) - 1),reported,-0.05774210517854627,0.02923650895116267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.975,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Car park,parking,A_structural,binary,binary,-0.134,0.132,,,,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,-13.299626998319336,11.54458885276401,100*(exp(b - se^2/2) - 1),reported,-0.02971595950969347,0.02927243772596671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0151515151515151,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Car park,parking,A_structural,binary,binary,0.045,0.142,,,,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,3.553479146516003,14.85359561070378,100*(exp(b - se^2/2) - 1),reported,0.009280175562689267,0.02928410955337502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31690140845070425,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Car park,parking,A_structural,binary,binary,0.206,0.097,,,**,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,22.29861108286095,11.918906078754526,100*(exp(b - se^2/2) - 1),reported,0.06207373732880237,0.029228895732494323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1237113402061856,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Car park,parking,A_structural,binary,binary,0.181,0.067,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,19.572835284223068,8.029381701264438,100*(exp(b - se^2/2) - 1),reported,0.07886777622378689,0.02919414920991007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7014925373134324,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Car park,parking,A_structural,binary,binary,0.221,0.056,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,24.536915997348487,6.985011211158763,100*(exp(b - se^2/2) - 1),reported,0.1148084169992712,0.029091725574475962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.946428571428571,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Car park,parking,A_structural,binary,binary,0.207,0.045,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,22.873784467625804,5.534921573013387,100*(exp(b - se^2/2) - 1),reported,0.13350674393288844,0.029023205202801834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Car park,parking,A_structural,binary,binary,0.206,0.046,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,22.745387057406695,5.652264738378435,100*(exp(b - se^2/2) - 1),reported,0.13003402546614728,0.029036724133217357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.478260869565217,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Car park,parking,A_structural,binary,binary,0.216,0.056,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,23.91578553753253,6.950173322403761,100*(exp(b - se^2/2) - 1),reported,0.11224404025854419,0.02910030673369664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.857142857142857,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Car park,parking,A_structural,binary,binary,0.218,0.064,,,***,,,high,Dummy = 1 if a car park / parking facility is included or nearby,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,24.104280751620678,7.958957232761996,100*(exp(b - se^2/2) - 1),reported,0.09926065871134777,0.029140743841863565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.40625,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Commercial,commercial_distance,D_location,binary,binary,0.066,0.075,,,,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,6.522655048600634,8.01170037874495,100*(exp(b - se^2/2) - 1),reported,0.02576257245852626,0.029275650521052566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8800000000000001,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Commercial,commercial_distance,D_location,binary,binary,0.111,0.063,,,*,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,11.517963549183774,7.0395879131830865,100*(exp(b - se^2/2) - 1),reported,0.05152948462271119,0.029246464245322568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7619047619047619,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Commercial,commercial_distance,D_location,binary,binary,0.197,0.07,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,21.47642195775923,8.52420828494136,100*(exp(b - se^2/2) - 1),reported,0.08213890284169346,0.02918641217725149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.814285714285714,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Commercial,commercial_distance,D_location,binary,binary,0.274,0.077,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,31.132162147586364,10.12715397723818,100*(exp(b - se^2/2) - 1),reported,0.10364899687435224,0.029127637807755916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.558441558441559,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Commercial,commercial_distance,D_location,binary,binary,0.465,0.073,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,58.777791336518305,11.621703578875838,100*(exp(b - se^2/2) - 1),reported,0.18338039489783087,0.028788750166756232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.369863013698631,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Commercial,commercial_distance,D_location,binary,binary,0.529,0.058,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,69.43818770232564,9.84395850785941,100*(exp(b - se^2/2) - 1),reported,0.25805599571764365,0.028293474010630117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.120689655172415,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Commercial,commercial_distance,D_location,binary,binary,0.593,0.052,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,80.6963839382382,9.40892423492299,100*(exp(b - se^2/2) - 1),reported,0.3167676740542835,0.027777266527525706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.403846153846153,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Commercial,commercial_distance,D_location,binary,binary,0.667,0.062,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,94.46421975127895,12.07997704251789,100*(exp(b - se^2/2) - 1),reported,0.3004933471633239,0.027931915328524855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.758064516129034,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Commercial,commercial_distance,D_location,binary,binary,0.773,0.093,,,***,,,high,"Dummy = 1 if commercial activities/services lie within 500 m (proximity, not distance)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,115.69075369848987,20.14617411535208,100*(exp(b - se^2/2) - 1),reported,0.2365091029228773,0.02845452337881965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.311827956989248,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,View river/mountains,view,E_environment,binary,binary,0.195,0.054,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,21.354035417604766,6.562679327044545,100*(exp(b - se^2/2) - 1),reported,0.10516629141209013,0.029122973006424954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.611111111111111,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,View river/mountains,view,E_environment,binary,binary,0.22,0.042,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,24.497817544503665,5.2335222684669995,100*(exp(b - se^2/2) - 1),reported,0.15162594640085109,0.028946771585617027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.238095238095238,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,View river/mountains,view,E_environment,binary,binary,0.215,0.036,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,23.905872671209472,4.463502829077822,100*(exp(b - se^2/2) - 1),reported,0.17228355019094516,0.028847478171507093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.972222222222222,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,View river/mountains,view,E_environment,binary,binary,0.219,0.035,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,24.40690509860106,4.356909468406359,100*(exp(b - se^2/2) - 1),reported,0.1802416635142635,0.028805745310498735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.257142857142856,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,View river/mountains,view,E_environment,binary,binary,0.229,0.037,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,25.64816828746168,4.6521655443364045,100*(exp(b - se^2/2) - 1),reported,0.17834680021663873,0.0288158585502866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1891891891891895,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,View river/mountains,view,E_environment,binary,binary,0.195,0.038,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,21.443384864235114,4.618181748660977,100*(exp(b - se^2/2) - 1),reported,0.1486114303120719,0.028960176163378114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.131578947368421,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,View river/mountains,view,E_environment,binary,binary,0.172,0.039,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,18.67749475875222,4.631943549534233,100*(exp(b - se^2/2) - 1),reported,0.12809204319854556,0.02904412607408882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.41025641025641,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,View river/mountains,view,E_environment,binary,binary,0.142,0.045,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,15.141025526659902,5.186594918416523,100*(exp(b - se^2/2) - 1),reported,0.09201953021957955,0.029161118731556898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1555555555555554,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,View river/mountains,view,E_environment,binary,binary,0.149,0.045,,,***,,,high,Dummy = 1 if the dwelling enjoys a panoramic view of the river and/or mountains,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,15.949840254235603,5.223028451440884,100*(exp(b - se^2/2) - 1),reported,0.09651443335888903,0.02914865437013427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.311111111111111,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Number of baths+,bathrooms,A_structural,log,count,0.036,0.055,,,,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",elasticity,0.036,0.055,b (log-log),reported,0.01916508558973059,0.029279991873199518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6545454545454545,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Number of baths+,bathrooms,A_structural,log,count,0.041,0.05,,,,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",elasticity,0.041,0.05,b (log-log),reported,0.024007082805774353,0.02927693025094433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.82,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Number of baths+,bathrooms,A_structural,log,count,0.043,0.044,,,,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",elasticity,0.043,0.044,b (log-log),reported,0.028608080092289552,0.02927338428048233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9772727272727273,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Number of baths+,bathrooms,A_structural,log,count,0.075,0.04,,,*,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",elasticity,0.075,0.04,b (log-log),reported,0.054827476552053105,0.029241320827761656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.875,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Number of baths+,bathrooms,A_structural,log,count,0.12,0.038,,,***,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",elasticity,0.12,0.038,b (log-log),reported,0.09208716512464571,0.02916093562280447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1578947368421053,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Number of baths+,bathrooms,A_structural,log,count,0.101,0.037,,,***,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",elasticity,0.101,0.037,b (log-log),reported,0.07968692924032748,0.029192241404872442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.72972972972973,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Number of baths+,bathrooms,A_structural,log,count,0.095,0.041,,,**,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",elasticity,0.095,0.041,b (log-log),reported,0.0677006622935388,0.02921818056879043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.317073170731707,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Number of baths+,bathrooms,A_structural,log,count,0.092,0.045,,,**,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",elasticity,0.092,0.045,b (log-log),reported,0.05976528898700784,0.029233021787123406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0444444444444443,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Number of baths+,bathrooms,A_structural,log,count,0.106,0.052,,,**,,,high,Log of number of bathrooms (+ = logarithmic form),"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",elasticity,0.106,0.052,b (log-log),reported,0.05959101255407085,0.02923332691331778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0384615384615383,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Economic Building,condition,A_structural,binary,binary,-0.381,0.032,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,-31.717180792937384,2.186169246783679,100*(exp(b - se^2/2) - 1),reported,-0.3292389239515902,0.027652613035304162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.90625,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Economic Building,condition,A_structural,binary,binary,-0.361,0.025,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,-30.32387734415152,1.742447496167182,100*(exp(b - se^2/2) - 1),reported,-0.38948673623346464,0.026972765667137442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.44,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Economic Building,condition,A_structural,binary,binary,-0.371,0.027,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,-31.020753362377285,1.8631186422087302,100*(exp(b - se^2/2) - 1),reported,-0.37331142051781113,0.027168216587549598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.74074074074074,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Economic Building,condition,A_structural,binary,binary,-0.369,0.025,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,-30.87906262329674,1.7285635261257715,100*(exp(b - se^2/2) - 1),reported,-0.39677167043906936,0.026881549487741827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.76,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Economic Building,condition,A_structural,binary,binary,-0.392,0.025,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,-32.45070105850583,1.689260284901565,100*(exp(b - se^2/2) - 1),reported,-0.41730142919947294,0.026613611556088837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.68,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Economic Building,condition,A_structural,binary,binary,-0.402,0.026,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,-33.12453315924835,1.7393499387949014,100*(exp(b - se^2/2) - 1),reported,-0.4124823766672258,0.02667796466006933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.461538461538463,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Economic Building,condition,A_structural,binary,binary,-0.415,0.026,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,-33.988287659418724,1.7168847298329553,100*(exp(b - se^2/2) - 1),reported,-0.42346034897489854,0.02653004595987316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.961538461538462,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Economic Building,condition,A_structural,binary,binary,-0.422,0.029,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,-34.45416388047199,1.9016287142409811,100*(exp(b - se^2/2) - 1),reported,-0.39203859270595465,0.026941040730977923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.551724137931034,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Economic Building,condition,A_structural,binary,binary,-0.376,0.038,,,***,,,high,Realtor/cadastral building class 'economic' (lower-quality building) vs base 'residential' building,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,-31.389321492781153,2.609088865560744,100*(exp(b - se^2/2) - 1),reported,-0.27832158690198466,0.028128245484775043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.894736842105264,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Historical Building,other,other,binary,binary,0.184,0.07,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",pct_premium,19.90744889353424,8.414110761674111,100*(exp(b - se^2/2) - 1),reported,0.0767516201623937,0.02919898593134544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.628571428571428,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Historical Building,other,other,binary,binary,0.253,0.071,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",pct_premium,28.464125451036182,9.143971265525876,100*(exp(b - se^2/2) - 1),reported,0.10379130159927354,0.029127203215606405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.563380281690141,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Historical Building,other,other,binary,binary,0.284,0.057,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",pct_premium,32.627664337541276,7.572067705290929,100*(exp(b - se^2/2) - 1),reported,0.1443841553041056,0.02897851004342964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.982456140350877,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Historical Building,other,other,binary,binary,0.275,0.047,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",pct_premium,31.507736947287768,6.187694171877821,100*(exp(b - se^2/2) - 1),reported,0.16888913127055546,0.02886468788987675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.851063829787234,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Historical Building,other,other,binary,binary,0.245,0.044,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",pct_premium,27.638517416152553,5.621533778101501,100*(exp(b - se^2/2) - 1),reported,0.16094055303542434,0.028903609524729267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.568181818181818,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Historical Building,other,other,binary,binary,0.235,0.04,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",pct_premium,26.389724638079404,5.059635074931567,100*(exp(b - se^2/2) - 1),reported,0.16956021653410744,0.028861313452614036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.874999999999999,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Historical Building,other,other,binary,binary,0.21,0.039,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",pct_premium,23.27402044569016,4.811344433831298,100*(exp(b - se^2/2) - 1),reported,0.1557656925289794,0.028927914326810463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.384615384615384,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Historical Building,other,other,binary,binary,0.173,0.044,,,***,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",pct_premium,18.771583951266635,5.23101086236974,100*(exp(b - se^2/2) - 1),reported,0.11438894734734817,0.029093142677938263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9318181818181817,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Historical Building,other,other,binary,binary,0.142,0.058,,,**,,,high,Realtor building class 'historical building' vs base 'residential'; printed name 'Historical building',"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",pct_premium,15.063964409157315,6.684944561514629,100*(exp(b - se^2/2) - 1),reported,0.07151508291884598,0.029210385980937094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4482758620689653,1166.0,binary,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,False,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Age+,age,A_structural,log,years,-0.006,0.033,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",elasticity,-0.006,0.033,b (log-log),reported,-0.005324537363559542,0.02928495549957748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18181818181818182,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Age+,age,A_structural,log,years,-0.012,0.025,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",elasticity,-0.012,0.025,b (log-log),reported,-0.014055589289793608,0.02928247768707002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Age+,age,A_structural,log,years,0.017,0.022,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",elasticity,0.017,0.022,b (log-log),reported,0.02262381250546078,0.029277875007066887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7727272727272728,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Age+,age,A_structural,log,years,0.034,0.022,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",elasticity,0.034,0.022,b (log-log),reported,0.045212925860845604,0.029255422615841267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5454545454545456,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Age+,age,A_structural,log,years,0.048,0.024,,,**,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",elasticity,0.048,0.024,b (log-log),reported,0.05847053462046861,0.029235267310234306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Age+,age,A_structural,log,years,0.034,0.023,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",elasticity,0.034,0.023,b (log-log),reported,0.04325090715379687,0.029257966604039058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4782608695652175,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Age+,age,A_structural,log,years,0.031,0.021,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",elasticity,0.031,0.021,b (log-log),reported,0.04319044473109238,0.029258043204933554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.476190476190476,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Age+,age,A_structural,log,years,-0.002,0.026,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",elasticity,-0.002,0.026,b (log-log),reported,-0.002252715101936214,0.02928529632517078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07692307692307693,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Age+,age,A_structural,log,years,-0.008,0.028,,,,,,high,"Log of age in years, measured from the date of the last renovation if any (years since construction or renewal)","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",elasticity,-0.008,0.028,b (log-log),reported,-0.008366955868521896,0.029284345539826635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2857142857142857,1166.0,years,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Total Surface+,living_area,A_structural,log,m2,1.002,0.052,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",elasticity,1.002,0.052,b (log-log),reported,0.4914559581711084,0.025504700424049537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.26923076923077,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Total Surface+,living_area,A_structural,log,m2,1.0,0.043,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",elasticity,1.0,0.043,b (log-log),reported,0.5629062152403098,0.024204967255333318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.255813953488374,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Total Surface+,living_area,A_structural,log,m2,0.95,0.041,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",elasticity,0.95,0.041,b (log-log),reported,0.561496906764523,0.024233024397205732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.17073170731707,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Total Surface+,living_area,A_structural,log,m2,0.933,0.037,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",elasticity,0.933,0.037,b (log-log),reported,0.5940454742107663,0.023558073468165436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.216216216216218,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Total Surface+,living_area,A_structural,log,m2,0.904,0.033,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",elasticity,0.904,0.033,b (log-log),reported,0.6257606452381149,0.022843032403603754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.393939393939394,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Total Surface+,living_area,A_structural,log,m2,0.911,0.03,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",elasticity,0.911,0.03,b (log-log),reported,0.6645350522435414,0.021883700952037587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.366666666666667,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Total Surface+,living_area,A_structural,log,m2,0.917,0.034,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",elasticity,0.917,0.034,b (log-log),reported,0.619823137086488,0.02298144674039323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.970588235294116,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Total Surface+,living_area,A_structural,log,m2,0.917,0.033,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",elasticity,0.917,0.033,b (log-log),reported,0.631189676590117,0.02271456851414816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.78787878787879,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Total Surface+,living_area,A_structural,log,m2,0.899,0.035,,,***,,,high,Log of total surface in square metres,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",elasticity,0.899,0.035,b (log-log),reported,0.6011326110809294,0.023403383078790353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.685714285714283,1166.0,m2,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Floor level,floor_level,A_structural,log,count,0.033,0.019,,,*,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",elasticity,0.033,0.019,b (log-log),reported,0.050798395617018564,0.02924756111282887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.736842105263158,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Floor level,floor_level,A_structural,log,count,0.028,0.015,,,*,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",elasticity,0.028,0.015,b (log-log),reported,0.054584526524690695,0.02924171063822716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8666666666666667,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Floor level,floor_level,A_structural,log,count,0.038,0.017,,,**,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",elasticity,0.038,0.017,b (log-log),reported,0.06532160834267409,0.029222824784880516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2352941176470584,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,True
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Floor level,floor_level,A_structural,log,count,0.034,0.016,,,**,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",elasticity,0.034,0.016,b (log-log),reported,0.06211125816683293,0.029228827372627265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.125,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Floor level,floor_level,A_structural,log,count,0.03,0.016,,,*,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",elasticity,0.03,0.016,b (log-log),reported,0.054827476552053105,0.029241320827761656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.875,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Floor level,floor_level,A_structural,log,count,0.02,0.015,,,,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",elasticity,0.02,0.015,b (log-log),reported,0.03901742761214631,0.02926307070910973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333335,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Floor level,floor_level,A_structural,log,count,0.017,0.016,,,,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",elasticity,0.017,0.016,b (log-log),reported,0.031100654312456486,0.029271204058782575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0625,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Floor level,floor_level,A_structural,log,count,0.015,0.017,,,,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",elasticity,0.015,0.017,b (log-log),reported,0.025831410439767433,0.0292755984984031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8823529411764705,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Floor level,floor_level,A_structural,log,count,0.029,0.022,,,,,,high,"Log of floor level (1-9) ; printed without the '+' log marker in Table 4 (model described as log-log), transform assumed as in Table 3","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",elasticity,0.029,0.022,b (log-log),reported,0.03857471129182722,0.029263574083455122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3181818181818183,1166.0,count,,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,True,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Movida 50 (#),noise,E_environment,level,count,0.025,0.024,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",semi_elasticity,2.5,2.4,100*b (unit as reported: count; noise unit not dB),reported,0.030491410188027205,0.029271753780506116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0416666666666667,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Movida 50 (#),noise,E_environment,level,count,0.016,0.017,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",semi_elasticity,1.6,1.7000000000000002,100*b (unit as reported: count; noise unit not dB),reported,0.027552238006627656,0.029274252882041885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9411764705882353,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Movida 50 (#),noise,E_environment,level,count,0.006,0.019,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",semi_elasticity,0.6,1.9,100*b (unit as reported: count; noise unit not dB),reported,0.00924761633272526,0.02928411838696332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31578947368421056,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Movida 50 (#),noise,E_environment,level,count,0.019,0.018,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",semi_elasticity,1.9,1.7999999999999998,100*b (unit as reported: count; noise unit not dB),reported,0.030897576755714026,0.029271388505413286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0555555555555556,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Movida 50 (#),noise,E_environment,level,count,0.022,0.017,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",semi_elasticity,2.1999999999999997,1.7000000000000002,100*b (unit as reported: count; noise unit not dB),reported,0.03787152702013679,0.029264361788287525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2941176470588234,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Movida 50 (#),noise,E_environment,level,count,0.02,0.016,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",semi_elasticity,2.0,1.6,100*b (unit as reported: count; noise unit not dB),reported,0.03658221047485797,0.029265768379886373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Movida 50 (#),noise,E_environment,level,count,0.016,0.015,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",semi_elasticity,1.6,1.5,100*b (unit as reported: count; noise unit not dB),reported,0.03122249899939896,0.029271092811936527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0666666666666667,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Movida 50 (#),noise,E_environment,level,count,0.023,0.017,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",semi_elasticity,2.3,1.7000000000000002,100*b (unit as reported: count; noise unit not dB),reported,0.03959032047130918,0.029262410783141568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.352941176470588,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Movida 50 (#),noise,E_environment,level,count,-0.002,0.02,,,,,,high,"Number of night-time recreational activities (bars, pubs, nightclubs open Saturday 10 pm - 2 am, Google Maps) within 50 m of the dwelling - recreational-noise proxy, not a dB measure","log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",semi_elasticity,-0.2,2.0,100*b (unit as reported: count; noise unit not dB),reported,-0.0029285245053187335,0.02928524505318733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Movida 51_75 (#),noise,E_environment,level,count,0.0,0.021,,,,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",semi_elasticity,0.0,2.1,100*b (unit as reported: count; noise unit not dB),reported,0.0,0.029285370632871136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Movida 51_75 (#),noise,E_environment,level,count,-0.031,0.019,,,,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",semi_elasticity,-3.1,1.9,100*b (unit as reported: count; noise unit not dB),reported,-0.047726943474639814,0.029251997613488918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.631578947368421,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Movida 51_75 (#),noise,E_environment,level,count,-0.026,0.02,,,,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",semi_elasticity,-2.6,2.0,100*b (unit as reported: count; noise unit not dB),reported,-0.038043421744295945,0.02926417057253535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2999999999999998,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Movida 51_75 (#),noise,E_environment,level,count,-0.029,0.016,,,*,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",semi_elasticity,-2.9000000000000004,1.6,100*b (unit as reported: count; noise unit not dB),reported,-0.05300511694248154,0.029244202451024298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8125,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Movida 51_75 (#),noise,E_environment,level,count,-0.032,0.014,,,**,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",semi_elasticity,-3.2,1.4000000000000001,100*b (unit as reported: count; noise unit not dB),reported,-0.0667885277597928,0.02921998089490935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2857142857142856,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Movida 51_75 (#),noise,E_environment,level,count,-0.028,0.014,,,*,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",semi_elasticity,-2.8000000000000003,1.4000000000000001,100*b (unit as reported: count; noise unit not dB),reported,-0.05847053462046861,0.029235267310234306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Movida 51_75 (#),noise,E_environment,level,count,-0.038,0.016,,,**,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",semi_elasticity,-3.8,1.6,100*b (unit as reported: count; noise unit not dB),reported,-0.06938512947722911,0.029214791358833307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.375,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Movida 51_75 (#),noise,E_environment,level,count,-0.039,0.02,,,**,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",semi_elasticity,-3.9,2.0,100*b (unit as reported: count; noise unit not dB),reported,-0.05701358350195308,0.029237735129206712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.95,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Movida 51_75 (#),noise,E_environment,level,count,-0.032,0.025,,,,,,high,Number of night-time recreational activities located 51-75 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",semi_elasticity,-3.2,2.5,100*b (unit as reported: count; noise unit not dB),reported,-0.03745896599429615,0.029264817183043867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.28,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c1,4,1,35,Movida 76_100 (#),noise,E_environment,level,count,-0.027,0.017,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.10; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.612; SE type not stated",semi_elasticity,-2.7,1.7000000000000002,100*b (unit as reported: count; noise unit not dB),reported,-0.04646182929002041,0.02925374436779063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.588235294117647,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c2,4,2,35,Movida 76_100 (#),noise,E_environment,level,count,0.0,0.018,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.20; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.616; SE type not stated",semi_elasticity,0.0,1.7999999999999998,100*b (unit as reported: count; noise unit not dB),reported,0.0,0.029285370632871136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c3,4,3,35,Movida 76_100 (#),noise,E_environment,level,count,0.0,0.019,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.30; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.624; SE type not stated",semi_elasticity,0.0,1.9,100*b (unit as reported: count; noise unit not dB),reported,0.0,0.029285370632871136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c4,4,4,35,Movida 76_100 (#),noise,E_environment,level,count,0.01,0.014,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.40; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.635; SE type not stated",semi_elasticity,1.0,1.4000000000000001,100*b (unit as reported: count; noise unit not dB),reported,0.020913546833461166,0.029278965566845633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7142857142857143,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c5,4,5,35,Movida 76_100 (#),noise,E_environment,level,count,0.014,0.011,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.50; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.643; SE type not stated",semi_elasticity,1.4000000000000001,1.0999999999999999,100*b (unit as reported: count; noise unit not dB),reported,0.03724642706793716,0.029265049839093473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.272727272727273,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c6,4,6,35,Movida 76_100 (#),noise,E_environment,level,count,0.007,0.011,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.60; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.65; SE type not stated",semi_elasticity,0.7000000000000001,1.0999999999999999,100*b (unit as reported: count; noise unit not dB),reported,0.018632909569382766,0.029280286466172912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6363636363636365,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c7,4,7,35,Movida 76_100 (#),noise,E_environment,level,count,0.017,0.012,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.70; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.654; SE type not stated",semi_elasticity,1.7000000000000002,1.2,100*b (unit as reported: count; noise unit not dB),reported,0.04145194973741346,0.029260199814644793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4166666666666667,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c8,4,8,35,Movida 76_100 (#),noise,E_environment,level,count,-0.001,0.015,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.80; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.658; SE type not stated",semi_elasticity,-0.1,1.5,100*b (unit as reported: count; noise unit not dB),reported,-0.0019523543212985924,0.029285314819478887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06666666666666667,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442,0aed4606387835599541d79050eabb05449e0442:T4-c9,4,9,35,Movida 76_100 (#),noise,E_environment,level,count,0.017,0.022,,,,,,high,Number of night-time recreational activities located 76-100 m from the dwelling - recreational-noise proxy,"log(price), real transaction price (EUR, CPI-deflated)",log,quantile,1176.0,,se,"Quantile regression tau = 0.90; central subsample, dwellings on the market for more than one year (N = 1,176); urban-area and time dummies; pseudo-R2 = 0.661; SE type not stated",semi_elasticity,1.7000000000000002,2.1999999999999997,100*b (unit as reported: count; noise unit not dB),reported,0.02262381250546078,0.029277875007066887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7727272727272728,1166.0,count,unit_not_harmonisable,2023.0,Italy,Europe,High income,2017.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2017.5,quantile,journal,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,square,living_area,A_structural,level,m2,-59.74631,5.937107,-10.06,0.0,,-71.38733,-48.1053,high,Total housing area,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,-59.74631,5.937107,"b (currency per unit, as reported)",reported,-0.1770039802325947,0.017594829048965675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.06,3129.0,m2,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,livingRoom,bedrooms,A_structural,level,count,542.6135,291.935,1.86,0.063,,-29.78979,1115.017,medium,"Printed meaning ""the living room's number""; in the Lianjia/Kaggle Beijing dataset this field (shi) is the number of bedrooms, hence mapped to bedrooms","price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,542.6135,291.935,"b (currency per unit, as reported)",reported,0.03323305030576575,0.01786723134718589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.86,3129.0,count,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,drawingRoom,other,other,level,count,1993.013,351.7496,5.67,0.0,,1303.329,2682.696,high,"Number of drawing rooms (ting, living/reception rooms)","price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,1993.013,351.7496,"b (currency per unit, as reported)",reported,0.10084644248775602,0.01778596869272593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.67,3129.0,count,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,False,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,ladderRatio,other,other,level,ratio (residents per floor per elevator),2716.725,932.9369,2.91,0.004,,887.4948,4545.954,high,Ladder ratio: proportion between number of residents on the same floor and number of elevators,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,2716.725,932.9369,"b (currency per unit, as reported)",reported,0.05195212659555195,0.017852964465825412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.91,3129.0,ratio,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,False,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,elevator,accessibility,B_interior,binary,binary,843.4289,327.006,2.58,0.01,,202.2612,1484.597,high,=1 if building has an elevator,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,843.4289,327.006,"b (currency per unit, as reported)",reported,0.04607395271744482,0.017858121208311945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.58,3129.0,binary,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,subway,transit_distance,D_location,binary,binary (proximity to subway),968.7746,315.3396,3.07,0.002,,350.4814,1587.068,high,Proximity to subway dummy (Lianjia field),"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,968.7746,315.3396,"b (currency per unit, as reported)",reported,0.05480024562542774,0.017850242874732164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.07,3129.0,binary,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T5-c1,5,1,6,communityAverage,other,other,level,currency (RMB per m2),0.8442177,0.0078306,107.81,0.0,,0.8288642,0.8595713,high,Average price per m2 in the community (neighbourhood price level control),"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,OLS,3139.0,0.8689,se,Full sample of first 3139 complete Lianjia rows; robust SEs (Table 3 R2 refers to the non-robust OLS with identical point estimates),monetary,0.8442177,0.0078306,"b (currency per unit, as reported)",reported,0.8876334327600011,0.008233312612559145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),107.81,3129.0,currency,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,ols,other,False,False,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,square,living_area,A_structural,level,m2,-52.27434,11.09248,-4.71,0.0,,-74.0152,-30.53347,high,Total housing area; z-statistic recorded in t,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,-52.27434,11.09248,"b (currency per unit, as reported)",reported,-0.08390426143651879,0.017814068245545392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.71,3129.0,m2,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,livingRoom,bedrooms,A_structural,level,count,299.7442,408.9099,0.73,0.464,,-501.7045,1101.193,medium,"Printed meaning ""the living room's number""; in the Lianjia/Kaggle Beijing dataset this field (shi) is the number of bedrooms, hence mapped to bedrooms; z-statistic recorded in t","price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,299.7442,408.9099,"b (currency per unit, as reported)",reported,0.013049176320281897,0.01787558400038616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.73,3129.0,count,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,drawingRoom,other,other,level,count,1812.467,407.2673,4.45,0.0,,1014.238,2610.696,high,"Number of drawing rooms (ting, living/reception rooms); z-statistic recorded in t","price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,1812.467,407.2673,"b (currency per unit, as reported)",reported,0.07930257698371476,0.017820803816565114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.45,3129.0,count,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,False,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,ladderRatio,other,other,level,ratio (residents per floor per elevator),2368.047,1112.697,2.13,0.033,,187.2018,4548.892,high,Ladder ratio: proportion between number of residents on the same floor and number of elevators; z-statistic recorded in t,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,2368.047,1112.697,"b (currency per unit, as reported)",reported,0.03805066023461781,0.017864159734562356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.13,3129.0,ratio,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,False,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,elevator,accessibility,B_interior,binary,binary,663.3836,404.546,1.64,0.101,,-129.512,1456.279,high,=1 if building has an elevator; z-statistic recorded in t,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,663.3836,404.546,"b (currency per unit, as reported)",reported,0.02930586151494152,0.017869427753013124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.64,3129.0,binary,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,subway,transit_distance,D_location,binary,binary (proximity to subway),974.9528,314.9833,3.1,0.002,,357.597,1592.309,high,Proximity to subway dummy (Lianjia field); z-statistic recorded in t,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,974.9528,314.9833,"b (currency per unit, as reported)",reported,0.05533412117040784,0.017849716506583176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,3129.0,binary,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,True,False
0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359,0b9eefecd90b818651be4c57fe476bab87958359:T7-c1,7,1,7,communityAverage,other,other,level,currency (RMB per m2),0.8450588,0.0079132,106.79,0.0,,0.8295493,0.8605683,high,Average price per m2 in the community (neighbourhood price level control); z-statistic recorded in t,"price (average price per square metre, Lianjia listing/transaction data, RMB per m2)",per_m2,IV,3139.0,0.8688,se,"2SLS, square instrumented by number of bathrooms and number of followers; robust SEs; z-statistics",monetary,0.8450588,0.0079132,"b (currency per unit, as reported)",reported,0.8858320602262537,0.008295084373314485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),106.79,3129.0,currency,,2025.0,China,Asia,Upper middle income,,,condo_apartment,transaction,conference,False,False,robust,,2022.0,iv,other,False,False,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,EPC band A/B,energy_efficiency,A_structural,dummy,binary,0.022,,,,***,0.011,0.033,high,EPC band A/B vs reference D,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,2.2227685049915236,0.5737187840784229,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.009503243481468375,0.002424341335215437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9199279999999996,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,EPC band C,energy_efficiency,A_structural,dummy,binary,0.013,,,,***,0.011,0.015,high,EPC band C vs reference D,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,1.3084339911964449,0.10337790565130883,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.030872213492282596,0.0024232951760874257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.739765999999998,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,EPC band E,energy_efficiency,A_structural,dummy,binary,-0.015,,,,***,-0.018,-0.013,high,EPC band E vs reference D,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,-1.488886177830162,0.12565434104951193,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.028499436980117476,0.0024234660245560182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.759784000000002,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,EPC band F/G,energy_efficiency,A_structural,dummy,binary,-0.041,,,,***,-0.047,-0.035,high,EPC band F/G vs reference D,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,-4.017536753203766,0.29383064075089094,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.032453777065937876,0.0024231737058240045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.393087333333337,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,EPC score,energy_efficiency,A_structural,level,index,0.001,,,,***,0.001,0.001,high,"Continuous EPC score 0-100 (SAP points); CI printed [0.001,0.001] (rounded)",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,semi_elasticity,0.1,0.0,100*b (unit as reported: index; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),,,,,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,False,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Tenure: Leasehold,other,other,dummy,binary,-0.007,,,,***,-0.009,-0.005,high,Printed name Tenure Leasehold vs freehold,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,-0.6976074070442428,0.1013308859686438,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.016629127429602188,0.0024241155784497184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.859874000000001,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,False,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Tenure: Leasehold,other,other,dummy,binary,-0.007,,,,***,-0.009,-0.005,high,Printed name Tenure Leasehold vs freehold,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,-0.6976074070442428,0.1013308859686438,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.016629127429602188,0.0024241155784497184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.859874000000001,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,False,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Property type: Bungalow,dwelling_type,A_structural,dummy,binary,0.094,,,,***,0.09,0.097,high,Bungalow vs house,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,9.855799432401934,0.19617498641353165,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.12659398904027977,0.002404945180066592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.639033142857095,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Property type: Bungalow,dwelling_type,A_structural,dummy,binary,0.094,,,,***,0.09,0.097,high,Bungalow vs house,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,9.855799432401934,0.19617498641353165,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.12659398904027977,0.002404945180066592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.639033142857095,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Total floor area (log),living_area,A_structural,log,m2,0.496,,,,***,0.491,0.501,high,Natural log of total floor area in sqm (EPC record),natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",elasticity,0.496,0.0025510672645007787,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.4263848859514537,0.0021930171867513134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),194.42842879999984,170127.0,m2,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Total floor area (log),living_area,A_structural,log,m2,0.496,,,,***,0.491,0.501,high,Natural log of total floor area in sqm (EPC record),natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,elasticity,0.496,0.0025510672645007787,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.4263848859514537,0.0021930171867513134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),194.42842879999984,170127.0,m2,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Habitable rooms: 4,bedrooms,A_structural,dummy,count,0.014,,,,***,0.01,0.017,high,"Habitable rooms (all rooms, not bedrooms) 4 vs reference 1-3",natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,1.4096842014219346,0.1810923367105071,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.019003912725595616,0.0024240129825848352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.839855999999999,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Habitable rooms: 4,bedrooms,A_structural,dummy,count,0.013,,,,***,0.01,0.017,high,"Habitable rooms (all rooms, not bedrooms) 4 vs reference 1-3",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,1.3083252051652172,0.18091133488979047,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.017646929361550153,0.0024240732822496717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.279866285714284,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Habitable rooms: 5,bedrooms,A_structural,dummy,count,0.054,,,,***,0.051,0.058,high,Habitable rooms 5 vs 1-3,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,5.548291924287563,0.18848285517197172,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.07311780714416082,0.00241796131444972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.239444571428546,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Habitable rooms: 5,bedrooms,A_structural,dummy,count,0.054,,,,***,0.051,0.058,high,Habitable rooms 5 vs 1-3,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,5.548291924287563,0.18848285517197172,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.07311780714416082,0.00241796131444972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.239444571428546,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Habitable rooms: 6-7,bedrooms,A_structural,dummy,count,0.126,,,,***,0.121,0.13,high,Habitable rooms 6-7 vs 1-3,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,13.427917863972173,0.260426709739241,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.1318891425853828,0.0024032719585188977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.878991999999954,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Habitable rooms: 6-7,bedrooms,A_structural,dummy,count,0.126,,,,***,0.121,0.13,high,Habitable rooms 6-7 vs 1-3,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,13.427917863972173,0.260426709739241,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.1318891425853828,0.0024032719585188977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.878991999999954,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Habitable rooms: 8-10,bedrooms,A_structural,dummy,count,0.196,,,,***,0.189,0.203,high,Habitable rooms 8-10 vs 1-3,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,21.651914661163897,0.4344818750415937,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.1318891425853828,0.0024032719585188977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.878991999999954,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Habitable rooms: 8-10,bedrooms,A_structural,dummy,count,0.196,,,,***,0.188,0.203,high,Habitable rooms 8-10 vs 1-3,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,21.65179986415677,0.4655162946874218,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.12323475610992542,0.0024059705522507754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.220392533333296,170127.0,count,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Built form: Detached,dwelling_type,A_structural,dummy,binary,0.174,,,,***,0.171,0.177,high,Detached vs semi-detached,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,19.00541717544426,0.1821547078140759,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.26570003745185977,0.002337305750758853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),113.67791200000042,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Built form: Detached,dwelling_type,A_structural,dummy,binary,0.174,,,,***,0.171,0.177,high,Detached vs semi-detached,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,19.00541717544426,0.1821547078140759,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.26570003745185977,0.002337305750758853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),113.67791200000042,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Built form: Terraced,dwelling_type,A_structural,dummy,binary,-0.172,,,,***,-0.175,-0.17,high,Terraced vs semi-detached,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,-15.80215117754048,0.10739727530307905,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.31074165726505293,0.002304426946411607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-134.8455232000006,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Built form: Terraced,dwelling_type,A_structural,dummy,binary,-0.172,,,,***,-0.175,-0.17,high,Terraced vs semi-detached,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,-15.80215117754048,0.10739727530307905,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.31074165726505293,0.002304426946411607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-134.8455232000006,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: Before 1900,age,A_structural,dummy,years,0.001,,,,,-0.003,0.005,high,"Construction period band vs reference 1950-1966 (year built, not age)",natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,0.09984155431550512,0.2042895686179355,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.001187958241408321,0.002424449105000543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48999099999999995,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: Before 1900,age,A_structural,dummy,years,0.002,,,,,-0.002,0.006,high,"Construction period band vs reference 1950-1966 (year built, not age)",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,0.19999146247808852,0.20449396036539455,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.0023759114533329993,0.0024244439727801118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9799819999999999,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: 1900-1929,age,A_structural,dummy,years,-0.032,,,,***,-0.035,-0.028,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,-3.149496215133285,0.17295078058970434,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.043404417033990256,0.002422165975034585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.91967085714285,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: 1900-1929,age,A_structural,dummy,years,-0.031,,,,***,-0.034,-0.027,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,-3.052597269950741,0.17312381787451667,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.042050466037150395,0.0024223063598407505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.359681142857138,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: 1930-1949,age,A_structural,dummy,years,0.014,,,,***,0.011,0.017,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,1.4097270993877142,0.1552220028947204,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.022169785926138132,0.002423854934449759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.146498666666664,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: 1930-1949,age,A_structural,dummy,years,0.015,,,,***,0.012,0.018,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,1.5111875482564985,0.15537730253449328,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.023752478417320273,0.0024237668056474784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.799820000000002,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: 1967-1982,age,A_structural,dummy,years,0.026,,,,***,0.023,0.029,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,2.6339746187616475,0.15709588775203662,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.04114770285531734,0.0024223974868524283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.98635466666666,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: 1967-1982,age,A_structural,dummy,years,0.025,,,,***,0.022,0.029,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,2.5313485715805273,0.1830953487837278,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.03392215380644527,0.0024230554912755228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.999742857142852,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: 1983-2002,age,A_structural,dummy,years,0.099,,,,***,0.095,0.103,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,10.406400029668173,0.22532379157145357,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.11680293551577783,0.0024078557187223897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.50910900000004,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: 1983-2002,age,A_structural,dummy,years,0.098,,,,***,0.094,0.102,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,10.296048814442038,0.22509858040423333,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.11563896438411791,0.002408185930947707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.01911800000004,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: 2003-2011,age,A_structural,dummy,years,0.132,,,,***,0.127,0.137,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,14.110460613864916,0.2911044078532147,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.12447286747990127,0.002405595890503937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.74304959999996,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: 2003-2011,age,A_structural,dummy,years,0.128,,,,***,0.123,0.133,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,13.654930439132329,0.2899423159550536,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.12075681160372963,0.002406708976935644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.17507839999996,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Building age: 2012-2024,age,A_structural,dummy,years,0.139,,,,***,0.115,0.162,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,14.904150365197877,1.3778016509530135,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.028095511036642273,0.002423493747334477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.592978553191491,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Building age: 2012-2024,age,A_structural,dummy,years,0.132,,,,***,0.109,0.156,high,Construction period band vs 1950-1966,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,14.102629907117304,1.3681907169101077,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.026681663143925383,0.0024235876653170684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.009159489361704,170127.0,years,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Modern kitchen,kitchen_quality,B_interior,dummy,binary,0.043,,,,***,0.041,0.045,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,4.393735133958954,0.10652623158900998,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.10163544363916299,0.002411896308659369,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.13922600000003,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Modern kitchen,kitchen_quality,B_interior,dummy,binary,0.043,,,,***,0.041,0.045,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,4.393735133958954,0.10652623158900998,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.10163544363916299,0.002411896308659369,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.13922600000003,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Garage,garage,A_structural,dummy,binary,0.057,,,,***,0.055,0.06,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,5.865494918673431,0.135035109113675,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.10771155533099691,0.00241034581415237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.687179200000024,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Garage,garage,A_structural,dummy,binary,0.058,,,,***,0.055,0.06,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,5.971413363988232,0.1351702117628547,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.10957873474954925,0.0024098510612499016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.471164800000025,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Basement,basement,A_structural,dummy,binary,0.051,,,,***,0.043,0.059,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,5.231412729263374,0.42952743755832407,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.030279066724253845,0.002423339166113843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.494770499999998,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Basement,basement,A_structural,dummy,binary,0.052,,,,***,0.044,0.06,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,5.336696775241956,0.429957179831207,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.030872213492282596,0.0024232951760874257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.739765999999998,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Garden,garden,C_exterior,dummy,binary,0.073,,,,***,0.07,0.076,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",pct_premium,7.5729276772468435,0.16465565748881641,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.1148627185837631,0.0024084042845802954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.6924573333334,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Garden,garden,C_exterior,dummy,binary,0.073,,,,***,0.07,0.075,high,Keyword from listing text,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,pct_premium,7.572966181577256,0.13721304790734695,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.13743692097416377,0.0024014440413080047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),57.23094880000011,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,School quality,school_quality,F_neighbourhood,level,index,0.008,,,,***,0.003,0.012,high,Average Ofsted rating (1-4) of nearest primary and secondary school,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",semi_elasticity,0.8,0.22959605380506992,100*b (unit as reported: index; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),0.008447407596786412,0.0024243643113814124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.484380444444444,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,School quality,school_quality,F_neighbourhood,level,index,0.008,,,,***,0.003,0.012,high,Average Ofsted rating (1-4) of nearest primary and secondary school,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,semi_elasticity,0.8,0.22959605380506992,100*b (unit as reported: index; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),0.008447407596786412,0.0024243643113814124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.484380444444444,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Population density (natural log),density,F_neighbourhood,log,per_km2,-0.006,,,,,-0.023,0.01,high,Log of 100s of residents per km2 (LSOA),natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",elasticity,-0.006,0.008418521972852562,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.0017279378998863915,0.0024244471963197167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7127141818181818,170127.0,per_km2,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Population density (natural log),density,F_neighbourhood,log,per_km2,-0.006,,,,,-0.023,0.01,high,Log of 100s of residents per km2 (LSOA),natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,elasticity,-0.006,0.008418521972852562,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.0017279378998863915,0.0024244471963197167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7127141818181818,170127.0,per_km2,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Accessibility PC1,transit_distance,D_location,level,index,0.017,,,,***,0.015,0.019,high,First principal component of PTAI public transport accessibility measures (LSOA),natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",semi_elasticity,1.7000000000000002,0.10204269058003107,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",(ci_hi-ci_lo)/(2*1.96),0.04035770232037555,0.0024224756061171078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.659694000000002,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Accessibility PC1,transit_distance,D_location,level,index,0.017,,,,***,0.015,0.019,high,First principal component of PTAI public transport accessibility measures (LSOA),natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,semi_elasticity,1.7000000000000002,0.10204269058003107,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",(ci_hi-ci_lo)/(2*1.96),0.04035770232037555,0.0024224756061171078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.659694000000002,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Accessibility PC2,transit_distance,D_location,level,index,-0.004,,,,,-0.011,0.003,high,Second principal component of PTAI measures,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",semi_elasticity,-0.4,0.35714941703010866,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",(ci_hi-ci_lo)/(2*1.96),-0.002715325029133385,0.0024244418780056285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1199794285714286,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Accessibility PC2,transit_distance,D_location,level,index,-0.004,,,,,-0.011,0.003,high,Second principal component of PTAI measures,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,semi_elasticity,-0.4,0.35714941703010866,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",(ci_hi-ci_lo)/(2*1.96),-0.002715325029133385,0.0024244418780056285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1199794285714286,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M1,3,Model 1: EPC bands,10,Income Deprivation,income,F_neighbourhood,level,share,-1.589,,,,***,-1.694,-1.484,high,Income Deprivation score = share (0-1) of residents on low-income benefits (LSOA); inverse of income,natural log of listing price (GBP),log,other,170137.0,0.896,none,"Full sample 2017-09 to 2024-05; two-level random-intercept model (LSOA); 95% CIs in brackets, no SE; year-quarter dummies",semi_elasticity,-158.9,5.35724125545163,100*b (unit as reported: share; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),-0.07172590724370108,0.002418206352238215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.660788533333335,170127.0,share,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T3-M2,3,Model 2: EPC score,10,Income Deprivation,income,F_neighbourhood,level,share,-1.59,,,,***,-1.694,-1.484,high,Income Deprivation score = share (0-1) of residents on low-income benefits (LSOA); inverse of income,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; continuous EPC score instead of bands,semi_elasticity,-159.0,5.35724125545163,100*b (unit as reported: share; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),-0.07177081382590055,0.002418198520537438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.67945485714286,170127.0,share,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,EPC A/B,energy_efficiency,A_structural,dummy,binary,0.02,,,,*,0.006,0.034,high,"EPC A/B vs D, pre-2022 baseline (interaction model)",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,pct_premium,2.0175313858280752,0.7287286276877831,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.006788181194975373,0.0024243949564802013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7999485714285712,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,After energy crisis * EPC A/B,energy_efficiency,A_structural,other,binary,0.005,,,,,-0.017,0.028,high,Interaction: change in A/B premium from 2022 onwards,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,other,0.005,0.011479802690253494,not harmonised,(ci_hi-ci_lo)/(2*1.96),0.0010559630376332082,0.002424449464045991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4355475555555556,170127.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,EPC C,energy_efficiency,A_structural,dummy,binary,0.012,,,,***,0.009,0.015,high,"EPC C vs D, pre-2022 baseline",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,pct_premium,1.2071103294977625,0.1549118691260775,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.019003912725595616,0.0024240129825848352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.839855999999999,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,After energy crisis * EPC C,energy_efficiency,A_structural,other,binary,0.001,,,,,-0.003,0.005,high,Interaction: change in C premium from 2022,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,other,0.001,0.0020408538116006214,not harmonised,(ci_hi-ci_lo)/(2*1.96),0.001187958241408321,0.002424449105000543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48999099999999995,170127.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,EPC E,energy_efficiency,A_structural,dummy,binary,-0.013,,,,***,-0.016,-0.01,high,"EPC E vs D, pre-2022 baseline",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,pct_premium,-1.2917021278957885,0.15108708144949923,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.020586926735257537,0.002423936994045755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.493177333333332,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,After energy crisis * EPC E,energy_efficiency,A_structural,other,binary,-0.008,,,,**,-0.013,-0.003,high,Interaction: change in E discount from 2022,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,other,-0.008,0.002551067264500776,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.00760271837668842,0.002424380746498538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1359424000000007,170127.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,EPC F/G,energy_efficiency,A_structural,dummy,binary,-0.036,,,,***,-0.043,-0.03,high,"EPC F/G vs D, pre-2022 baseline",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,pct_premium,-3.5365011264577806,0.3199120957140182,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.02630875315045954,0.002423611628098881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.855185230769232,170127.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M3,4,Model 3: Interaction with EPC bands,10,After energy crisis * EPC F/G,energy_efficiency,A_structural,other,binary,-0.019,,,,**,-0.032,-0.006,high,Interaction: change in F/G discount from 2022,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC bands interacted with post-2022 energy-crisis dummy; only variables of interest reported,other,-0.019,0.00663277488770202,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.006944824060113862,0.002424392348706948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8645627692307687,170127.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M4,4,Model 4: Interaction with EPC score,10,EPC score,energy_efficiency,A_structural,level,index,0.0012,,,,***,0.0011,0.0013,high,"Continuous EPC score, pre-2022 baseline",natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC score interacted with post-2022 dummy; only variables of interest reported,semi_elasticity,0.12,0.0051021345290015495,100*b (unit as reported: index; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),0.05692955735071012,0.002420518835665268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.519568000000014,170127.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T4-M4,4,Model 4: Interaction with EPC score,10,After energy crisis * EPC score,energy_efficiency,A_structural,other,index,0.0003,,,,***,0.0001,0.0005,high,Interaction: change in per-point premium from 2022,natural log of listing price (GBP),log,other,170137.0,0.896,none,Full sample; EPC score interacted with post-2022 dummy; only variables of interest reported,other,0.0003,0.00010204269058003106,not harmonised,(ci_hi-ci_lo)/(2*1.96),0.007127573422484356,0.0024243892311234146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9399459999999995,170127.0,index,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-matched-bands,5,Matched listings,11,EPC band A/B,energy_efficiency,A_structural,dummy,binary,0.018,,,,*,0.001,0.035,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,64292.0,,none,"Listings matched to Land Registry transactions, 2017-09 to 2023-10; EPC bands model; only EPC rows reported",pct_premium,1.8124677975686954,0.8831167612581912,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.008184882331912786,0.003944035016360771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0752559999999995,64282.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-matched-bands,5,Matched listings,11,EPC band C,energy_efficiency,A_structural,dummy,binary,0.012,,,,***,0.009,0.015,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,64292.0,,none,"Listings matched to Land Registry transactions, 2017-09 to 2023-10; EPC bands model; only EPC rows reported",pct_premium,1.2071103294977625,0.1549118691260775,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.030906930036345353,0.0039422828730968215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.839855999999999,64282.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-matched-bands,5,Matched listings,11,EPC band E,energy_efficiency,A_structural,dummy,binary,-0.014,,,,***,-0.018,-0.011,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,64292.0,,none,"Listings matched to Land Registry transactions, 2017-09 to 2023-10; EPC bands model; only EPC rows reported",pct_premium,-1.3904028015491177,0.17609208153415146,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.03090693003634536,0.0039422828730968215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.839856000000001,64282.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-matched-bands,5,Matched listings,11,EPC band F/G,energy_efficiency,A_structural,dummy,binary,-0.045,,,,***,-0.053,-0.037,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,64292.0,,none,"Listings matched to Land Registry transactions, 2017-09 to 2023-10; EPC bands model; only EPC rows reported",pct_premium,-4.401048175389821,0.3902102209359356,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.04344259212423912,0.003940443543225091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.024797499999998,64282.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-matched-score,5,Matched listings,11,EPC score,energy_efficiency,A_structural,level,index,0.001,,,,***,0.001,0.001,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,64292.0,,none,Matched listings; EPC score model; only EPC row reported,semi_elasticity,0.1,0.0,100*b (unit as reported: index; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),,,,,64282.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,False,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-all-bands,5,All listings,11,EPC band A/B,energy_efficiency,A_structural,dummy,binary,0.019,,,,**,0.007,0.031,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,149526.0,,none,All listings 2017-09 to 2023-10; EPC bands model; only EPC rows reported,pct_premium,1.9162546397838431,0.6240002256882727,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.008025324989907988,0.00258608133078749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.103276333333333,149516.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-all-bands,5,All listings,11,EPC band C,energy_efficiency,A_structural,dummy,binary,0.012,,,,***,0.01,0.014,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,149526.0,,none,All listings 2017-09 to 2023-10; EPC bands model; only EPC rows reported,pct_premium,1.207176194541737,0.10327457941738499,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),0.030398682104257495,0.0025849694266712295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.759784,149516.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-all-bands,5,All listings,11,EPC band E,energy_efficiency,A_structural,dummy,binary,-0.016,,,,***,-0.018,-0.013,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,149526.0,,none,All listings 2017-09 to 2023-10; EPC bands model; only EPC rows reported,pct_premium,-1.5873480525098405,0.1255287495146958,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.032423196953931345,0.0025848048862386103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.543769600000003,149516.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-all-bands,5,All listings,11,EPC band F/G,energy_efficiency,A_structural,dummy,binary,-0.04,,,,***,-0.046,-0.035,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,149526.0,,none,All listings 2017-09 to 2023-10; EPC bands model; only EPC rows reported,pct_premium,-3.9214343762477677,0.26961423349295066,100*(exp(b - se^2/2) - 1),(ci_hi-ci_lo)/(2*1.96),-0.03683890134708433,0.0025844091703848093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.254283636363644,149516.0,binary,,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,True,True,False
0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89,0ce3fe88a1c7202d8a2a9d78f61d5b7af8475b89:T5-all-score,5,All listings,11,EPC score,energy_efficiency,A_structural,level,index,0.001,,,,***,0.001,0.001,high,Robustness model; reference band D; only EPC variables reported,natural log of listing price (GBP),log,other,149526.0,,none,All listings 2017-09 to 2023-10; EPC score model; only EPC row reported,semi_elasticity,0.1,0.0,100*b (unit as reported: index; no harmonised unit defined for this characteristic),(ci_hi-ci_lo)/(2*1.96),,,,,149516.0,index,unit_not_harmonisable,2026.0,United Kingdom,Europe,High income,2017.0,2024.0,single_family,listing,journal,True,False,unknown,,2020.5,other,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c1,4,1,8,Area,living_area,A_structural,level,100 m2,0.443,0.004,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.846,se,Baseline hedonic model without geomantic attributes; district + quarter FE,semi_elasticity,0.44299999999999995,0.004,100*b/100 (X per 100m2->m2),reported,0.8465660229731788,0.00764393700201516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),110.75,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c1,4,1,8,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.846,se,Baseline hedonic model without geomantic attributes; district + quarter FE,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c1,4,1,8,Subway,transit_distance,D_location,dummy,binary,0.027,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.846,se,Baseline hedonic model without geomantic attributes; district + quarter FE,pct_premium,2.735958385394288,0.4109471211053958,100*(exp(b - se^2/2) - 1),reported,0.09648211909650203,0.014293647273555856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c1,4,1,8,Bus,transit_distance,D_location,dummy,binary,0.024,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.846,se,Baseline hedonic model without geomantic attributes; district + quarter FE,pct_premium,2.4282123600855687,0.4097161271562486,100*(exp(b - se^2/2) - 1),reported,0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c1,4,1,8,Log_Park,green_space,E_environment,log,m,-0.023,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.846,se,Baseline hedonic model without geomantic attributes; district + quarter FE,elasticity,-0.023,0.005,b (log-log),reported,-0.0659152975669559,0.01432941251455563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.6,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c1,4,1,8,Log_CBD,cbd_distance,D_location,log,m,-0.027,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.846,se,Baseline hedonic model without geomantic attributes; district + quarter FE,elasticity,-0.027,0.004,b (log-log),reported,-0.09648211909650203,0.014293647273555856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.75,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Graveyard,other,other,dummy,binary,-0.043,0.01,,,***,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-4.213650430510341,0.9579113900670306,100*(exp(b - se^2/2) - 1),reported,-0.061633370963941864,0.014333342084637645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.3,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Funeral home,other,other,dummy,binary,-0.023,0.011,,,**,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-2.279663881851479,1.0749887321506046,100*(exp(b - se^2/2) - 1),reported,-0.030013273454319065,0.014354174260761292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.090909090909091,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Hospital,hospital_distance,D_location,dummy,binary,-0.024,0.007,,,***,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-2.373820894897438,0.6833999968305365,100*(exp(b - se^2/2) - 1),reported,-0.049176920724083,0.01434326854452421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4285714285714284,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.143,0.034,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-13.374677019989056,2.9469638342446625,100*(exp(b - se^2/2) - 1),reported,-0.06028930861245698,0.01433452092883593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.205882352941176,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Highway,highway_distance,D_location,dummy,binary,-0.015,0.007,,,**,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-1.491219534380328,0.6895783577221438,100*(exp(b - se^2/2) - 1),reported,-0.030758247930394986,0.014353849034184327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.142857142857143,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Unlucky floor,other,other,dummy,binary,-0.004,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-0.40044606780624825,0.49800399467199574,100*(exp(b - se^2/2) - 1),reported,-0.011487756895536202,0.014359696119420251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Unlucky address,other,other,dummy,binary,-0.009,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,-0.8972009154426308,0.4955201893864418,100*(exp(b - se^2/2) - 1),reported,-0.025840527102106714,0.014355848390059286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7999999999999998,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Unlucky unit,other,other,dummy,binary,0.005,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,0.5004480791394483,0.4020050083437604,100*(exp(b - se^2/2) - 1),reported,0.017947913201683697,0.014358330561346958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Area,living_area,A_structural,level,100 m2,0.442,0.004,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,semi_elasticity,0.442,0.004,100*b/100 (X per 100m2->m2),reported,0.8460232750802543,0.007656319231495512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),110.5,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Subway,transit_distance,D_location,dummy,binary,0.026,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,2.6332737778697046,0.4105363793893768,100*(exp(b - se^2/2) - 1),reported,0.09294016222362012,0.014298486495941556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Bus,transit_distance,D_location,dummy,binary,0.022,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,pct_premium,2.2235606552874154,0.4088975137881753,100*(exp(b - se^2/2) - 1),reported,0.07873832178890346,0.014316058507073357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Log_Park,green_space,E_environment,log,m,-0.026,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,elasticity,-0.026,0.005,b (log-log),reported,-0.07446800406992858,0.014320770013447804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.199999999999999,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c2,4,2,8,Log_CBD,cbd_distance,D_location,log,m,-0.025,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.847,se,Stepwise: adds unfavorable geomantic attributes and unlucky numbers,elasticity,-0.025,0.004,b (log-log),reported,-0.08939467343475828,0.014303147749561325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Graveyard,other,other,dummy,binary,-0.037,0.01,,,***,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-3.6372047253138917,0.9636761353490535,100*(exp(b - se^2/2) - 1),reported,-0.053059534085687664,0.014340414617753422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6999999999999997,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Funeral home,other,other,dummy,binary,-0.024,0.011,,,**,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-2.377335374084222,1.0739142807337003,100*(exp(b - se^2/2) - 1),reported,-0.03131694521817075,0.01435359989166159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.181818181818182,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Hospital,hospital_distance,D_location,dummy,binary,-0.021,0.007,,,***,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-2.080502600128664,0.6854532751986218,100*(exp(b - se^2/2) - 1),reported,-0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.139,0.031,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-13.019001566627853,2.69770688822058,100*(exp(b - se^2/2) - 1),reported,-0.0642581964599424,0.014330964678116648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.483870967741936,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Highway,highway_distance,D_location,dummy,binary,-0.017,0.007,,,**,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-1.6880402090300284,0.6882005792444368,100*(exp(b - se^2/2) - 1),reported,-0.03485465820066124,0.014351918082625216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.428571428571429,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Unlucky floor,other,other,dummy,binary,-0.006,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-0.5994461092958092,0.49700898202696764,100*(exp(b - se^2/2) - 1),reported,-0.017230214247822628,0.014358511873185521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Unlucky address,other,other,dummy,binary,-0.008,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,-0.798048548437269,0.49601595741853033,100*(exp(b - se^2/2) - 1),reported,-0.022970967072928113,0.014356854420580071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Unlucky unit,other,other,dummy,binary,0.004,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,0.3999978645384594,0.40160320427093676,100*(exp(b - se^2/2) - 1),reported,0.01435916317235476,0.014359163172354762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Pearl River,water_proximity,E_environment,dummy,binary,0.025,0.004,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,2.5306918036274606,0.41012604820977155,100*(exp(b - se^2/2) - 1),reported,0.08939467343475828,0.014303147749561325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Orientation,sunlight,E_environment,dummy,binary,0.001,0.003,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,0.09959956745926313,0.30030015005001254,100*(exp(b - se^2/2) - 1),reported,0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Area,living_area,A_structural,level,100 m2,0.44,0.005,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,semi_elasticity,0.44,0.005,100*b/100 (X per 100m2->m2),reported,0.7841841688079839,0.008911183736454362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),88.0,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Subway,transit_distance,D_location,dummy,binary,0.023,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,2.3258353447645597,0.40930661581888705,100*(exp(b - se^2/2) - 1),reported,0.08229362196487157,0.014311934254760272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Bus,transit_distance,D_location,dummy,binary,0.021,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,pct_premium,2.1213881893794584,0.4084888206550114,100*(exp(b - se^2/2) - 1),reported,0.0751800146601043,0.014320002792400817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.25,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Log_Park,green_space,E_environment,log,m,-0.027,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,elasticity,-0.027,0.005,b (log-log),reported,-0.0773153523516994,0.014317657842907295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3999999999999995,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c3,4,3,8,Log_CBD,cbd_distance,D_location,log,m,-0.024,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.848,se,Stepwise: adds favorable geomantic attributes,elasticity,-0.024,0.004,b (log-log),reported,-0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Graveyard,other,other,dummy,binary,-0.037,0.01,,,***,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-3.6372047253138917,0.9636761353490535,100*(exp(b - se^2/2) - 1),reported,-0.053059534085687664,0.014340414617753422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6999999999999997,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Funeral home,other,other,dummy,binary,-0.025,0.011,,,**,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-2.4749092436441966,1.0728409032311659,100*(exp(b - se^2/2) - 1),reported,-0.03262045716983971,0.014353001154729475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.272727272727273,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Hospital,hospital_distance,D_location,dummy,binary,-0.021,0.007,,,***,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-2.080502600128664,0.6854532751986218,100*(exp(b - se^2/2) - 1),reported,-0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.137,0.031,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-12.84486549173156,2.703107701009539,100*(exp(b - se^2/2) - 1),reported,-0.06333735411429134,0.014331810055058621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.419354838709678,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Highway,highway_distance,D_location,dummy,binary,-0.018,0.007,,,**,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-1.7863030292223359,0.6875127226508105,100*(exp(b - se^2/2) - 1),reported,-0.036902217659278196,0.014350862423052632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.571428571428571,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Unlucky floor,other,other,dummy,binary,-0.004,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-0.40044606780624825,0.49800399467199574,100*(exp(b - se^2/2) - 1),reported,-0.011487756895536202,0.014359696119420251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Unlucky address,other,other,dummy,binary,-0.007,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-0.6987969794721827,0.49651222146661755,100*(exp(b - se^2/2) - 1),reported,-0.020100839178326335,0.014357742270233096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Unlucky unit,other,other,dummy,binary,0.004,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,0.3999978645384594,0.40160320427093676,100*(exp(b - se^2/2) - 1),reported,0.01435916317235476,0.014359163172354762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Pearl River,water_proximity,E_environment,dummy,binary,0.026,0.004,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,2.6332737778697046,0.4105363793893768,100*(exp(b - se^2/2) - 1),reported,0.09294016222362012,0.014298486495941556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Orientation,sunlight,E_environment,dummy,binary,0.001,0.003,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,0.09959956745926313,0.30030015005001254,100*(exp(b - se^2/2) - 1),reported,0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Lucky floor,other,other,dummy,binary,0.012,0.005,,,***,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,1.205963804153476,0.5060361444330389,100*(exp(b - se^2/2) - 1),reported,0.03444509280618143,0.014352122002575596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Lucky address,other,other,dummy,binary,0.012,0.004,,,***,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,1.2064192320152989,0.40482891554643113,100*(exp(b - se^2/2) - 1),reported,0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Lucky unit,other,other,dummy,binary,-0.004,0.006,,,,,,high,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,-0.40099386384644387,0.597604793606395,100*(exp(b - se^2/2) - 1),reported,-0.009573323764269162,0.014359985646403742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Area,living_area,A_structural,level,100 m2,0.44,0.005,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,semi_elasticity,0.44,0.005,100*b/100 (X per 100m2->m2),reported,0.7841841688079839,0.008911183736454362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),88.0,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Subway,transit_distance,D_location,dummy,binary,0.023,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,2.3258353447645597,0.40930661581888705,100*(exp(b - se^2/2) - 1),reported,0.08229362196487157,0.014311934254760272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Bus,transit_distance,D_location,dummy,binary,0.021,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,pct_premium,2.1213881893794584,0.4084888206550114,100*(exp(b - se^2/2) - 1),reported,0.0751800146601043,0.014320002792400817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.25,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Log_Park,green_space,E_environment,log,m,-0.027,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,elasticity,-0.027,0.005,b (log-log),reported,-0.0773153523516994,0.014317657842907295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3999999999999995,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c4,4,4,8,Log_CBD,cbd_distance,D_location,log,m,-0.025,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,Stepwise: adds lucky numbers,elasticity,-0.025,0.004,b (log-log),reported,-0.08939467343475828,0.014303147749561325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Graveyard,other,other,dummy,binary,-0.037,0.01,,,***,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-3.6372047253138917,0.9636761353490535,100*(exp(b - se^2/2) - 1),reported,-0.053059534085687664,0.014340414617753422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6999999999999997,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Funeral home,other,other,dummy,binary,-0.026,0.011,,,**,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-2.572385588105297,1.0717685985696241,100*(exp(b - se^2/2) - 1),reported,-0.03392380268520237,0.01435237805912408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3636363636363638,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Hospital,hospital_distance,D_location,dummy,binary,-0.021,0.007,,,***,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-2.080502600128664,0.6854532751986218,100*(exp(b - se^2/2) - 1),reported,-0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.134,0.033,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-12.588602006103011,2.8861472131910024,100*(exp(b - se^2/2) - 1),reported,-0.0582140255717507,0.014336289879610248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0606060606060606,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Highway,highway_distance,D_location,dummy,binary,-0.018,0.007,,,**,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-1.7863030292223359,0.6875127226508105,100*(exp(b - se^2/2) - 1),reported,-0.036902217659278196,0.014350862423052632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.571428571428571,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Unlucky floor,other,other,dummy,binary,-0.004,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-0.40044606780624825,0.49800399467199574,100*(exp(b - se^2/2) - 1),reported,-0.011487756895536202,0.014359696119420251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Unlucky address,other,other,dummy,binary,-0.007,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-0.6987969794721827,0.49651222146661755,100*(exp(b - se^2/2) - 1),reported,-0.020100839178326335,0.014357742270233096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Unlucky unit,other,other,dummy,binary,0.003,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,0.29964804994371264,0.40120180180135084,100*(exp(b - se^2/2) - 1),reported,0.010769858144132208,0.014359810858842944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Pearl River,water_proximity,E_environment,dummy,binary,0.027,0.004,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,2.735958385394288,0.4109471211053958,100*(exp(b - se^2/2) - 1),reported,0.09648211909650203,0.014293647273555856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Orientation,sunlight,E_environment,dummy,binary,-0.001,0.003,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-0.10039956587594157,0.2997001499500125,100*(exp(b - se^2/2) - 1),reported,-0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Lucky floor,other,other,dummy,binary,0.012,0.005,,,**,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,1.205963804153476,0.5060361444330389,100*(exp(b - se^2/2) - 1),reported,0.03444509280618143,0.014352122002575596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Lucky address,other,other,dummy,binary,0.012,0.004,,,***,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,1.2064192320152989,0.40482891554643113,100*(exp(b - se^2/2) - 1),reported,0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Lucky unit,other,other,dummy,binary,-0.003,0.006,,,,,,high,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,-0.3013450416032293,0.5982026973020238,100*(exp(b - se^2/2) - 1),reported,-0.007180136772856009,0.014360273545712019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Area,living_area,A_structural,level,100 m2,0.436,0.005,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,semi_elasticity,0.436,0.005,100*b/100 (X per 100m2->m2),reported,0.781415966596542,0.00896119227748328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),87.2,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Subway,transit_distance,D_location,dummy,binary,0.024,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,2.4282123600855687,0.4097161271562486,100*(exp(b - se^2/2) - 1),reported,0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Bus,transit_distance,D_location,dummy,binary,0.019,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,pct_premium,1.9173495196832757,0.40767265944696324,100*(exp(b - se^2/2) - 1),reported,0.06805491098351593,0.014327349680740195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Log_Park,green_space,E_environment,log,m,-0.028,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,elasticity,-0.028,0.005,b (log-log),reported,-0.08016081010727731,0.014314430376299519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.6,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T4-c5,4,5,8,Log_CBD,cbd_distance,D_location,log,m,-0.024,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Full model incl. buyer demographic characteristics,elasticity,-0.024,0.004,b (log-log),reported,-0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,BFS_1,other,other,level,count,-0.022,0.004,,,***,,,high,"Count of unfavorable geomantic attributes near the house (graveyard, funeral home, highway, hospital); printed name 'BFS_1'","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,semi_elasticity,-2.1999999999999997,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.07873832178890346,0.014316058507073357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,4849.0,count,unit_not_harmonisable,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Pearl River,water_proximity,E_environment,dummy,binary,0.028,0.004,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,2.8387457288858275,0.41135827376857004,100*(exp(b - se^2/2) - 1),reported,0.10002041441278796,0.014288630630398279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Orientation,sunlight,E_environment,dummy,binary,-0.001,0.003,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,-0.10039956587594157,0.2997001499500125,100*(exp(b - se^2/2) - 1),reported,-0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Unlucky floor,other,other,dummy,binary,-0.004,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,-0.40044606780624825,0.49800399467199574,100*(exp(b - se^2/2) - 1),reported,-0.011487756895536202,0.014359696119420251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Unlucky address,other,other,dummy,binary,-0.007,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,-0.6987969794721827,0.49651222146661755,100*(exp(b - se^2/2) - 1),reported,-0.020100839178326335,0.014357742270233096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Unlucky unit,other,other,dummy,binary,0.003,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,0.29964804994371264,0.40120180180135084,100*(exp(b - se^2/2) - 1),reported,0.010769858144132208,0.014359810858842944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Lucky floor,other,other,dummy,binary,0.012,0.005,,,**,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,1.205963804153476,0.5060361444330389,100*(exp(b - se^2/2) - 1),reported,0.03444509280618143,0.014352122002575596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Lucky address,other,other,dummy,binary,0.012,0.004,,,***,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,1.2064192320152989,0.40482891554643113,100*(exp(b - se^2/2) - 1),reported,0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Lucky unit,other,other,dummy,binary,-0.002,0.006,,,,,,high,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,-0.20159652069675627,0.5988011992003999,100*(exp(b - se^2/2) - 1),reported,-0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Area,living_area,A_structural,level,100 m2,0.436,0.005,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,semi_elasticity,0.436,0.005,100*b/100 (X per 100m2->m2),reported,0.781415966596542,0.00896119227748328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),87.2,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Bus,transit_distance,D_location,dummy,binary,0.02,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,2.019317844868196,0.4080805360107023,100*(exp(b - se^2/2) - 1),reported,0.07161883330203965,0.01432376666040793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Subway,transit_distance,D_location,dummy,binary,0.022,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,pct_premium,2.2235606552874154,0.4088975137881753,100*(exp(b - se^2/2) - 1),reported,0.07873832178890346,0.014316058507073357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Log_Park,green_space,E_environment,log,m,-0.027,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,elasticity,-0.027,0.005,b (log-log),reported,-0.0773153523516994,0.014317657842907295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3999999999999995,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c2,5,2,10,Log_CBD,cbd_distance,D_location,log,m,-0.024,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_1 (count of 4 unfavorable attributes) replaces individual unfavorable dummies; incl. demographics,elasticity,-0.024,0.004,b (log-log),reported,-0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,BFS_2,other,other,level,count,-0.024,0.004,,,***,,,high,Count of unfavorable geomantic attributes incl. slaughterhouse; printed name 'BFS_2',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,semi_elasticity,-2.4,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,4849.0,count,unit_not_harmonisable,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Pearl River,water_proximity,E_environment,dummy,binary,0.028,0.004,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,2.8387457288858275,0.41135827376857004,100*(exp(b - se^2/2) - 1),reported,0.10002041441278796,0.014288630630398279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Orientation,sunlight,E_environment,dummy,binary,-0.001,0.003,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,-0.10039956587594157,0.2997001499500125,100*(exp(b - se^2/2) - 1),reported,-0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Unlucky floor,other,other,dummy,binary,-0.004,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,-0.40044606780624825,0.49800399467199574,100*(exp(b - se^2/2) - 1),reported,-0.011487756895536202,0.014359696119420251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Unlucky address,other,other,dummy,binary,-0.007,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,-0.6987969794721827,0.49651222146661755,100*(exp(b - se^2/2) - 1),reported,-0.020100839178326335,0.014357742270233096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Unlucky unit,other,other,dummy,binary,0.003,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,0.29964804994371264,0.40120180180135084,100*(exp(b - se^2/2) - 1),reported,0.010769858144132208,0.014359810858842944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Lucky floor,other,other,dummy,binary,0.012,0.005,,,**,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,1.205963804153476,0.5060361444330389,100*(exp(b - se^2/2) - 1),reported,0.03444509280618143,0.014352122002575596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Lucky address,other,other,dummy,binary,0.012,0.004,,,***,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,1.2064192320152989,0.40482891554643113,100*(exp(b - se^2/2) - 1),reported,0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Lucky unit,other,other,dummy,binary,-0.002,0.006,,,,,,high,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,-0.20159652069675627,0.5988011992003999,100*(exp(b - se^2/2) - 1),reported,-0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Area,living_area,A_structural,level,100 m2,0.436,0.005,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,semi_elasticity,0.436,0.005,100*b/100 (X per 100m2->m2),reported,0.781415966596542,0.00896119227748328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),87.2,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), i.e. rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Bus,transit_distance,D_location,dummy,binary,0.02,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,2.019317844868196,0.4080805360107023,100*(exp(b - se^2/2) - 1),reported,0.07161883330203965,0.01432376666040793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Subway,transit_distance,D_location,dummy,binary,0.022,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,pct_premium,2.2235606552874154,0.4088975137881753,100*(exp(b - se^2/2) - 1),reported,0.07873832178890346,0.014316058507073357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Log_Park,green_space,E_environment,log,m,-0.027,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,elasticity,-0.027,0.005,b (log-log),reported,-0.0773153523516994,0.014317657842907295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3999999999999995,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T5-c3,5,3,10,Log_CBD,cbd_distance,D_location,log,m,-0.024,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.85,se,Group variable BFS_2 (count of 5 unfavorable attributes incl. slaughterhouse); incl. demographics,elasticity,-0.024,0.004,b (log-log),reported,-0.08584578303380906,0.014307630505634843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Graveyard,other,other,dummy,binary,-0.036,0.01,,,***,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-3.5407937325770855,0.9646402934831231,100*(exp(b - se^2/2) - 1),reported,-0.05162936813863331,0.014341491149620363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5999999999999996,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Funeral home,other,other,dummy,binary,-0.027,0.011,,,**,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-2.669764504943861,1.0706973656767704,100*(exp(b - se^2/2) - 1),reported,-0.035226975144375006,0.014351730614375002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4545454545454546,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Hospital,hospital_distance,D_location,dummy,binary,-0.021,0.007,,,***,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-2.080502600128664,0.6854532751986218,100*(exp(b - se^2/2) - 1),reported,-0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.136,0.032,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-12.760414855340807,2.7930964239639016,100*(exp(b - se^2/2) - 1),reported,-0.06091937912010326,0.014333971557671356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.25,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Highway,highway_distance,D_location,dummy,binary,-0.018,0.007,,,**,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-1.7863030292223359,0.6875127226508105,100*(exp(b - se^2/2) - 1),reported,-0.036902217659278196,0.014350862423052632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.571428571428571,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Unlucky floor,other,other,dummy,binary,-0.004,0.005,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-0.40044606780624825,0.49800399467199574,100*(exp(b - se^2/2) - 1),reported,-0.011487756895536202,0.014359696119420251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Unlucky address,other,other,dummy,binary,-0.007,0.005,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-0.6987969794721827,0.49651222146661755,100*(exp(b - se^2/2) - 1),reported,-0.020100839178326335,0.014357742270233096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Unlucky unit,other,other,dummy,binary,0.004,0.004,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,0.3999978645384594,0.40160320427093676,100*(exp(b - se^2/2) - 1),reported,0.01435916317235476,0.014359163172354762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Pearl River,water_proximity,E_environment,dummy,binary,0.027,0.004,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,2.735958385394288,0.4109471211053958,100*(exp(b - se^2/2) - 1),reported,0.09648211909650203,0.014293647273555856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Orientation,sunlight,E_environment,dummy,binary,-0.001,0.003,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-0.10039956587594157,0.2997001499500125,100*(exp(b - se^2/2) - 1),reported,-0.004786826399559017,0.014360479198677053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Lucky floor,other,other,dummy,binary,0.012,0.005,,,**,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,1.205963804153476,0.5060361444330389,100*(exp(b - se^2/2) - 1),reported,0.03444509280618143,0.014352122002575596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,11,Lucky address,other,other,dummy,binary,0.012,0.004,,,***,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,1.2064192320152989,0.40482891554643113,100*(exp(b - se^2/2) - 1),reported,0.04304200558228067,0.014347335194093556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Lucky unit,other,other,dummy,binary,-0.003,0.006,,,,,,high,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,-0.3013450416032293,0.5982026973020238,100*(exp(b - se^2/2) - 1),reported,-0.007180136772856009,0.014360273545712019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Area,living_area,A_structural,level,100 m2,0.441,0.005,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",semi_elasticity,0.441,0.005,100*b/100 (X per 100m2->m2),reported,0.7848689918986934,0.008898741404747092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),88.2,4849.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,4849.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Subway,transit_distance,D_location,dummy,binary,0.023,0.004,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,2.3258353447645597,0.40930661581888705,100*(exp(b - se^2/2) - 1),reported,0.08229362196487157,0.014311934254760272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Bus,transit_distance,D_location,dummy,binary,0.02,0.004,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",pct_premium,2.019317844868196,0.4080805360107023,100*(exp(b - se^2/2) - 1),reported,0.07161883330203965,0.01432376666040793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,4849.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Log_Park,green_space,E_environment,log,m,-0.028,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",elasticity,-0.028,0.005,b (log-log),reported,-0.08016081010727731,0.014314430376299519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.6,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c1,6,1,12,Log_CBD,cbd_distance,D_location,log,m,-0.025,0.004,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,4859.0,0.849,se,"Whole sample; demographics (Guangdong, gender, age) included, improvement-demand dummy excluded",elasticity,-0.025,0.004,b (log-log),reported,-0.08939467343475828,0.014303147749561325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,4849.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Graveyard,other,other,dummy,binary,-0.041,0.012,,,***,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-4.023997526173995,1.1517949559373588,100*(exp(b - se^2/2) - 1),reported,-0.06467673500466659,0.018929776098926807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4166666666666665,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Funeral home,other,other,dummy,binary,-0.008,0.014,,,,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-0.8065299527011738,1.3888446807718848,100*(exp(b - se^2/2) - 1),reported,-0.010839073587659658,0.0189683787784044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5714285714285714,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Hospital,hospital_distance,D_location,dummy,binary,-0.01,0.011,,,,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-1.0010062453895352,1.0890548171240848,100*(exp(b - se^2/2) - 1),reported,-0.01724243008498071,0.01896667309347878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9090909090909092,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.122,0.039,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-11.552453092917869,3.4520786371602457,100*(exp(b - se^2/2) - 1),reported,-0.05923626328798121,0.0189361825264858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.128205128205128,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Highway,highway_distance,D_location,dummy,binary,-0.008,0.009,,,,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-0.8008261641911041,0.8928287233533545,100*(exp(b - se^2/2) - 1),reported,-0.016859375119526475,0.018966797009467285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.888888888888889,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Unlucky floor,other,other,dummy,binary,-0.006,0.007,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-0.6006389087856889,0.6958125748377547,100*(exp(b - se^2/2) - 1),reported,-0.016257416668445376,0.01896698611318627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8571428571428571,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Unlucky address,other,other,dummy,binary,-0.012,0.007,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-1.1952494598492014,0.6916501990033513,100*(exp(b - se^2/2) - 1),reported,-0.032501950316601975,0.01895947101801782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7142857142857142,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Unlucky unit,other,other,dummy,binary,-0.001,0.005,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-0.10119875948263468,0.4995002499166875,100*(exp(b - se^2/2) - 1),reported,-0.00379387132253485,0.018969356612674248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Pearl River,water_proximity,E_environment,dummy,binary,0.023,0.006,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,2.324812091527373,0.6139599237283306,100*(exp(b - se^2/2) - 1),reported,0.07252489914843116,0.01891953890828639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.833333333333333,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Orientation,sunlight,E_environment,dummy,binary,0.002,0.005,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,0.19894763872647658,0.5010010006670002,100*(exp(b - se^2/2) - 1),reported,0.007587578829573931,0.018968947073934823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Lucky floor,other,other,dummy,binary,0.007,0.006,,,,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,0.7006430987954726,0.6042147343601092,100*(exp(b - se^2/2) - 1),reported,0.02212565758135182,0.018964849355444416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1666666666666667,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,11,Lucky address,other,other,dummy,binary,0.011,0.006,,,*,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,1.1042523515506497,0.6066364334668317,100*(exp(b - se^2/2) - 1),reported,0.034756392056691995,0.018958032030922906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8333333333333333,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Lucky unit,other,other,dummy,binary,-0.001,0.008,,,,,,high,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,-0.10314676711368209,0.7992003998667,100*(exp(b - se^2/2) - 1),reported,-0.0023711799754339985,0.018969439803471988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.125,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Area,living_area,A_structural,level,100 m2,0.462,0.007,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,semi_elasticity,0.462,0.007000000000000001,100*b/100 (X per 100m2->m2),reported,0.781352393430129,0.011838672627729223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),66.0,2779.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,2779.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Subway,transit_distance,D_location,dummy,binary,0.03,0.005,,,***,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,3.0441653352346387,0.5152272669767585,100*(exp(b - se^2/2) - 1),reported,0.1130868353382506,0.01884780588970843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Bus,transit_distance,D_location,dummy,binary,0.021,0.006,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,pct_premium,2.120366980603605,0.6127332309825171,100*(exp(b - se^2/2) - 1),reported,0.0662473752954962,0.01892782151299891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,2779.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Log_Park,green_space,E_environment,log,m,-0.025,0.008,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,elasticity,-0.025,0.008,b (log-log),reported,-0.05917578303662831,0.018936250571721058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.125,2779.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c2,6,2,12,Log_CBD,cbd_distance,D_location,log,m,-0.02,0.005,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,2789.0,0.827,se,First-time demand buyers subsample,elasticity,-0.02,0.005,b (log-log),reported,-0.07566047878575344,0.01891511969643836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,2779.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Graveyard,other,other,dummy,binary,-0.027,0.012,,,**,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-2.670883796216117,1.1680334898292042,100*(exp(b - se^2/2) - 1),reported,-0.052324697061673565,0.02325542091629936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Funeral home,other,other,dummy,binary,-0.057,0.011,,,***,,,high,Dummy = 1 if near a funeral home; printed name 'Funeral home',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-5.546307684598295,1.0390534763031756,100*(exp(b - se^2/2) - 1),reported,-0.11980157909551503,0.023119602983345002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.1818181818181825,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Hospital,hospital_distance,D_location,dummy,binary,-0.042,0.012,,,***,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-4.119925556638582,1.1506437366869815,100*(exp(b - se^2/2) - 1),reported,-0.08123623944599233,0.023210354127426377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.193,0.017,,,***,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-17.563715489887464,1.401619356036484,100*(exp(b - se^2/2) - 1),reported,-0.2555977531787489,0.02251379173077063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.352941176470587,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Highway,highway_distance,D_location,dummy,binary,-0.024,0.012,,,**,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-2.3784580282721457,1.1715428517094912,100*(exp(b - se^2/2) - 1),reported,-0.046524210519923545,0.023262105259961773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Unlucky floor,other,other,dummy,binary,0.0,0.008,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-0.003199948800547414,0.8,100*(exp(b - se^2/2) - 1),reported,0.0,0.023287321641631116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Unlucky address,other,other,dummy,binary,-0.003,0.008,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-0.30274081300222244,0.7976035964026984,100*(exp(b - se^2/2) - 1),reported,-0.008732412651371431,0.02328643373699048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.375,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Unlucky unit,other,other,dummy,binary,0.007,0.007,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,0.6999885467426248,0.704917190086794,100*(exp(b - se^2/2) - 1),reported,0.02328100985898594,0.02328100985898594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Pearl River,water_proximity,E_environment,dummy,binary,0.024,0.007,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,2.4265223085245813,0.7170032225234351,100*(exp(b - se^2/2) - 1),reported,0.07958896739033604,0.02321344882218135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4285714285714284,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Orientation,sunlight,E_environment,dummy,binary,-0.005,0.005,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-0.4999958385572545,0.4975062395963412,100*(exp(b - se^2/2) - 1),reported,-0.02328100985898594,0.02328100985898594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Lucky floor,other,other,dummy,binary,0.021,0.007,,,***,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,2.1197032003755334,0.71485543614627,100*(exp(b - se^2/2) - 1),reported,0.06969209902870227,0.02323069967623409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,11,Lucky address,other,other,dummy,binary,0.012,0.007,,,*,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,1.2047493398746223,0.7084506022062544,100*(exp(b - se^2/2) - 1),reported,0.039889349718872015,0.02326878733600868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7142857142857142,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Lucky unit,other,other,dummy,binary,-0.009,0.0011,,,,,,medium,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit'; SE printed as (-0.0011)/(-0.0031): likely typo for 0.011/0.031 (implied |t| would be ~8-15 for an unstarred coefficient); recorded as printed,"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,-0.8960220806364161,0.10901444166501721,100*(exp(b - se^2/2) - 1),reported,-0.1871656102057875,0.02287579680292959,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.18181818181818,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Area,living_area,A_structural,level,100 m2,0.4,0.007,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,semi_elasticity,0.4,0.007000000000000001,100*b/100 (X per 100m2->m2),reported,0.7994310076118102,0.013990042633206672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),57.142857142857146,1844.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Floor,floor_level,A_structural,level,count,0.002,,,,***,,,high,"Floor level of the unit (mean 13.8, max 47); SE printed as (0.000), rounds to zero at 3 decimals; not usable","Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,semi_elasticity,0.2,,100*b/1 (per unit count),,,,,,1844.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Subway,transit_distance,D_location,dummy,binary,0.009,0.007,,,,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,0.9015900581470282,0.7063284352417075,100*(exp(b - se^2/2) - 1),reported,0.02992743083271046,0.023276890647663695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2857142857142856,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Bus,transit_distance,D_location,dummy,binary,0.02,0.007,,,***,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,pct_premium,2.0176345400110485,0.714140938018729,100*(exp(b - se^2/2) - 1),reported,0.06638841840874359,0.023235946443060252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.857142857142857,1844.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Log_Park,green_space,E_environment,log,m,-0.032,0.008,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,elasticity,-0.032,0.008,b (log-log),reported,-0.09274777915203367,0.023186944788008413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1844.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c3,6,3,12,Log_CBD,cbd_distance,D_location,log,m,-0.031,0.007,,,***,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,1854.0,0.865,se,Improvement demand buyers subsample,elasticity,-0.031,0.007,b (log-log),reported,-0.10258547558030442,0.023164462227810673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.428571428571429,1844.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Graveyard,other,other,dummy,binary,0.018,0.044,,,,,,high,Dummy = 1 if house located near a graveyard (unfavorable geomantic attribute); printed name 'Graveyard',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,1.7177871496339092,4.479917096115091,100*(exp(b - se^2/2) - 1),reported,0.028491143400931253,0.06964501720227641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.40909090909090906,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Hospital,hospital_distance,D_location,dummy,binary,-0.017,0.023,,,,,,high,"Dummy = 1 if near a hospital (unfavorable geomantic attribute); proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-1.711632248221917,2.2612304746602923,100*(exp(b - se^2/2) - 1),reported,-0.05142950698125123,0.06958109768051637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7391304347826088,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Slaughterhouse,hazard_site,E_environment,dummy,binary,-0.08,0.06,,,,,,high,Dummy = 1 if near a slaughterhouse (unfavorable geomantic attribute); printed name 'Slaughterhouse',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-7.854376848524447,5.538698078319815,100*(exp(b - se^2/2) - 1),reported,-0.09249945801257607,0.06937459350943205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3333333333333335,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Highway,highway_distance,D_location,dummy,binary,-0.063,0.033,,,*,,,high,"Dummy = 1 if near a highway; proximity indicator, not distance","Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-6.156764186845198,3.0985134631741396,100*(exp(b - se^2/2) - 1),reported,-0.13185139841594756,0.0690650182178773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.909090909090909,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Unlucky floor,other,other,dummy,binary,-0.012,0.025,,,,,,high,Dummy = 1 if unit on 4th/14th/24th/34th/44th floor; printed name 'Unlucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-1.223701130767496,2.4701792821548265,100*(exp(b - se^2/2) - 1),reported,-0.03342449815132083,0.06963437114858505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Unlucky address,other,other,dummy,binary,0.019,0.037,,,,,,high,Dummy = 1 if address number ends with 4; printed name 'Unlucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,1.8484257488273093,3.7709720998844096,100*(exp(b - se^2/2) - 1),reported,0.035755304339383655,0.06962875055564186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5135135135135135,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Unlucky unit,other,other,dummy,binary,-0.014,0.018,,,,,,high,Dummy = 1 if unit number ends with 4; printed name 'Unlucky unit',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-1.4062190600435454,1.7749755796731512,100*(exp(b - se^2/2) - 1),reported,-0.05411095286168635,0.06957122510788244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7777777777777779,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Pearl River,water_proximity,E_environment,dummy,binary,0.107,0.02,,,***,,,high,Dummy = 1 if located near the Pearl River (favorable geomantic attribute),"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,11.271168988563108,2.2258685089586514,100*(exp(b - se^2/2) - 1),reported,0.34927616748125073,0.0652852649497665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.35,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Orientation,sunlight,E_environment,dummy,binary,0.0,0.016,,,,,,high,Dummy = 1 if south-facing,"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-0.012799180834954793,1.6,100*(exp(b - se^2/2) - 1),reported,0.0,0.06967330142916177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Lucky floor,other,other,dummy,binary,0.001,0.022,,,,,,high,Dummy = 1 if unit on 8th/18th/28th/38th floor; printed name 'Lucky floor',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,0.07582873546003732,2.202201100366758,100*(exp(b - se^2/2) - 1),reported,0.0031669523650480324,0.06967295203105671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.045454545454545456,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,11,Lucky address,other,other,dummy,binary,0.031,0.021,,,,,,high,Dummy = 1 if address number ends with 8; printed name 'Lucky address',"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,3.1258086406614316,2.166119558161698,100*(exp(b - se^2/2) - 1),reported,0.10231134542259064,0.06930768560885173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.476190476190476,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Lucky unit,other,other,dummy,binary,-0.047,0.0031,,,,,,medium,Dummy = 1 if unit number ends with 8; printed name 'Lucky unit'; SE printed as (-0.0011)/(-0.0031): likely typo for 0.011/0.031 (implied |t| would be ~8-15 for an unstarred coefficient); recorded as printed,"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-4.591718678856028,0.29576709325301503,100*(exp(b - se^2/2) - 1),reported,-0.7262067866346039,0.04789874550143133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.161290322580646,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,False,False,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Area,living_area,A_structural,level,100 m2,0.531,0.026,,,***,,,high,Ground floor area in units of 100 m2 (sample mean 1.041 = 104 m2),"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,semi_elasticity,0.531,0.026000000000000002,100*b/100 (X per 100m2->m2),reported,0.8181662786458921,0.04006087240074048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.423076923076923,206.0,100m2,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Floor,floor_level,A_structural,level,count,0.001,0.001,,,,,,high,"Floor level of the unit (mean 13.8, max 47)","Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,semi_elasticity,0.1,0.1,100*b/1 (per unit count),reported,0.06950480468569159,0.06950480468569159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,206.0,count,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Subway,transit_distance,D_location,dummy,binary,0.025,0.018,,,,,,high,Dummy = 1 if subway station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,2.5149032928362347,1.8455672169439719,100*(exp(b - se^2/2) - 1),reported,0.09631855492848873,0.06934935954851187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.388888888888889,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Bus,transit_distance,D_location,dummy,binary,-0.005,0.02,,,,,,high,Dummy = 1 if bus station within 800 m,"Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,pct_premium,-0.5186503404233211,1.9900249583853649,100*(exp(b - se^2/2) - 1),reported,-0.01741568361535407,0.06966273446141628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,206.0,binary,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Log_Park,green_space,E_environment,log,m,-0.037,0.024,,,,,,high,"Log (base 10, inferred from descriptives) of distance to nearest park in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,elasticity,-0.037,0.024,b (log-log),reported,-0.10679867551740724,0.06927481655183174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5416666666666665,206.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801,0d749c58da0b3282e774a34a3cbd378d57ba0801:T6-c4,6,4,12,Log_CBD,cbd_distance,D_location,log,m,0.02,0.02,,,,,,high,"Log (base 10, inferred from descriptives) of distance to nearest CBD in metres","Log_Price (log10 of transaction price, RMB)",log,OLS,216.0,0.873,se,Investment demand buyers subsample,elasticity,0.02,0.02,b (log-log),reported,0.06950480468569159,0.06950480468569159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,206.0,m,,2023.0,China,Asia,Upper middle income,2013.0,2016.0,condo_apartment,transaction,journal,True,True,robust,,2014.5,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,net.area,living_area,A_structural,level,m2,0.004,0.0,13.863,0.0,***,,,high,"net surface area of the dwelling in m2; SE printed as 0.000 (rounded to 3 decimals), t=13.863",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,0.4,0.028853783452355192,100*b/1 (X per m2->m2),|b/t|,0.2545413067829516,0.018361199363986988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.863,2774.0,m2,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,bedroom,bedrooms,A_structural,level,count,-0.013,0.015,-0.862,0.389,,,,high,number of bedrooms,ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-1.3,1.5,100*b/1 (per unit count),reported,-0.01636424150215754,0.018984038865611993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.862,2774.0,count,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,livingroom,other,other,level,count,-0.048,0.052,-0.916,0.36,,,,high,number of living rooms,ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-4.8,5.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.01738907875398242,0.018983710430111808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.916,2774.0,count,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,bath: more.than.one,bathrooms,A_structural,binary,binary,0.286,0.019,14.87,0.0,***,,,high,more than one bathroom (=1) vs one,ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,33.08522147464765,2.529075664979354,100*(exp(b - se^2/2) - 1),reported,0.2717090347914872,0.01827229554751091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.87,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,floor: ground.floor,floor_level,A_structural,dummy,binary,-0.212,0.02,-10.461,0.0,***,,,high,ground floor vs middle floors (reference),ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,-19.11970791947981,1.6179293951329996,100*(exp(b - se^2/2) - 1),reported,-0.19481316670985435,0.018622805344599404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.461,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,floor: top.floor,floor_level,A_structural,dummy,binary,-0.073,0.019,-3.912,0.0,***,,,high,top floor vs middle floors (reference),ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,-7.056694778162431,1.766241577049006,100*(exp(b - se^2/2) - 1),reported,-0.07407146618235935,0.01893442387074626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.912,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,floor: basement.floor,floor_level,A_structural,dummy,binary,-0.447,0.024,-18.323,0.0,***,,,high,basement level vs middle floors (reference),ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,-36.06402538501634,1.5349053798595222,100*(exp(b - se^2/2) - 1),reported,-0.32857542422356073,0.01793240322128258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.323,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,True
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,total.floor,storeys,A_structural,level,count,0.024,0.002,15.813,0.0,***,,,high,total number of floors in the building,ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,2.4,0.2,100*b/1 (per unit count),reported,0.2875542015303803,0.018184670937227615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.813,2774.0,count,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,age,age,A_structural,level,years,-0.007,0.001,-9.138,0.0,***,,,high,age of the building in years,ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per year),reported,-0.1709455511816297,0.01870710781151562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.138,2774.0,years,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,carpark.indoor: yes,parking,A_structural,binary,binary,0.17,0.018,9.57,0.0,***,,,high,building has indoor parking (=1),ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,18.511284748718193,2.1335487323766578,100*(exp(b - se^2/2) - 1),reported,0.17877438708303808,0.018680709204079216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.57,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,carpark.outdoor: yes,parking,A_structural,binary,binary,-0.035,0.015,-2.328,0.02,*,,,high,building has outdoor parking (=1),ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,-3.4503208241519867,1.4484081243863496,100*(exp(b - se^2/2) - 1),reported,-0.04415764659111069,0.018968061250477102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.328,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,gated.community: yes,other,other,binary,binary,-0.01,0.016,-0.663,0.507,,,,high,located in a gated community (=1),ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",pct_premium,-1.007688451940969,1.584079733998669,100*(exp(b - se^2/2) - 1),reported,-0.012587106115689884,0.018985077097571468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.663,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,num.views,view,E_environment,level,count,-0.007,0.008,-0.879,0.379,,,,high,"number of facade orientations/views (east, west, south, north)",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-0.7000000000000001,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.016686881167189905,0.018983937619101145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.879,2774.0,count,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,False,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,dist.transportation,transit_distance,D_location,level,km,-0.015,0.01,-1.518,0.129,,,,high,"distance to closest transportation station, km",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-1.5,1.0,100*b/1 (X per km->km),reported,-0.028809666890607964,0.018978700191441347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.518,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,dist.school,school_distance,D_location,level,km,0.21,0.011,18.965,0.0,***,,,high,"distance to closest primary school, km",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,21.0,1.0999999999999999,100*b/1 (X per km->km),reported,0.3387865280803806,0.01786377685633433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.965,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,True
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,dist.mall,commercial_distance,D_location,level,km,-0.16,0.008,-19.614,0.0,***,,,high,"distance to closest shopping mall, km",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-16.0,0.8,100*b/1 (X per km->km),reported,-0.34898869781204694,0.01779283663771015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.614,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,True
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,dist.hospital,hospital_distance,D_location,level,km,-0.091,0.011,-8.619,0.0,***,,,high,"distance to closest hospital, km",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-9.1,1.0999999999999999,100*b/1 (X per km->km),reported,-0.1614971990908349,0.018737347614669327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.619,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,dist.university,university_distance,D_location,level,km,-0.049,0.008,-6.395,0.0,***,,,high,"distance to closest university, km",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,-4.9,0.8,100*b/1 (X per km->km),reported,-0.12053394470955732,0.018848153981166117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.395,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,True,True,True
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T3-ols,3,Coef.,4,dist.police,public_services,F_neighbourhood,level,km,0.06,0.007,8.394,0.0,***,,,high,"distance to closest police station, km",ln asking price (TRY),log,OLS,2784.0,0.6974,se,"Global OLS; SE, t and p in separate columns; adj R2 0.6953",semi_elasticity,6.0,0.7000000000000001,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.15738709078180008,0.01874995124872529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.394,2774.0,km,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,ols,journal,False,True,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,net.area,living_area,A_structural,level,m2,0.003,,,,,,,medium,"net surface area of the dwelling in m2; GWR median of local coefficients; min=-0.001, Q1=0.002, Q3=0.004, max=0.012; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,0.3,,100*b/1 (X per m2->m2),,,,,,2774.0,m2,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,bedroom,bedrooms,A_structural,level,count,0.06,,,,,,,medium,"number of bedrooms; GWR median of local coefficients; min=-0.401, Q1=0.007, Q3=0.093, max=0.205; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,6.0,,100*b/1 (per unit count),,,,,,2774.0,count,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,livingroom,other,other,level,count,0.039,,,,,,,medium,"number of living rooms; GWR median of local coefficients; min=-14.027, Q1=-0.14, Q3=0.184, max=0.969; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,3.9,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,2774.0,count,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,bath: more.than.one,bathrooms,A_structural,binary,binary,0.147,,,,,,,medium,"more than one bathroom (=1) vs one; GWR median of local coefficients; min=-0.07, Q1=0.094, Q3=0.21, max=0.4; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,15.835396302985538,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,floor: ground.floor,floor_level,A_structural,dummy,binary,-0.271,,,,,,,medium,"ground floor vs middle floors (reference); GWR median of local coefficients; min=-0.472, Q1=-0.348, Q3=-0.205, max=0.225; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,-23.738350359493467,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,floor: top.floor,floor_level,A_structural,dummy,binary,-0.073,,,,,,,medium,"top floor vs middle floors (reference); GWR median of local coefficients; min=-0.278, Q1=-0.105, Q3=-0.034, max=0.119; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,-7.039916997420725,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,floor: basement.floor,floor_level,A_structural,dummy,binary,-0.481,,,,,,,medium,"basement level vs middle floors (reference); GWR median of local coefficients; min=-0.856, Q1=-0.566, Q3=-0.41, max=0.679; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,-38.18350822970742,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,total.floor,storeys,A_structural,level,count,0.025,,,,,,,medium,"total number of floors in the building; GWR median of local coefficients; min=-0.023, Q1=0.018, Q3=0.031, max=0.053; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,2.5,,100*b/1 (per unit count),,,,,,2774.0,count,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,age,age,A_structural,level,years,-0.011,,,,,,,medium,"age of the building in years; GWR median of local coefficients; min=-0.036, Q1=-0.016, Q3=-0.008, max=0.003; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-1.0999999999999999,,100*b/1 (per year),,,,,,2774.0,years,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,carpark.indoor: yes,parking,A_structural,binary,binary,0.071,,,,,,,medium,"building has indoor parking (=1); GWR median of local coefficients; min=-0.105, Q1=0.026, Q3=0.125, max=0.384; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,7.358122586835747,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,carpark.outdoor: yes,parking,A_structural,binary,binary,0.005,,,,,,,medium,"building has outdoor parking (=1); GWR median of local coefficients; min=-0.195, Q1=-0.019, Q3=0.036, max=0.175; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,0.501252085940096,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,gated.community: yes,other,other,binary,binary,-0.021,,,,,,,medium,"located in a gated community (=1); GWR median of local coefficients; min=-0.141, Q1=-0.047, Q3=0.009, max=0.112; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,pct_premium,-2.078103543054044,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,num.views,view,E_environment,level,count,-0.002,,,,,,,medium,"number of facade orientations/views (east, west, south, north); GWR median of local coefficients; min=-0.069, Q1=-0.021, Q3=0.013, max=0.104; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-0.2,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,2774.0,count,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,dist.transportation,transit_distance,D_location,level,km,-0.033,,,,,,,medium,"distance to closest transportation station, km; GWR median of local coefficients; min=-76.786, Q1=-0.141, Q3=0.068, max=2.473; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-3.3000000000000003,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,dist.school,school_distance,D_location,level,km,0.083,,,,,,,medium,"distance to closest primary school, km; GWR median of local coefficients; min=-552.54, Q1=-0.112, Q3=0.262, max=3.246; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,8.3,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,dist.mall,commercial_distance,D_location,level,km,-0.096,,,,,,,medium,"distance to closest shopping mall, km; GWR median of local coefficients; min=-2.735, Q1=-0.208, Q3=-0.019, max=304.215; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-9.6,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,dist.hospital,hospital_distance,D_location,level,km,-0.056,,,,,,,medium,"distance to closest hospital, km; GWR median of local coefficients; min=-1.741, Q1=-0.139, Q3=0.054, max=409.446; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-5.6000000000000005,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,dist.university,university_distance,D_location,level,km,-0.021,,,,,,,medium,"distance to closest university, km; GWR median of local coefficients; min=-2.022, Q1=-0.158, Q3=0.068, max=38.315; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-2.1,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr1,5,GWR 1 (CV approach) Median,6,dist.police,public_services,F_neighbourhood,level,km,-0.007,,,,,,,medium,"distance to closest police station, km; GWR median of local coefficients; min=-170.9, Q1=-0.103, Q3=0.089, max=3.612; no SE",ln asking price (TRY),log,GWR,2784.0,0.9,none,GWR adaptive bandwidth 45 (CV); median of local coefficients; adj R2 0.866,semi_elasticity,-0.7000000000000001,,100*b (unit as reported: km; no harmonised unit defined for this characteristic),,,,,,2774.0,km,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,net.area,living_area,A_structural,level,m2,0.003,,,,,,,medium,"net surface area of the dwelling in m2; GWR median of local coefficients; min=0.0, Q1=0.002, Q3=0.004, max=0.009; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,0.3,,100*b/1 (X per m2->m2),,,,,,2774.0,m2,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,bedroom,bedrooms,A_structural,level,count,0.053,,,,,,,medium,"number of bedrooms; GWR median of local coefficients; min=-0.234, Q1=0.005, Q3=0.094, max=0.192; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,5.3,,100*b/1 (per unit count),,,,,,2774.0,count,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,livingroom,other,other,level,count,0.04,,,,,,,medium,"number of living rooms; GWR median of local coefficients; min=-0.999, Q1=-0.111, Q3=0.165, max=0.431; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,4.0,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,2774.0,count,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,bath: more.than.one,bathrooms,A_structural,binary,binary,0.155,,,,,,,medium,"more than one bathroom (=1) vs one; GWR median of local coefficients; min=-0.027, Q1=0.104, Q3=0.207, max=0.396; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,16.765796110512497,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,floor: ground.floor,floor_level,A_structural,dummy,binary,-0.27,,,,,,,medium,"ground floor vs middle floors (reference); GWR median of local coefficients; min=-0.444, Q1=-0.347, Q3=-0.209, max=0.093; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,-23.662050566314687,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,floor: top.floor,floor_level,A_structural,dummy,binary,-0.075,,,,,,,medium,"top floor vs middle floors (reference); GWR median of local coefficients; min=-0.199, Q1=-0.106, Q3=-0.04, max=0.06; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,-7.225651367144714,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,floor: basement.floor,floor_level,A_structural,dummy,binary,-0.476,,,,,,,medium,"basement level vs middle floors (reference); GWR median of local coefficients; min=-0.774, Q1=-0.564, Q3=-0.413, max=-0.097; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,-37.873651775253826,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,total.floor,storeys,A_structural,level,count,0.025,,,,,,,medium,"total number of floors in the building; GWR median of local coefficients; min=-0.005, Q1=0.02, Q3=0.03, max=0.05; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,2.5,,100*b/1 (per unit count),,,,,,2774.0,count,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,age,age,A_structural,level,years,-0.011,,,,,,,medium,"age of the building in years; GWR median of local coefficients; min=-0.034, Q1=-0.015, Q3=-0.008, max=0.002; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-1.0999999999999999,,100*b/1 (per year),,,,,,2774.0,years,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,carpark.indoor: yes,parking,A_structural,binary,binary,0.075,,,,,,,medium,"building has indoor parking (=1); GWR median of local coefficients; min=-0.068, Q1=0.035, Q3=0.123, max=0.36; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,7.788415088463152,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,carpark.outdoor: yes,parking,A_structural,binary,binary,0.004,,,,,,,medium,"building has outdoor parking (=1); GWR median of local coefficients; min=-0.177, Q1=-0.016, Q3=0.032, max=0.137; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,0.4008010677341911,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,gated.community: yes,other,other,binary,binary,-0.019,,,,,,,medium,"located in a gated community (=1); GWR median of local coefficients; min=-0.096, Q1=-0.043, Q3=0.006, max=0.052; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,pct_premium,-1.8820637757194003,,100*(exp(b) - 1),,,,,,2774.0,binary,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,num.views,view,E_environment,level,count,-0.002,,,,,,,medium,"number of facade orientations/views (east, west, south, north); GWR median of local coefficients; min=-0.061, Q1=-0.019, Q3=0.012, max=0.058; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-0.2,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,2774.0,count,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,dist.transportation,transit_distance,D_location,level,km,-0.02,,,,,,,medium,"distance to closest transportation station, km; GWR median of local coefficients; min=-1.223, Q1=-0.109, Q3=0.07, max=1.89; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-2.0,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,dist.school,school_distance,D_location,level,km,0.127,,,,,,,medium,"distance to closest primary school, km; GWR median of local coefficients; min=-1.42, Q1=-0.076, Q3=0.301, max=2.545; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,12.7,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,dist.mall,commercial_distance,D_location,level,km,-0.118,,,,,,,medium,"distance to closest shopping mall, km; GWR median of local coefficients; min=-2.14, Q1=-0.213, Q3=-0.045, max=1.43; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-11.799999999999999,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,dist.hospital,hospital_distance,D_location,level,km,-0.071,,,,,,,medium,"distance to closest hospital, km; GWR median of local coefficients; min=-1.448, Q1=-0.138, Q3=0.014, max=0.85; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-7.1,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,dist.university,university_distance,D_location,level,km,-0.024,,,,,,,medium,"distance to closest university, km; GWR median of local coefficients; min=-1.666, Q1=-0.135, Q3=0.055, max=3.673; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-2.4,,100*b/1 (X per km->km),,,,,,2774.0,km,,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211,0d7a1f874f9358489f6f5882b5a4900f21774211:T5-gwr2,5,GWR 2 (AIC approach) Median,6,dist.police,public_services,F_neighbourhood,level,km,-0.013,,,,,,,medium,"distance to closest police station, km; GWR median of local coefficients; min=-5.637, Q1=-0.084, Q3=0.074, max=1.923; no SE",ln asking price (TRY),log,GWR,2784.0,0.889,none,GWR adaptive bandwidth 59 (AIC); median of local coefficients; adj R2 0.860,semi_elasticity,-1.3,,100*b (unit as reported: km; no harmonised unit defined for this characteristic),,,,,,2774.0,km,unit_not_harmonisable,2022.0,Turkey,Asia,Upper middle income,2021.0,2021.0,condo_apartment,listing,journal,False,False,OLS,,2021.0,spatial,journal,False,False,False
0e568775e77ac2d90b96f6f8ecad954135ed43b0,0e568775e77ac2d90b96f6f8ecad954135ed43b0,0e568775e77ac2d90b96f6f8ecad954135ed43b0:T4-c1,4,1,7,log(Size),living_area,A_structural,log,m2,0.73296,0.03107,,,***,,,high,"floor area in square metres, log","listing price (ZAR), gamma GLM with log link",other,other,1314.0,,se,,other,0.73296,0.03107,not harmonised,reported,0.5469180524580449,0.02318372611039,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.590601866752493,1304.0,m2,,2019.0,South Africa,Africa,Upper middle income,2016.0,2016.0,condo_apartment,listing,journal,False,True,OLS,,2016.0,other,journal,False,True,False
0e568775e77ac2d90b96f6f8ecad954135ed43b0,0e568775e77ac2d90b96f6f8ecad954135ed43b0,0e568775e77ac2d90b96f6f8ecad954135ed43b0:T4-c1,4,1,7,NumBaths,bathrooms,A_structural,level,count,0.20298,0.01878,,,***,,,high,,"listing price (ZAR), gamma GLM with log link",other,other,1314.0,,se,,other,0.20298,0.01878,not harmonised,reported,0.2867399510078067,0.02652959050116568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.808306709265175,1304.0,count,,2019.0,South Africa,Africa,Upper middle income,2016.0,2016.0,condo_apartment,listing,journal,False,True,OLS,,2016.0,other,journal,False,True,False
0e568775e77ac2d90b96f6f8ecad954135ed43b0,0e568775e77ac2d90b96f6f8ecad954135ed43b0,0e568775e77ac2d90b96f6f8ecad954135ed43b0:T4-c1,4,1,7,NumBeds,bedrooms,A_structural,level,count,0.03851,0.0183,,,*,,,high,,"listing price (ZAR), gamma GLM with log link",other,other,1314.0,,se,,other,0.03851,0.0183,not harmonised,reported,0.05817648143597083,0.027645536491255937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1043715846994537,1304.0,count,,2019.0,South Africa,Africa,Upper middle income,2016.0,2016.0,condo_apartment,listing,journal,False,True,OLS,,2016.0,other,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Property size,living_area,A_structural,level,m2,-0.003,0.001,,,***,,,high,Total weighted area of dwelling incl. garage (m2); dep var is price per m2,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,-0.003,0.001,"b (currency per unit, as reported)",reported,-0.14205411714311353,0.047351372381037836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,437.0,m2,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Property size,living_area,A_structural,level,m2,-0.003,0.001,,,***,,,high,Total weighted area of dwelling incl. garage (m2); dep var is price per m2,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,-0.003,0.001,"b (currency per unit, as reported)",reported,-0.14205411714311353,0.047351372381037836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,437.0,m2,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,EPC ABCD,energy_efficiency,A_structural,dummy,binary,0.324,0.0764,,,**,,,high,EPC class A4-D vs reference G,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,0.324,0.0764,"b (currency per unit, as reported)",reported,0.19881687954723912,0.0468815111031144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.240837696335079,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,EPC ABCD,energy_efficiency,A_structural,dummy,binary,0.337,0.074,,,***,,,high,EPC class A4-D vs reference G,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,0.337,0.074,"b (currency per unit, as reported)",reported,0.2128575353403857,0.04674023031213217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.554054054054054,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,EPC E,energy_efficiency,A_structural,dummy,binary,0.226,0.0706,,,**,,,high,EPC class E vs reference G,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,0.226,0.0706,"b (currency per unit, as reported)",reported,0.1513665446147719,0.04728530110532254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.201133144475921,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,EPC E,energy_efficiency,A_structural,dummy,binary,0.233,0.069,,,***,,,high,EPC class E vs reference G,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,0.233,0.069,"b (currency per unit, as reported)",reported,0.15946766861987527,0.04722433105052101,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3768115942028984,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,EPC F,energy_efficiency,A_structural,dummy,binary,0.202,0.058,,,***,,,high,EPC class F vs reference G,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,0.202,0.058,"b (currency per unit, as reported)",reported,0.16433782433400151,0.047186107977089546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4827586206896552,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,EPC F,energy_efficiency,A_structural,dummy,binary,0.205,0.057,,,***,,,high,EPC class F vs reference G,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,0.205,0.057,"b (currency per unit, as reported)",reported,0.16955251555224102,0.04714387017794019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.596491228070175,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Building age,age,A_structural,level,years,-0.011,0.001,,,***,,,high,Age = 2024 minus construction year; quadratic term also included,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,-0.011,0.001,"b (currency per unit, as reported)",reported,-0.46566713233403184,0.042333375666730164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.0,437.0,years,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Building age,age,A_structural,level,years,-0.012,0.001,,,***,,,high,Age = 2024 minus construction year; quadratic term also included,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,-0.012,0.001,"b (currency per unit, as reported)",reported,-0.49784388818701336,0.04148699068225112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,437.0,years,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Building age (^2),age,A_structural,squared,years,0.0,0.0,,,***,,,medium,Quadratic term printed as 0.000*** (SE printed 0.000); rounding hides magnitude,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),other,0.0,,not harmonised,,,,,,437.0,years,nonstandard_x_transform,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,False,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Building age (^2),age,A_structural,squared,years,0.0,0.0,,,***,,,medium,Quadratic term printed as 0.000*** (SE printed 0.000); rounding hides magnitude,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),other,0.0,,not harmonised,,,,,,437.0,years,nonstandard_x_transform,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,False,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,House,dwelling_type,A_structural,dummy,binary,-0.104,0.107,,,,,,high,House vs reference apartment,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,-0.104,0.107,"b (currency per unit, as reported)",reported,-0.04644510173971982,0.04778486428990405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9719626168224299,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,House,dwelling_type,A_structural,dummy,binary,-0.087,0.104,,,,,,high,House vs reference apartment,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,-0.087,0.104,"b (currency per unit, as reported)",reported,-0.03998505897658912,0.04779823142029044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8365384615384616,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Portion of the house,dwelling_type,A_structural,dummy,binary,-0.21,0.052,,,***,,,high,Portion of house vs reference apartment,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,-0.21,0.052,"b (currency per unit, as reported)",reported,-0.18967875836133052,0.046968073498996135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.038461538461538,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Portion of the house,dwelling_type,A_structural,dummy,binary,-0.2,0.051,,,***,,,high,Portion of house vs reference apartment,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,-0.2,0.051,"b (currency per unit, as reported)",reported,-0.1843778494835518,0.0470163516183057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9215686274509807,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Bathrooms,bathrooms,A_structural,level,count,0.165,0.055,,,**,,,high,,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,0.165,0.055,"b (currency per unit, as reported)",reported,0.14205411714311353,0.047351372381037836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,437.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Bathrooms,bathrooms,A_structural,level,count,0.157,0.054,,,**,,,high,,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,0.157,0.054,"b (currency per unit, as reported)",reported,0.137754228819589,0.047380435390177104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9074074074074074,437.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Mediation,other,other,dummy,binary,0.121,0.044,,,**,,,high,Printed name Mediation: sale mediated by real estate agency = 1,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,0.121,0.044,"b (currency per unit, as reported)",reported,0.13042663234499272,0.047427866307270077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,False,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Mediation,other,other,dummy,binary,0.119,0.042,,,**,,,high,Printed name Mediation: sale mediated by real estate agency = 1,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,0.119,0.042,"b (currency per unit, as reported)",reported,0.13430868938174842,0.047403066840617084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.833333333333333,437.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,False,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Number of hotels,neighbourhood_amenities,F_neighbourhood,level,count,0.046,0.02,,,*,,,high,Number of hotels within 250 m buffer,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,0.046,0.02,"b (currency per unit, as reported)",reported,0.10936397221725609,0.04754955313793744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3,437.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Number of hotels,neighbourhood_amenities,F_neighbourhood,level,count,0.049,0.019,,,*,,,high,Number of hotels within 250 m buffer,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,0.049,0.019,"b (currency per unit, as reported)",reported,0.1224395603844085,0.047476564230689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5789473684210527,437.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Forest cover (250 m radius),green_space,E_environment,level,share,-0.004,0.001,,,***,,,high,Percentage of 250 m buffer area covered by forest (unit: percentage points),log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,-0.004,0.001,"b (currency per unit, as reported)",reported,-0.18793639429397582,0.046984098573493956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,437.0,share,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Forest cover (250 m radius),green_space,E_environment,level,share,-0.003,0.001,,,**,,,high,Percentage of 250 m buffer area covered by forest (unit: percentage points),log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,-0.003,0.001,"b (currency per unit, as reported)",reported,-0.14205411714311353,0.047351372381037836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,437.0,share,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Log of distance to the ski lift,other,other,log,m,-0.053,0.029,,,,,,high,Printed name: log of distance to nearest ski lift (metres),log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),other,-0.053,0.029,not harmonised,reported,-0.08709310367648646,0.047654717106002026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8275862068965516,437.0,m,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,False,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Log of distance to the ski lift,other,other,log,m,-0.027,0.029,,,,,,high,Printed name: log of distance to nearest ski lift (metres),log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),other,-0.027,0.029,not harmonised,reported,-0.04449331303243011,0.047789113997795306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9310344827586207,437.0,m,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,False,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Log of distance to primary road,highway_distance,D_location,log,m,0.059,0.016,,,***,,,high,Log of distance to nearest primary road (metres),log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),other,0.059,0.016,not harmonised,reported,0.17371509763660992,0.047109179020097605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6874999999999996,437.0,m,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Log of distance to primary road,highway_distance,D_location,log,m,0.053,0.016,,,***,,,high,Log of distance to nearest primary road (metres),log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),other,0.053,0.016,not harmonised,reported,0.15650569085291163,0.04724700101219973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3125,437.0,m,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Winter temperature,urban_heat,E_environment,level,degrees_C,-0.136,0.031,,,***,,,high,"Mean DJF air temperature 2018-2022 (degrees C), with quadratic term",log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),monetary,-0.136,0.031,"b (currency per unit, as reported)",reported,-0.20538909693623433,0.04681663238987694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.387096774193549,437.0,degrees_c,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Winter temperature,urban_heat,E_environment,level,degrees_C,-0.088,0.032,,,**,,,high,"Mean DJF air temperature 2018-2022 (degrees C), with quadratic term",log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),monetary,-0.088,0.032,"b (currency per unit, as reported)",reported,-0.13042663234499272,0.047427866307270077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.75,437.0,degrees_c,,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-OLS,2,OLS,10,Winter temperature (^2),urban_heat,E_environment,squared,degrees_C,-0.032,0.011,,,**,,,high,Quadratic term of winter temperature,log of transaction price per m2,per_m2,OLS,447.0,0.606,se,Full sample; dep var log(price/m2),other,-0.032,0.011,not harmonised,reported,-0.13783247893276948,0.04737991463313951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.909090909090909,437.0,degrees_c,nonstandard_x_transform,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,ols,journal,False,True,False
0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679,0e8221fad5dffd447b52c9be164621765eb67679:T2-SAR,2,SAR,10,Winter temperature (^2),urban_heat,E_environment,squared,degrees_C,-0.023,0.011,,,*,,,high,Quadratic term of winter temperature,log of transaction price per m2,per_m2,spatial_lag,447.0,0.615,se,Full sample; pseudo R2; spatial lag rho=0.39 (SE 0.115),other,-0.023,0.011,not harmonised,reported,-0.09952514301070159,0.04759898143990076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.090909090909091,437.0,degrees_c,nonstandard_x_transform,2025.0,Italy,Europe,High income,2022.0,2023.0,mixed,transaction,journal,False,False,OLS,,2022.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,TTBellecour,cbd_distance,D_location,log,"minutes (travel time by car to Bellecour, morning peak)",-0.224,,,0.001,,,,high,travel time by car to Bellecour (central Lyon public-transport junction); log; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,elasticity,-0.224,0.06807420765077343,b (log-log),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,TTBellecour,cbd_distance,D_location,log,"minutes (travel time by car to Bellecour, morning peak)",-0.093,,,0.001,,,,high,travel time by car to Bellecour (central Lyon public-transport junction); log; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,elasticity,-0.093,0.02826295228358004,b (log-log),|b/z(p)| (two-sided normal),-0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,863.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,TTBellecour,cbd_distance,D_location,log,"minutes (travel time by car to Bellecour, morning peak)",-0.176,,,0.001,,,,high,travel time by car to Bellecour (central Lyon public-transport junction); log; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,elasticity,-0.176,0.0534868774398934,b (log-log),|b/z(p)| (two-sided normal),-0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,862.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,TTBellecour,cbd_distance,D_location,log,"minutes (travel time by car to Bellecour, morning peak)",-0.136,,,,,,,high,travel time by car to Bellecour (central Lyon public-transport junction); log; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",elasticity,-0.136,,b (log-log),,,,,,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Area,living_area,A_structural,log,m2,0.932,,,0.001,,,,high,p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,elasticity,0.932,0.28323732826125375,b (log-log),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,m2,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Area,living_area,A_structural,log,m2,0.69,,,0.001,,,,high,p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,elasticity,0.69,0.20969287178140028,b (log-log),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,m2,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Area,living_area,A_structural,log,m2,0.383,,,0.001,,,,high,p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,elasticity,0.383,0.1163947389743135,b (log-log),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,m2,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Area,living_area,A_structural,log,m2,0.937,,,,,,,high,mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",elasticity,0.937,,b (log-log),,,,,,4352.0,m2,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Bath2,bathrooms,A_structural,dummy,binary (2 bathrooms vs 1),0.094,,,0.001,,,,high,reference Bath1; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,9.81115891628872,3.138239696639544,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Bath2,bathrooms,A_structural,dummy,binary (2 bathrooms vs 1),0.046,,,0.001,,,,high,reference Bath1,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,4.697210259074258,1.4637602680158552,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Bath2,bathrooms,A_structural,dummy,binary (2 bathrooms vs 1),0.053,,,0.78,,,,high,reference Bath1,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,3.561764091167685,20.007505504190398,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0095132135593042,0.034058593887534856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27931903444745415,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Bath2,bathrooms,A_structural,dummy,binary (2 bathrooms vs 1),0.081,,,,,,,high,reference Bath1; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,8.437089656676044,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,ParkUn,parking,A_structural,dummy,binary,0.092,,,0.001,,,,high,dummy for no data about parking places; reference Park0; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,9.59363869896599,3.0653318408297396,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,ParkUn,parking,A_structural,dummy,binary,0.016,,,0.48,,,,high,dummy for no data about parking places; reference Park0,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,1.5867997188913519,2.3018547322160114,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.024035872798804793,0.03403056149499872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7063025628400874,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,ParkUn,parking,A_structural,dummy,binary,0.043,,,0.09,,,,high,dummy for no data about parking places; reference Park0,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,4.360218154581386,2.647716769145038,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.05764943798712797,0.03400348935110595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6953977102721358,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,ParkUn,parking,A_structural,dummy,binary,0.043,,,,,,,high,dummy for no data about parking places; reference Park0; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,4.393789485061261,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Park1,parking,A_structural,dummy,binary (1 parking place vs 0),0.154,,,0.001,,,,high,reference Park0; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,16.521408300128826,5.45929607088871,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Park1,parking,A_structural,dummy,binary (1 parking place vs 0),0.039,,,0.2,,,,high,reference Park0,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,3.9289130313920007,3.1642151554880136,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04358307089338646,0.03400805091667588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2815515655446004,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Park1,parking,A_structural,dummy,binary (1 parking place vs 0),0.09,,,0.001,,,,high,reference Park0,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,9.376508951426498,2.9927027971239415,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Park1,parking,A_structural,dummy,binary (1 parking place vs 0),0.099,,,,,,,high,reference Park0; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,10.406629955888192,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Park2,parking,A_structural,dummy,binary (2 parking places vs 0),0.193,,,0.001,,,,high,reference Park0; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,21.079830671894094,7.113948563580198,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Park2,parking,A_structural,dummy,binary (2 parking places vs 0),0.08,,,0.01,,,,high,reference Park0,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,8.276472603029529,3.3644684954315505,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08734712204436584,0.03391029130852016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Park2,parking,A_structural,dummy,binary (2 parking places vs 0),0.067,,,0.58,,,,high,reference Park0,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,6.14868914374298,12.946291161639246,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.018845011198182028,0.03405408666381895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5533847195556729,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Park2,parking,A_structural,dummy,binary (2 parking places vs 0),0.139,,,,,,,high,reference Park0; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,14.91241000036052,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Floor1,floor_level,A_structural,dummy,binary (storey 1 vs ground floor),0.067,,,0.001,,,,high,reference FloorGr; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,6.907384157384722,2.1772440367082515,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Floor1,floor_level,A_structural,dummy,binary (storey 1 vs ground floor),0.027,,,0.36,,,,high,reference FloorGr,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,2.6920974809605314,3.0303679966629367,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.031144274525401014,0.03402388286273497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.915365087842814,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Floor1,floor_level,A_structural,dummy,binary (storey 1 vs ground floor),0.078,,,0.01,,,,high,reference FloorGr,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,8.062709385367217,3.2738026258216126,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08739738596862962,0.03392980499453753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Floor1,floor_level,A_structural,dummy,binary (storey 1 vs ground floor),0.072,,,,,,,high,reference FloorGr; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,7.465534406381358,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Floor2_4,floor_level,A_structural,dummy,binary (storeys 2-4 vs ground floor),0.104,,,0.001,,,,high,reference FloorGr; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,10.90463857150079,3.506990118232529,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Floor2_4,floor_level,A_structural,dummy,binary (storeys 2-4 vs ground floor),0.038,,,0.16,,,,high,reference FloorGr,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,3.8351424894471853,2.8092367651854393,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04777457804920982,0.034001526611699286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4050715603096329,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Floor2_4,floor_level,A_structural,dummy,binary (storeys 2-4 vs ground floor),0.103,,,0.001,,,,high,reference FloorGr; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,10.794848442449245,3.4697975263997636,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11137845681048096,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491894,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Floor2_4,floor_level,A_structural,dummy,binary (storeys 2-4 vs ground floor),0.118,,,,,,,high,reference FloorGr; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,12.524411136734237,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Floor5_8,floor_level,A_structural,dummy,binary (storeys 5-8 vs ground floor),0.133,,,0.001,,,,high,reference FloorGr; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,14.131733212443386,4.616867212293704,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Floor5_8,floor_level,A_structural,dummy,binary (storeys 5-8 vs ground floor),0.073,,,0.01,,,,high,reference FloorGr,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,7.5298622337698795,3.0486620012661305,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08734712204436584,0.03391029130852016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Floor5_8,floor_level,A_structural,dummy,binary (storeys 5-8 vs ground floor),0.134,,,0.001,,,,high,reference FloorGr; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,14.244513428068561,4.656234406669494,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Floor5_8,floor_level,A_structural,dummy,binary (storeys 5-8 vs ground floor),0.147,,,,,,,high,reference FloorGr; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,15.835396302985538,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Floor9+,floor_level,A_structural,dummy,binary (storey 9+ vs ground floor),0.132,,,0.001,,,,high,reference FloorGr; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,14.019053801062121,4.577574061371702,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Floor9+,floor_level,A_structural,dummy,binary (storey 9+ vs ground floor),0.166,,,0.002,,,,high,reference FloorGr,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,17.887100770002085,6.341760601457564,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.10461551144386591,0.03385360745697441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Floor9+,floor_level,A_structural,dummy,binary (storey 9+ vs ground floor),0.194,,,0.01,,,,high,reference FloorGr,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,21.065772218991107,9.144032897249696,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08739738596862962,0.03392980499453753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Floor9+,floor_level,A_structural,dummy,binary (storey 9+ vs ground floor),0.165,,,,,,,high,reference FloorGr; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,17.93931187113906,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Constr1850_1913,age,A_structural,dummy,binary (construction period 1850-1913),-0.092,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-8.825128094685885,2.550159308996181,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Constr1850_1913,age,A_structural,dummy,binary (construction period 1850-1913),0.042,,,0.18,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,4.238291054776244,3.266932959928941,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.045592372499090386,0.034004998193894215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3407550336902165,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Constr1850_1913,age,A_structural,dummy,binary (construction period 1850-1913),-0.119,,,0.01,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-11.313912735955133,4.101557822372462,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.08739738596862962,0.03392980499453753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Constr1850_1913,age,A_structural,dummy,binary (construction period 1850-1913),-0.083,,,,,,,high,"construction-period dummy, not age; reference Constr1981_1991; mean of local GWR coefficients",log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Constr1914_1947,age,A_structural,dummy,binary (construction period 1914-1947),-0.082,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-7.901405530365002,2.295811788736009,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Constr1914_1947,age,A_structural,dummy,binary (construction period 1914-1947),0.112,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,11.786513602338001,3.8070938367805707,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Constr1914_1947,age,A_structural,dummy,binary (construction period 1914-1947),-0.11,,,0.01,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-10.498235463344496,3.8256321855974775,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.08739738596862962,0.03392980499453753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Constr1914_1947,age,A_structural,dummy,binary (construction period 1914-1947),-0.062,,,,,,,high,"construction-period dummy, not age; reference Constr1981_1991; mean of local GWR coefficients",log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-6.011711320891111,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Constr1948_1969,age,A_structural,dummy,binary (construction period 1948-1969),-0.153,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-14.279740736994073,3.9900556400469003,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Constr1948_1969,age,A_structural,dummy,binary (construction period 1948-1969),0.008,,,0.73,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,0.7761308474019568,2.336615506153631,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01174739905645614,0.03403804698656786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34512553147047237,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Constr1948_1969,age,A_structural,dummy,binary (construction period 1948-1969),-0.122,,,0.002,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-11.554116324846175,3.494497832469954,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.10467551120055282,0.03387302339427051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.090232306167813,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Constr1948_1969,age,A_structural,dummy,binary (construction period 1948-1969),-0.119,,,,,,,high,"construction-period dummy, not age; reference Constr1981_1991; mean of local GWR coefficients",log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-11.21921992380499,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Constr1970_1980,age,A_structural,dummy,binary (construction period 1970-1980),-0.123,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-11.635389787059381,3.305380547559575,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Constr1970_1980,age,A_structural,dummy,binary (construction period 1970-1980),-0.005,,,0.83,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,-0.5257300481633531,2.3171989111523335,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.007308331187479285,0.03403948678856365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21470156800174456,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Constr1970_1980,age,A_structural,dummy,binary (construction period 1970-1980),-0.088,,,0.03,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-8.499175629065437,3.7135299978312752,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.07371249220491717,0.033967475717284895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1700903775845606,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Constr1970_1980,age,A_structural,dummy,binary (construction period 1970-1980),-0.083,,,,,,,high,"construction-period dummy, not age; reference Constr1981_1991; mean of local GWR coefficients",log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Constr1992<,age,A_structural,dummy,binary (constructed 1992 or later),0.099,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,10.356671797022155,3.3217345603137693,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Constr1992<,age,A_structural,dummy,binary (constructed 1992 or later),0.099,,,0.001,,,,high,"construction-period dummy, not age; reference Constr1981_1991; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,10.356671797022155,3.3217345603137693,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Constr1992<,age,A_structural,dummy,binary (constructed 1992 or later),-0.085,,,0.12,,,,high,"construction-period dummy, not age; reference Constr1981_1991",log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-8.28593363372695,5.021537826822176,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.05288170217512109,0.03401247767449581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5547735945968535,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Constr1992<,age,A_structural,dummy,binary (constructed 1992 or later),0.104,,,,,,,high,"construction-period dummy, not age; reference Constr1981_1991; mean of local GWR coefficients",log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,10.960045491558246,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,CondMed,condition,A_structural,dummy,binary (some maintenance needed vs good),-0.109,,,0.001,,,,high,reference CondGood; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-10.376143504762714,2.970454990438312,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,CondMed,condition,A_structural,dummy,binary (some maintenance needed vs good),0.009,,,0.63,,,,high,reference CondGood,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,0.8864536080777974,1.8851690753366448,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.016395968374840194,0.03403582006893375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48172684958473033,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,CondMed,condition,A_structural,dummy,binary (some maintenance needed vs good),-0.072,,,0.001,,,,high,reference CondGood,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-6.969183636807719,2.0360942172936074,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,CondMed,condition,A_structural,dummy,binary (some maintenance needed vs good),-0.107,,,,,,,high,reference CondGood; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-10.14743270096945,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,CondBad,condition,A_structural,dummy,binary (renovation needed vs good),-0.215,,,0.001,,,,high,reference CondGood; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-19.51783652868023,5.269867829321795,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.049817439149501146,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,CondBad,condition,A_structural,dummy,binary (renovation needed vs good),-0.092,,,0.04,,,,high,reference CondGood,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,-8.880954906425576,4.085877943683622,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.06974020718060914,0.033957513900350164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.053748910631823,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,CondBad,condition,A_structural,dummy,binary (renovation needed vs good),-0.197,,,0.001,,,,high,reference CondGood; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-18.027973622738756,4.916372604250096,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,CondBad,condition,A_structural,dummy,binary (renovation needed vs good),-0.225,,,,,,,high,reference CondGood; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-20.148378124062294,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,ViewNo,view,E_environment,dummy,binary (no data about view vs good view),-0.04,,,0.001,,,,high,missing-data dummy; reference ViewGood; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-3.9281546620892005,1.1679460676700961,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,ViewNo,view,E_environment,dummy,binary (no data about view vs good view),-0.027,,,0.03,,,,high,missing-data dummy; reference ViewGood,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,-2.6714093881469725,1.2110441939477714,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0736700046213568,0.03394789700111748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1700903775845606,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,ViewNo,view,E_environment,dummy,binary (no data about view vs good view),-0.017,,,0.37,,,,high,missing-data dummy; reference ViewGood,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-1.703307008409083,1.8643546266877977,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.030519780080790287,0.034044268693659764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8964733640019161,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,ViewNo,view,E_environment,dummy,binary (no data about view vs good view),-0.035,,,,,,,high,missing-data dummy; reference ViewGood; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-3.4394583742433538,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,ViewBad,view,E_environment,dummy,binary (view decreasing value vs good view),-0.098,,,0.001,,,,high,reference ViewGood,log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,-9.3753103740273,2.7002240011464598,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,ViewBad,view,E_environment,dummy,binary (view decreasing value vs good view),0.0,,,0.99,,,,low,"reference ViewGood; coefficient printed as <0.001 (recorded as 0.0, not usable)",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,0.0,,100*(exp(b) - 1),,,,,,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,ViewBad,view,E_environment,dummy,binary (view decreasing value vs good view),-0.002,,,0.96,,,,high,reference ViewGood,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,-0.27912052040550694,3.9797834161504926,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.001708235339414418,0.034060085469577506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05015358346473367,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,ViewBad,view,E_environment,dummy,binary (view decreasing value vs good view),-0.083,,,,,,,high,reference ViewGood; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Cellar1,basement,A_structural,dummy,binary (1 cellar vs 0),0.099,,,0.001,,,,high,reference Cellar0,log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,10.356671797022155,3.3217345603137693,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Cellar1,basement,A_structural,dummy,binary (1 cellar vs 0),-0.002,,,0.88,,,,high,reference Cellar0,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,-0.20855733905336127,1.3221264949722646,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.005138984001514712,0.034039946386449985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15096921549677725,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Cellar1,basement,A_structural,dummy,binary (1 cellar vs 0),0.092,,,0.001,,,,high,reference Cellar0; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,9.59363869896599,3.0653318408297396,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Cellar1,basement,A_structural,dummy,binary (1 cellar vs 0),0.076,,,,,,,high,reference Cellar0; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,7.896257415728392,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Cellar2,basement,A_structural,dummy,binary (2 cellars vs 0),0.032,,,0.001,,,,high,reference Cellar0,log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,3.246868210186604,1.0041115865599883,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.049817439149501146,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491894,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Cellar2,basement,A_structural,dummy,binary (2 cellars vs 0),0.001,,,0.98,,,,high,reference Cellar0,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,0.020441285114136676,3.9929959846531253,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0008533552506547738,0.03404038348412105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.02506890825871106,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Cellar2,basement,A_structural,dummy,binary (2 cellars vs 0),0.021,,,0.84,,,,high,reference Cellar0,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,1.5712578795442855,10.622266343392063,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.006876356610852283,0.034059329900501326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.20189347914185088,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Cellar2,basement,A_structural,dummy,binary (2 cellars vs 0),-0.005,,,,,,,high,reference Cellar0; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,-0.498752080731768,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Garden,garden,C_exterior,binary,binary,0.049,,,0.02,,,,high,,log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,4.998741018300423,2.212085207053515,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.03524198409591599,0.01514906024553458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3263478740408408,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Garden,garden,C_exterior,binary,binary,-0.011,,,0.73,,,,high,,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,-1.1441963965284963,3.1523785041848975,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.01174739905645614,0.03403804698656786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34512553147047237,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Garden,garden,C_exterior,binary,binary,0.101,,,0.16,,,,high,,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,10.342221622026981,7.9521886268542215,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.047802218203583914,0.034021198317507634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4050715603096329,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Garden,garden,C_exterior,binary,binary,0.046,,,,,,,high,mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,4.707441095693721,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,Terrace,terrace_balcony,C_exterior,binary,binary,0.043,,,0.01,,,,high,,log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,4.379244369960511,1.7427136734848523,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.03901591835390113,0.015146934736764643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,Terrace,terrace_balcony,C_exterior,binary,binary,0.049,,,0.01,,,,high,,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,5.003034393934214,1.9978341389066512,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08734712204436584,0.03391029130852016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,Terrace,terrace_balcony,C_exterior,binary,binary,0.037,,,0.69,,,,high,,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,3.3237708711354674,9.626213900324695,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01358380403342462,0.03405699263603073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39885506564233686,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,Terrace,terrace_balcony,C_exterior,binary,binary,0.041,,,,,,,high,mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,4.185210554547947,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,NewApartment,age,A_structural,dummy,binary (new apartment sold on primary market),0.046,,,0.01,,,,high,"new-build indicator, not age in years",log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,pct_premium,4.690745787946238,1.8698996411624882,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.03901591835390113,0.015146934736764643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,NewApartment,age,A_structural,dummy,binary (new apartment sold on primary market),-0.067,,,0.001,,,,high,"new-build indicator, not age in years",log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,pct_premium,-6.499863983219445,1.9041960027218936,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,863.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,NewApartment,age,A_structural,dummy,binary (new apartment sold on primary market),0.256,,,0.001,,,,high,"new-build indicator, not age in years; p printed as <0.001 (upper bound)",log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,pct_premium,28.784934213789025,10.04971924973948,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11137845681048096,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491894,862.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,NewApartment,age,A_structural,dummy,binary (new apartment sold on primary market),0.047,,,,,,,high,"new-build indicator, not age in years; mean of local GWR coefficients",log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",pct_premium,4.812200907965569,,100*(exp(b) - 1),,,,,,4352.0,binary,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,%MidIncome,income,F_neighbourhood,log,"share (% of middle-income households in IRIS zone), log",0.671,,,0.001,,,,high,log of percentage of middle-income households; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,elasticity,0.671,0.20391872023959362,b (log-log),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,%MidIncome,income,F_neighbourhood,log,"share (% of middle-income households in IRIS zone), log",0.313,,,0.05,,,,high,log of percentage of middle-income households,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,elasticity,0.313,0.15969681201741665,b (log-log),|b/z(p)| (two-sided normal),0.06656995353935329,0.03396488612262703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9599639845400545,863.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,%MidIncome,income,F_neighbourhood,log,"share (% of middle-income households in IRIS zone), log",0.558,,,0.01,,,,high,log of percentage of middle-income households,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,elasticity,0.558,0.2166292615862411,b (log-log),|b/z(p)| (two-sided normal),0.08739738596862962,0.03392980499453753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,862.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,%MidIncome,income,F_neighbourhood,log,"share (% of middle-income households in IRIS zone), log",0.77,,,,,,,high,log of percentage of middle-income households; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",elasticity,0.77,,b (log-log),,,,,,4352.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-overall,Appendix 2,Overall,14,%HighIncome,income,F_neighbourhood,log,"share (% of high-income households in IRIS zone), log",0.136,,,0.001,,,,high,log of percentage of high-income households; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,0.838,p,Overall sample; model with TTBellecour by car,elasticity,0.136,0.041330768930826725,b (log-log),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-expensive,Appendix 2,Expensive,14,%HighIncome,income,F_neighbourhood,log,"share (% of high-income households in IRIS zone), log",0.097,,,,,,,high,log of percentage of high-income households; no significance level printed for this cell,log transaction price (Euro),log,OLS,873.0,0.839,p,20% most expensive apartments; model with TTBellecour by car,elasticity,0.097,,b (log-log),,,,,,863.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-OLS-cheap,Appendix 2,Cheap,14,%HighIncome,income,F_neighbourhood,log,"share (% of high-income households in IRIS zone), log",0.029,,,0.61,,,,high,log of percentage of high-income households,log transaction price (Euro),log,OLS,872.0,0.705,p,20% cheapest apartments; model with TTBellecour by car,elasticity,0.029,0.056854556150302736,b (log-log),|b/z(p)| (two-sided normal),0.017370549634933136,0.03405499619244575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5100734569685949,862.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A2-GWR-overall,Appendix 2,GWR,14,%HighIncome,income,F_neighbourhood,log,"share (% of high-income households in IRIS zone), log",0.028,,,,,,,high,log of percentage of high-income households; mean of local GWR coefficients,log transaction price (Euro),log,GWR,4362.0,0.875,none,"Average of local GWR estimates over all 4,362 observations; no significance reported in Appendix 2; model with TTBellecour by car",elasticity,0.028,,b (log-log),,,,,,4352.0,share log,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-2,4,Overall / TTCityHall by car,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.278,,,0.001,,,,high,TTCityHall by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with TTCityHall by car as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,elasticity,-0.278,0.08448495413801346,b (log-log),|b/z(p)| (two-sided normal),-0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-3,4,Overall / Centrality Index by car,10,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.0085,,,0.001,,,,high,Centrality Index by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with Centrality Index by car as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,semi_elasticity,0.8500000000000001,0.25831730581766704,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-4,4,Overall / Accessibility Index by car,10,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.0051,,,0.001,,,,high,Accessibility Index by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with Accessibility Index by car as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,semi_elasticity,0.51,0.15499038349060024,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-5,4,Overall / TTBellecour by public transport,10,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.223,,,0.001,,,,high,TTBellecour by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with TTBellecour by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,elasticity,-0.223,0.06777030493804675,b (log-log),|b/z(p)| (two-sided normal),-0.04981743914950116,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491895,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-6,4,Overall / TTCityHall by public transport,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.228,,,0.001,,,,high,TTCityHall by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with TTCityHall by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,elasticity,-0.228,0.0692898185016801,b (log-log),|b/z(p)| (two-sided normal),-0.049817439149501146,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-7,4,Overall / Centrality Index by public transport,10,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.0078,,,0.001,,,,high,Centrality Index by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with Centrality Index by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,semi_elasticity,0.7799999999999999,0.23704411592680033,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.049817439149501146,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491894,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-overall-8,4,Overall / Accessibility Index by public transport,10,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.0049,,,0.001,,,,high,Accessibility Index by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,4362.0,,p,Overall sample; variant with Accessibility Index by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.85; full covariate set as Appendix 2,semi_elasticity,0.49,0.14891232923606687,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.04981743914950115,0.015139654898629068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-2,4,Expensive / TTCityHall by car,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.129,,,0.001,,,,high,TTCityHall by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with TTCityHall by car as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,elasticity,-0.129,0.039203449941740055,b (log-log),|b/z(p)| (two-sided normal),-0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,863.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-3,4,Expensive / Centrality Index by car,10,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.0042,,,0.001,,,,high,Centrality Index by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with Centrality Index by car as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,semi_elasticity,0.42,0.12763913934520016,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-4,4,Expensive / Accessibility Index by car,10,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.0024,,,0.001,,,,high,Accessibility Index by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with Accessibility Index by car as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,semi_elasticity,0.24,0.07293665105440009,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-5,4,Expensive / TTBellecour by public transport,10,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.062,,,0.001,,,,high,TTBellecour by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with TTBellecour by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,elasticity,-0.062,0.01884196818905336,b (log-log),|b/z(p)| (two-sided normal),-0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,863.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-6,4,Expensive / TTCityHall by public transport,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.083,,,0.001,,,,high,TTCityHall by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with TTCityHall by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,elasticity,-0.083,0.02522392515631337,b (log-log),|b/z(p)| (two-sided normal),-0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,863.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-7,4,Expensive / Centrality Index by public transport,10,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.0028,,,0.001,,,,high,Centrality Index by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with Centrality Index by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,semi_elasticity,0.27999999999999997,0.08509275956346678,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.111314708340827,0.03382884183115508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,863.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-expensive-8,4,Expensive / Accessibility Index by public transport,10,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.0011,,,0.017,,,,high,Accessibility Index by public transport; same specification as Appendix 2 with this accessibility measure,log transaction price (Euro),log,OLS,873.0,,p,Expensive sample; variant with Accessibility Index by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.84-0.86; full covariate set as Appendix 2,semi_elasticity,0.11,0.046088592419717224,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.08097766248380009,0.033928604375611716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3867077344922505,863.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-2,4,Cheap / TTCityHall by car,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.217,,,0.001,,,,high,TTCityHall by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with TTCityHall by car as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,elasticity,-0.217,0.06594688866168676,b (log-log),|b/z(p)| (two-sided normal),-0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,862.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-3,4,Cheap / Centrality Index by car,10,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.0069,,,0.001,,,,high,Centrality Index by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with Centrality Index by car as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,semi_elasticity,0.69,0.20969287178140028,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-4,4,Cheap / Accessibility Index by car,10,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.0043,,,0.001,,,,high,Accessibility Index by car; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with Accessibility Index by car as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,semi_elasticity,0.43,0.13067816647246686,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-5,4,Cheap / TTBellecour by public transport,10,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.173,,,0.001,,,,high,TTBellecour by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with TTBellecour by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,elasticity,-0.173,0.0525751693017134,b (log-log),|b/z(p)| (two-sided normal),-0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,862.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-6,4,Cheap / TTCityHall by public transport,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.157,,,0.001,,,,high,TTCityHall by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with TTCityHall by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,elasticity,-0.157,0.047712725898086734,b (log-log),|b/z(p)| (two-sided normal),-0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,862.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-7,4,Cheap / Centrality Index by public transport,10,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.006,,,0.001,,,,high,Centrality Index by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with Centrality Index by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,semi_elasticity,0.6,0.18234162763600026,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-OLS-cheap-8,4,Cheap / Accessibility Index by public transport,10,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.0038,,,0.001,,,,high,Accessibility Index by public transport; same specification as Appendix 2 with this accessibility measure; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,872.0,,p,Cheap sample; variant with Accessibility Index by public transport as the accessibility measure; adj. R2 range across the 8 variants 0.70-0.72; full covariate set as Appendix 2,semi_elasticity,0.38,0.11548303083613348,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.11137845681048097,0.033848215164015094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,862.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-2,4,GWR overall average / TTCityHall by car,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.235,,,,,,,high,"TTCityHall by car; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with TTCityHall by car; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),elasticity,-0.235,,b (log-log),,,,,,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-3,4,GWR overall average / Centrality Index by car,10,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.0071,,,,,,,high,"Centrality Index by car; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with Centrality Index by car; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),semi_elasticity,0.7100000000000001,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-4,4,GWR overall average / Accessibility Index by car,10,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.0049,,,,,,,high,"Accessibility Index by car; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with Accessibility Index by car; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),semi_elasticity,0.49,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-5,4,GWR overall average / TTBellecour by public transport,10,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.151,,,,,,,high,"TTBellecour by public transport; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with TTBellecour by public transport; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),elasticity,-0.151,,b (log-log),,,,,,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-6,4,GWR overall average / TTCityHall by public transport,10,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.159,,,,,,,high,"TTCityHall by public transport; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with TTCityHall by public transport; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),elasticity,-0.159,,b (log-log),,,,,,4352.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-7,4,GWR overall average / Centrality Index by public transport,10,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.0052,,,,,,,high,"Centrality Index by public transport; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with Centrality Index by public transport; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),semi_elasticity,0.52,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:T4-GWR-overall-8,4,GWR overall average / Accessibility Index by public transport,10,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.004,,,,,,,high,"Accessibility Index by public transport; mean of local GWR coefficients, no inference",log transaction price (Euro),log,GWR,4362.0,,none,Average of local GWR estimates over all observations; variant with Accessibility Index by public transport; R2 range 0.87-0.88; parentheses in table give share of observations significant at 5% (not extracted),semi_elasticity,0.4,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,4352.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,False,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-1,Appendix 3,OLS / Sainte-Foy-les-Lyon / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),-0.525,,,0.02,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with TTBellecour by car; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.525,0.22567562051159648,b (log-log),|b/z(p)| (two-sided normal),-0.26105491835379324,0.11221662988018372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-2,Appendix 3,OLS / Sainte-Foy-les-Lyon / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.381,,,0.11,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with TTCityHall by car; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.381,0.2383942156192711,b (log-log),|b/z(p)| (two-sided normal),-0.18266052035198002,0.11429189363233119,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5981931399228173,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-3,Appendix 3,OLS / Sainte-Foy-les-Lyon / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.022,,,0.12,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with Centrality Index by car; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,2.1999999999999997,1.4149970179873366,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.177857118619888,0.11439422385225523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5547735945968535,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-4,Appendix 3,OLS / Sainte-Foy-les-Lyon / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.006,,,0.06,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with Accessibility Index by car; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.6,0.3190142700398569,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.21359228719073456,0.11356497930715943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.880793608151251,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-5,Appendix 3,OLS / Sainte-Foy-les-Lyon / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.304,,,0.05,,,,high,TTBellecour by public transport; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with TTBellecour by public transport; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.304,0.15510489090509477,b (log-log),|b/z(p)| (two-sided normal),-0.22214810239204405,0.11334295127069675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9599639845400545,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-6,Appendix 3,OLS / Sainte-Foy-les-Lyon / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.203,,,0.21,,,,high,TTCityHall by public transport; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with TTCityHall by public transport; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.203,0.1619380957468741,b (log-log),|b/z(p)| (two-sided normal),-0.14420097617368294,0.11503266742072203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2535654384704504,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-7,Appendix 3,OLS / Sainte-Foy-les-Lyon / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.013,,,0.19,,,,high,Centrality Index by public transport; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with Centrality Index by public transport; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,1.3,0.9919279102879741,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.15061380194188295,0.11492156447749137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3105791121681283,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-SFL-8,Appendix 3,OLS / Sainte-Foy-les-Lyon / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.003,,,0.13,,,,high,Accessibility Index by public transport; submarket model,log transaction price (Euro),log,OLS,84.0,,p,Sainte-Foy-les-Lyon submarket; OLS model with Accessibility Index by public transport; R2 (adj. for OLS) 0.83-0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.3,0.1981372604136361,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.1733461357740645,0.11448776148521111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5141018876192838,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-1,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),-0.599,,,0.01,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with TTBellecour by car; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.599,0.2325464653945491,b (log-log),|b/z(p)| (two-sided normal),-0.2868504680733633,0.11136237470322635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-2,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.427,,,0.1,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with TTCityHall by car; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.427,0.2595975672263253,b (log-log),|b/z(p)| (two-sided normal),-0.18780791063299643,0.11417910235640513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6448536269514729,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-3,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.027,,,0.1,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with Centrality Index by car; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,2.7,1.641483446161776,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.1878079106329964,0.11417910235640513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6448536269514726,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-4,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.007,,,0.05,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with Accessibility Index by car; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.7000000000000001,0.35714941984725773,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.22214810239204405,0.11334295127069675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9599639845400545,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-5,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.486,,,0.01,,,,high,TTBellecour by public transport; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with TTBellecour by public transport; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.486,0.18867709880091965,b (log-log),|b/z(p)| (two-sided normal),-0.2868504680733633,0.11136237470322635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-6,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.268,,,0.17,,,,high,TTCityHall by public transport; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with TTCityHall by public transport; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.268,0.19530626445028973,b (log-log),|b/z(p)| (two-sided normal),-0.15752392941738522,0.11479630677627095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3722038089987258,74.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-7,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.021,,,0.1,,,,high,Centrality Index by public transport; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with Centrality Index by public transport; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,2.1,1.2767093470147146,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.18780791063299643,0.11417910235640513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6448536269514729,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-SFL-8,Appendix 3,spatial_error / Sainte-Foy-les-Lyon / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.005,,,0.07,,,,high,Accessibility Index by public transport; submarket model,log transaction price (Euro),log,spatial_error,84.0,,p,Sainte-Foy-les-Lyon submarket; spatial_error model with Accessibility Index by public transport; R2 (adj. for OLS) 0.88; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.5,0.275951793575577,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.2061079482432737,0.11375171597582712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8119106729525978,74.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-1,Appendix 3,OLS / Oullins / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),0.098,,,0.62,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with TTBellecour by car; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.098,0.19764027699940126,b (log-log),|b/z(p)| (two-sided normal),0.05219596841227129,0.1052655679107479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4958503473474533,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-2,Appendix 3,OLS / Oullins / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),0.306,,,0.25,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with TTCityHall by car; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.306,0.2660061414558937,b (log-log),|b/z(p)| (two-sided normal),0.1203757361007471,0.10464276171592372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1503493803760079,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-3,Appendix 3,OLS / Oullins / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",-0.008,,,0.39,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with Centrality Index by car; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.8,0.9306466263690619,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.09024191941416214,0.10497917232482344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8596173642419115,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-4,Appendix 3,OLS / Oullins / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",-0.003,,,0.33,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with Accessibility Index by car; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.3,0.3079722063971115,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.1021435646629523,0.1048579299283848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9741138770593091,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-5,Appendix 3,OLS / Oullins / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),0.307,,,0.18,,,,high,TTBellecour by public transport; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with TTBellecour by public transport; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.307,0.22897545956253543,b (log-log),|b/z(p)| (two-sided normal),0.13993737266852577,0.10437206585260414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3407550336902165,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-6,Appendix 3,OLS / Oullins / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),0.396,,,0.03,,,,high,TTCityHall by public transport; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with TTCityHall by public transport; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.396,0.1824808791792218,b (log-log),|b/z(p)| (two-sided normal),0.2229880171227916,0.1027551752802989,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1700903775845606,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-7,Appendix 3,OLS / Oullins / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",-0.02,,,0.08,,,,high,Centrality Index by public transport; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with Centrality Index by public transport; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-2.0,1.1424092719087608,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.18147437731988722,0.10365900563205405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7506860712521697,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-OUL-8,Appendix 3,OLS / Oullins / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",-0.005,,,0.06,,,,high,Accessibility Index by public transport; submarket model,log transaction price (Euro),log,OLS,100.0,,p,Oullins submarket; OLS model with Accessibility Index by public transport; R2 (adj. for OLS) 0.88-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.5,0.26584522503321406,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.1944681707092118,0.10339686920797579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.880793608151251,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-1,Appendix 3,spatial_error / Oullins / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),0.117,,,0.59,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with TTBellecour by car; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.117,0.21713470040141672,b (log-log),|b/z(p)| (two-sided normal),0.056706908765723284,0.10523963799603243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5388360302784503,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-2,Appendix 3,spatial_error / Oullins / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),0.311,,,0.27,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with TTCityHall by car; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.311,0.2819423053135756,b (log-log),|b/z(p)| (two-sided normal),0.11549491204365028,0.10470386415940645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1030625561995973,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-3,Appendix 3,spatial_error / Oullins / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",-0.008,,,0.43,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with Centrality Index by car; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.8,1.0136954658673492,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.0829017481317947,0.10504640774209656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7891916526582226,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-4,Appendix 3,spatial_error / Oullins / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",-0.003,,,0.35,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with Accessibility Index by car; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.3,0.3209966162306616,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.09803976806543747,0.10490144601681434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9345892910734802,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-5,Appendix 3,spatial_error / Oullins / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),0.318,,,0.18,,,,high,TTBellecour by public transport; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with TTBellecour by public transport; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.318,0.23717979198985756,b (log-log),|b/z(p)| (two-sided normal),0.13993737266852577,0.10437206585260414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3407550336902165,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-6,Appendix 3,spatial_error / Oullins / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),0.422,,,0.02,,,,high,TTCityHall by public transport; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with TTCityHall by public transport; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.422,0.18140021305884516,b (log-log),|b/z(p)| (two-sided normal),0.23816250997637697,0.1023761375647965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3263478740408408,90.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-7,Appendix 3,spatial_error / Oullins / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",-0.022,,,0.06,,,,high,Centrality Index by public transport; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with Centrality Index by public transport; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-2.1999999999999997,1.1697189901461418,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.19446817070921182,0.10339686920797579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8807936081512513,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-OUL-8,Appendix 3,spatial_error / Oullins / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",-0.005,,,0.05,,,,high,Accessibility Index by public transport; submarket model,log transaction price (Euro),log,spatial_error,100.0,,p,Oullins submarket; spatial_error model with Accessibility Index by public transport; R2 (adj. for OLS) 0.91; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.5,0.2551067284623269,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.2023255435290532,0.10322921498811775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9599639845400545,90.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-1,Appendix 3,OLS / Venissieux / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),-0.237,,,0.17,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with TTBellecour by car; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.237,0.17271486818924875,b (log-log),|b/z(p)| (two-sided normal),-0.1455490605974166,0.10606956462511302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3722038089987258,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-2,Appendix 3,OLS / Venissieux / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.478,,,0.02,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with TTCityHall by car; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.478,0.20547227924674877,b (log-log),|b/z(p)| (two-sided normal),-0.24199741198602165,0.10402460211837311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-3,Appendix 3,OLS / Venissieux / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.022,,,0.001,,,,high,Centrality Index by car; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with Centrality Index by car; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,2.1999999999999997,0.6685859679986675,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-4,Appendix 3,OLS / Venissieux / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.006,,,0.001,,,,high,Accessibility Index by car; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with Accessibility Index by car; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.6,0.18234162763600026,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-5,Appendix 3,OLS / Venissieux / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.935,,,0.001,,,,high,TTBellecour by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with TTBellecour by public transport; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.935,0.28414903639943373,b (log-log),|b/z(p)| (two-sided normal),-0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-6,Appendix 3,OLS / Venissieux / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.911,,,0.001,,,,high,TTCityHall by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with TTCityHall by public transport; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.911,0.27685537129399374,b (log-log),|b/z(p)| (two-sided normal),-0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-7,Appendix 3,OLS / Venissieux / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.061,,,0.001,,,,high,Centrality Index by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with Centrality Index by public transport; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,6.1,1.8538065476326693,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEN-8,Appendix 3,OLS / Venissieux / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.013,,,0.001,,,,high,Accessibility Index by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,OLS,97.0,,p,Venissieux submarket; OLS model with Accessibility Index by public transport; R2 (adj. for OLS) 0.78-0.85; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,1.3,0.3950735265446672,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-1,Appendix 3,spatial_error / Venissieux / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),0.284,,,0.39,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with TTBellecour by car; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.284,0.33037955236101696,b (log-log),|b/z(p)| (two-sided normal),0.09177174358955426,0.10675882945953157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8596173642419115,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-2,Appendix 3,spatial_error / Venissieux / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.26,,,0.45,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with TTCityHall by car; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.26,0.3441816629630201,b (log-log),|b/z(p)| (two-sided normal),-0.08072467925276114,0.10686136287450669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7554150263604693,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-3,Appendix 3,spatial_error / Venissieux / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.022,,,0.04,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with Centrality Index by car; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,2.1999999999999997,1.071211767227759,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.21503410609060636,0.10470321127252721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.053748910631823,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-4,Appendix 3,spatial_error / Venissieux / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.005,,,0.11,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with Accessibility Index by car; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.5,0.3128533013376261,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.16888311514396043,0.10567128022594091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5981931399228173,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-5,Appendix 3,spatial_error / Venissieux / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),-0.947,,,0.001,,,,high,TTBellecour by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with TTBellecour by public transport; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.947,0.2877958689521537,b (log-log),|b/z(p)| (two-sided normal),-0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-6,Appendix 3,spatial_error / Venissieux / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-1.162,,,0.001,,,,high,TTCityHall by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with TTCityHall by public transport; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-1.162,0.35313495218838714,b (log-log),|b/z(p)| (two-sided normal),-0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,87.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-7,Appendix 3,spatial_error / Venissieux / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",0.061,,,0.001,,,,high,Centrality Index by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with Centrality Index by public transport; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,6.1,1.8538065476326693,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEN-8,Appendix 3,spatial_error / Venissieux / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",0.014,,,0.001,,,,high,Accessibility Index by public transport; submarket model; p printed as <0.001 (upper bound),log transaction price (Euro),log,spatial_error,97.0,,p,Venissieux submarket; spatial_error model with Accessibility Index by public transport; R2 (adj. for OLS) 0.87-0.89; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,1.4000000000000001,0.42546379781733396,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.33268620942909904,0.10110424153225529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,87.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-1,Appendix 3,OLS / Vaulx-en-Velin / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),1.97,,,0.19,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with TTBellecour by car; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,1.97,1.503152294821007,b (log-log),|b/z(p)| (two-sided normal),0.19383020119324726,0.14789660493870413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3105791121681283,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-2,Appendix 3,OLS / Vaulx-en-Velin / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),0.016,,,0.99,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with TTCityHall by car; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.016,1.2765818746117272,b (log-log),|b/z(p)| (two-sided normal),0.0018894882488989502,0.1507554031772657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.012533469508069278,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-3,Appendix 3,OLS / Vaulx-en-Velin / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",-0.014,,,0.74,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with Centrality Index by car; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-1.4000000000000001,4.218730999798893,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.0499662836579981,0.15056736415195815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33185334643681663,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-4,Appendix 3,OLS / Vaulx-en-Velin / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",-0.005,,,0.69,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with Accessibility Index by car; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.5,1.2535881904740864,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.06002125574840193,0.15048387476724306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39885506564233686,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-5,Appendix 3,OLS / Vaulx-en-Velin / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),0.559,,,0.54,,,,high,TTBellecour by public transport; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with TTBellecour by public transport; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.559,0.912186928466784,b (log-log),|b/z(p)| (two-sided normal),0.09199328745630209,0.15011641202920592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6128129910166272,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-6,Appendix 3,OLS / Vaulx-en-Velin / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),-0.355,,,0.67,,,,high,TTCityHall by public transport; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with TTCityHall by public transport; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.355,0.8330439036857408,b (log-log),|b/z(p)| (two-sided normal),-0.06411206012604123,0.1504455234950206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4261480078412781,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-7,Appendix 3,OLS / Vaulx-en-Velin / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",-0.007,,,0.87,,,,high,Centrality Index by public transport; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with Centrality Index by public transport; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.7000000000000001,4.277199527574807,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.024664939121342058,0.1507098085106652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16365848623314128,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-OLS-VEV-8,Appendix 3,OLS / Vaulx-en-Velin / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",-0.004,,,0.72,,,,high,Accessibility Index by public transport; submarket model,log transaction price (Euro),log,OLS,54.0,,p,Vaulx-en-Velin submarket; OLS model with Accessibility Index by public transport; R2 (adj. for OLS) 0.66-0.68; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.4,1.1158883741476409,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.053960963006079075,0.15053602819073644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3584587932511938,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,ols,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-1,Appendix 3,spatial_error / Vaulx-en-Velin / TTBellecour by car,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by car to Bellecour),0.147,,,0.94,,,,high,TTBellecour by car; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with TTBellecour by car; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.147,1.9529728884720807,b (log-log),|b/z(p)| (two-sided normal),0.01134662817708404,0.15074596738379942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0752698620998299,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-2,Appendix 3,spatial_error / Vaulx-en-Velin / TTCityHall by car,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by car to Lyon City Hall),-0.562,,,0.65,,,,high,TTCityHall by car; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with TTCityHall by car; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,-0.562,1.2385342194103888,b (log-log),|b/z(p)| (two-sided normal),-0.06824772614960625,0.150404171233517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45376219016987945,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-3,Appendix 3,spatial_error / Vaulx-en-Velin / Centrality Index by car,15,Centrality Index,employment_access,D_location,level,"index (gravity-based tertiary employment centrality, car, 0-100)",0.019,,,0.7,,,,high,Centrality Index by car; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with Centrality Index by car; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,1.9,4.930960500785597,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.05799148626940331,0.15050196219804107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.38532046640756773,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-4,Appendix 3,spatial_error / Vaulx-en-Velin / Accessibility Index by car,15,Accessibility Index,employment_access,D_location,level,"index (fuzzy-logic tertiary employment accessibility, car, 0-100)",0.003,,,0.81,,,,high,Accessibility Index by car; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with Accessibility Index by car; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,0.3,1.2477850196779663,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.036221802706491606,0.15065674267630344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24042603114230793,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-5,Appendix 3,spatial_error / Vaulx-en-Velin / TTBellecour by public transport,15,TTBellecour,cbd_distance,D_location,log,minutes (travel time by public transport to Bellecour),0.685,,,0.5,,,,high,TTBellecour by public transport; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with TTBellecour by public transport; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.685,1.0155825196763375,b (log-log),|b/z(p)| (two-sided normal),0.10116152265797279,0.1499822979201152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6744897501960816,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,True
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-6,Appendix 3,spatial_error / Vaulx-en-Velin / TTCityHall by public transport,15,TTCityHall,cbd_distance,D_location,log,minutes (travel time by public transport to Lyon City Hall),0.499,,,0.57,,,,high,TTCityHall by public transport; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with TTCityHall by public transport; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,elasticity,0.499,0.8784414819063171,b (log-log),|b/z(p)| (two-sided normal),0.08532468452758538,0.15020589643206633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5680514983389829,44.0,minutes,,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,True,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-7,Appendix 3,spatial_error / Vaulx-en-Velin / Centrality Index by public transport,15,Centrality Index,employment_access,D_location,level,"index (tertiary employment centrality, public transport, 0-100)",-0.008,,,0.86,,,,high,Centrality Index by public transport; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with Centrality Index by public transport; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.8,4.535811698918443,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.026580011458538323,0.15070240866378046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.17637416478086135,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4,0f2b62072edfd32a439d1606541bbbb8547810e4:A3-SE-VEV-8,Appendix 3,spatial_error / Vaulx-en-Velin / Accessibility Index by public transport,15,Accessibility Index,employment_access,D_location,level,"index (tertiary employment accessibility, public transport, 0-100)",-0.005,,,0.65,,,,high,Accessibility Index by public transport; submarket model,log transaction price (Euro),log,spatial_error,54.0,,p,Vaulx-en-Velin submarket; spatial_error model with Accessibility Index by public transport; R2 (adj. for OLS) 0.84; submarket models exclude construction-period and some structural dummies; n printed for OLS rows only,semi_elasticity,-0.5,1.1018987717174278,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.06824772614960625,0.150404171233517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45376219016987945,44.0,index,unit_not_harmonisable,2014.0,France,Europe,High income,1997.0,2008.0,condo_apartment,transaction,journal,True,True,unknown,,2002.5,spatial,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Distance,transit_distance,D_location,level,miles,0.028,0.007,,,***,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",semi_elasticity,1.7398393382645352,0.4349598345661338,100*b/1.60934 (X per miles->km),reported,0.016620849347518573,0.004155212336879643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,57902.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Distance squared,transit_distance,D_location,squared,miles,-0.002,0.001,,,**,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared,"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",other,-0.002,0.001,not harmonised,reported,-0.008311285723823635,0.004155642861911818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,57902.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Age,age,A_structural,level,years,-0.001,0.001,,,,,,high,age of home; with Age squared,"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",semi_elasticity,-0.1,0.1,100*b/1 (per year),reported,-0.004155750514081046,0.004155750514081046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,57902.0,years,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Age squared,age,A_structural,squared,years,-0.0,0.0,,,,,,medium,printed -0.000 (0.000),"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",other,-0.0,,not harmonised,,,,,,57902.0,years,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,False,False,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Structural area (ln),living_area,A_structural,log,sqft,0.411,0.019,,,***,,,high,log structural area in sq ft,"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",elasticity,0.411,0.019,b (log-log),reported,0.08953516793185722,0.004139095354514081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.63157894736842,57902.0,sqft,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Basement,basement,A_structural,binary,binary,0.161,0.01,,,***,,,high,,"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",pct_premium,17.462623603376272,1.1746849688138716,100*(exp(b - se^2/2) - 1),reported,0.06675889805818996,0.004146515407340992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.1,57902.0,binary,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,True,True,True
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Below average,condition,A_structural,binary,binary,-0.109,0.017,,,***,,,high,1 = below average condition,"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",pct_premium,-10.339915068556627,1.5244417097470002,100*(exp(b - se^2/2) - 1),reported,-0.02663647022508242,0.004154311869966983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.411764705882352,57902.0,binary,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Split level,dwelling_type,A_structural,binary,binary,0.06,0.012,,,***,,,high,1 = split-level house,"ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",pct_premium,6.176009706622265,1.2742038558544315,100*(exp(b - se^2/2) - 1),reported,0.020774447654510805,0.004154889530902161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,57902.0,binary,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c1,3,1,11,Parking,other,other,level,count,0.0,0.0,,,,,,medium,"number of all-day parking spaces within one mile of nearest station (station attribute, not property parking); printed 0.000 (0.000)","ln(sales price), 2000 dollars",log,OLS,57912.0,,se,"full sample hedonic; ZIP code FE, year-quarter FE, ZIP linear trends; SE clustered at ZIP",semi_elasticity,0.0,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,57902.0,count,unit_not_harmonisable,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,ols,journal,False,False,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c2,3,2,11,Distance,transit_distance,D_location,level,miles,0.05,0.011,,,***,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; repeat sales with house FE; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,16511.0,,se,"repeat-sales sample with house FE, time FE, ZIP linear trends",semi_elasticity,3.1068559611866697,0.6835083114610673,100*b/1.60934 (X per miles->km),reported,0.03536311095699128,0.007779884410538081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.545454545454546,16501.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c2,3,2,11,Distance squared,transit_distance,D_location,squared,miles,-0.002,0.001,,,*,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared,"ln(sales price), 2000 dollars",log,FE_panel,16511.0,,se,"repeat-sales sample with house FE, time FE, ZIP linear trends",other,-0.002,0.001,not harmonised,reported,-0.01556762033154182,0.0077838101657709095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,16501.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T3-c2,3,2,11,Parking,other,other,level,count,0.0,0.0,,,,,,medium,station park-and-ride spaces within one mile; printed 0.000 (0.000),"ln(sales price), 2000 dollars",log,FE_panel,16511.0,,se,"repeat-sales sample with house FE, time FE, ZIP linear trends",semi_elasticity,0.0,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,16501.0,count,unit_not_harmonisable,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,False,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c2,4,2,14,Distance,transit_distance,D_location,level,miles,0.044,0.01,,,***,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties built before 1990; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,11243.0,,se,repeat-sales robustness: properties built before 1990; robust SE,semi_elasticity,2.734033245844269,0.621371192237334,100*b/1.60934 (X per miles->km),reported,0.04147924734559151,0.009427101669452617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.3999999999999995,11233.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c2,4,2,14,Distance squared,transit_distance,D_location,squared,miles,-0.001,0.001,,,+,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties built before 1990,"ln(sales price), 2000 dollars",log,FE_panel,11243.0,,se,repeat-sales robustness: properties built before 1990; robust SE,other,-0.001,0.001,not harmonised,reported,-0.009434801995539331,0.009434801995539331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,11233.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c3,4,3,14,Distance,transit_distance,D_location,level,miles,0.058,0.031,,,+,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties < 5 miles from station; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,8956.0,,se,repeat-sales robustness: properties < 5 miles from station; robust SE,semi_elasticity,3.6039529149765372,1.9262506959357353,100*b/1.60934 (X per miles->km),reported,0.01977729542593132,0.010570623417308118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.870967741935484,8946.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c3,4,3,14,Distance squared,transit_distance,D_location,squared,miles,-0.0,0.005,,,,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties < 5 miles from station,"ln(sales price), 2000 dollars",log,FE_panel,8956.0,,se,repeat-sales robustness: properties < 5 miles from station; robust SE,other,-0.0,0.005,not harmonised,reported,-0.0,0.010572691328259186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,8946.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c4,4,4,14,Distance,transit_distance,D_location,level,miles,0.078,0.021,,,***,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties < 7 miles; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,12132.0,,se,repeat-sales robustness: properties < 7 miles; robust SE,semi_elasticity,4.846695299451205,1.3048795036984013,100*b/1.60934 (X per miles->km),reported,0.03371639830561187,0.00907749185151089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.714285714285714,12122.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c4,4,4,14,Distance squared,transit_distance,D_location,squared,miles,-0.005,0.003,,,*,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties < 7 miles,"ln(sales price), 2000 dollars",log,FE_panel,12132.0,,se,repeat-sales robustness: properties < 7 miles; robust SE,other,-0.005,0.003,not harmonised,reported,-0.015136025677076038,0.009081615406245623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6666666666666667,12122.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c5,4,5,14,Distance,transit_distance,D_location,level,miles,0.062,0.012,,,***,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties < 10 miles; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,15980.0,,se,repeat-sales robustness: properties < 10 miles; robust SE,semi_elasticity,3.8525013918714706,0.7456454306848007,100*b/1.60934 (X per miles->km),reported,0.04085030644173168,0.007906510924206132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.166666666666667,15970.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c5,4,5,14,Distance squared,transit_distance,D_location,squared,miles,-0.003,0.001,,,**,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; properties < 10 miles,"ln(sales price), 2000 dollars",log,FE_panel,15980.0,,se,repeat-sales robustness: properties < 10 miles; robust SE,other,-0.003,0.001,not harmonised,reported,-0.023732662124540872,0.007910887374846957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,15970.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c6,4,6,14,Distance,transit_distance,D_location,level,miles,0.042,0.007,,,***,,,high,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; all repeat sales 1994-2011 (extended window); stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,32231.0,,se,repeat-sales robustness: all repeat sales 1994-2011 (extended window); robust SE,semi_elasticity,2.6097590073968027,0.4349598345661338,100*b/1.60934 (X per miles->km),reported,0.033407137399294844,0.005567856233215807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,32221.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c6,4,6,14,Distance squared,transit_distance,D_location,squared,miles,-0.001,0.001,,,*,,,high,Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; all repeat sales 1994-2011 (extended window),"ln(sales price), 2000 dollars",log,FE_panel,32231.0,,se,repeat-sales robustness: all repeat sales 1994-2011 (extended window); robust SE,other,-0.001,0.001,not harmonised,reported,-0.005570879355273488,0.0055708793552734885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,32221.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c7,4,7,14,Distance,transit_distance,D_location,level,miles,0.019,0.012,,,,,,medium,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; placebo-type: treatment date = construction groundbreaking, 1994-2011 sample; stars: *** p<0.001, ** p<0.01, * p<0.05, + p<0.10 (non-standard legend)","ln(sales price), 2000 dollars",log,FE_panel,32231.0,,se,"repeat-sales robustness: placebo-type: treatment date = construction groundbreaking, 1994-2011 sample; robust SE",semi_elasticity,1.1806052652509345,0.7456454306848007,100*b/1.60934 (X per miles->km),reported,0.00882035272838947,0.0055707490916144025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5833333333333333,32221.0,miles,,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,True,True,False
0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b,0f88c3ee1b9c25f3d6602c7c07d55400a39f254b:T4-c7,4,7,14,Distance squared,transit_distance,D_location,squared,miles,-0.001,0.001,,,,,,medium,"Euclidean distance (miles) to nearest Metro station in sales year; quadratic with Distance squared; placebo-type: treatment date = construction groundbreaking, 1994-2011 sample","ln(sales price), 2000 dollars",log,FE_panel,32231.0,,se,"repeat-sales robustness: placebo-type: treatment date = construction groundbreaking, 1994-2011 sample; robust SE",other,-0.001,0.001,not harmonised,reported,-0.005570879355273488,0.0055708793552734885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,32221.0,miles,nonstandard_x_transform,2023.0,United States,North America,High income,1998.0,2007.0,single_family,transaction,journal,True,True,clustered,,2002.5,panel,journal,False,True,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Land area (ln),lot_size,A_structural,log,sqft,0.175,,,,***,,,high,"Unit of land area not stated in table (US data, sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,0.175,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Land area (ln),lot_size,A_structural,log,sqft,0.184,,,,***,,,high,"Unit of land area not stated in table (US data, sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,0.184,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Land area (ln),lot_size,A_structural,log,sqft,0.178,,,,***,,,high,"Unit of land area not stated in table (US data, sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,0.178,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Land area (ln),lot_size,A_structural,log,sqft,0.161,,,,***,,,high,"Unit of land area not stated in table (US data, sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,0.161,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Land area (ln),lot_size,A_structural,log,sqft,0.222,,,,***,,,high,"Unit of land area not stated in table (US data, sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,0.222,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Land area (ln),lot_size,A_structural,log,sqft,0.241,,,,***,,,high,"Unit of land area not stated in table (US data, sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,0.241,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Floor area (ln),living_area,A_structural,log,sqft,0.657,,,,***,,,high,Unit not stated in table (sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,0.657,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Floor area (ln),living_area,A_structural,log,sqft,0.68,,,,***,,,high,Unit not stated in table (sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,0.68,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Floor area (ln),living_area,A_structural,log,sqft,0.682,,,,***,,,high,Unit not stated in table (sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,0.682,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Floor area (ln),living_area,A_structural,log,sqft,0.686,,,,***,,,high,Unit not stated in table (sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,0.686,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Floor area (ln),living_area,A_structural,log,sqft,0.589,,,,***,,,high,Unit not stated in table (sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,0.589,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Floor area (ln),living_area,A_structural,log,sqft,0.454,,,,***,,,high,Unit not stated in table (sq ft assumed); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,0.454,,b (log-log),,,,,,13922.0,sqft,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Age,age,A_structural,level,years,-0.009,,,,***,,,high,No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,13922.0,years,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Age,age,A_structural,level,years,-0.01,,,,***,,,high,No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,semi_elasticity,-1.0,,100*b/1 (per year),,,,,,13922.0,years,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Age,age,A_structural,level,years,-0.01,,,,***,,,high,No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,semi_elasticity,-1.0,,100*b/1 (per year),,,,,,13922.0,years,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Age,age,A_structural,level,years,-0.009,,,,***,,,high,No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,13922.0,years,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Age,age,A_structural,level,years,-0.011,,,,***,,,high,No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),semi_elasticity,-1.0999999999999999,,100*b/1 (per year),,,,,,13922.0,years,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Age,age,A_structural,level,years,-0.01,,,,***,,,high,No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,semi_elasticity,-1.0,,100*b/1 (per year),,,,,,13922.0,years,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Age squared,age,A_structural,squared,years,7.03e-05,,,,***,,,high,Printed as 7.03x10-5 etc.; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,other,7.03e-05,,not harmonised,,,,,,13922.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Age squared,age,A_structural,squared,years,6.69e-05,,,,***,,,high,Printed as 7.03x10-5 etc.; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,other,6.69e-05,,not harmonised,,,,,,13922.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Age squared,age,A_structural,squared,years,6.94e-05,,,,***,,,high,Printed as 7.03x10-5 etc.; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,other,6.94e-05,,not harmonised,,,,,,13922.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Age squared,age,A_structural,squared,years,4.7e-05,,,,***,,,high,Printed as 7.03x10-5 etc.; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,other,4.7e-05,,not harmonised,,,,,,13922.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Age squared,age,A_structural,squared,years,6.29e-05,,,,***,,,high,Printed as 7.03x10-5 etc.; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),other,6.29e-05,,not harmonised,,,,,,13922.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Age squared,age,A_structural,squared,years,5.85e-05,,,,***,,,high,Printed as 7.03x10-5 etc.; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,other,5.85e-05,,not harmonised,,,,,,13922.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Quality: Average,condition,A_structural,binary,binary,0.789,,,,***,,,high,Construction quality category (reference: below average); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,120.11941311607531,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Quality: Average,condition,A_structural,binary,binary,0.688,,,,***,,,high,Construction quality category (reference: below average); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,98.97320869507038,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Quality: Average,condition,A_structural,binary,binary,0.688,,,,***,,,high,Construction quality category (reference: below average); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,98.97320869507038,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Quality: Average,condition,A_structural,binary,binary,0.671,,,,***,,,high,Construction quality category (reference: below average); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,95.61925354010235,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Quality: Average,condition,A_structural,binary,binary,0.559,,,,***,,,high,Construction quality category (reference: below average); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,74.89227028403494,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Quality: Average,condition,A_structural,binary,binary,0.452,,,,***,,,high,Construction quality category (reference: below average); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,57.145194857764906,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Quality: Above average,condition,A_structural,binary,binary,0.224,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,25.10710194283623,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Quality: Above average,condition,A_structural,binary,binary,0.198,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,21.896239382164275,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Quality: Above average,condition,A_structural,binary,binary,0.193,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,21.288279351911953,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Quality: Above average,condition,A_structural,binary,binary,0.178,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,19.482532123480077,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Quality: Above average,condition,A_structural,binary,binary,0.165,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,17.93931187113906,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Quality: Above average,condition,A_structural,binary,binary,0.12,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,12.749685157937574,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Quality: Excellent or superior,condition,A_structural,binary,binary,0.499,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,64.7073373515345,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Quality: Excellent or superior,condition,A_structural,binary,binary,0.438,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,54.960490741950885,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Quality: Excellent or superior,condition,A_structural,binary,binary,0.44,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,55.2707218511336,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Quality: Excellent or superior,condition,A_structural,binary,binary,0.404,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,49.78039469581388,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Quality: Excellent or superior,condition,A_structural,binary,binary,0.345,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,41.19899196676591,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Quality: Excellent or superior,condition,A_structural,binary,binary,0.273,,,,***,,,high,Construction quality category; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,31.390024482473923,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Special features' value (ln),other,other,log,currency,0.007,,,,***,,,high,Assessor's value of special features (pools etc.); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,0.007,,b (log-log),,,,,,13922.0,currency,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Special features' value (ln),other,other,log,currency,0.006,,,,***,,,high,Assessor's value of special features (pools etc.); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,0.006,,b (log-log),,,,,,13922.0,currency,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Special features' value (ln),other,other,log,currency,0.006,,,,***,,,high,Assessor's value of special features (pools etc.); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,0.006,,b (log-log),,,,,,13922.0,currency,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Special features' value (ln),other,other,log,currency,0.006,,,,***,,,high,Assessor's value of special features (pools etc.); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,0.006,,b (log-log),,,,,,13922.0,currency,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Special features' value (ln),other,other,log,currency,0.006,,,,***,,,high,Assessor's value of special features (pools etc.); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,0.006,,b (log-log),,,,,,13922.0,currency,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Special features' value (ln),other,other,log,currency,0.005,,,,***,,,high,Assessor's value of special features (pools etc.); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,0.005,,b (log-log),,,,,,13922.0,currency,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Distance to ocean (ln),water_proximity,E_environment,log,unknown,-0.192,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,-0.192,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Distance to ocean (ln),water_proximity,E_environment,log,unknown,-0.205,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,-0.205,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Distance to ocean (ln),water_proximity,E_environment,log,unknown,-0.204,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,-0.204,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Distance to ocean (ln),water_proximity,E_environment,log,unknown,-0.12,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,-0.12,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Distance to ocean (ln),water_proximity,E_environment,log,unknown,-0.187,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,-0.187,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Distance to ocean (ln),water_proximity,E_environment,log,unknown,-0.16,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,-0.16,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Distance to nearest water body (ln),water_proximity,E_environment,log,unknown,-0.033,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,-0.033,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Distance to nearest water body (ln),water_proximity,E_environment,log,unknown,-0.016,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,-0.016,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Distance to nearest water body (ln),water_proximity,E_environment,log,unknown,-0.018,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,-0.018,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Distance to nearest water body (ln),water_proximity,E_environment,log,unknown,-0.039,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,-0.039,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Distance to nearest water body (ln),water_proximity,E_environment,log,unknown,-0.031,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,-0.031,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Distance to nearest water body (ln),water_proximity,E_environment,log,unknown,-0.036,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,-0.036,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Distance to nearest railroad (ln),transit_distance,D_location,log,unknown,-0.007,,,,***,,,medium,"Railroad line (disamenity), not a station; Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,-0.007,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Distance to nearest railroad (ln),transit_distance,D_location,log,unknown,0.006,,,,***,,,medium,"Railroad line (disamenity), not a station; Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,0.006,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Distance to nearest railroad (ln),transit_distance,D_location,log,unknown,0.008,,,,***,,,medium,"Railroad line (disamenity), not a station; Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,0.008,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Distance to nearest railroad (ln),transit_distance,D_location,log,unknown,0.023,,,,***,,,medium,"Railroad line (disamenity), not a station; Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,0.023,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Distance to nearest railroad (ln),transit_distance,D_location,log,unknown,0.004,,,,,,,medium,"Railroad line (disamenity), not a station; Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,0.004,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Distance to nearest railroad (ln),transit_distance,D_location,log,unknown,0.006,,,,,,,medium,"Railroad line (disamenity), not a station; Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,0.006,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Distance to nearest expressway (ln),highway_distance,D_location,log,unknown,0.017,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,elasticity,0.017,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Distance to nearest expressway (ln),highway_distance,D_location,log,unknown,0.027,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,0.027,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Distance to nearest expressway (ln),highway_distance,D_location,log,unknown,0.028,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,0.028,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Distance to nearest expressway (ln),highway_distance,D_location,log,unknown,0.029,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,0.029,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Distance to nearest expressway (ln),highway_distance,D_location,log,unknown,0.01,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),elasticity,0.01,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Distance to nearest expressway (ln),highway_distance,D_location,log,unknown,0.021,,,,**,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,elasticity,0.021,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Airport noise > 60 LEQ,noise,E_environment,binary,binary,0.006,,,,,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,0.6018036054064924,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Airport noise > 60 LEQ,noise,E_environment,binary,binary,0.002,,,,,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,0.2002001334000303,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Airport noise > 60 LEQ,noise,E_environment,binary,binary,0.006,,,,,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,0.6018036054064924,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Airport noise > 60 LEQ,noise,E_environment,binary,binary,-0.149,,,,***,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,-13.843088510104174,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Airport noise > 60 LEQ,noise,E_environment,binary,binary,-0.016,,,,,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,-1.5872679944714863,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Airport noise > 60 LEQ,noise,E_environment,binary,binary,-0.216,,,,**,,,medium,Row block garbled in dump (5 amenity rows x 6 models listed as 30 values); reconstructed column-wise and cross-checked with text (airport noise significant only in Models 4 and 6); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,-19.426469812652037,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Elementary school A,school_quality,F_neighbourhood,binary,binary,0.106,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,11.182187650653086,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Elementary school A,school_quality,F_neighbourhood,binary,binary,0.098,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,10.296278510850776,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Elementary school A,school_quality,F_neighbourhood,binary,binary,0.089,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,9.308065632633022,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Elementary school A,school_quality,F_neighbourhood,binary,binary,0.095,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,9.965885512610285,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Elementary school A,school_quality,F_neighbourhood,binary,binary,0.122,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,12.975410178031876,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Elementary school A,school_quality,F_neighbourhood,binary,binary,0.092,,,,**,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,9.636482208081688,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Elementary school B,school_quality,F_neighbourhood,binary,binary,-0.002,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Elementary school B,school_quality,F_neighbourhood,binary,binary,-0.002,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Elementary school B,school_quality,F_neighbourhood,binary,binary,-0.006,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Elementary school B,school_quality,F_neighbourhood,binary,binary,0.012,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,1.2072288866077807,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Elementary school B,school_quality,F_neighbourhood,binary,binary,0.095,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,9.965885512610285,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Elementary school B,school_quality,F_neighbourhood,binary,binary,0.093,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,9.7461735268082,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Elementary school C,school_quality,F_neighbourhood,binary,binary,-0.059,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,-5.729323084290028,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Elementary school C,school_quality,F_neighbourhood,binary,binary,-0.051,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,-4.972132946757302,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Elementary school C,school_quality,F_neighbourhood,binary,binary,-0.046,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,-4.495803780928531,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Elementary school C,school_quality,F_neighbourhood,binary,binary,-0.018,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,-1.7838967641699233,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Elementary school C,school_quality,F_neighbourhood,binary,binary,0.056,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,5.759768373661123,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Elementary school C,school_quality,F_neighbourhood,binary,binary,0.101,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,10.627664176342355,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Middle school A,school_quality,F_neighbourhood,binary,binary,0.238,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,26.87091928249108,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Middle school A,school_quality,F_neighbourhood,binary,binary,0.215,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,23.986189696606175,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Middle school A,school_quality,F_neighbourhood,binary,binary,0.193,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,21.288279351911953,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Middle school A,school_quality,F_neighbourhood,binary,binary,0.193,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,21.288279351911953,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Middle school A,school_quality,F_neighbourhood,binary,binary,0.05,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Middle school A,school_quality,F_neighbourhood,binary,binary,0.034,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,3.4584606728117917,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Middle school B,school_quality,F_neighbourhood,binary,binary,0.333,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,39.514729846980366,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Middle school B,school_quality,F_neighbourhood,binary,binary,0.321,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,37.85055808937538,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Middle school B,school_quality,F_neighbourhood,binary,binary,0.307,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,35.93409680719806,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Middle school B,school_quality,F_neighbourhood,binary,binary,0.269,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,30.865514104646664,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Middle school B,school_quality,F_neighbourhood,binary,binary,0.122,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,12.975410178031876,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Middle school B,school_quality,F_neighbourhood,binary,binary,0.062,,,,*,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,6.396234472803375,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,Middle school C,school_quality,F_neighbourhood,binary,binary,0.196,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,21.652690533431617,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,Middle school C,school_quality,F_neighbourhood,binary,binary,0.155,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,16.765796110512497,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,Middle school C,school_quality,F_neighbourhood,binary,binary,0.148,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,15.951289636297394,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,Middle school C,school_quality,F_neighbourhood,binary,binary,0.128,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,13.655300269706029,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,Middle school C,school_quality,F_neighbourhood,binary,binary,0.038,,,,***,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,3.873123287849767,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,Middle school C,school_quality,F_neighbourhood,binary,binary,0.0,,,,,,,high,School grade dummy (Florida school accountability grades; reference: D/F or lower grades); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,0.0,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,High school B,school_quality,F_neighbourhood,binary,binary,0.275,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,31.653067486762154,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,High school B,school_quality,F_neighbourhood,binary,binary,0.261,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,29.822766543368996,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,High school B,school_quality,F_neighbourhood,binary,binary,0.253,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,28.788327683463045,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,High school B,school_quality,F_neighbourhood,binary,binary,0.161,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,17.468496881387164,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,High school B,school_quality,F_neighbourhood,binary,binary,0.047,,,,*,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,4.812200907965569,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,High school B,school_quality,F_neighbourhood,binary,binary,-0.029,,,,,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,-2.858353553339521,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M1,4,Model 1,24,High school C,school_quality,F_neighbourhood,binary,binary,0.264,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.809,none,Base model: control variables only,pct_premium,30.212819630089417,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,24,High school C,school_quality,F_neighbourhood,binary,binary,0.277,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,pct_premium,31.91663710349588,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,24,High school C,school_quality,F_neighbourhood,binary,binary,0.267,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,pct_premium,30.60404463306543,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,24,High school C,school_quality,F_neighbourhood,binary,binary,0.201,,,,***,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,pct_premium,22.262477182332717,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M5,4,Model 5,24,High school C,school_quality,F_neighbourhood,binary,binary,0.002,,,,,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,OLS,13932.0,0.897,none,Adds 68 submarket (area) dummies (not reported),pct_premium,0.2002001334000303,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M6,4,Model 6,24,High school C,school_quality,F_neighbourhood,binary,binary,-0.06,,,,,,,medium,"School grade dummy (Florida school accountability grades; reference: D/F or lower grades); row garbled in dump (High school B, High school C and February values interleaved), reconstructed in groups of three per model; No SE/t printed; stars only (*0.10 **0.05 ***0.01)",ln(sale price),log,SAC,13932.0,0.935,none,Spatial autoregressive + spatial error model with lags of control variables (5 NN inverse distance); lagged controls not reported,pct_premium,-5.823546641575128,,100*(exp(b) - 1),,,,,,13922.0,binary,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M2,4,Model 2,25,Travel demand automobile (ln),employment_access,D_location,log,index,0.126,,,,***,,,high,Automobile job accessibility index from regional travel demand model; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.817,none,Adds travel-demand-model automobile accessibility index,elasticity,0.126,,b (log-log),,,,,,13922.0,index,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M3,4,Model 3,25,Sugar Access automobile (ln),employment_access,D_location,log,index,0.175,,,,***,,,high,Big-data (Sugar Access) automobile job accessibility index; No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.819,none,Adds Sugar Access (big data) automobile accessibility index,elasticity,0.175,,b (log-log),,,,,,13922.0,index,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,25,Nearest CBD (ln),cbd_distance,D_location,log,unknown,-0.061,,,,***,,,high,Distance to nearest CBD (unit not stated in table); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,-0.061,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a,0ff4eb143517f757d1f3f4078cf390068474a83a:M4,4,Model 4,25,Nearest employment center (ln),employment_access,D_location,log,unknown,-0.183,,,,***,,,high,Distance to nearest employment subcenter (unit not stated); No SE/t printed; stars only (*0.10 **0.05 ***0.01),ln(sale price),log,OLS,13932.0,0.84,none,Adds distances to nearest CBD and employment subcenter,elasticity,-0.183,,b (log-log),,,,,,13922.0,unknown,,2021.0,United States,North America,High income,2016.0,2016.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Building Type,dwelling_type,A_structural,binary,binary,-0.473,0.022,-21.673,0.0,,,,high,"1 = apartment, reference = villa (text p.5/9)",ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",pct_premium,-37.70207080545441,1.3708861565908472,100*(exp(b - se^2/2) - 1),reported,-0.4286250389482461,0.019776913161456477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.673,2087.0,binary,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Bed Room,bedrooms,A_structural,level,count,0.012,0.004,2.837,0.005,,,,high,,ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.061981529360413457,0.021847560578221167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.837,2087.0,count,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Living Room,other,other,level,count,0.045,0.008,5.349,0.0,,,,high,number of living rooms (printed 'Living Room'),ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,4.5,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.11629327670238004,0.021741124827515432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.349,2087.0,count,unit_not_harmonisable,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,False,False,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Bathroom,bathrooms,A_structural,level,count,0.03,0.006,5.007,0.0,,,,high,,ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,3.0,0.6,100*b/1 (per unit count),reported,0.10894904617211154,0.021759346149812574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.007,2087.0,count,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Kitchen,other,other,level,count,-0.005,0.019,-0.259,0.795,,,,high,"number of kitchens (printed 'Kitchen'), not kitchen quality",ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,-0.5,1.9,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.005669327688567826,0.021889296094856472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.259,2087.0,count,unit_not_harmonisable,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,False,False,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Size,living_area,A_structural,level,m2,0.002,,27.336,0.0,,,,high,"Size in square metres (Table 1: Size (m2), 56-930). Std. Error printed as '0' (rounded to 3 decimals) -> not recorded; t = 27.336 printed",ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,0.2,0.0073163593795727245,100*b/1 (X per m2->m2),|b/t|,0.5134703372103845,0.01878366758890783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.336,2087.0,m2,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Street Wide,other,other,level,m,0.001,0.001,1.468,0.142,,,,medium,"street width; unit not stated in paper, metres presumed",ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,0.1,0.1,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.03211742523100958,0.021878355061995626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.468,2087.0,m,unit_not_harmonisable,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,False,False,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Floor,floor_level,A_structural,level,count,-0.022,0.006,-3.599,0.0,,,,high,floor number 0-10,ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,-2.1999999999999997,0.6,100*b/1 (per unit count),reported,-0.07853750129411285,0.021822034257880756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.599,2087.0,count,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Age,age,A_structural,level,years,-0.007,0.001,-5.051,0.0,,,,high,"age in years (Table 1: Age (years), 0-20)",ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per year),reported,-0.10989494460269605,0.021757066838783617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.051,2087.0,years,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Entrance Number,other,other,level,count,0.047,0.016,2.972,0.003,,,,high,number of entrances (1-2),ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",semi_elasticity,4.7,1.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0649188010279375,0.021843472755026076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.972,2087.0,count,unit_not_harmonisable,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,False,False,False
1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd,1076844f99e9f35c7768a4e64f2c167ab26b5afd:T2-c1,2,1,9,Elevator,accessibility,B_interior,binary,binary,0.065,0.011,5.861,0.0,,,,high,presence of elevator (dummy),ln(listing price),log,OLS,2097.0,0.8178,se,"Full sample of apartments and villas; district dummies (reference East Riyadh); N and R2 taken from Table 3 training results (70% split), Table 2 itself does not print N",pct_premium,6.7094463216212485,1.1738749268226119,100*(exp(b - se^2/2) - 1),reported,0.12725223592446358,0.0217116935547626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.861,2087.0,binary,,2025.0,Saudi Arabia,Asia,High income,2024.0,2024.0,mixed,listing,journal,False,True,OLS,ANN;random_forest,2024.0,ols,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c1,2,1,10,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.182,0.026,,,***,,,high,DiD interaction: sold after the 2014 wildfire x located within 20 km of the wildfire perimeter,Log(selling price),log,OLS,98269.0,0.062,se,"DiD only, no characteristics, no FE; treatment < 20 km",other,-0.182,0.026,not harmonised,reported,-0.022325624173376336,0.0031893748819109054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c2,2,2,10,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.14,0.022,,,***,,,high,DiD interaction: sold after the 2014 wildfire x located within 20 km of the wildfire perimeter,Log(selling price),log,OLS,98269.0,0.335,se,"DiD + basic characteristics, no FE; treatment < 20 km",other,-0.14,0.022,not harmonised,reported,-0.020296899886310237,0.003189512839277323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.363636363636364,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c3,2,3,10,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.096,0.015,,,***,,,high,DiD interaction: sold after the 2014 wildfire x located within 20 km of the wildfire perimeter,Log(selling price),log,spatial_lag,98269.0,0.682,se,+ spatial price lag W(Price); no FE; treatment < 20 km,other,-0.096,0.015,not harmonised,reported,-0.020412833980675015,0.0031895053094804717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.4,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.104,0.015,,,***,,,high,DiD interaction: sold after the 2014 wildfire x located within 20 km of the wildfire perimeter,Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,other,-0.104,0.015,not harmonised,reported,-0.022113103652221817,0.003189389949839685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.933333333333334,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.027,0.011,,,**,,,high,DiD interaction: sold after the 2014 wildfire x located within 20 km of the wildfire perimeter,Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,other,-0.027,0.011,not harmonised,reported,-0.007830177281918677,0.003190072225966868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4545454545454546,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c2,2,2,10,Living Area (m2),living_area,A_structural,level,m2,0.005,0.0001,,,***,,,high,,Log(selling price),log,OLS,98269.0,0.335,se,"DiD + basic characteristics, no FE; treatment < 20 km",semi_elasticity,0.5,0.01,100*b/1 (X per m2->m2),reported,0.15751723510049742,0.0031503447020099482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.0,98259.0,m2,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c2,2,2,10,Age,age,A_structural,level,years,-0.004,0.0001,,,***,,,high,Quadratic in age (Age2 also included),Log(selling price),log,OLS,98269.0,0.335,se,"DiD + basic characteristics, no FE; treatment < 20 km",semi_elasticity,-0.4,0.01,100*b/1 (per year),reported,-0.12658037705155908,0.003164509426288977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,98259.0,years,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c2,2,2,10,Age2,age,A_structural,squared,years,0.0,0.0,,,***,,,low,Printed as 0.00000 (0.00000); value rounded to zero in the table,Log(selling price),log,OLS,98269.0,0.335,se,"DiD + basic characteristics, no FE; treatment < 20 km",other,0.0,,not harmonised,,,,,,98259.0,years,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c2,2,2,10,Number of rooms,bedrooms,A_structural,level,count,0.076,0.002,,,***,,,high,"Total number of rooms, not bedrooms",Log(selling price),log,OLS,98269.0,0.335,se,"DiD + basic characteristics, no FE; treatment < 20 km",semi_elasticity,7.6,0.2,100*b/1 (per unit count),reported,0.12034539689254041,0.003166984128751063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.0,98259.0,count,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c3,2,3,10,Living Area (m2),living_area,A_structural,level,m2,0.005,0.0001,,,***,,,high,,Log(selling price),log,spatial_lag,98269.0,0.682,se,+ spatial price lag W(Price); no FE; treatment < 20 km,semi_elasticity,0.5,0.01,100*b/1 (X per m2->m2),reported,0.15751723510049742,0.0031503447020099482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.0,98259.0,m2,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c3,2,3,10,Age,age,A_structural,level,years,-0.0003,0.0001,,,***,,,high,Quadratic in age (Age2 also included),Log(selling price),log,spatial_lag,98269.0,0.682,se,+ spatial price lag W(Price); no FE; treatment < 20 km,semi_elasticity,-0.03,0.01,100*b/1 (per year),reported,-0.009570071800690036,0.003190023933563346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,98259.0,years,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c3,2,3,10,Age2,age,A_structural,squared,years,0.0,0.0,,,***,,,low,Printed as 0.00000 (0.00000); value rounded to zero in the table,Log(selling price),log,spatial_lag,98269.0,0.682,se,+ spatial price lag W(Price); no FE; treatment < 20 km,other,0.0,,not harmonised,,,,,,98259.0,years,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c3,2,3,10,Number of rooms,bedrooms,A_structural,level,count,0.05,0.002,,,***,,,high,"Total number of rooms, not bedrooms",Log(selling price),log,spatial_lag,98269.0,0.682,se,+ spatial price lag W(Price); no FE; treatment < 20 km,semi_elasticity,5.0,0.2,100*b/1 (per unit count),reported,0.07950180623795465,0.003180072249518186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.0,98259.0,count,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Living Area (m2),living_area,A_structural,level,m2,0.005,0.0001,,,***,,,high,,Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,0.5,0.01,100*b/1 (X per m2->m2),reported,0.15751723510049742,0.0031503447020099482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.0,98259.0,m2,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Age,age,A_structural,level,years,-0.0004,0.0001,,,***,,,high,Quadratic in age (Age2 also included),Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,-0.04,0.01,100*b/1 (per year),reported,-0.012759641283677221,0.003189910320919305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,98259.0,years,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Age2,age,A_structural,squared,years,0.0,0.0,,,***,,,low,Printed as 0.00000 (0.00000); value rounded to zero in the table,Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,other,0.0,,not harmonised,,,,,,98259.0,years,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Number of rooms,bedrooms,A_structural,level,count,0.052,0.002,,,***,,,high,"Total number of rooms, not bedrooms",Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,5.2,0.2,100*b/1 (per unit count),reported,0.08266056490092927,0.0031792524961895873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,98259.0,count,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Living Area (m2),living_area,A_structural,level,m2,0.005,5e-05,,,***,,,high,,Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,0.5,0.005,100*b/1 (X per m2->m2),reported,0.30392609804812076,0.0030392609804812077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),100.0,98259.0,m2,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Age,age,A_structural,level,years,-0.002,5e-05,,,***,,,high,Quadratic in age (Age2 also included),Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,-0.2,0.005,100*b/1 (per year),reported,-0.12658037705155908,0.003164509426288977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,98259.0,years,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Age2,age,A_structural,squared,years,0.0,0.0,,,***,,,low,Printed as 0.00000 (0.00000); value rounded to zero in the table,Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,other,0.0,,not harmonised,,,,,,98259.0,years,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Number of rooms,bedrooms,A_structural,level,count,0.06,0.001,,,***,,,high,"Total number of rooms, not bedrooms",Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,6.0,0.1,100*b/1 (per unit count),reported,0.18799727155273147,0.003133287859212191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.0,98259.0,count,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Distance fire,wildfire_risk,E_environment,level,m,0.0,0.0,,,***,,,low,"Distance from the wildfire perimeter in meters; printed as -0.00000 (0.00000), rounded to zero",Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,0.0,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,98259.0,m,unit_not_harmonisable,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Distance fire,wildfire_risk,E_environment,level,m,0.0,0.0,,,,,,low,"Distance from the wildfire perimeter in meters; printed as -0.00000 (0.00000), rounded to zero",Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,0.0,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,98259.0,m,unit_not_harmonisable,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Distance forest,green_space,E_environment,level,m,0.0001,0.0,,,***,,,medium,Distance to closest forest in meters; SE printed as 0.00000,Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,10.0,,100*b/0.001 (X per m->km),,,,,,98259.0,m,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Distance forest,green_space,E_environment,level,m,2e-05,1e-05,,,***,,,medium,Distance to closest forest in meters,Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,2.0,1.0,100*b/0.001 (X per m->km),reported,0.006380210185769235,0.003190105092884617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,98259.0,m,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,True,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Distance train,transit_distance,D_location,level,m,-1e-05,0.0,,,***,,,medium,Distance to closest train station in meters; SE printed as 0.00000,Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,-1.0,,100*b/0.001 (X per m->km),,,,,,98259.0,m,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Distance train,transit_distance,D_location,level,m,-1e-05,0.0,,,***,,,medium,Distance to closest train station in meters; SE printed as 0.00000,Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,-1.0,,100*b/0.001 (X per m->km),,,,,,98259.0,m,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c4,2,4,10,Distance lake,water_proximity,E_environment,level,m,-0.0001,0.0,,,***,,,medium,Distance to closest lake in meters; SE printed as 0.00000,Log(selling price),log,spatial_lag,98269.0,0.694,se,+ distances; no FE; treatment < 20 km,semi_elasticity,-10.0,,100*b/0.001 (X per m->km),,,,,,98259.0,m,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T2-c5,2,5,10,Distance lake,water_proximity,E_environment,level,m,-0.0001,0.0,,,***,,,medium,Distance to closest lake in meters; SE printed as 0.00000,Log(selling price),log,spatial_lag,98269.0,0.829,se,full model with month FE and postal code FE; treatment < 20 km,semi_elasticity,-10.0,,100*b/0.001 (X per m->km),,,,,,98259.0,m,,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,False,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T3-c1,3,1,13,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.199,0.057,,,***,,,high,"DiD interaction, treatment group = within < 5 km of the wildfire perimeter",Log(selling price),log,spatial_lag,98269.0,0.693,se,treatment = within < 5 km of wildfire perimeter; no FE; full characteristic controls omitted from table,other,-0.199,0.057,not harmonised,reported,-0.011136920413298987,0.0031899721786836296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.491228070175439,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T3-c2,3,2,13,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.107,0.043,,,**,,,high,"DiD interaction, treatment group = within < 5 km of the wildfire perimeter",Log(selling price),log,spatial_lag,98269.0,0.829,se,treatment = within < 5 km of wildfire perimeter; month + postal code FE; full characteristic controls omitted from table,other,-0.107,0.043,not harmonised,reported,-0.00793807994833038,0.0031900695119458536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.488372093023256,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T3-c3,3,3,13,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.159,0.037,,,***,,,high,"DiD interaction, treatment group = within < 10 km of the wildfire perimeter",Log(selling price),log,spatial_lag,98269.0,0.694,se,treatment = within < 10 km of wildfire perimeter; no FE; full characteristic controls omitted from table,other,-0.159,0.037,not harmonised,reported,-0.01370782096434031,0.0031898702872993167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.297297297297297,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T3-c4,3,4,13,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.093,0.029,,,***,,,high,"DiD interaction, treatment group = within < 10 km of the wildfire perimeter",Log(selling price),log,spatial_lag,98269.0,0.829,se,treatment = within < 10 km of wildfire perimeter; month + postal code FE; full characteristic controls omitted from table,other,-0.093,0.029,not harmonised,reported,-0.010230009908792395,0.0031900030898384894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.206896551724138,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T3-c5,3,5,13,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.092,0.013,,,***,,,high,"DiD interaction, treatment group = within < 25 km of the wildfire perimeter",Log(selling price),log,spatial_lag,98269.0,0.694,se,treatment = within < 25 km of wildfire perimeter; no FE; full characteristic controls omitted from table,other,-0.092,0.013,not harmonised,reported,-0.02257083639678447,0.003189357316936936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0769230769230775,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T3-c6,3,6,13,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.024,0.01,,,**,,,high,"DiD interaction, treatment group = within < 25 km of the wildfire perimeter",Log(selling price),log,spatial_lag,98269.0,0.829,se,treatment = within < 25 km of wildfire perimeter; month + postal code FE; full characteristic controls omitted from table,other,-0.024,0.01,not harmonised,reported,-0.007656183657832961,0.0031900765240970674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T4-c1,4,1,14,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.163,0.05,,,***,,,high,"DiD interaction, treatment group = 1st ring postal codes",Log(selling price),log,spatial_lag,98269.0,0.694,se,treatment = 1st ring postal codes around wildfire; no FE; characteristic controls omitted from table,other,-0.163,0.05,not harmonised,reported,-0.010399391901890813,0.0031899975159174277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.26,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T4-c2,4,2,14,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.127,0.045,,,***,,,medium,"DiD interaction, treatment group = 1st ring postal codes; SE printed as (0.0.045), read as 0.045",Log(selling price),log,spatial_lag,98269.0,0.7522,se,treatment = 1st ring postal codes around wildfire; month + municipality FE; characteristic controls omitted from table,other,-0.127,0.045,not harmonised,reported,-0.009003003849391596,0.0031900407340363923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8222222222222224,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T4-c3,4,3,14,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.129,0.018,,,***,,,high,"DiD interaction, treatment group = 1st and 2nd ring postal codes",Log(selling price),log,spatial_lag,98269.0,0.694,se,treatment = 1st and 2nd ring postal codes around wildfire; no FE; characteristic controls omitted from table,other,-0.129,0.018,not harmonised,reported,-0.02285691217254752,0.003189336582215933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.166666666666667,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T4-c4,4,4,14,Post * Treatment,wildfire_risk,E_environment,other,binary,-0.099,0.014,,,***,,,high,"DiD interaction, treatment group = 1st and 2nd ring postal codes",Log(selling price),log,spatial_lag,98269.0,0.7521,se,treatment = 1st and 2nd ring postal codes around wildfire; month + municipality FE; characteristic controls omitted from table,other,-0.099,0.014,not harmonised,reported,-0.022553321372536926,0.0031893585779345147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.071428571428571,98259.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,spatial,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T6-c1,6,1,16,Treatmentd*Treatmentt,wildfire_risk,E_environment,other,binary,-0.183,0.073,,,**,,,high,Repeat-sales DiD: within 10 km x wildfire occurred between the two consecutive sales; dependent variable is change in log price,Δ log(selling price) between consecutive sales,other,OLS,5865.0,0.171,se,"Repeat-sales, single/multi-family houses only, 2005-2019, treatment within 10 km; no year controls/FE",other,-0.183,0.073,not harmonised,reported,-0.0327440078863994,0.013061817353591014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5068493150684934,5855.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,False,True,False
11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5,11135c13b52c3fe849cfd9899af8e92968906ec5:T6-c2,6,2,16,Treatmentd*Treatmentt,wildfire_risk,E_environment,other,binary,-0.115,0.07,,,*,,,high,Repeat-sales DiD: within 10 km x wildfire occurred between the two consecutive sales; dependent variable is change in log price,Δ log(selling price) between consecutive sales,other,OLS,5865.0,0.257,se,"Repeat-sales, single/multi-family houses only, 2005-2019, treatment within 10 km; year controls + postal code FE",other,-0.115,0.07,not harmonised,reported,-0.02146526603220372,0.013065814106558786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6428571428571428,5855.0,binary,nonstandard_x_transform,2026.0,Sweden,Europe,High income,2012.0,2016.0,mixed,transaction,journal,True,True,OLS,,2014.0,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,P_LGPERMOPEN,green_space,E_environment,level,share,0.6303,,3.59,,*,,,high,"Share of permanently preserved open space within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,63.029999999999994,17.557103064066855,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.08761603047239966,0.02440557951877428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.59,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,SMPERMOPEN,green_space,E_environment,level,share,-0.1134,,-1.72,,,,,high,Share of permanently preserved open space within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,-11.34,6.593023255813954,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.04210228735496661,0.024478074043585235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.72,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,P_LGAGFOREST,green_space,E_environment,level,share,-0.3492,,-7.42,,*,,,high,"Share of non-preserved agricultural and forest land within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,-34.92,4.706199460916443,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.17885716966733314,0.024104739847349482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.42,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,SMAGFOREST,green_space,E_environment,level,share,-0.0482,,-2.38,,,,,high,Share of non-preserved agricultural and forest land within 100 m buffer; no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,-4.82,2.025210084033614,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.05821064482394276,0.024458254127707045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.38,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,LN_ACRE,lot_size,A_structural,log,acres,0.0982,,13.83,,*,,,high,Natural log of parcel size in acres,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",elasticity,0.0982,0.007100506146059291,b (log-log),|b/t|,0.32091113522581693,0.023203986639610766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.83,1666.0,acre,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,LN_AGE,age,A_structural,log,years,-0.0226,,-5.31,,*,,,high,Natural log of age of structure in years,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",elasticity,-0.0226,0.0042561205273069684,b (log-log),|b/t|,-0.12900682361774457,0.02429507036115717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.31,1666.0,years,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,LN_SQFT,living_area,A_structural,log,sqft,0.3894,,20.46,,*,,,high,Natural log of structure size in square feet,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",elasticity,0.3894,0.01903225806451613,b (log-log),|b/t|,0.4481186865003056,0.021902184090924027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.46,1666.0,sqft,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,AVERAGE,condition,A_structural,dummy,binary,0.2106,,13.88,,*,,,high,Assessor quality grade average (omitted category: fair),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",pct_premium,23.427640488172095,1.872972145260582,100*(exp(b - se^2/2) - 1),|b/t|,0.32195127107974847,0.023195336533123084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.88,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,GOOD_VGOOD,condition,A_structural,dummy,binary,0.4859,,12.29,,*,,,high,Assessor quality grade good or very good (omitted: fair),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",pct_premium,62.436739421167275,6.427153982807359,100*(exp(b - se^2/2) - 1),|b/t|,0.2883162687888979,0.023459419754995756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.29,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,NUSTORY,storeys,A_structural,level,count,0.079,,5.99,,*,,,high,Number of stories,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,7.9,1.318864774624374,100*b/1 (per unit count),|b/t|,0.14519857104440848,0.02424016211091961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.99,1666.0,count,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,BASEMENT,basement,A_structural,binary,binary,-0.0126,,-1.12,,,,,high,,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",pct_premium,-1.2583439279781339,1.110913928608001,100*(exp(b - se^2/2) - 1),|b/t|,-0.027429449184989674,0.024490579629455066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.12,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,FRAME,roof_materials,A_structural,binary,binary,-0.0052,,-0.22,,,,,high,Framed structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",pct_premium,-0.5464354646219527,2.351377355589994,100*(exp(b - se^2/2) - 1),|b/t|,-0.0053898772412844225,0.02449944200583828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,ALUM,roof_materials,A_structural,binary,binary,-0.0422,,-1.82,,,,,high,Aluminum-sided structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",pct_premium,-4.15796457944646,2.2228688290353276,100*(exp(b - se^2/2) - 1),|b/t|,-0.04454537078742743,0.024475478454630457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.82,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,LN_POPDEN,density,F_neighbourhood,log,households per sq mile,-0.0396,,-3.67,,*,,,high,Natural log of census tract/block-group population density (households per square mile),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",elasticity,-0.0396,0.010790190735694824,b (log-log),|b/t|,-0.08955298804763184,0.0244013591410441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.67,1666.0,households per sq mile,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,%EDUCB,education_level,F_neighbourhood,level,share,0.9934,,3.94,,*,,,high,Share of population with bachelor's degree or higher (1990 Census block group),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,99.33999999999999,25.213197969543145,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.09608259817624949,0.024386446237626775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.94,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,MEDHSINC,income,F_neighbourhood,level,USD,0.0,,-0.3,,,,,medium,"Median household income of census tract in dollars; coefficient printed as 0.0000 (rounded) in most columns; beta printed as 0.0000 — magnitude not recoverable, use t only","LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",semi_elasticity,0.0,0.0,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.007349740843519691,0.024499136145065638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,1666.0,usd,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,LN_CITYDIST,cbd_distance,D_location,log,km,-0.1669,,-4.27,,*,,,high,"Natural log of Euclidean distance to nearest of Washington DC or Baltimore; printed in units of 100 km (log, so scale does not affect the elasticity)","LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",elasticity,-0.1669,0.03908665105386417,b (log-log),|b/t|,-0.1040463385378416,0.024366824013546045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.27,1666.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c1,3,1,8,LN_TOWNDIST,other,other,log,km,0.0241,,2.67,,*,,,high,Natural log of distance to nearest town (units of 100 km),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,1676.0,0.7549,t,"Calvert County, OLS; adjusted R2",elasticity,0.0241,0.009026217228464419,b (log-log),|b/t|,0.06527495196998089,0.02444754755429996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.67,1666.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,False,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,P_LGPERMOPEN,green_space,E_environment,level,share,0.7118,,2.83,,*,,,high,"Share of permanently preserved open space within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,71.17999999999999,25.151943462897524,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.0691683720481263,0.024441120865062296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.83,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,SMPERMOPEN,green_space,E_environment,level,share,-0.1117,,-1.67,,,,,high,Share of permanently preserved open space within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,-11.17,6.688622754491018,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.04088045962240075,0.024479317139162127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.67,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,P_LGAGFOREST,green_space,E_environment,level,share,-0.3907,,-6.03,,*,,,high,"Share of non-preserved agricultural and forest land within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,-39.07,6.47927031509121,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.146147534837096,0.024236738778954558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.03,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,SMAGFOREST,green_space,E_environment,level,share,-0.0496,,-2.42,,,,,high,Share of non-preserved agricultural and forest land within 100 m buffer; no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,-4.96,2.0495867768595044,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.059185576189521374,0.024456849665091474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.42,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,LN_ACRE,lot_size,A_structural,log,acres,0.1002,,12.87,,*,,,high,Natural log of parcel size in acres,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",elasticity,0.1002,0.007785547785547786,b (log-log),|b/t|,0.3007176056420591,0.02336578132416932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.87,1666.0,acre,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,LN_AGE,age,A_structural,log,years,-0.0287,,-6.28,,*,,,high,Natural log of age of structure in years,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",elasticity,-0.0287,0.004570063694267516,b (log-log),|b/t|,-0.15206932738475204,0.024214861048527395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.28,1666.0,years,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,LN_SQFT,living_area,A_structural,log,sqft,0.3765,,19.49,,*,,,high,Natural log of structure size in square feet,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",elasticity,0.3765,0.019317598768599283,b (log-log),|b/t|,0.43089739929096815,0.022108640291994268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.49,1666.0,sqft,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,AVERAGE,condition,A_structural,dummy,binary,0.202,,12.94,,*,,,high,Assessor quality grade average (omitted category: fair),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",pct_premium,22.36988986442603,1.910489162584964,100*(exp(b - se^2/2) - 1),|b/t|,0.302204202155599,0.023354266008933467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.94,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,GOOD_VGOOD,condition,A_structural,dummy,binary,0.4699,,11.56,,*,,,high,Assessor quality grade good or very good (omitted: fair),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",pct_premium,59.85130253689541,6.503132278760893,100*(exp(b - se^2/2) - 1),|b/t|,0.2724995488272578,0.023572625331077665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.56,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,NUSTORY,storeys,A_structural,level,count,0.0655,,5.14,,*,,,high,Number of stories,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,6.550000000000001,1.27431906614786,100*b/1 (per unit count),|b/t|,0.12494218477005789,0.024307818048649395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.14,1666.0,count,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,BASEMENT,basement,A_structural,binary,binary,-0.0008,,-0.07,,,,,high,,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",pct_premium,-0.0864931853066464,1.1419432227596387,100*(exp(b - se^2/2) - 1),|b/t|,-0.0017149833293926351,0.024499761848466212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,FRAME,roof_materials,A_structural,binary,binary,-0.0311,,-1.36,,,,,high,Framed structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",pct_premium,-3.0874794737354883,2.216740838557395,100*(exp(b - se^2/2) - 1),|b/t|,-0.03330124465238143,0.024486209303221637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.36,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,ALUM,roof_materials,A_structural,binary,binary,-0.0464,,-2.07,,,,,high,Aluminum-sided structure (omitted: all other construction types); no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",pct_premium,-4.557978387716865,2.1399142517620304,100*(exp(b - se^2/2) - 1),|b/t|,-0.05064948898121878,0.02446835216483999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.07,1666.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,LN_POPDEN,density,F_neighbourhood,log,households per sq mile,-0.0393,,-2.85,,*,,,high,Natural log of census tract/block-group population density (households per square mile),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",elasticity,-0.0393,0.013789473684210527,b (log-log),|b/t|,-0.06965483108056955,0.02444029160721739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.85,1666.0,households per sq mile,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,%EDUCB,education_level,F_neighbourhood,level,share,1.0015,,2.83,,*,,,high,Share of population with bachelor's degree or higher (1990 Census block group),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,100.15,35.3886925795053,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.0691683720481263,0.024441120865062296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.83,1666.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,MEDHSINC,income,F_neighbourhood,level,USD,0.0,,-0.18,,,,,medium,"Median household income of census tract in dollars; coefficient printed as 0.0000 (rounded) in most columns; beta printed as 0.0000 — magnitude not recoverable, use t only","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",semi_elasticity,0.0,0.0,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.004409920736576521,0.024499559647647336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18,1666.0,usd,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,LN_CITYDIST,cbd_distance,D_location,log,km,-0.1432,,-2.59,,*,,,high,"Natural log of Euclidean distance to nearest of Washington DC or Baltimore; printed in units of 100 km (log, so scale does not affect the elasticity)","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",elasticity,-0.1432,0.05528957528957529,b (log-log),|b/t|,-0.06332711220371977,0.024450622472478672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.59,1666.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T3-c2,3,2,8,LN_TOWNDIST,other,other,log,km,0.0265,,2.02,,,,,high,Natural log of distance to nearest town (units of 100 km); no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,1676.0,0.7785,t,"Calvert County, spatial error model (lambda 0.4067); adjusted R2",elasticity,0.0265,0.013118811881188118,b (log-log),|b/t|,0.04942909737166429,0.024469850183992226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.02,1666.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,False,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,P_LGPERMOPEN,green_space,E_environment,level,share,-0.1181,,-1.86,,,,,high,"Share of permanently preserved open space within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,-11.81,6.349462365591398,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.03326492385484662,0.01788436766389603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.86,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,SMPERMOPEN,green_space,E_environment,level,share,0.0443,,2.01,,,,,high,Share of permanently preserved open space within 100 m buffer; no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,4.43,2.2039800995024876,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.03594424221668729,0.01788270757049119,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,P_LGAGFOREST,green_space,E_environment,level,share,-0.1932,,-3.39,,*,,,high,"Share of non-preserved agricultural and forest land within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,-19.32,5.699115044247788,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.06055027339531463,0.017861437579738827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.39,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,SMAGFOREST,green_space,E_environment,level,share,0.0027,,0.2,,,,,high,Share of non-preserved agricultural and forest land within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,0.27,1.35,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.0035788312615184933,0.017894156307592467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,LN_ACRE,lot_size,A_structural,log,acres,0.1334,,31.68,,*,,,high,Natural log of parcel size in acres,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",elasticity,0.1334,0.004210858585858585,b (log-log),|b/t|,0.4931599584073992,0.01556691787902144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.68,3123.0,acre,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,LN_AGE,age,A_structural,log,years,-0.0641,,-21.8,,*,,,high,Natural log of age of structure in years,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",elasticity,-0.0641,0.0029403669724770644,b (log-log),|b/t|,-0.36342218072685334,0.01667074223517676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.8,3123.0,years,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,LN_SQFT,living_area,A_structural,log,sqft,0.2692,,24.29,,*,,,high,Natural log of structure size in square feet,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",elasticity,0.2692,0.011082750102923014,b (log-log),|b/t|,0.3986252461364907,0.016411084649505588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.29,3123.0,sqft,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,AVERAGE,condition,A_structural,dummy,binary,0.1546,,17.51,,*,,,high,Assessor quality grade average (omitted category: fair),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",pct_premium,16.714549773662423,1.0305409894814728,100*(exp(b - se^2/2) - 1),|b/t|,0.2989953295573191,0.017075689866208967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.51,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,GOOD_VGOOD,condition,A_structural,dummy,binary,0.4036,,17.17,,*,,,high,Assessor quality grade good or very good (omitted: fair),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",pct_premium,49.67913714123302,3.5193472095384637,100*(exp(b - se^2/2) - 1),|b/t|,0.29369490128746456,0.017105119469275745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.17,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,NUSTORY,storeys,A_structural,level,count,-0.0212,,-2.52,,,,,high,Number of stories; no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,-2.12,0.8412698412698414,100*b/1 (per unit count),|b/t|,-0.04504778519019629,0.017876105234204874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.52,3123.0,count,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,BASEMENT,basement,A_structural,binary,binary,0.1324,,8.74,,*,,,high,,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",pct_premium,14.143387576658363,1.7293270784416628,100*(exp(b - se^2/2) - 1),|b/t|,0.1545176178012709,0.017679361304493237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.74,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,FRAME,roof_materials,A_structural,binary,binary,-0.0528,,-4.05,,*,,,high,Framed structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",pct_premium,-5.151090063079156,1.2366538408284746,100*(exp(b - se^2/2) - 1),|b/t|,-0.07228222678528726,0.017847463403774633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.05,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,ALUM,roof_materials,A_structural,binary,binary,-0.0015,,-0.16,,,,,high,Aluminum-sided structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",pct_premium,-0.15427540421003583,0.9360948041603538,100*(exp(b - se^2/2) - 1),|b/t|,-0.0028630716098793246,0.01789419756174578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,LN_POPDEN,density,F_neighbourhood,log,households per sq mile,0.0286,,4.75,,*,,,high,Natural log of census tract/block-group population density (households per square mile),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",elasticity,0.0286,0.006021052631578948,b (log-log),|b/t|,0.08469240200009637,0.017829979368441343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,3123.0,households per sq mile,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,%EDUCB,education_level,F_neighbourhood,level,share,0.0814,,0.62,,,,,high,Share of population with bachelor's degree or higher (1990 Census block group),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,8.14,13.129032258064516,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.011093765233076194,0.017893169730768054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.62,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,MEDHSINC,income,F_neighbourhood,level,USD,0.0,,6.82,,*,,,medium,"Median household income of census tract in dollars; coefficient printed as 0.0000 (rounded) in most columns; beta printed as 0.0000 — magnitude not recoverable, use t only","LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",semi_elasticity,0.0,0.0,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,0.12114016115045902,0.01776248697220807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.82,3123.0,usd,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,LN_CITYDIST,cbd_distance,D_location,log,km,-0.1581,,-6.45,,*,,,high,"Natural log of Euclidean distance to nearest of Washington DC or Baltimore; printed in units of 100 km (log, so scale does not affect the elasticity)","LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",elasticity,-0.1581,0.02451162790697674,b (log-log),|b/t|,-0.11465688303006547,0.01777626093489387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.45,3123.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c1,4,1,8,LN_TOWNDIST,other,other,log,km,-0.0044,,-0.76,,,,,high,Natural log of distance to nearest town (units of 100 km),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,3133.0,0.7394,t,"Carroll County, OLS; adjusted R2",elasticity,-0.0044,0.005789473684210527,b (log-log),|b/t|,-0.013598388431072295,0.017892616356674072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.76,3123.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,False,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,P_LGPERMOPEN,green_space,E_environment,level,share,-0.1171,,-1.57,,,,,high,"Share of permanently preserved open space within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,-11.709999999999999,7.458598726114649,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.02808292495479287,0.017887213347001826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.57,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,SMPERMOPEN,green_space,E_environment,level,share,0.0317,,1.33,,,,,high,Share of permanently preserved open space within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,3.17,2.3834586466165413,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.02379264305363286,0.01788920530348335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.33,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,P_LGAGFOREST,green_space,E_environment,level,share,-0.2074,,-3.26,,*,,,high,"Share of non-preserved agricultural and forest land within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,-20.74,6.36196319018405,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.05823631791775709,0.017863901201765977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.26,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,SMAGFOREST,green_space,E_environment,level,share,0.0027,,0.2,,,,,high,Share of non-preserved agricultural and forest land within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,0.27,1.35,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.0035788312615184933,0.017894156307592467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,LN_ACRE,lot_size,A_structural,log,acres,0.1338,,31.06,,*,,,high,Natural log of parcel size in acres,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",elasticity,0.1338,0.004307791371538957,b (log-log),|b/t|,0.4858035568453203,0.01564080994350677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.06,3123.0,acre,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,LN_AGE,age,A_structural,log,years,-0.061,,-20.15,,*,,,high,Natural log of age of structure in years,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",elasticity,-0.061,0.0030272952853598015,b (log-log),|b/t|,-0.33919374648425427,0.016833436550087063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.15,3123.0,years,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,LN_SQFT,living_area,A_structural,log,sqft,0.2629,,24.0,,*,,,high,Natural log of structure size in square feet,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",elasticity,0.2629,0.010954166666666668,b (log-log),|b/t|,0.3946108989335921,0.01644212078889967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.0,3123.0,sqft,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,AVERAGE,condition,A_structural,dummy,binary,0.1551,,16.76,,*,,,high,Assessor quality grade average (omitted category: fair),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",pct_premium,16.772472982191488,1.0806793618611608,100*(exp(b - se^2/2) - 1),|b/t|,0.2872670211626098,0.01714003706220822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.76,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,GOOD_VGOOD,condition,A_structural,dummy,binary,0.3973,,16.69,,*,,,high,Assessor quality grade good or very good (omitted: fair),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",pct_premium,48.73807226381579,3.5416645629540997,100*(exp(b - se^2/2) - 1),|b/t|,0.28616566044920383,0.01714593531750772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.69,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,NUSTORY,storeys,A_structural,level,count,-0.0155,,-1.83,,,,,high,Number of stories,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,-1.55,0.8469945355191256,100*b/1 (per unit count),|b/t|,-0.032728972254982246,0.01788468429234002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.83,3123.0,count,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,BASEMENT,basement,A_structural,binary,binary,0.133,,8.87,,*,,,high,,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",pct_premium,14.212159898768672,1.712731113619041,100*(exp(b - se^2/2) - 1),|b/t|,0.15675986057481772,0.017673039523654763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.87,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,FRAME,roof_materials,A_structural,binary,binary,-0.0488,,-3.79,,*,,,high,Framed structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",pct_premium,-4.770735912690649,1.2262726476650037,100*(exp(b - se^2/2) - 1),|b/t|,-0.06766385678698773,0.01785326036595982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.79,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,ALUM,roof_materials,A_structural,binary,binary,-0.0007,,-0.08,,,,,high,Aluminum-sided structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",pct_premium,-0.0738008787453448,0.8743877143249877,100*(exp(b - se^2/2) - 1),|b/t|,-0.00143154020541819,0.017894252567727375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08,3123.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,LN_POPDEN,density,F_neighbourhood,log,households per sq mile,0.0274,,4.02,,*,,,high,Natural log of census tract/block-group population density (households per square mile),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",elasticity,0.0274,0.006815920398009951,b (log-log),|b/t|,0.07174956947740223,0.017848151611294088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.02,3123.0,households per sq mile,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,%EDUCB,education_level,F_neighbourhood,level,share,0.0083,,0.06,,,,,high,Share of population with bachelor's degree or higher (1990 Census block group),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,0.83,13.833333333333334,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.0010736556353685243,0.017894260589475408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.06,3123.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,MEDHSINC,income,F_neighbourhood,level,USD,0.0,,23.02,,*,,,medium,"Median household income of census tract in dollars; coefficient printed as 0.0000 (rounded) in most columns; beta printed as 0.0000 — magnitude not recoverable, use t only","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",semi_elasticity,0.0,0.0,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,0.3808774097449915,0.016545499988922307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.02,3123.0,usd,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,LN_CITYDIST,cbd_distance,D_location,log,km,-0.1687,,-5.8,,*,,,high,"Natural log of Euclidean distance to nearest of Washington DC or Baltimore; printed in units of 100 km (log, so scale does not affect the elasticity)","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",elasticity,-0.1687,0.02908620689655172,b (log-log),|b/t|,-0.10323226728434982,0.017798666773163763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8,3123.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T4-c2,4,2,8,LN_TOWNDIST,other,other,log,km,-0.0048,,-0.75,,,,,high,Natural log of distance to nearest town (units of 100 km),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,3133.0,0.7458,t,"Carroll County, spatial error model (lambda 0.2149); adjusted R2",elasticity,-0.0048,0.0063999999999999994,b (log-log),|b/t|,-0.01341949470380791,0.017892659605077212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,3123.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,False,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,P_LGPERMOPEN,green_space,E_environment,level,share,0.495,,6.4,,*,,,high,"Share of permanently preserved open space within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,49.5,7.734374999999999,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.08744213889476062,0.013662834202306346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,SMPERMOPEN,green_space,E_environment,level,share,0.0367,,1.69,,,,,high,Share of permanently preserved open space within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,3.6700000000000004,2.171597633136095,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.02317275042516678,0.01371168664211052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.69,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,P_LGAGFOREST,green_space,E_environment,level,share,-0.1612,,-2.89,,*,,,high,"Share of non-preserved agricultural and forest land within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,-16.12,5.577854671280277,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.03960631697858232,0.013704607951066547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.89,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,P_SMAGFOREST,green_space,E_environment,level,share,0.2066,,3.18,,*,,,high,Share of non-preserved agricultural and forest land within 100 m buffer; predicted (IV) value in Howard County,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,20.66,6.496855345911949,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.0435734509050657,0.013702343051907455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.18,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,LN_ACRE,lot_size,A_structural,log,acres,0.1045,,26.82,,*,,,high,Natural log of parcel size in acres,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",elasticity,0.1045,0.00389634601043997,b (log-log),|b/t|,0.3452302923451221,0.012872121265664508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.82,5316.0,acre,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,LN_AGE,age,A_structural,log,years,-0.0559,,-20.79,,*,,,high,Natural log of age of structure in years,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",elasticity,-0.0559,0.002688792688792689,b (log-log),|b/t|,-0.27421274971003734,0.013189646450699248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.79,5316.0,years,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,LN_SQFT,living_area,A_structural,log,sqft,0.2518,,34.22,,,,,high,Natural log of structure size in square feet; no star printed although t = 34.22 (likely typographic omission); no star printed despite |t| > 1.96 (as printed),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",elasticity,0.2518,0.007358270017533607,b (log-log),|b/t|,0.42487167127676495,0.012415887530004822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.22,5316.0,sqft,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,AVERAGE,condition,A_structural,dummy,binary,0.2258,,14.67,,*,,,high,Assessor quality grade average (omitted category: fair),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",pct_premium,25.317651994344146,1.929112334039134,100*(exp(b - se^2/2) - 1),|b/t|,0.1972513950162982,0.013445902864096675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.67,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,GOOD_VGOOD,condition,A_structural,dummy,binary,0.4152,,24.96,,*,,,high,Assessor quality grade good or very good (omitted: fair),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",pct_premium,51.4464096628954,2.519601351994465,100*(exp(b - se^2/2) - 1),|b/t|,0.3238827811548376,0.0129760729629342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.96,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,NUSTORY,storeys,A_structural,level,count,0.0675,,8.04,,*,,,high,Number of stories,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,6.75,0.8395522388059703,100*b/1 (per unit count),|b/t|,0.109607183023019,0.01363273420684316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.04,5316.0,count,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,BASEMENT,basement,A_structural,binary,binary,0.0847,,8.13,,*,,,high,,"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",pct_premium,8.833143577197667,1.1339074464688774,100*(exp(b - se^2/2) - 1),|b/t|,0.1108191435099518,0.013630890960633676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.13,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,FRAME,roof_materials,A_structural,binary,binary,-0.0736,,-7.85,,*,,,high,Framed structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",pct_premium,-7.099759631036373,0.871052002931637,100*(exp(b - se^2/2) - 1),|b/t|,-0.10704699999237993,0.013636560508583431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.85,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,ALUM,roof_materials,A_structural,binary,binary,-0.0919,,-8.58,,*,,,high,Aluminum-sided structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",pct_premium,-8.785595954741199,0.9770494861147367,100*(exp(b - se^2/2) - 1),|b/t|,-0.11687143052879623,0.01362137884951005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.58,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,LN_POPDEN,density,F_neighbourhood,log,households per sq mile,0.0089,,2.62,,*,,,high,Natural log of census tract/block-group population density (households per square mile),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",elasticity,0.0089,0.003396946564885496,b (log-log),|b/t|,0.03591109021798119,0.013706522983962285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.62,5316.0,households per sq mile,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,%EDUCB,education_level,F_neighbourhood,level,share,0.2816,,4.86,,*,,,high,Share of population with bachelor's degree or higher (1990 Census block group),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,28.16,5.794238683127572,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.06650910596976418,0.013685001228346538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.86,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,MEDHSINC,income,F_neighbourhood,level,USD,0.0,,13.31,,*,,,medium,"Median household income of census tract in dollars; coefficient printed as 0.0000 (rounded) in most columns; beta printed as 0.0000 — magnitude not recoverable, use t only","LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",semi_elasticity,0.0,0.0,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,0.17958377029183417,0.013492394462196406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.31,5316.0,usd,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,LN_CITYDIST,cbd_distance,D_location,log,km,-0.0033,,-0.24,,,,,high,"Natural log of Euclidean distance to nearest of Washington DC or Baltimore; printed in units of 100 km (log, so scale does not affect the elasticity)","LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",elasticity,-0.0033,0.01375,b (log-log),|b/t|,-0.0032916708593588974,0.013715295247328741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24,5316.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c1,5,1,9,LN_TOWNDIST,other,other,log,km,-0.0059,,-1.32,,,,,high,Natural log of distance to nearest town (units of 100 km),"LN_PRICE (natural log of full transaction price, USD)",log,OLS,5326.0,0.739,t,"Howard County, OLS; adjusted R2",elasticity,-0.0059,0.004469696969696969,b (log-log),|b/t|,-0.018101321558660177,0.013713122392924375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.32,5316.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,ols,journal,False,False,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,P_LGPERMOPEN,green_space,E_environment,level,share,0.5869,,3.91,,*,,,high,"Share of permanently preserved open space within 1,600 m buffer; predicted (IV) value","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,58.69,15.010230179028131,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.05355014871967448,0.013695690209635415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.91,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,SMPERMOPEN,green_space,E_environment,level,share,0.0489,,2.02,,*,,,high,Share of permanently preserved open space within 100 m buffer,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,4.89,2.4207920792079207,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.02769441983483412,0.013710108829125803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.02,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,P_LGAGFOREST,green_space,E_environment,level,share,-0.2035,,-2.31,,,,,high,"Share of non-preserved agricultural and forest land within 1,600 m buffer; predicted (IV) value; no star printed despite |t| > 1.96 (as printed)","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,-20.349999999999998,8.809523809523808,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.031666614476031106,0.013708491115164981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.31,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,P_SMAGFOREST,green_space,E_environment,level,share,0.1482,,1.75,,,,,high,Share of non-preserved agricultural and forest land within 100 m buffer; predicted (IV) value in Howard County,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,14.82,8.468571428571428,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.023994986061457436,0.013711420606547104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,LN_ACRE,lot_size,A_structural,log,acres,0.1114,,23.79,,*,,,high,Natural log of parcel size in acres,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",elasticity,0.1114,0.004682639764606978,b (log-log),|b/t|,0.31019390298276023,0.013038835770607828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.79,5316.0,acre,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,LN_AGE,age,A_structural,log,years,-0.0566,,-18.56,,*,,,high,Natural log of age of structure in years,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",elasticity,-0.0566,0.0030495689655172416,b (log-log),|b/t|,-0.24669002402393345,0.013291488363358486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.56,5316.0,years,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,LN_SQFT,living_area,A_structural,log,sqft,0.2031,,29.46,,*,,,high,Natural log of structure size in square feet,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",elasticity,0.2031,0.006894093686354378,b (log-log),|b/t|,0.37462935836639993,0.012716543053849285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.46,5316.0,sqft,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,AVERAGE,condition,A_structural,dummy,binary,0.23,,14.07,,*,,,high,Assessor quality grade average (omitted category: fair),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",pct_premium,25.843186018963603,2.057412951554939,100*(exp(b - se^2/2) - 1),|b/t|,0.18947944275502163,0.013466911354301465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.07,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,GOOD_VGOOD,condition,A_structural,dummy,binary,0.412,,23.06,,*,,,high,Assessor quality grade good or very good (omitted: fair),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",pct_premium,50.95934788003724,2.6975359400149297,100*(exp(b - se^2/2) - 1),|b/t|,0.3015535177997151,0.013076908837802046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.06,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,NUSTORY,storeys,A_structural,level,count,0.0734,,8.97,,*,,,high,Number of stories,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,7.340000000000001,0.8182831661092531,100*b/1 (per unit count),|b/t|,0.12210625894964183,0.013612737898510795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.97,5316.0,count,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,BASEMENT,basement,A_structural,binary,binary,0.0731,,7.15,,*,,,high,,"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",pct_premium,7.578189050407147,1.0999128144319201,100*(exp(b - se^2/2) - 1),|b/t|,0.09759673474669803,0.013649892971566158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.15,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,FRAME,roof_materials,A_structural,binary,binary,-0.0538,,-6.0,,*,,,high,Framed structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",pct_premium,-5.241648227654149,0.8497007119794603,100*(exp(b - se^2/2) - 1),|b/t|,-0.08201498277207121,0.013669163795345203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,ALUM,roof_materials,A_structural,binary,binary,-0.0601,,-5.65,,*,,,high,Aluminum-sided structure (omitted: all other construction types),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",pct_premium,-5.838291134630225,1.0016705972842004,100*(exp(b - se^2/2) - 1),|b/t|,-0.0772602119664432,0.013674373799370476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.65,5316.0,binary,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,LN_POPDEN,density,F_neighbourhood,log,households per sq mile,0.0041,,0.74,,,,,high,Natural log of census tract/block-group population density (households per square mile),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",elasticity,0.0041,0.005540540540540541,b (log-log),|b/t|,0.010148850765904547,0.01371466319716831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.74,5316.0,households per sq mile,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,%EDUCB,education_level,F_neighbourhood,level,share,0.1601,,1.62,,,,,high,Share of population with bachelor's degree or higher (1990 Census block group),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,16.009999999999998,9.882716049382715,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.022213416195886793,0.013711985306102955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.62,5316.0,share,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,MEDHSINC,income,F_neighbourhood,level,USD,0.0021,,5.7,,*,,,medium,"Median household income of census tract in dollars; printed 0.0021 in this column (scale inconsistent with other columns, possibly in thousands of dollars)","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",semi_elasticity,0.21,0.03684210526315789,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,0.07793979543749507,0.013673648322367556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7,5316.0,usd,unit_not_harmonisable,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,LN_CITYDIST,cbd_distance,D_location,log,km,0.0154,,1.68,,,,,high,"Natural log of Euclidean distance to nearest of Washington DC or Baltimore; printed in units of 100 km (log, so scale does not affect the elasticity)","LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",elasticity,0.0154,0.009166666666666667,b (log-log),|b/t|,0.023035706535390024,0.0137117300805893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.68,5316.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,True,True,False
1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e,1196e55c7401009f4e60d7c8db96469c1e13b00e:T5-c2,5,2,9,LN_TOWNDIST,other,other,log,km,0.0004,,0.05,,,,,high,Natural log of distance to nearest town (units of 100 km),"LN_PRICE (natural log of full transaction price, USD)",log,spatial_error,5326.0,0.7741,t,"Howard County, spatial error model (lambda 0.6339); adjusted R2",elasticity,0.0004,0.008,b (log-log),|b/t|,0.0006857683163095076,0.013715366326190153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05,5316.0,km,,2003.0,United States,North America,High income,1993.0,1996.0,single_family,transaction,journal,True,False,unknown,,1994.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.099,0.13,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,pct_premium,9.477624508545347,14.352861894265466,100*(exp(b - se^2/2) - 1),reported,0.04940682629518067,0.06487765069064129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7615384615384615,237.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.216,0.182,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,pct_premium,22.071652266032117,22.588063297812223,100*(exp(b - se^2/2) - 1),reported,0.10771043274935202,0.09075601277954662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1868131868131868,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.1,0.206,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,pct_premium,8.196842724157394,22.76652091235834,100*(exp(b - se^2/2) - 1),reported,0.04687736452826501,0.09656737092822591,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48543689320388356,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.114,0.11,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",pct_premium,11.399204439173483,12.328273373725683,100*(exp(b - se^2/2) - 1),reported,0.06283022367412246,0.060625654422398874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0363636363636364,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.223,0.159,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",pct_premium,23.412164694928215,19.872147122402144,100*(exp(b - se^2/2) - 1),reported,0.1269949578404898,0.09054797442438511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4025157232704402,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.068,0.157,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",pct_premium,5.725454896486637,16.804735343116757,100*(exp(b - se^2/2) - 1),reported,0.036451129639381255,0.08415922578504202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43312101910828027,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.079,0.107,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,pct_premium,7.602694150548106,11.579586246152369,100*(exp(b - se^2/2) - 1),reported,0.044804570440006145,0.06068467135545136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7383177570093458,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.216,0.157,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,pct_premium,22.590028426318188,19.485307350310546,100*(exp(b - se^2/2) - 1),reported,0.12461348593324412,0.09057554301629318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.375796178343949,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),-0.007,0.146,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,pct_premium,-1.7503011123342627,14.49815686682523,100*(exp(b - se^2/2) - 1),reported,-0.004037681776733626,0.08421450562901561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.04794520547945206,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.08,0.03,,,***,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,-0.08,0.03,b (log-log),reported,-0.17067698345391666,0.06400386879521874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.666666666666667,237.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.038,0.045,,,,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,-0.038,0.045,b (log-log),reported,-0.07685885419252698,0.0910170641753609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8444444444444444,120.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.111,0.05,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,-0.111,0.05,b (log-log),reported,-0.20983736625854027,0.09452133615249562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2199999999999998,107.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.091,0.024,,,***,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",elasticity,-0.091,0.024,b (log-log),reported,-0.22445064994098182,0.059195775808610594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7916666666666665,271.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.061,0.038,,,,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",elasticity,-0.061,0.038,b (log-log),reported,-0.1449913104257717,0.09032245567507088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.605263157894737,120.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.1,0.028,,,***,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",elasticity,-0.1,0.028,b (log-log),reported,-0.2880230206414618,0.08064644577960929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5714285714285716,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.088,0.024,,,***,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,elasticity,-0.088,0.024,b (log-log),reported,-0.21740658913657607,0.05929270612815711,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6666666666666665,271.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.065,0.038,,,*,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,elasticity,-0.065,0.038,b (log-log),reported,-0.154279442630607,0.09019413569173947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.710526315789474,120.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.094,0.028,,,***,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,elasticity,-0.094,0.028,b (log-log),reported,-0.27205835409245577,0.0810386586658379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.357142857142857,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.516,0.113,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,0.516,0.113,b (log-log),reported,0.2843715965401644,0.06227517521131507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.566371681415929,237.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.498,0.125,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,0.498,0.125,b (log-log),reported,0.34178573798174144,0.08578959286690298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.984,120.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.523,0.163,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,0.523,0.163,b (log-log),reported,0.29626084341757886,0.09233368542459913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.208588957055215,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.496,0.089,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",elasticity,0.496,0.089,b (log-log),reported,0.3206608887890714,0.05753794173836161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.573033707865169,271.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.455,0.105,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",elasticity,0.455,0.105,b (log-log),reported,0.36784269273329034,0.08488677524614391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.333333333333334,120.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.48,0.131,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",elasticity,0.48,0.131,b (log-log),reported,0.29485603717916387,0.08047112681348013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.66412213740458,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.494,0.088,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,elasticity,0.494,0.088,b (log-log),reported,0.3227545086923724,0.057494730293378066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.613636363636364,271.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.46,0.104,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,elasticity,0.46,0.104,b (log-log),reported,0.37440220339954616,0.08464745468163651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.423076923076923,120.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,House size (sq. ft.),living_area,A_structural,log,sqft,0.504,0.129,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,elasticity,0.504,0.129,b (log-log),reported,0.31254367056844606,0.07999629663359036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9069767441860463,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.15,0.061,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,0.15,0.061,b (log-log),reported,0.15773079948181284,0.06414385845593722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.459016393442623,237.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.37,0.097,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,0.37,0.097,b (log-log),reported,0.32884277293851416,0.08621013236496183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8144329896907214,120.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.057,0.073,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,0.057,0.073,b (log-log),reported,0.07527076324511374,0.09639939854198776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7808219178082193,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.133,0.053,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",elasticity,0.133,0.053,b (log-log),reported,0.15069643664636465,0.060051963475618995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.509433962264151,271.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.373,0.089,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",elasticity,0.373,0.089,b (log-log),reported,0.3573267419035699,0.08526026817538263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.191011235955056,120.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.079,0.065,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",elasticity,0.079,0.065,b (log-log),reported,0.10182187969902326,0.08377749595489256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2153846153846153,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.137,0.053,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,elasticity,0.137,0.053,b (log-log),reported,0.15512115818673045,0.06001037506493951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5849056603773586,271.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.375,0.089,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,elasticity,0.375,0.089,b (log-log),reported,0.3589963510264612,0.08520180064361345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.213483146067416,120.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.076,0.063,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,elasticity,0.076,0.063,b (log-log),reported,0.10107267732237708,0.0837839298856547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2063492063492063,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.387,0.04,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,-0.387,0.04,b (log-log),reported,-0.5321031800599463,0.05499774470903838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.675,237.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.392,0.058,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,-0.392,0.058,b (log-log),reported,-0.5250787007287627,0.07769021592415368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.758620689655173,120.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,True
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.375,0.057,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,-0.375,0.057,b (log-log),reported,-0.5366635961010194,0.08157286660735497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.578947368421052,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.374,0.036,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",elasticity,-0.374,0.036,b (log-log),reported,-0.5336913605824131,0.05137136091167612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.38888888888889,271.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.379,0.055,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",elasticity,-0.379,0.055,b (log-log),reported,-0.5324625207701638,0.07727028665530081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.890909090909091,120.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.355,0.048,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",elasticity,-0.355,0.048,b (log-log),reported,-0.5286807931996682,0.07148360020727909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.395833333333333,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.372,0.036,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,elasticity,-0.372,0.036,b (log-log),reported,-0.5316456139417775,0.05144957554275265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.333333333333334,271.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.38,0.055,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,elasticity,-0.38,0.055,b (log-log),reported,-0.5334679898331169,0.07721247221268797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.909090909090909,120.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.359,0.047,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,elasticity,-0.359,0.047,b (log-log),reported,-0.5409977367350923,0.0708270017452628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.638297872340425,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Years since built,age,A_structural,log,years,-0.174,0.095,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,-0.174,0.095,b (log-log),reported,-0.11814064845373716,0.06450207817876455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.831578947368421,237.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Years since built,age,A_structural,log,years,-0.134,0.125,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,-0.134,0.125,b (log-log),reported,-0.09739452384754915,0.09085310060405703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.072,120.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Years since built,age,A_structural,log,years,-0.218,0.174,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,-0.218,0.174,b (log-log),reported,-0.12024110040184208,0.09597225444917669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.252873563218391,107.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Years since built,age,A_structural,log,years,-0.064,0.065,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",elasticity,-0.064,0.065,b (log-log),reported,-0.05970442799683131,0.0606373096842818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9846153846153846,271.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Years since built,age,A_structural,log,years,-0.05,0.099,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",elasticity,-0.05,0.099,b (log-log),reported,-0.0460556696310418,0.09119022586946277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5050505050505051,120.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Years since built,age,A_structural,log,years,-0.106,0.085,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",elasticity,-0.106,0.085,b (log-log),reported,-0.10444688089653878,0.08375457430382827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2470588235294116,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Years since built,age,A_structural,log,years,-0.07,0.065,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,elasticity,-0.07,0.065,b (log-log),reported,-0.06527888443869251,0.06061610697878589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.076923076923077,271.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Years since built,age,A_structural,log,years,-0.052,0.097,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,elasticity,-0.052,0.097,b (log-log),reported,-0.048878916307784,0.09117797849721246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5360824742268041,120.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Years since built,age,A_structural,log,years,-0.102,0.087,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,elasticity,-0.102,0.087,b (log-log),reported,-0.09825727842407525,0.0838076786558289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1724137931034482,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.232,0.075,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,pct_premium,25.757781274525037,9.45839796621247,100*(exp(b - se^2/2) - 1),reported,0.1969961501855258,0.06368410027549325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0933333333333337,237.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.267,0.096,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,pct_premium,30.003605669228506,12.537988284774281,100*(exp(b - se^2/2) - 1),reported,0.24608462161750064,0.08847986395235978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.78125,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,True
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.181,0.115,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,pct_premium,19.051680171080033,13.781774561871796,100*(exp(b - se^2/2) - 1),reported,0.15042460832676538,0.09557364617446419,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5739130434782607,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.213,0.074,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",pct_premium,23.400132595444845,9.156646419202644,100*(exp(b - se^2/2) - 1),reported,0.17223603927753361,0.05983787280064548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8783783783783785,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.255,0.105,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",pct_premium,28.33675222615357,13.549847019165343,100*(exp(b - se^2/2) - 1),reported,0.21644199897526475,0.08912317604863841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.428571428571429,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,True
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.187,0.104,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",pct_premium,19.91248511214763,12.538523764519217,100*(exp(b - se^2/2) - 1),reported,0.1497186241441001,0.08326597278602359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7980769230769231,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.211,0.074,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,pct_premium,23.15357896606787,9.138351427454317,100*(exp(b - se^2/2) - 1),reported,0.17066612137963472,0.05985446910944536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8513513513513513,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.241,0.103,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,pct_premium,26.57888187834758,13.10696666367476,100*(exp(b - se^2/2) - 1),reported,0.2088823524635587,0.08927337055496493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3398058252427183,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Fireplace (0−1),fireplace,B_interior,binary,binary,0.191,0.104,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,pct_premium,20.393095632823698,12.588778301645513,100*(exp(b - se^2/2) - 1),reported,0.15284710862460937,0.08322565076942082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8365384615384617,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.29,0.106,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,pct_premium,32.89404890698086,14.166131373070003,100*(exp(b - se^2/2) - 1),reported,0.17497102607354148,0.06395492677170826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7358490566037736,237.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.165,0.119,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,pct_premium,17.107191962361856,14.034778112665549,100*(exp(b - se^2/2) - 1),reported,0.1255726308445825,0.09056450345760797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3865546218487397,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.333,0.159,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,pct_premium,37.76229307680692,22.18284204566988,100*(exp(b - se^2/2) - 1),reported,0.19844095171273018,0.0947510850520243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0943396226415096,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.375,0.088,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",pct_premium,44.936858069225224,12.80392444864017,100*(exp(b - se^2/2) - 1),reported,0.25059942678096137,0.05880733215126559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.261363636363637,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.196,0.134,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",pct_premium,20.565380914195973,16.30146053147984,100*(exp(b - se^2/2) - 1),reported,0.13234980002579078,0.09048404695640798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.462686567164179,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.463,0.117,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",pct_premium,57.799570402127685,18.589350106268135,100*(exp(b - se^2/2) - 1),reported,0.31616671223483006,0.07989525989519464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9572649572649574,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c7,2,7,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.365,0.086,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.513,se,Combined; Reduced model 2 (also drops nourishment dummies); adj. R2 0.497,pct_premium,43.51968248784115,12.388420470083267,100*(exp(b - se^2/2) - 1),reported,0.2496523380256856,0.05882219471290125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.244186046511628,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c8,2,8,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.207,0.133,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.572,se,San Diego; Reduced model 2 (also drops nourishment dummies); adj. R2 0.544,pct_premium,21.91519572120537,16.35876820468401,100*(exp(b - se^2/2) - 1),reported,0.1406657426574951,0.09037943851906691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5563909774436089,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c9,2,9,9,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.428,0.112,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.486,se,Santa Cruz; Reduced model 2 (also drops nourishment dummies); adj. R2 0.457,pct_premium,52.45937787725665,17.18288410683689,100*(exp(b - se^2/2) - 1),reported,0.30634889768187484,0.08016606668310743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.821428571428571,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.147,0.232,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,pct_premium,-15.962915296084956,20.02842027605861,100*(exp(b - se^2/2) - 1),reported,-0.04112327019485004,0.06490203187214429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6336206896551724,237.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),0.147,0.62,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,pct_premium,-4.419369854459165,71.81794570785104,100*(exp(b - se^2/2) - 1),reported,0.02163880742065881,0.09126571837284668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23709677419354838,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.216,0.234,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,pct_premium,-21.60248848377828,18.854206063839428,100*(exp(b - se^2/2) - 1),reported,-0.08888401198498765,0.09629101298373663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.923076923076923,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.27,0.205,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",pct_premium,-25.249366586948486,15.649279633905488,100*(exp(b - se^2/2) - 1),reported,-0.0797516577606298,0.06055218459603373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3170731707317074,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),0.118,0.582,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",pct_premium,-5.006526808965928,65.48920728157933,100*(exp(b - se^2/2) - 1),reported,0.01850521036156661,0.09127146127484548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2027491408934708,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.447,0.204,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",pct_premium,-37.3626224122058,13.046695728805938,100*(exp(b - se^2/2) - 1),reported,-0.18146660974903123,0.08281697626130284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1911764705882355,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.056,0.18,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,pct_premium,4.060263285237764,19.036758307259,100*(exp(b - se^2/2) - 1),reported,0.020204712946413492,0.06494372018490051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3111111111111111,237.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),-0.001,0.456,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,pct_premium,-9.964666656804056,45.5544227924019,100*(exp(b - se^2/2) - 1),reported,-0.00020019098922872188,0.09128709108829718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0021929824561403508,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.124,0.168,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,pct_premium,11.615305431601453,19.017866632786145,100*(exp(b - se^2/2) - 1),reported,0.0711734022273372,0.09642848043703751,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.738095238095238,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c4,2,4,9,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.087,0.137,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,281.0,0.516,se,"Combined; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.496",pct_premium,8.070704691430496,14.945284512140407,100*(exp(b - se^2/2) - 1),reported,0.038547049932524835,0.060700526905240276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6350364963503649,271.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c5,2,5,9,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),-0.205,0.408,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.574,se,"San Diego; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.538",pct_premium,-25.041233227433445,33.237610509585714,100*(exp(b - se^2/2) - 1),reported,-0.04581911733831775,0.09119121889772508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5024509803921569,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c6,2,6,9,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.209,0.104,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,151.0,0.497,se,"Santa Cruz; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); adj. R2 0.461",pct_premium,22.579792586296854,12.817427984714648,100*(exp(b - se^2/2) - 1),reported,0.1668672912714168,0.08303444158960453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0096153846153846,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Bedrooms,bedrooms,A_structural,log,count,-0.036,0.088,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,-0.036,0.088,b (log-log),reported,-0.02656393281731724,0.0649340579978866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.40909090909090906,237.0,count,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Bedrooms,bedrooms,A_structural,log,count,-0.066,0.116,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,-0.066,0.116,b (log-log),reported,-0.05186929175046857,0.09116420974324778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5689655172413793,120.0,count,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,True
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Bedrooms,bedrooms,A_structural,log,count,0.032,0.137,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,0.032,0.137,b (log-log),reported,0.02257495169509073,0.0966490119446072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23357664233576642,107.0,count,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,9,Geotime,other,other,log,"years (erosion risk: years until erosion reference feature reaches property), log",0.008,0.066,,,,,,high,erosion-risk measure,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,0.008,0.066,b (log-log),reported,0.007873329320681133,0.06495496689561935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12121212121212122,237.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,9,Geotime,other,other,log,"years (erosion risk: years until erosion reference feature reaches property), log",-0.034,0.322,,,,,,high,erosion-risk measure,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,-0.034,0.322,b (log-log),reported,-0.009638562059708491,0.09128285244782748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.10559006211180125,120.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,9,Geotime,other,other,log,"years (erosion risk: years until erosion reference feature reaches property), log",-0.001,0.076,,,,,,high,erosion-risk measure,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,-0.001,0.076,b (log-log),reported,-0.0012720206670234833,0.09667357069378472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.013157894736842106,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,10,Geotime squared,other,other,squared,years (squared log Geotime),-0.005,0.005,,,,,,high,square of log Geotime,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,other,-0.005,0.005,not harmonised,reported,-0.06482037235521644,0.06482037235521644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,237.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,10,Geotime squared,other,other,squared,years (squared log Geotime),-0.005,0.021,,,,,,high,square of log Geotime,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,other,-0.005,0.021,not harmonised,reported,-0.021729890007703836,0.09126553803235612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23809523809523808,120.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,10,Geotime squared,other,other,squared,years (squared log Geotime),-0.0,0.007,,,,,,high,square of log Geotime,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,other,-0.0,0.007,not harmonised,reported,-0.0,0.09667364890456635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,107.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,10,Elevation (ft.),flood_risk,E_environment,log,"ft (first-floor height above base flood elevation), log",0.013,0.016,,,,,,high,flood protection measure; entered in logs per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,0.013,0.016,b (log-log),reported,0.05270419453355939,0.06486670096438078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8125,237.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,10,Elevation (ft.),flood_risk,E_environment,log,"ft (first-floor height above base flood elevation), log",-0.004,0.027,,,,,,high,flood protection measure; entered in logs per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,-0.004,0.027,b (log-log),reported,-0.013522777171238454,0.09127874590585956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14814814814814814,120.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,10,Elevation (ft.),flood_risk,E_environment,log,"ft (first-floor height above base flood elevation), log",0.02,0.023,,,,,,high,flood protection measure; entered in logs per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,0.02,0.023,b (log-log),reported,0.08376857725110064,0.09633386383876572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8695652173913044,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,10,Distance to water (ft.),water_proximity,E_environment,log,ft,-0.009,0.043,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,-0.009,0.043,b (log-log),reported,-0.013594390681734828,0.06495097770162195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.20930232558139536,237.0,ft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,10,Distance to water (ft.),water_proximity,E_environment,log,ft,0.027,0.109,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,0.027,0.109,b (log-log),reported,0.022606620286399405,0.09126376337842723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2477064220183486,120.0,ft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,10,Distance to water (ft.),water_proximity,E_environment,log,ft,-0.023,0.049,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,-0.023,0.049,b (log-log),reported,-0.04533078048534455,0.09657427146877752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4693877551020408,107.0,ft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,10,Beach width,other,other,log,"ft (beach width), log",0.015,0.021,,,,,,high,beach width in front of property,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,elasticity,0.015,0.021,b (log-log),reported,0.046347981816437046,0.06488717454301186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7142857142857142,237.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,10,Beach width,other,other,log,"ft (beach width), log",0.019,0.031,,,,,,high,beach width in front of property,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,elasticity,0.019,0.031,b (log-log),reported,0.055862785063884424,0.09114454405160091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6129032258064516,120.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,10,Beach width,other,other,log,"ft (beach width), log",0.013,0.029,,,,,,high,beach width in front of property,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,elasticity,0.013,0.029,b (log-log),reported,0.0432958265295858,0.09658299764292218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.44827586206896547,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c1,2,1,10,Built Post-FIRM,flood_risk,E_environment,binary,binary (built after Flood Insurance Rate Map publication),-0.54,0.239,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,247.0,0.557,se,Combined; All variables; adj. R2 0.522,pct_premium,-43.36599012205379,13.927683231738351,100*(exp(b - se^2/2) - 1),reported,-0.14520916296684647,0.06426849990569684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.259414225941423,237.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c2,2,2,10,Built Post-FIRM,flood_risk,E_environment,binary,binary (built after Flood Insurance Rate Map publication),-0.58,0.356,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,130.0,0.623,se,San Diego; All variables; adj. R2 0.566,pct_premium,-47.44805095450383,19.932381849728312,100*(exp(b - se^2/2) - 1),reported,-0.1471080848926163,0.09029392796857137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6292134831460674,120.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,True
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T2-c3,2,3,10,Built Post-FIRM,flood_risk,E_environment,binary,binary (built after Flood Insurance Rate Map publication),-0.476,0.401,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,OLS,117.0,0.55,se,Santa Cruz; All variables; adj. R2 0.473,pct_premium,-42.67311642753631,24.91266563812322,100*(exp(b - se^2/2) - 1),reported,-0.11400655402856086,0.09604333648204393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1870324189526182,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,ols,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.094,0.161,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",pct_premium,8.441373006313068,17.686811909266495,100*(exp(b - se^2/2) - 1),reported,0.05635330583597575,0.09652002382544783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5838509316770186,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.068,0.138,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",pct_premium,6.0221660561489365,14.771041257007086,100*(exp(b - se^2/2) - 1),reported,0.041461657298941815,0.08414277510667603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49275362318840576,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),-0.008,0.128,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",pct_premium,-1.6061614251891565,12.698008509914377,100*(exp(b - se^2/2) - 1),reported,-0.005263376599139347,0.08421402558622955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0625,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.103,0.163,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",pct_premium,9.386303539866713,18.068409967938916,100*(exp(b - se^2/2) - 1),reported,0.060974590884706546,0.09649377004084629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6319018404907975,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.06,0.136,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",pct_premium,5.206194956402821,14.44097703301689,100*(exp(b - se^2/2) - 1),reported,0.03712814417918838,0.08415712680616035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4411764705882352,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),-0.011,0.126,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",pct_premium,-1.8759802750869192,12.462159512569645,100*(exp(b - se^2/2) - 1),reported,-0.007351921249349771,0.08421291612891557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0873015873015873,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.084,0.157,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",pct_premium,7.430667497928356,17.07577363279857,100*(exp(b - se^2/2) - 1),reported,0.051654431067300145,0.09654459139959669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.535031847133758,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),0.046,0.132,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",pct_premium,3.799191946749514,13.821382224631572,100*(exp(b - se^2/2) - 1),reported,0.02933508819218998,0.08417894872541473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34848484848484845,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Seawall (0−1),other,other,binary,binary (property protected by seawall/armoring),-0.026,0.123,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",pct_premium,-3.3007461798819637,11.984321602187617,100*(exp(b - se^2/2) - 1),reported,-0.017798765364058024,0.08420185152996683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2113821138211382,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.098,0.053,,,*,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,-0.098,0.053,b (log-log),reported,-0.17596580512680035,0.09516518032367775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.849056603773585,107.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.1,0.041,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",elasticity,-0.1,0.041,b (log-log),reported,-0.2012023561915739,0.0824929660385453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4390243902439024,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.093,0.041,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",elasticity,-0.093,0.041,b (log-log),reported,-0.18763199042148276,0.08271947964818059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.268292682926829,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.112,0.054,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,-0.112,0.054,b (log-log),reported,-0.19659533195568357,0.09478703505006174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.074074074074074,107.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.1,0.037,,,***,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",elasticity,-0.1,0.037,b (log-log),reported,-0.22193252905184793,0.08211503574918373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.702702702702703,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.094,0.038,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",elasticity,-0.094,0.038,b (log-log),reported,-0.20394342439505694,0.08244521411715067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.473684210526316,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.081,0.047,,,*,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,-0.081,0.047,b (log-log),reported,-0.16434246890664486,0.09535921035323837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.723404255319149,107.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.09,0.035,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",elasticity,-0.09,0.035,b (log-log),reported,-0.21164756317894431,0.08230738568070058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.571428571428571,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Percent seawalled (%),other,other,log,"share (% of waterfront structures in beach community with seawalls), log",-0.084,0.035,,,**,,,high,extent of coastal armoring in the beach community; log per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",elasticity,-0.084,0.035,b (log-log),reported,-0.1981104526038634,0.08254602191827641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,141.0,share log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,House size (sq. ft.),living_area,A_structural,log,sqft,0.496,0.155,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,0.496,0.155,b (log-log),reported,0.2955371449174024,0.09235535778668826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,House size (sq. ft.),living_area,A_structural,log,sqft,0.48,0.117,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",elasticity,0.48,0.117,b (log-log),reported,0.32655713503848804,0.07959830166563148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.102564102564102,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,House size (sq. ft.),living_area,A_structural,log,sqft,0.502,0.118,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",elasticity,0.502,0.118,b (log-log),reported,0.337278470428434,0.07928059663457213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.254237288135593,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,House size (sq. ft.),living_area,A_structural,log,sqft,0.519,0.155,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,0.519,0.155,b (log-log),reported,0.3079678883983387,0.09197499557175819,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3483870967741938,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,House size (sq. ft.),living_area,A_structural,log,sqft,0.484,0.114,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",elasticity,0.484,0.114,b (log-log),reported,0.3366723748523598,0.07929886515117567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.245614035087719,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,House size (sq. ft.),living_area,A_structural,log,sqft,0.508,0.115,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",elasticity,0.508,0.115,b (log-log),reported,0.34866656086737075,0.07893042224359771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.417391304347826,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,House size (sq. ft.),living_area,A_structural,log,sqft,0.472,0.149,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,0.472,0.149,b (log-log),reported,0.2928182252858344,0.0924362617957401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1677852348993287,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,House size (sq. ft.),living_area,A_structural,log,sqft,0.468,0.112,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",elasticity,0.468,0.112,b (log-log),reported,0.3319459508250636,0.07944005660770753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.178571428571429,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,House size (sq. ft.),living_area,A_structural,log,sqft,0.489,0.113,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",elasticity,0.489,0.113,b (log-log),reported,0.3424062480227311,0.07912455220157182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.327433628318584,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.056,0.075,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,0.056,0.075,b (log-log),reported,0.07199567230858431,0.09642277541328255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7466666666666667,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.079,0.06,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",elasticity,0.079,0.06,b (log-log),reported,0.11020789674104979,0.08370220005649352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3166666666666667,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.076,0.059,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",elasticity,0.076,0.059,b (log-log),reported,0.10784786361554825,0.08372399938575456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.288135593220339,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.056,0.077,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,0.056,0.077,b (log-log),reported,0.07013497532921857,0.09643559107767553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7272727272727273,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.073,0.058,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",elasticity,0.073,0.058,b (log-log),reported,0.10540453036112986,0.08374606521843193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2586206896551724,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.071,0.058,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",elasticity,0.071,0.058,b (log-log),reported,0.10254752614540359,0.08377121854131563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2241379310344827,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.064,0.07,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,0.064,0.07,b (log-log),reported,0.08804409086431449,0.09629822438284395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9142857142857143,107.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.064,0.055,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",elasticity,0.064,0.055,b (log-log),reported,0.09752868563767927,0.08381371421988063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1636363636363636,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Lot size (sq. ft.),lot_size,A_structural,log,sqft,0.062,0.055,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",elasticity,0.062,0.055,b (log-log),reported,0.0945085715312309,0.08383824893899516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1272727272727272,141.0,sqft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.379,0.057,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,-0.379,0.057,b (log-log),reported,-0.5407205806196017,0.08132209259978179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.649122807017544,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.355,0.053,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",elasticity,-0.355,0.053,b (log-log),reported,-0.49130818789829556,0.0733502365031258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.69811320754717,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.36,0.053,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",elasticity,-0.36,0.053,b (log-log),reported,-0.49653094230271533,0.07310038872789976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.7924528301886795,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.374,0.058,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,-0.374,0.058,b (log-log),reported,-0.5290089861038253,0.08203882672198361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.448275862068965,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.358,0.053,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",elasticity,-0.358,0.053,b (log-log),reported,-0.494448255181082,0.07320044001284175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.754716981132075,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.363,0.053,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",elasticity,-0.363,0.053,b (log-log),reported,-0.49963896346549513,0.07295004149771692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.849056603773585,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.382,0.057,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,-0.382,0.057,b (log-log),reported,-0.5437387762422808,0.081133796455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.701754385964912,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.363,0.053,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",elasticity,-0.363,0.053,b (log-log),reported,-0.49963896346549513,0.07295004149771692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.849056603773585,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Years since last transaction,transaction_timing,G_market,log,"years (since purchase), log",-0.367,0.052,,,***,,,high,captures price appreciation since purchase year (survey-reported purchase price),"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",elasticity,-0.367,0.052,b (log-log),reported,-0.5109295171692934,0.07239328308665738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0576923076923075,141.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Years since built,age,A_structural,log,years,-0.196,0.124,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,-0.196,0.124,b (log-log),reported,-0.15105337109242795,0.09556437762990339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5806451612903227,107.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Years since built,age,A_structural,log,years,-0.106,0.08,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",elasticity,-0.106,0.08,b (log-log),reported,-0.11089686356028378,0.08369574608323305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.325,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Years since built,age,A_structural,log,years,-0.1,0.081,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",elasticity,-0.1,0.081,b (log-log),reported,-0.10341195279836873,0.08376368176667869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2345679012345678,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Years since built,age,A_structural,log,years,-0.21,0.127,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,-0.21,0.127,b (log-log),reported,-0.15784999120797497,0.09546166134958486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6535433070866141,107.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Years since built,age,A_structural,log,years,-0.113,0.079,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",elasticity,-0.113,0.079,b (log-log),reported,-0.11959513518351467,0.083610758225643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4303797468354431,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Years since built,age,A_structural,log,years,-0.107,0.08,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",elasticity,-0.107,0.08,b (log-log),reported,-0.11193001377719779,0.08368599160911984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3375,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Years since built,age,A_structural,log,years,-0.212,0.125,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,-0.212,0.125,b (log-log),reported,-0.1617981700653408,0.09539986442531889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.696,107.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Years since built,age,A_structural,log,years,-0.102,0.078,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",elasticity,-0.102,0.078,b (log-log),reported,-0.10946575479084625,0.08370910660476477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3076923076923077,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Years since built,age,A_structural,log,years,-0.097,0.079,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",elasticity,-0.097,0.079,b (log-log),reported,-0.10285504848079838,0.08376854463900074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2278481012658229,141.0,years,year_built_not_age,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.176,0.116,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",pct_premium,18.444226340870063,13.832281480347769,100*(exp(b - se^2/2) - 1),reported,0.1451244482865132,0.09565020455247461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5172413793103448,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.187,0.108,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",pct_premium,19.861652995630298,13.020774678539187,100*(exp(b - se^2/2) - 1),reported,0.14429111139299797,0.08333390390611647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7314814814814814,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.191,0.108,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",pct_premium,20.342059780640632,13.072962082478034,100*(exp(b - se^2/2) - 1),reported,0.14731126106326328,0.08329641986823264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7685185185185186,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.183,0.117,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",pct_premium,19.262349718626215,14.04952857454572,100*(exp(b - se^2/2) - 1),reported,0.14950800765199054,0.09558708685946937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5641025641025639,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.177,0.108,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",pct_premium,18.66900962122424,12.891215805773099,100*(exp(b - se^2/2) - 1),reported,0.13672324227297133,0.08342435121740625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6388888888888888,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.184,0.108,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",pct_premium,19.50260687510874,12.981770889440055,100*(exp(b - se^2/2) - 1),reported,0.14202334516028056,0.08336152868103423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7037037037037037,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.142,0.119,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",pct_premium,14.444465284851704,13.715662117590359,100*(exp(b - se^2/2) - 1),reported,0.11459847783053664,0.09603675254812578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1932773109243697,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.165,0.108,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",pct_premium,17.253491600071968,12.737445682083019,100*(exp(b - se^2/2) - 1),reported,0.12761020881670534,0.08352668213457076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.527777777777778,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Fireplace (0−1),fireplace,B_interior,binary,binary,0.171,0.108,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",pct_premium,17.959127339986193,12.814100089434488,100*(exp(b - se^2/2) - 1),reported,0.13217091487147517,0.08347636728724749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5833333333333335,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.333,0.131,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",pct_premium,38.32274493411394,18.27642960995443,100*(exp(b - se^2/2) - 1),reported,0.23864279163818658,0.09388049761141874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5419847328244276,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.463,0.101,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",pct_premium,58.07501296901327,16.04721675840241,100*(exp(b - se^2/2) - 1),reported,0.36014937345667997,0.07856390220113323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.584158415841584,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c3,4,Spatial lag model (3),17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.426,0.099,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.966",pct_premium,52.36358726361434,15.158095673819151,100*(exp(b - se^2/2) - 1),reported,0.34070003964439893,0.07917676977651524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.303030303030303,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.326,0.136,,,**,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",pct_premium,37.26621095067062,18.84164901666987,100*(exp(b - se^2/2) - 1),reported,0.22575029689993298,0.09417803797052418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3970588235294117,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.48,0.1,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",pct_premium,60.80141974857829,16.160744021928934,100*(exp(b - se^2/2) - 1),reported,0.3747713274987064,0.07807735989556383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c6,4,Spatial error model (3),17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.441,0.098,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.833",pct_premium,54.6815033807404,15.231754882954599,100*(exp(b - se^2/2) - 1),reported,0.354374653931171,0.07874992309581577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.371,0.133,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",pct_premium,43.64222882605515,19.274134876042375,100*(exp(b - se^2/2) - 1),reported,0.2603676104967943,0.09333933206488854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.789473684210526,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.47,0.096,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",pct_premium,59.26383807827635,15.359944254886658,100*(exp(b - se^2/2) - 1),reported,0.3811757045894443,0.07785716519273755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.895833333333333,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c9,4,"Spatial ARAR(1,1) model (3)",17,Waterfront (0−1),waterfront,C_exterior,binary,binary,0.434,0.094,,,***,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model 2 (also drops nourishment dummies); ML estimation, inverse-distance weights; log-likelihood -107.458",pct_premium,53.66150841826198,14.50813736029498,100*(exp(b - se^2/2) - 1),reported,0.3623931879506572,0.07849069047779211,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.617021276595745,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.208,0.263,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",pct_premium,-21.540255370667914,21.361045065523996,100*(exp(b - se^2/2) - 1),reported,-0.07623423221594965,0.09639232246535943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7908745247148288,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.446,0.251,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",pct_premium,-37.96680245698869,16.068612678747343,100*(exp(b - se^2/2) - 1),reported,-0.14799353607134602,0.08328784204912074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7768924302788844,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.216,0.264,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",pct_premium,-22.185936005239594,21.271411969459866,100*(exp(b - se^2/2) - 1),reported,-0.07885035164901595,0.09637265201546394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8181818181818181,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.458,0.252,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",pct_premium,-38.72216556287819,15.940196400425558,100*(exp(b - se^2/2) - 1),reported,-0.15129584924137862,0.08324575111097689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8174603174603174,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.2,0.264,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",pct_premium,-20.930897446898367,21.614491881258722,100*(exp(b - se^2/2) - 1),reported,-0.07304198538078996,0.09641542070264275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7575757575757576,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Waterfront nourishment (1−0),other,other,binary,binary (waterfront property with beach nourishment),-0.453,0.251,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",pct_premium,-38.39951856648387,15.956525154022998,100*(exp(b - se^2/2) - 1),reported,-0.15026425418549544,0.0832590017672392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8047808764940239,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.132,0.207,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",pct_premium,11.692067337967504,23.620942208831764,100*(exp(b - se^2/2) - 1),reported,0.06153015737809207,0.09649047407018982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6376811594202899,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c2,4,Spatial lag model (2),17,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.209,0.166,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,151.0,,se,"Santa Cruz only; Spatial lag model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.39",pct_premium,21.558081547864095,20.45858697560223,100*(exp(b - se^2/2) - 1),reported,0.10543893633586525,0.08374575804666809,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2590361445783131,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.127,0.208,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",pct_premium,11.11194270779794,23.616673969854915,100*(exp(b - se^2/2) - 1),reported,0.05892413804433712,0.0965056749072608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6105769230769231,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c5,4,Spatial error model (2),17,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.22,0.163,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,151.0,,se,"Santa Cruz only; Spatial error model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -106.167",pct_premium,22.963269151190936,20.311050708574307,100*(exp(b - se^2/2) - 1),reported,0.11293746104458101,0.08367639159212138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3496932515337423,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.128,0.203,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",pct_premium,11.337450703117558,23.072025954750327,100*(exp(b - se^2/2) - 1),reported,0.06084384871670612,0.0964945413241511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6305418719211823,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c8,4,"Spatial ARAR(1,1) model (2)",17,Beach nourishment (1−0),other,other,binary,binary (beach nourishment reported in community),0.196,0.163,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,151.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; Reduced model (drops bedrooms, Geotime, elevation, distance to water, beach width, post-FIRM); ML estimation, inverse-distance weights; log-likelihood -105.846",pct_premium,20.047282497423268,19.829388556949354,100*(exp(b - se^2/2) - 1),reported,0.10074963859015194,0.08378668923568758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2024539877300613,141.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Bedrooms,bedrooms,A_structural,log,count,0.033,0.134,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,0.033,0.134,b (log-log),reported,0.02380094535079756,0.09664626293960224,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2462686567164179,107.0,count,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Bedrooms,bedrooms,A_structural,log,count,0.028,0.136,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,0.028,0.136,b (log-log),reported,0.01989945715630465,0.09665450618776544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.20588235294117646,107.0,count,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Bedrooms,bedrooms,A_structural,log,count,0.054,0.132,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,0.054,0.132,b (log-log),reported,0.039517419007088266,0.09659813535066021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.40909090909090906,107.0,count,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),17,Geotime,other,other,log,"years (erosion risk: years until erosion reference feature reaches property), log",0.021,0.08,,,,,,high,erosion-risk measure,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,0.021,0.08,b (log-log),reported,0.02536866564923594,0.09664253580661311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2625,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),17,Geotime,other,other,log,"years (erosion risk: years until erosion reference feature reaches property), log",0.01,0.093,,,,,,high,erosion-risk measure,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,0.01,0.093,b (log-log),reported,0.010394454432969753,0.0966684262266187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.10752688172043011,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",17,Geotime,other,other,log,"years (erosion risk: years until erosion reference feature reaches property), log",-0.007,0.076,,,,,,high,erosion-risk measure,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,-0.007,0.076,b (log-log),reported,-0.008903798915741803,0.09666981679948243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09210526315789475,107.0,years log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),18,Geotime squared,other,other,squared,years (squared log Geotime),-0.002,0.006,,,,,,high,square of log Geotime,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",other,-0.002,0.006,not harmonised,reported,-0.03220783132004154,0.09662349396012462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,107.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),18,Geotime squared,other,other,squared,years (squared log Geotime),-0.001,0.007,,,,,,high,square of log Geotime,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",other,-0.001,0.007,not harmonised,reported,-0.013809204416652687,0.0966644309165688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14285714285714285,107.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",18,Geotime squared,other,other,squared,years (squared log Geotime),-0.0,0.006,,,,,,high,square of log Geotime,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",other,-0.0,0.006,not harmonised,reported,-0.0,0.09667364890456635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,107.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),18,Elevation (ft.),flood_risk,E_environment,log,"ft (first-floor height above base flood elevation), log",0.027,0.021,,,,,,high,flood protection measure; entered in logs per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,0.027,0.021,b (log-log),reported,0.12334555005453965,0.09593542782019752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2857142857142856,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),18,Elevation (ft.),flood_risk,E_environment,log,"ft (first-floor height above base flood elevation), log",0.02,0.02,,,,,,high,flood protection measure; entered in logs per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,0.02,0.02,b (log-log),reported,0.09622504486493763,0.09622504486493763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",18,Elevation (ft.),flood_risk,E_environment,log,"ft (first-floor height above base flood elevation), log",0.036,0.02,,,*,,,high,flood protection measure; entered in logs per Eq. 4,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,0.036,0.02,b (log-log),reported,0.17143634649587836,0.09524241471993242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7999999999999998,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),18,Distance to water (ft.),water_proximity,E_environment,log,ft,-0.033,0.049,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,-0.033,0.049,b (log-log),reported,-0.0649691901931334,0.09646940362010717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.673469387755102,107.0,ft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),18,Distance to water (ft.),water_proximity,E_environment,log,ft,-0.03,0.059,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,-0.03,0.059,b (log-log),reported,-0.04909681161283754,0.0965570628385805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5084745762711864,107.0,ft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",18,Distance to water (ft.),water_proximity,E_environment,log,ft,-0.009,0.049,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,-0.009,0.049,b (log-log),reported,-0.017753585956375002,0.0966584124291528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18367346938775508,107.0,ft,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),18,Beach width,other,other,log,"ft (beach width), log",0.01,0.022,,,,,,high,beach width in front of property,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",elasticity,0.01,0.022,b (log-log),reported,0.04390020359205627,0.09658044790252379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4545454545454546,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),18,Beach width,other,other,log,"ft (beach width), log",0.013,0.022,,,,,,high,beach width in front of property,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",elasticity,0.013,0.022,b (log-log),reported,0.05703235681781884,0.09651629615323189,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5909090909090909,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",18,Beach width,other,other,log,"ft (beach width), log",0.005,0.023,,,,,,high,beach width in front of property,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",elasticity,0.005,0.023,b (log-log),reported,0.021011371069137467,0.09665230691803235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2173913043478261,107.0,ft log,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,False,False,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c1,4,Spatial lag model (1),18,Built Post-FIRM,flood_risk,E_environment,binary,binary (built after Flood Insurance Rate Map publication),-0.426,0.29,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_lag,117.0,,se,"Santa Cruz only; Spatial lag model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.103",pct_premium,-37.37777882358231,18.940373921499592,100*(exp(b - se^2/2) - 1),reported,-0.1405996033133861,0.09571334497859617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4689655172413794,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c4,4,Spatial error model (1),18,Built Post-FIRM,flood_risk,E_environment,binary,binary (built after Flood Insurance Rate Map publication),-0.449,0.312,,,,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,spatial_error,117.0,,se,"Santa Cruz only; Spatial error model; All variables; ML estimation, inverse-distance weights; log-likelihood -71.586",pct_premium,-39.20556920634717,19.913902279245587,100*(exp(b - se^2/2) - 1),reported,-0.1377961448173108,0.09575144138753001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.439102564102564,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d,11ddac3108d5fcc806866635ea2276160f2ca53d:T4-c7,4,"Spatial ARAR(1,1) model (1)",18,Built Post-FIRM,flood_risk,E_environment,binary,binary (built after Flood Insurance Rate Map publication),-0.512,0.302,,,*,,,high,,"ln(property price), owner-reported purchase price in 2014 US$",log,SAC,117.0,,se,"Santa Cruz only; Spatial ARAR(1,1) model; All variables; ML estimation, inverse-distance weights; log-likelihood -70.669",pct_premium,-42.74195331610673,18.09873279293526,100*(exp(b - se^2/2) - 1),reported,-0.16173910540265907,0.09540080045234968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.695364238410596,107.0,binary,,2020.0,United States,North America,High income,1944.0,1999.0,single_family,transaction,journal,False,False,robust,,1971.5,spatial,journal,True,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,Ar,living_area,A_structural,level,m2,0.0029,0.0002,13.05,0.0,,,,high,Building area in m2 (range 40-200),Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",semi_elasticity,0.29,0.02,100*b/1 (X per m2->m2),reported,0.5375778548110431,0.041193705349505214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.05,419.0,m2,,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,True,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,D,condition,A_structural,other,index,0.0219,0.0025,8.88,0.0,,,,high,"Decoration/finish level ordinal score: blank 2, simple 4, medium 6, hardcover 8, luxury 10",Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,0.0219,0.0025,not harmonised,reported,0.3979804978022399,0.04481762362637835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.88,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,F,floor_level,A_structural,other,index,0.0285,0.0048,5.93,0.0,,,,high,"Floor position ordinal score 1-3/4 (high-rise: low 1, middle 3, high 2; low-rise: low 3, medium 2, high 1), not floor number",Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,0.0285,0.0048,not harmonised,reported,0.27825816059653924,0.046923804485082515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.93,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,EH,accessibility,B_interior,level,share,0.099,0.019,5.22,0.0,,,,high,"Elevators per household on the floor (number of elevators / number of units on the floor), proxy for elevator access",Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",semi_elasticity,9.9,1.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.24710536065926014,0.04733819169717628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.22,419.0,share,unit_not_harmonisable,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,Ag,age,A_structural,level,years,-0.0157,0.001,-15.74,0.0,,,,high,"Age = 2022.4.1 minus year built, years",Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",semi_elasticity,-1.5699999999999998,0.1,100*b/1 (per year),reported,-0.6095705788818194,0.038727482775210896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.74,419.0,years,,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,True,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,B,transit_distance,D_location,other,index,0.0005,0.0002,2.99,0.003,,,,high,"Bus: number of bus routes divided by distance to bus stop (quantity/distance), higher = better access; sign opposite to a distance measure",Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,0.0005,0.0002,not harmonised,reported,0.1445372196945721,0.04834020725570973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.99,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,MR,highway_distance,D_location,other,index,-0.0037,0.0014,-2.68,0.007,,,,high,Main road: inverse geographical distance (anti-distance); negative sign means closer to main road lowers price,Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,-0.0037,0.0014,not harmonised,reported,-0.12981863508968494,0.048439789212569015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.68,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,SM,commercial_distance,D_location,other,index,0.0035,0.0018,1.98,0.048,,,,high,"Shopping mall: service-capacity score (municipal 3, district 2, street 1) divided by distance",Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,0.0035,0.0018,not harmonised,reported,0.09627995377974705,0.04862623928270053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.98,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,CBD,cbd_distance,D_location,other,index,0.4269,0.1393,3.06,0.002,,,,high,CBD: inverse geographical distance (anti-distance); positive sign means closer to CBD raises price,Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,0.4269,0.1393,not harmonised,reported,0.14784789534453935,0.048316305668150115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.06,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1,122e07c191e40cdc3321c3817fb731f2d55466e1:T9-SDM,9,1,11,P,green_space,E_environment,other,index,0.0048,0.0018,2.61,0.009,,,,high,Park: inverse geographical distance (anti-distance),Ln unit listing price (yuan/m2),log,spatial_lag,429.0,0.9037,none,"Spatial Durbin model (coefficient, SE, t, p in separate columns); R2 from Table 7",other,0.0048,0.0018,not harmonised,reported,0.12648281077592066,0.04846084704058263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.61,419.0,index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,unknown,,2022.0,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,surf _build,living_area,A_structural,other,m2,0.0048,,,,***,,,medium,"Surface area of building in m2 (mean 101.7); form not stated, magnitude consistent with level; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.0048,,not harmonised,,,,,,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,surf _build,living_area,A_structural,other,m2,0.0047,,,0.0001,***,,,medium,"Surface area of building in m2 (mean 101.7); form not stated, magnitude consistent with level","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.0047,0.0012080424103112842,not harmonised,|b/z(p)| (two-sided normal),0.024667447318697737,0.006340281386192817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,surf_area,lot_size,A_structural,other,m2,0.045,,,,***,,,medium,"Land surface in m2 (mean 762.8); form not stated, magnitude suggests log; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.045,,not harmonised,,,,,,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,surf_area,lot_size,A_structural,other,m2,0.0455,,,0.0016,***,,,medium,"Land surface in m2 (mean 762.8); form not stated, magnitude suggests log","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.0455,0.01441740947693969,not harmonised,|b/z(p)| (two-sided normal),0.020011419264337714,0.006340941225245626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.155906757921816,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Rooms,bedrooms,A_structural,level,count,0.0115,,,,***,,,high,Number of main rooms; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,semi_elasticity,1.15,,100*b/1 (per unit count),,,,,,24861.0,count,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Rooms,bedrooms,A_structural,level,count,0.0112,,,0.0021,***,,,high,Number of main rooms,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",semi_elasticity,1.1199999999999999,0.36414322947520283,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.01950311017235363,0.00634100493122458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.075712822161009,24861.0,count,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Terrace,terrace_balcony,C_exterior,binary,binary,0.077,,,,***,,,high,; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,8.004207639260041,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Terrace,terrace_balcony,C_exterior,binary,binary,0.0688,,,0.0046,***,,,high,,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,7.090627721014298,2.600762621934009,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01796957300869945,0.006341187193242696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.833786870043548,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Swim,pool,C_exterior,binary,binary,0.186,,,,***,,,high,; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,20.44222603776298,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Swim,pool,C_exterior,binary,binary,0.1777,,,0.0068,***,,,high,,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,19.189510887461147,7.842530270385172,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.017162560686599285,0.006341277114516502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7064833119042557,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Bathroom,bathrooms,A_structural,binary,binary,0.0763,,,,***,,,high,More than one bathroom/shower room; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,7.928631148770271,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Bathroom,bathrooms,A_structural,binary,binary,0.0709,,,0.005,***,,,high,More than one bathroom/shower room,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,7.313150807061541,2.711378055442943,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01779998058953763,0.0063412064330241056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Garage,garage,A_structural,binary,binary,0.0287,,,,***,,,high,; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,2.9115813416263014,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Garage,garage,A_structural,binary,binary,0.0331,,,0.0041,***,,,high,,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,3.358517476147793,1.1919724621349144,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.018201436760968054,0.006341160593634446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8703636333133575,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Condition,condition,A_structural,binary,binary,0.0611,,,,***,,,high,House in good condition; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,6.30052093932858,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Condition,condition,A_structural,binary,binary,0.0579,,,0.005,***,,,high,House in good condition,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,5.9383641236731455,2.1856296671762543,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01779998058953763,0.0063412064330241056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,dist_road,highway_distance,D_location,other,m,0.1278,,,,***,,,medium,"Distance to nearest main road in m (mean 3,654); form not stated, magnitude suggests log; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.1278,,not harmonised,,,,,,24861.0,m,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,dist_road,highway_distance,D_location,other,m,0.1231,,,0.0187,**,,,medium,"Distance to nearest main road in m (mean 3,654); form not stated, magnitude suggests log","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.1231,0.05235049887075916,not harmonised,|b/z(p)| (two-sided normal),0.014911785263368178,0.006341506073037847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.351458011964784,24861.0,m,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,dist_BX,cbd_distance,D_location,level,m,0.0,,,,***,,,low,Distance to Bordeaux town hall in m; coefficient printed '<=0.000' (negative per text); magnitude not recoverable; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,semi_elasticity,0.0,,100*b/0.001 (X per m->km),,,,,,24861.0,m,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,dist_BX,cbd_distance,D_location,level,m,0.0,,,0.0,***,,,low,Distance to Bordeaux town hall in m; coefficient printed '<=0.000' (negative per text); magnitude not recoverable,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",semi_elasticity,0.0,,100*b/0.001 (X per m->km),,,,,,24861.0,m,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,dist_river,water_proximity,E_environment,other,m,0.0472,,,,***,,,medium,"Distance to the Garonne river in m (mean 4,584); form not stated, magnitude suggests log; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.0472,,not harmonised,,,,,,24861.0,m,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,dist_river,water_proximity,E_environment,other,m,0.0487,,,0.0036,***,,,medium,"Distance to the Garonne river in m (mean 4,584); form not stated, magnitude suggests log","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.0487,0.01672828005799721,not harmonised,|b/z(p)| (two-sided normal),0.01846053823875864,0.006341130465694477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.911237726243005,24861.0,m,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,Isolation,other,other,other,m,0.1041,,,,***,,,medium,"Isolation index (average distance to reference facilities, m); form not stated; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.1041,,not harmonised,,,,,,24861.0,m,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,Isolation,other,other,other,m,0.0986,,,0.0166,**,,,medium,"Isolation index (average distance to reference facilities, m); form not stated","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.0986,0.041161376730033304,not harmonised,|b/z(p)| (two-sided normal),0.015190693874723256,0.006341479445923867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.395449516829614,24861.0,m,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,dist_road∗isolation,other,other,other,index,-0.0129,,,,***,,,medium,Interaction dist_road x isolation; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,-0.0129,,not harmonised,,,,,,24861.0,index,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,dist_road∗isolation,other,other,other,index,-0.0123,,,0.0021,***,,,medium,Interaction dist_road x isolation,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,-0.0123,0.003999072966558031,not harmonised,|b/z(p)| (two-sided normal),-0.019503110172353632,0.00634100493122458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0757128221610093,24861.0,index,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,surf_nature,green_space,E_environment,other,m2,0.0125,,,,***,,,medium,"Green and water surface within 200 m radius (m2); form not stated, magnitude suggests log; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.0125,,not harmonised,,,,,,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,surf_nature,green_space,E_environment,other,m2,0.0121,,,0.0022,***,,,medium,"Green and water surface within 200 m radius (m2); form not stated, magnitude suggests log","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.0121,0.003951905439145513,not harmonised,|b/z(p)| (two-sided normal),0.019415011930724608,0.006341015805793852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0618141517617588,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,surf_built_neigh,density,F_neighbourhood,other,m2,-0.076,,,,***,,,medium,"Built (living) area within 200 m radius (m2); form not stated, magnitude suggests log; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,-0.076,,not harmonised,,,,,,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,surf_built_neigh,density,F_neighbourhood,other,m2,-0.0786,,,0.0049,***,,,medium,"Built (living) area within 200 m radius (m2); form not stated, magnitude suggests log","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,-0.0786,0.027936382713157383,not harmonised,|b/z(p)| (two-sided normal),-0.017841195281560092,0.006341201774120107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8135353387387996,24861.0,m2,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,height_built_neigh,other,other,level,m,0.1493,,,,***,,,medium,"Average building height within 200 m (m, mean 6.71); perceived-density proxy; p printed as <0.0000","log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,semi_elasticity,14.93,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,24861.0,m,unit_not_harmonisable,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,height_built_neigh,other,other,level,m,0.1485,,,0.0053,***,,,medium,"Average building height within 200 m (m, mean 6.71); perceived-density proxy","log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",semi_elasticity,14.85,5.325995634818699,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.01768065942635585,0.006341219860302177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7882110722956885,24861.0,m,unit_not_harmonisable,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,social_compo_G2,demographics,F_neighbourhood,dummy,binary,0.0279,,,0.0006,***,,,high,IRIS social composition group 2 (least favourable socioeconomic conditions; ref. G1 mixed),"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,2.82588646965074,0.8360313001531575,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.021758870772517914,0.006340709710725022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.431614403623269,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,social_compo_G2,demographics,F_neighbourhood,dummy,binary,0.0318,,,0.0094,***,,,high,IRIS social composition group 2 (least favourable socioeconomic conditions; ref. G1 mixed),"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,3.2233643400298773,1.2639797700185942,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.016469459891849735,0.00634135104465227,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.597153158038555,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,social_compo_G3,demographics,F_neighbourhood,dummy,binary,0.156,,,,***,,,high,IRIS social composition group 3 (majority favourable socioeconomic conditions; ref. G1 mixed); p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,16.88262030898691,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,social_compo_G3,demographics,F_neighbourhood,dummy,binary,0.1615,,,0.0071,***,,,high,IRIS social composition group 3 (majority favourable socioeconomic conditions; ref. G1 mixed),"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,17.315957719369045,7.050454761239859,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01707148751253235,0.006341287002849209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6921171530104133,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,BM,other,other,binary,binary,0.1946,,,,***,,,high,Located within Bordeaux Metropole; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,21.48249593070517,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,BM,other,other,binary,binary,0.1962,,,0.0096,***,,,high,Located within Bordeaux Metropole,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,21.32837880658156,9.217694076480026,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01642356423759934,0.006341355832549502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.589913682701567,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,CatNat,flood_risk,E_environment,binary,binary,0.0748,,,,***,,,high,Municipality with >= 9 natural-disaster (CatNat) declarations; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,7.766859561070061,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,CatNat,flood_risk,E_environment,binary,binary,0.075,,,0.0066,***,,,high,Municipality with >= 9 natural-disaster (CatNat) declarations,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,7.747337908915419,2.976067190605143,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01722530344014705,0.0063412702715614205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7163805834608654,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,PPRI,flood_risk,E_environment,binary,binary,-0.1143,,,,***,,,high,Located within a PPRI flood-risk prevention zone; p printed as <0.0000,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,pct_premium,-10.800968135676325,,100*(exp(b) - 1),,,,,,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,False,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,PPRI,flood_risk,E_environment,binary,binary,-0.1153,,,0.0158,**,,,high,Located within a PPRI flood-risk prevention zone,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",pct_premium,-10.991750032969916,4.2570359683815315,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.015305152629811861,0.006341468375839984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4135029495884783,24861.0,binary,,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,True,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c1,2,1,6,CatNat∗PPRI,flood_risk,E_environment,other,binary,0.0583,,,0.002,***,,,high,Interaction PPRI x CatNat,"log P (sale price, EUR)",log,OLS,24871.0,,p,MCO (OLS) benchmark; year dummies included,other,0.0583,0.018865895578024563,not harmonised,|b/z(p)| (two-sided normal),0.019595143023695357,0.006340993518378956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,24861.0,binary,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,ols,journal,False,True,False
13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500,13561959c7aabb108ebf10b83cf499e51f169500:T2-c2,2,2,6,CatNat∗PPRI,flood_risk,E_environment,other,binary,0.0532,,,0.0178,**,,,high,Interaction PPRI x CatNat,"log P (sale price, EUR)",log,spatial_error,24871.0,,p,"SEM, ML, W = 1 nearest neighbour; lambda 0.1609; year dummies included",other,0.0532,0.022449602539856086,not harmonised,|b/z(p)| (two-sided normal),0.015027773279216956,0.006341495059727264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.369752422366986,24861.0,binary,nonstandard_x_transform,2025.0,France,Europe,High income,2011.0,2016.0,single_family,transaction,journal,True,False,unknown,,2013.5,spatial,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Bedrooms,bedrooms,A_structural,level,count,0.142,0.00158,90.152,,***,,,high,Number of bedrooms; printed p-value '<2.00e-6',"log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,14.2,0.158,100*b/1 (per unit count),reported,0.4498198295359178,0.004989571274468872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),90.152,32040.0,count,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Bathrooms,bathrooms,A_structural,level,count,0.0489,0.00148,33.016,,***,,,high,Number of bathrooms; printed p-value '<2.00e-6',"log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,4.89,0.148,100*b/1 (per unit count),reported,0.18139001186241188,0.005494003266973948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.016,32040.0,count,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Sydney,cbd_distance,D_location,level,m,-1.45e-05,1.69e-07,-85.66,,***,,,medium,"Network/Euclidean distance to Sydney CBD; distance unit not stated in the paper, coefficient magnitudes (1e-5 per unit) are consistent with metres; printed p-value '<2.00e-6'","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,-1.45,0.0169,100*b/0.001 (X per m->km),reported,-0.43167137653113336,0.005039357652709939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-85.66,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Secondary City,other,other,level,m,7.07e-07,1.74e-07,4.065,4.82e-05,***,,,medium,"Distance to the nearest secondary city centre (e.g. Parramatta); printed name 'Distance to Secondary City'; unit not stated, assumed metres","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,7.07e-05,1.74e-05,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.02270399770159934,0.005585239286986307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.065,32040.0,m,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Beach,water_proximity,E_environment,level,m,-2.44e-06,2.24e-07,-10.916,,***,,,medium,"Distance to nearest beach; unit not stated, assumed metres; printed p-value '<2.00e-6'","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,-0.244,0.0224,100*b/0.001 (X per m->km),reported,-0.06087110493596404,0.005576319616706122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.916,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Bus Interchanges,transit_distance,D_location,level,m,2.91e-06,4.83e-07,6.031,1.65e-09,***,,,medium,"Distance to nearest bus interchange; unit not stated, assumed metres","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,0.29100000000000004,0.048299999999999996,100*b/0.001 (X per m->km),reported,0.03367415458346659,0.005583510957298391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.031,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Highway,highway_distance,D_location,level,m,7.5e-06,4.44e-07,16.883,,***,,,medium,"Distance to nearest highway/motorway; unit not stated, assumed metres; printed p-value '<2.00e-6'","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,0.75,0.0444,100*b/0.001 (X per m->km),reported,0.09390313982521951,0.005561993711142541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.883,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Primary School,school_distance,D_location,level,m,1.02e-05,3.26e-06,3.118,0.00182,**,,,medium,"Distance to nearest primary school; unit not stated, assumed metres","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,1.02,0.326,100*b/0.001 (X per m->km),reported,0.017416624073292905,0.005585831967059945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.118,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to University,university_distance,D_location,level,m,1.12e-06,4.27e-07,2.617,0.00889,**,,,medium,"Distance to nearest university; unit not stated, assumed metres","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,0.11200000000000002,0.0427,100*b/0.001 (X per m->km),reported,0.014618777554039257,0.005586082366847252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.617,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Swimming Pool,neighbourhood_amenities,F_neighbourhood,level,m,-4.61e-06,9.32e-07,-4.952,7.4e-07,***,,,medium,"Distance to nearest (public) swimming pool facility - a recreational amenity, NOT a private pool; unit not stated, assumed metres","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,-0.000461,9.32e-05,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,-0.027654655256336907,0.005584542660811169,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.952,32040.0,m,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Shopping Centre,commercial_distance,D_location,level,m,1.11e-05,7.5e-07,14.774,,***,,,medium,"Distance to nearest shopping centre; unit not stated, assumed metres; printed p-value '<2.00e-6'","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,1.11,0.07500000000000001,100*b/0.001 (X per m->km),reported,0.08225788733360413,0.0055677465367269625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.774,32040.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Distance to Sport Centre,neighbourhood_amenities,F_neighbourhood,level,m,9e-06,7.27e-07,12.378,,***,,,medium,"Distance to nearest sports centre; unit not stated, assumed metres; printed p-value '<2.00e-6'","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,0.0009,7.27e-05,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.06898716595047473,0.005573369361001352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.378,32040.0,m,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Population Above 65 (%),demographics,F_neighbourhood,level,share,0.00508,0.000217,23.355,,***,,,medium,Share of SA1 population aged over 65 (printed as %); scale (0-1 vs 0-100) not stated; printed p-value '<2.00e-6',"log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,0.508,0.0217,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12938024524234257,0.005539723624163671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.355,32040.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Crime Rate (%),crime,F_neighbourhood,level,share,-0.0435,0.0162,-2.681,0.00735,**,,,medium,Local crime rate (printed as %); scale not stated,"log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,-4.35,1.6199999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01497620759777063,0.005586052815281847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.681,32040.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Low Income (%),income,F_neighbourhood,level,share,-0.187,0.0575,-3.244,0.00118,**,,,medium,Share of SA1 population in the low-income group (2016 census); printed as % but magnitude of coefficient suggests a 0-1 proportion; authors read -0.187 as -0.2 per cent,"log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,-18.7,5.75,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01812021229560702,0.0055857621133190575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.244,32040.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Middle Income (%),income,F_neighbourhood,level,share,-0.102,0.058,-1.758,0.0788,.,,,medium,"Share of SA1 population in the middle-income group; printed as %, scale not stated","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,-10.2,5.800000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009820908665409692,0.005586409934817799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.758,32040.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,High Income (%),income,F_neighbourhood,level,share,2.51,0.0597,41.983,,***,,,medium,"Share of SA1 population in the high-income group; printed as %, scale not stated; authors read 2.51 as 2.5 per cent; printed p-value '<2.00e-6'","log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,250.99999999999997,5.970000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.2283487264708014,0.005439075970530964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.983,32040.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T4-c1,4,1,19,Accessibility Score,employment_access,D_location,level,index,0.273,0.0868,3.142,0.00168,**,,,high,Gravity-model employment accessibility score standardised to 0-1,"log(house sale price), AUD",log,OLS,32050.0,0.8103,se,"Greater Sydney detached houses sold in two quarters of 2016 (APM); SE, t and p in separate columns; adj. R2 0.8102",semi_elasticity,27.3,8.68,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.017550642904334206,0.0055858188747085315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.142,32040.0,index,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,ols,journal,False,True,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Bedrooms,bedrooms,A_structural,level,count,0.14,,,,,,,medium,"Number of bedrooms; GWR summary: beta = median of local coefficients; min 0.11, Q1 0.12, Q3 0.16, max 0.19; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,14.000000000000002,,100*b/1 (per unit count),,,,,,32058.0,count,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Bathrooms,bathrooms,A_structural,level,count,0.051,,,,,,,medium,"Number of bathrooms; GWR summary: beta = median of local coefficients; min 0.028, Q1 0.04, Q3 0.058, max 0.081; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,5.1,,100*b/1 (per unit count),,,,,,32058.0,count,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Sydney,cbd_distance,D_location,level,m,-2.3e-05,,,,,,,medium,"Network/Euclidean distance to Sydney CBD; distance unit not stated in the paper, coefficient magnitudes (1e-5 per unit) are consistent with metres; GWR summary: beta = median of local coefficients; min -3.4e-05, Q1 -3e-05, Q3 -1.5e-05, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-2.3,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Secondary City,other,other,level,m,-5.6e-07,,,,,,,medium,"Distance to the nearest secondary city centre (e.g. Parramatta); printed name 'Distance to Secondary City'; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -1.6e-05, Q1 -4.7e-06, Q3 4.1e-06, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-5.6000000000000006e-05,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,32058.0,m,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Beach,water_proximity,E_environment,level,m,2.3e-06,,,,,,,medium,"Distance to nearest beach; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -3.8e-05, Q1 -8.7e-06, Q3 8.7e-06, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.23,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Bus Interchanges,transit_distance,D_location,level,m,-3.2e-06,,,,,,,medium,"Distance to nearest bus interchange; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -3.8e-05, Q1 -9.7e-06, Q3 4.4e-06, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-0.31999999999999995,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Highway,highway_distance,D_location,level,m,7.6e-06,,,,,,,medium,"Distance to nearest highway/motorway; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -1.8e-05, Q1 3e-06, Q3 1.1e-05, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.76,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Primary School,school_distance,D_location,level,m,2.7e-05,,,,,,,medium,"Distance to nearest primary school; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -3.7e-05, Q1 1.3e-05, Q3 4.5e-05, max 0.0001; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,2.6999999999999997,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to University,university_distance,D_location,level,m,6.5e-06,,,,,,,medium,"Distance to nearest university; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -2.1e-05, Q1 2.3e-06, Q3 1.1e-05, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.6499999999999999,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Swimming Pool,neighbourhood_amenities,F_neighbourhood,level,m,-4.5e-06,,,,,,,medium,"Distance to nearest (public) swimming pool facility - a recreational amenity, NOT a private pool; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -2.7e-05, Q1 -1.3e-05, Q3 6.8e-06, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-0.00045,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,32058.0,m,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Shopping Centres,commercial_distance,D_location,level,m,7.2e-06,,,,,,,medium,"Distance to nearest shopping centre; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -1.5e-05, Q1 -4e-07, Q3 1.8e-05, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.72,,100*b/0.001 (X per m->km),,,,,,32058.0,m,,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Distance to Sports Centres,neighbourhood_amenities,F_neighbourhood,level,m,6.7e-06,,,,,,,medium,"Distance to nearest sports centre; unit not stated, assumed metres; GWR summary: beta = median of local coefficients; min -1.2e-05, Q1 3.6e-06, Q3 1.4e-05, max 0.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.00067,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,32058.0,m,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Population Above 65,demographics,F_neighbourhood,level,share,0.0037,,,,,,,medium,"Share of SA1 population aged over 65 (printed as %); scale (0-1 vs 0-100) not stated; GWR summary: beta = median of local coefficients; min 0.0012, Q1 0.0026, Q3 0.0058, max 0.0089; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.37,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,32058.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Crime Rate,crime,F_neighbourhood,level,share,-0.021,,,,,,,medium,"Local crime rate (printed as %); scale not stated; GWR summary: beta = median of local coefficients; min -0.31, Q1 -0.08, Q3 0.1, max 0.68; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-2.1,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,32058.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Low Income (%),income,F_neighbourhood,level,share,-0.28,,,,,,,medium,"Share of SA1 population in the low-income group (2016 census); printed as % but magnitude of coefficient suggests a 0-1 proportion; authors read -0.187 as -0.2 per cent; GWR summary: beta = median of local coefficients; min -7.2, Q1 -0.38, Q3 -0.16, max 5.0; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-28.000000000000004,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,32058.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Middle Income (%),income,F_neighbourhood,level,share,-0.18,,,,,,,medium,"Share of SA1 population in the middle-income group; printed as %, scale not stated; GWR summary: beta = median of local coefficients; min -6.6, Q1 -0.31, Q3 -0.067, max 5.4; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,-18.0,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,32058.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,High Income (%),income,F_neighbourhood,level,share,1.7,,,,,,,medium,"Share of SA1 population in the high-income group; printed as %, scale not stated; authors read 2.51 as 2.5 per cent; GWR summary: beta = median of local coefficients; min -4.5, Q1 1.5, Q3 1.8, max 7.7; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,170.0,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,32058.0,share,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526,13a61953f01e5f8f119d4fa1872eb72503252526:T5-c1,5,Median,20,Accessibility Score,employment_access,D_location,level,index,0.00033,,,,,,,medium,"Gravity-model employment accessibility score standardised to 0-1; GWR summary: beta = median of local coefficients; min -0.78, Q1 -0.28, Q3 0.57, max 1.1; no standard error reported","log(house sale price), AUD",log,GWR,32068.0,0.8458,none,"GWR (adaptive bi-square kernel, AIC bandwidth); Table 5 reports the distribution (min, Q1, median, Q3, max) of local coefficients - the MEDIAN local estimate is recorded as beta, no SE; adj. R2 0.8449",semi_elasticity,0.033,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,32058.0,index,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2016.0,2016.0,single_family,transaction,journal,False,False,OLS,,2016.0,spatial,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,age,age,A_structural,level,years,-0.004,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),semi_elasticity,-0.4,,100*b/1 (per year),,,,,,1582.0,years,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,age,age,A_structural,level,years,-0.001,,,,,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),semi_elasticity,-0.1,,100*b/1 (per year),,,,,,2558.0,years,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,age,age,A_structural,level,years,-0.003,,,,,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),semi_elasticity,-0.3,,100*b/1 (per year),,,,,,1298.0,years,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,age,age,A_structural,level,years,-0.006,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),semi_elasticity,-0.6,,100*b/1 (per year),,,,,,903.0,years,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,squared age,age,A_structural,squared,years,4.37e-05,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),other,4.37e-05,,not harmonised,,,,,,1582.0,years,nonstandard_x_transform,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,squared age,age,A_structural,squared,years,2.59e-05,,,,,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),other,2.59e-05,,not harmonised,,,,,,2558.0,years,nonstandard_x_transform,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,squared age,age,A_structural,squared,years,4.16e-05,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),other,4.16e-05,,not harmonised,,,,,,1298.0,years,nonstandard_x_transform,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,squared age,age,A_structural,squared,years,8.21e-05,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),other,8.21e-05,,not harmonised,,,,,,903.0,years,nonstandard_x_transform,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,height,storeys,A_structural,level,count,0.125,,,,*,,,medium,"story height (number of stories); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),semi_elasticity,12.5,,100*b/1 (per unit count),,,,,,1582.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,height,storeys,A_structural,level,count,0.062,,,,*,,,medium,"story height (number of stories); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),semi_elasticity,6.2,,100*b/1 (per unit count),,,,,,2558.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,height,storeys,A_structural,level,count,0.076,,,,*,,,medium,"story height (number of stories); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),semi_elasticity,7.6,,100*b/1 (per unit count),,,,,,1298.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,height,storeys,A_structural,level,count,0.053,,,,,,,medium,"story height (number of stories); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),semi_elasticity,5.3,,100*b/1 (per unit count),,,,,,903.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,no fuel,heating,A_structural,binary,binary,-0.796,,,,*,,,medium,"dummy for no heating fuel; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),pct_premium,-54.88701205969577,,100*(exp(b) - 1),,,,,,1582.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,no fuel,heating,A_structural,binary,binary,0.302,,,,,,,medium,"dummy for no heating fuel; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),pct_premium,35.25612267094822,,100*(exp(b) - 1),,,,,,2558.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,no fuel,heating,A_structural,binary,binary,0.032,,,,,,,medium,"dummy for no heating fuel; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),pct_premium,3.251750530511832,,100*(exp(b) - 1),,,,,,1298.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,no fuel,heating,A_structural,binary,binary,-0.794,,,,*,,,medium,"dummy for no heating fuel; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),pct_premium,-54.796695797658536,,100*(exp(b) - 1),,,,,,903.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,central air conditioning,air_conditioning,A_structural,binary,binary,0.045,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),pct_premium,4.602785990871694,,100*(exp(b) - 1),,,,,,1582.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,central air conditioning,air_conditioning,A_structural,binary,binary,0.08,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),pct_premium,8.328706767495863,,100*(exp(b) - 1),,,,,,2558.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,central air conditioning,air_conditioning,A_structural,binary,binary,0.09,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,1298.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,central air conditioning,air_conditioning,A_structural,binary,binary,0.101,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),pct_premium,10.627664176342355,,100*(exp(b) - 1),,,,,,903.0,binary,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,building grade,condition,A_structural,level,ordinal (1-5),0.034,,,,*,,,medium,"quality of structure, below average to excellent (1-5); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),semi_elasticity,3.4000000000000004,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,1582.0,ordinal,unit_not_harmonisable,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,building grade,condition,A_structural,level,ordinal (1-5),0.027,,,,*,,,medium,"quality of structure, below average to excellent (1-5); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),semi_elasticity,2.7,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,2558.0,ordinal,unit_not_harmonisable,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,building grade,condition,A_structural,level,ordinal (1-5),0.032,,,,*,,,medium,"quality of structure, below average to excellent (1-5); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),semi_elasticity,3.2,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,1298.0,ordinal,unit_not_harmonisable,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,building grade,condition,A_structural,level,ordinal (1-5),0.059,,,,*,,,medium,"quality of structure, below average to excellent (1-5); no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),semi_elasticity,5.8999999999999995,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,903.0,ordinal,unit_not_harmonisable,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,# fireplaces,fireplace,B_interior,level,count,0.089,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),semi_elasticity,8.9,,100*b/1 (per unit count),,,,,,1582.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,# fireplaces,fireplace,B_interior,level,count,0.064,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),semi_elasticity,6.4,,100*b/1 (per unit count),,,,,,2558.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,# fireplaces,fireplace,B_interior,level,count,0.057,,,,*,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),semi_elasticity,5.7,,100*b/1 (per unit count),,,,,,1298.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,# fireplaces,fireplace,B_interior,level,count,0.004,,,,,,,medium,"no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),semi_elasticity,0.4,,100*b/1 (per unit count),,,,,,903.0,count,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,ln (heated area),living_area,A_structural,log,sqft,0.337,,,,*,,,medium,"heated area in square feet, log; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),elasticity,0.337,,b (log-log),,,,,,1582.0,sqft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,ln (heated area),living_area,A_structural,log,sqft,0.392,,,,*,,,medium,"heated area in square feet, log; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),elasticity,0.392,,b (log-log),,,,,,2558.0,sqft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,ln (heated area),living_area,A_structural,log,sqft,0.338,,,,*,,,medium,"heated area in square feet, log; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),elasticity,0.338,,b (log-log),,,,,,1298.0,sqft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,ln (heated area),living_area,A_structural,log,sqft,0.455,,,,*,,,medium,"heated area in square feet, log; no SE printed, * = significant at p<0.05","ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),elasticity,0.455,,b (log-log),,,,,,903.0,sqft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,False,False,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T1,3,T1,6,ln (network distance),transit_distance,D_location,log,ft,0.123,0.028,,,*,,,high,log of network distance (feet) to nearest light rail station; SE printed in table; * = p<0.05,"ln(sales price, 2005 constant USD)",log,OLS,1592.0,0.779,none,1997-1998 (pre-planning),elasticity,0.123,0.028,b (log-log),reported,0.10977693697165758,0.024989871830946444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.392857142857142,1582.0,ft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,True,True,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T2,3,T2,6,ln (network distance),transit_distance,D_location,log,ft,0.169,0.022,,,*,,,high,log of network distance (feet) to nearest light rail station; SE printed in table; * = p<0.05,"ln(sales price, 2005 constant USD)",log,OLS,2568.0,0.786,none,1999-2005 (planning),elasticity,0.169,0.022,b (log-log),reported,0.15016243610493318,0.01954777274738775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6818181818181825,2558.0,ft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,True,True,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T3,3,T3,6,ln (network distance),transit_distance,D_location,log,ft,0.148,0.027,,,*,,,high,log of network distance (feet) to nearest light rail station; SE printed in table; * = p<0.05,"ln(sales price, 2005 constant USD)",log,OLS,1308.0,0.811,none,2005-2007 (construction),elasticity,0.148,0.027,b (log-log),reported,0.15041504835770125,0.027440583146337395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.481481481481481,1298.0,ft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,True,True,False
14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a,14eecd7db66f7a02fa146490275ce97a19765a1a:T3-T4,3,T4,6,ln (network distance),transit_distance,D_location,log,ft,0.052,0.033,,,,,,high,log of network distance (feet) to nearest light rail station; SE printed in table; * = p<0.05,"ln(sales price, 2005 constant USD)",log,OLS,913.0,0.837,none,2007-2008 (operation),elasticity,0.052,0.033,b (log-log),reported,0.05236598178935655,0.03323225767401474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5757575757575757,903.0,ft,,2012.0,United States,North America,High income,1997.0,2008.0,single_family,transaction,journal,False,True,unknown,,2002.5,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,log(LIVAREA),living_area,A_structural,log,sqft,0.803,0.004,,,***,0.796,0.81,high,air-conditioned living area in sq ft,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,elasticity,0.803,0.004,b (log-log),reported,0.7542220543122693,0.003757021441157008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),200.75,30545.0,sqft,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,log(ACREAGE),lot_size,A_structural,log,acres,0.078,0.001,,,***,0.075,0.08,high,,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,elasticity,0.078,0.001,b (log-log),reported,0.40755110179042103,0.005225014125518219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),78.0,30545.0,acre,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,log(MHHI),income,F_neighbourhood,log,currency,0.222,0.004,,,***,0.214,0.23,high,"median household income by zip code, USD","log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,elasticity,0.222,0.004,b (log-log),reported,0.3026636200063351,0.005453398558672705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.5,30545.0,currency,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,log(NBEDS),bedrooms,A_structural,log,count,0.042,0.003,,,***,0.034,0.049,high,,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,elasticity,0.042,0.003,b (log-log),reported,0.07984891885448572,0.005703494203891837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.0,30545.0,count,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,log(NBATHS),bathrooms,A_structural,log,count,-0.06,0.004,,,***,-0.067,-0.052,high,,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,elasticity,-0.06,0.004,b (log-log),reported,-0.08551208798801606,0.005700805865867737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.0,30545.0,count,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,log(AGE),age,A_structural,log,years,-0.173,0.002,,,***,-0.177,-0.168,high,see study note on years vs decades,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,elasticity,-0.173,0.002,b (log-log),reported,-0.443576620215228,0.00512805341289281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-86.49999999999999,30545.0,years,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,Pool,pool,C_exterior,binary,binary,0.024,0.002,,,***,0.02,0.028,high,,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,pct_premium,2.4288269312034316,0.2048580635781243,100*(exp(b - se^2/2) - 1),reported,0.06849989053107838,0.005708324210923199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,30545.0,binary,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T6-c1,6,1,18,County*Pool,pool,C_exterior,other,binary,0.035,0.003,,,***,0.029,0.042,high,interaction Orange County x pool,"log(JMV, just market value USD)",log,OLS,30555.0,0.911,se,Full cleaned sample; log-log model; HC2 robust SEs; adjusted R2 0.911,other,0.035,0.003,not harmonised,reported,0.06660567714439729,0.005709058040948339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.666666666666668,30545.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,ols,journal,False,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T4-c1,4,Production frontier,17,log(LIVAREA),living_area,A_structural,log,sqft,0.8086,0.0032,,,***,,,high,,"log(JMV, just market value USD)",log,other,30555.0,,se,"Stochastic frontier analysis (SFA) production-frontier equation; Wald chi2(6)=253,536; log-likelihood 20,211",elasticity,0.8086,0.0032,b (log-log),reported,0.8224453050765427,0.0032547922041119668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),252.68749999999997,30545.0,sqft,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,other,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T4-c1,4,Production frontier,17,log(NBEDS),bedrooms,A_structural,log,count,0.048,0.0032,,,***,,,high,,"log(JMV, just market value USD)",log,other,30555.0,,se,"Stochastic frontier analysis (SFA) production-frontier equation; Wald chi2(6)=253,536; log-likelihood 20,211",elasticity,0.048,0.0032,b (log-log),reported,0.08551208798801606,0.005700805865867737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,30545.0,count,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,other,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T4-c1,4,Production frontier,17,log(NBATHS),bathrooms,A_structural,log,count,-0.0615,0.0038,,,***,,,high,,"log(JMV, just market value USD)",log,other,30555.0,,se,"Stochastic frontier analysis (SFA) production-frontier equation; Wald chi2(6)=253,536; log-likelihood 20,211",elasticity,-0.0615,0.0038,b (log-log),reported,-0.09220772726035974,0.005697388025843367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.184210526315788,30545.0,count,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,other,journal,True,True,True
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T4-c1,4,Production frontier,17,log(MHHI),income,F_neighbourhood,log,currency,0.2214,0.0037,,,***,,,high,"median household income by zip code, USD","log(JMV, just market value USD)",log,other,30555.0,,se,"Stochastic frontier analysis (SFA) production-frontier equation; Wald chi2(6)=253,536; log-likelihood 20,211",elasticity,0.2214,0.0037,b (log-log),reported,0.3239186513302515,0.005413274660893995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.83783783783784,30545.0,currency,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,other,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T4-c1,4,Production frontier,17,log(ACREAGE),lot_size,A_structural,log,acres,0.075,0.0013,,,***,,,high,,"log(JMV, just market value USD)",log,other,30555.0,,se,"Stochastic frontier analysis (SFA) production-frontier equation; Wald chi2(6)=253,536; log-likelihood 20,211",elasticity,0.075,0.0013,b (log-log),reported,0.31346459867719695,0.005433386377071414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),57.69230769230769,30545.0,acre,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,other,journal,True,True,False
154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3,154f55871a6811fb1dd94f5c020e97dabe79c8c3:T4-c1,4,Production frontier,17,log(AGE),age,A_structural,log,years,-0.196,0.0024,,,***,,,medium,Table cell is typeset as '-8***' (typo); value -0.196 taken from the text on p. 19 ('a 1% increase in the age of the property reduces the JMV by 0.196%'); SE as printed in table,"log(JMV, just market value USD)",log,other,30555.0,,se,"Stochastic frontier analysis (SFA) production-frontier equation; Wald chi2(6)=253,536; log-likelihood 20,211",elasticity,-0.196,0.0024,b (log-log),reported,-0.4233397603728087,0.00518375216783031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-81.66666666666667,30545.0,years,,2024.0,United States,North America,High income,,,single_family,appraisal,journal,False,True,robust,,2021.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.493,0.014,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,63.70600816397503,2.292108729944838,100*(exp(b - se^2/2) - 1),reported,0.2784887197851878,0.007908401778889714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.214285714285715,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.543,0.018,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,72.08838067707653,3.0980927025542138,100*(exp(b - se^2/2) - 1),reported,0.24107097658648488,0.007991303091264692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.16666666666667,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.488,0.014,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,62.88952104197427,2.2806767899616043,100*(exp(b - se^2/2) - 1),reported,0.27588027432962026,0.007914598034046485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.857142857142854,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.031,0.006,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,3.1466937314552412,0.6188913023319136,100*(exp(b - se^2/2) - 1),reported,0.04250465473018455,0.00822670736713249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.166666666666667,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,-0.103,0.009,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-9.79095619210626,0.8119142761333648,100*(exp(b - se^2/2) - 1),reported,-0.09381960712629994,0.008197829748899994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.444444444444445,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.265,0.025,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,30.30237172358996,3.258577439445922,100*(exp(b - se^2/2) - 1),reported,0.11879138903676556,0.011206734814789203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.6,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.31,0.027,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,36.29282362395738,3.68124780815688,100*(exp(b - se^2/2) - 1),reported,0.12851293573377348,0.011193062144554466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.481481481481481,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.488,0.014,,,***,,,low,house vs flat; in models with interaction the house effect also depends on surface; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,62.88952104197427,2.2806767899616043,100*(exp(b - se^2/2) - 1),reported,0.36610646093252475,0.01050305420708063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.857142857142854,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.239,0.012,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,26.98871014209079,1.5239742439006039,100*(exp(b - se^2/2) - 1),reported,0.21931950234270448,0.011011857858211105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.916666666666664,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Type of Dwelling: House (ref. Flat),dwelling_type,A_structural,dummy,binary,0.082,0.016,,,***,,,high,house vs flat; in models with interaction the house effect also depends on surface,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,8.531688037756545,1.7367292957272784,100*(exp(b - se^2/2) - 1),reported,0.057747567163375285,0.011267817983097615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.125,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Surface,living_area,A_structural,level,m2,0.006,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Surface,living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Surface,living_area,A_structural,level,m2,0.006,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Surface,living_area,A_structural,level,m2,0.003,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.3,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Surface,living_area,A_structural,level,m2,0.003,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.3,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Surface,living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Surface,living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Surface,living_area,A_structural,level,m2,0.006,0.0,,,***,,,low,dwelling surface in m2; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Surface,living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Surface,living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,dwelling surface in m2; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,14749.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,low,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use; SE printed as 0.000 (rounded)",log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Plot size,lot_size,A_structural,level,m2,0.0,0.0,,,,,,high,"plot surface in m2; coded 0 when not mentioned (mostly flats); coefficient printed as 0.000 (rounded, not significant); SE printed as 0.000 (rounded)",log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,7850.0,m2,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.089,0.014,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,9.2973539670812,1.5303129188568623,100*(exp(b - se^2/2) - 1),reported,0.052274092087622105,0.008222890890187748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.357142857142857,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.134,0.018,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,14.3207605010655,2.0581070753603643,100*(exp(b - se^2/2) - 1),reported,0.06118382134542943,0.008218722270281564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.4444444444444455,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.093,0.014,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,9.735418928786487,1.5364464293753148,100*(exp(b - se^2/2) - 1),reported,0.054616631571139336,0.008221858516085492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.642857142857142,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.066,0.01,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,6.817330716539627,1.0682267171659934,100*(exp(b - se^2/2) - 1),reported,0.054265307183664284,0.008222016239949134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6000000000000005,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.051,0.016,,,**,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,5.218820457312501,1.683716629253126,100*(exp(b - se^2/2) - 1),reported,0.026237313872980776,0.008231314156229264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1874999999999996,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.228,0.036,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,25.527164580213647,4.521907171556438,100*(exp(b - se^2/2) - 1),reported,0.071300206337368,0.011257927316426523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.333333333333334,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.12,0.037,,,**,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,12.672534406304049,4.1717383508436905,100*(exp(b - se^2/2) - 1),reported,0.03658086084768735,0.011279098761370263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2432432432432434,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.093,0.014,,,***,,,low,"construction-period dummy, not age in years; reference category pre-1950; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use",log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,9.735418928786487,1.5364464293753148,100*(exp(b - se^2/2) - 1),reported,0.07476577564999223,0.0112550630010741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.642857142857142,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,0.042,0.022,,,.,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,4.264212882247853,2.294367853251679,100*(exp(b - se^2/2) - 1),reported,0.021542246281323105,0.01128403376640734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9090909090909094,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Construction year: 1950-1969 (ref. pre 1950),age,A_structural,dummy,binary,-0.02,0.029,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-2.0213413588028417,2.8425761525895905,100*(exp(b - se^2/2) - 1),reported,-0.007783662793968638,0.011286311051254525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.689655172413793,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.118,0.022,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,12.49718352391318,2.4755370450081533,100*(exp(b - se^2/2) - 1),reported,0.04412197006659816,0.008226130012416608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.363636363636363,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.229,0.028,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,25.684925752202915,3.5205577092275497,100*(exp(b - se^2/2) - 1),reported,0.06719138492900319,0.008215540515336635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.178571428571429,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.119,0.022,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,12.60973697478307,2.478013820234376,100*(exp(b - se^2/2) - 1),reported,0.04449514788777382,0.008225993727151461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.409090909090909,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.078,0.012,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,8.10448206408616,1.2973471904040996,100*(exp(b - se^2/2) - 1),reported,0.05344547212497714,0.00822238032691956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.223,0.018,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,24.96181192022986,2.249677032724771,100*(exp(b - se^2/2) - 1),reported,0.10148527141778564,0.008191636257937854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.38888888888889,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.2035,0.043,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,22.455253534829357,5.270446177857189,100*(exp(b - se^2/2) - 1),reported,0.05333870427858621,0.011270586162059986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.732558139534884,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.1091,0.045,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,11.414522882902723,5.018732424686158,100*(exp(b - se^2/2) - 1),reported,0.027353624043755086,0.011282429715572674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4244444444444446,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.119,0.022,,,***,,,low,"construction-period dummy, not age in years; reference category pre-1950; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use",log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,12.60973697478307,2.478013820234376,100*(exp(b - se^2/2) - 1),reported,0.060937076193602335,0.011265677951758415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.409090909090909,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,0.028,0.024,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,2.809954910984347,2.4681496426114204,100*(exp(b - se^2/2) - 1),reported,0.013166620355922715,0.0112856745907909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1666666666666667,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Construction year: 1970-1989 (ref. pre 1950),age,A_structural,dummy,binary,-0.0641,0.031,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-6.253935435749569,2.907524731534,100*(exp(b - se^2/2) - 1),reported,-0.023331532660681394,0.011283580537927038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.067741935483871,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.162,0.038,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,17.501157663158494,4.468268917019799,100*(exp(b - se^2/2) - 1),reported,0.03508186833414055,0.008229080226526794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2631578947368425,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.192,0.049,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,21.021677928991743,5.937185533128012,100*(exp(b - se^2/2) - 1),reported,0.03224763946899186,0.00822986632281563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9183673469387754,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.164,0.038,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,17.736395137546722,4.477214397352345,100*(exp(b - se^2/2) - 1),reported,0.035514434876309814,0.00822895442255959,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.315789473684211,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.118,0.013,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,12.514903225708075,1.4628173447775452,100*(exp(b - se^2/2) - 1),reported,0.07453284853928092,0.008211246025514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.076923076923077,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.088,0.02,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,9.176974624209834,2.183976244056395,100*(exp(b - se^2/2) - 1),reported,0.0362064997264135,0.008228749937821252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.3999999999999995,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.266,0.099,,,**,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,29.8356845513003,12.916877081000587,100*(exp(b - se^2/2) - 1),reported,0.030311819466641594,0.01128146664359969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.686868686868687,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.038,0.107,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,3.2802003065597107,11.114424191799925,100*(exp(b - se^2/2) - 1),reported,0.00400831184146211,0.011286562290432784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35514018691588783,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.164,0.038,,,***,,,low,"construction-period dummy, not age in years; reference category pre-1950; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use",log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,17.736395137546722,4.477214397352345,100*(exp(b - se^2/2) - 1),reported,0.04865313152704938,0.011273286573340709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.315789473684211,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,0.037,0.026,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,3.734233986553681,2.6980018541792083,100*(exp(b - se^2/2) - 1),reported,0.016059703953005506,0.011285197372382247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4230769230769231,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Construction year: 1990-1999 (ref. pre 1950),age,A_structural,dummy,binary,-0.145,0.034,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-13.547754530745815,2.941075796576521,100*(exp(b - se^2/2) - 1),reported,-0.048078590692526055,0.011273600576178527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.26470588235294,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.146,0.073,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,15.411694295378897,8.447532172770812,100*(exp(b - se^2/2) - 1),reported,0.01646606496190767,0.008233032480953835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.196,0.094,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,21.116414452802147,11.435352910142573,100*(exp(b - se^2/2) - 1),reported,0.017166546305415985,0.008232935473005625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0851063829787235,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.14,0.073,,,.,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,14.721297391507715,8.39699873165776,100*(exp(b - se^2/2) - 1),reported,0.01578954967980219,0.008233122333039713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9178082191780825,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.113,0.012,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,11.955132235142996,1.3435583195383032,100*(exp(b - se^2/2) - 1),reported,0.077306193715196,0.008209507297188955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.416666666666666,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.102,0.019,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,10.718360704961638,2.1040285962830736,100*(exp(b - se^2/2) - 1),reported,0.04416125281499638,0.008226115720440503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.368421052631579,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.292,0.119,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,32.965498658668956,15.935325909907593,100*(exp(b - se^2/2) - 1),reported,0.027684365287358854,0.011282326949300354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.453781512605042,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.155,0.128,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,15.81315804183674,14.9460219021456,100*(exp(b - se^2/2) - 1),reported,0.01366615496809662,0.011285598941395919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2109375,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.14,0.073,,,.,,,low,"construction-period dummy, not age in years; reference category pre-1950; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use",log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,14.721297391507715,8.39699873165776,100*(exp(b - se^2/2) - 1),reported,0.021640566740961594,0.011284009800644259,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9178082191780825,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,0.054,0.024,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,5.518066635851482,2.5331630451721923,100*(exp(b - se^2/2) - 1),reported,0.025386784453373425,0.011283015312610412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Construction year: 2000-2009 (ref. pre 1950),age,A_structural,dummy,binary,-0.111,0.031,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-10.549116584453344,2.774310121679996,100*(exp(b - se^2/2) - 1),reported,-0.04038053698590395,0.011277447266333536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5806451612903225,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.216,0.203,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,21.579173704704367,25.194378293713637,100*(exp(b - se^2/2) - 1),reported,0.008761122588300667,0.008233832802893684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0640394088669949,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.33,0.262,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,34.40373071340941,36.443364965751044,100*(exp(b - se^2/2) - 1),reported,0.010370698416320705,0.008233706015381892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2595419847328244,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.205,0.204,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,20.224663229130968,25.041511325248823,100*(exp(b - se^2/2) - 1),reported,0.008274229045712582,0.008233866952806666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0049019607843137,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.04,0.011,,,***,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,4.07478070453251,1.144891851611627,100*(exp(b - se^2/2) - 1),reported,0.029928946045263406,0.008230460162447434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6363636363636367,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.028,0.018,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,2.8229097814428172,1.851112231958565,100*(exp(b - se^2/2) - 1),reported,0.01280762536860484,0.008233473451245968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5555555555555558,14749.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.296,0.105,,,**,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,33.707915525686616,14.116936646736553,100*(exp(b - se^2/2) - 1),reported,0.03180151892506308,0.011280944213282513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.819047619047619,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.288,0.116,,,*,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,32.48139043141578,15.47158472783126,100*(exp(b - se^2/2) - 1),reported,0.02801103947593843,0.01128222423336409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.482758620689655,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.205,0.204,,,,,,low,"construction-period dummy, not age in years; reference category pre-1950; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use",log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,20.224663229130968,25.041511325248823,100*(exp(b - se^2/2) - 1),reported,0.011341250241245375,0.011285927069336862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0049019607843137,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,0.078,0.024,,,**,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,8.081134017640101,2.5946943808081993,100*(exp(b - se^2/2) - 1),reported,0.036656968674672726,0.011279067284514685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Construction year: 2010-2020 (ref. pre 1950),age,A_structural,dummy,binary,-0.024,0.032,,,,,,high,"construction-period dummy, not age in years; reference category pre-1950",log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-2.421402058360256,3.12411427122531,100*(exp(b - se^2/2) - 1),reported,-0.008464686452177277,0.011286248602903037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,7850.0,binary,year_built_not_age,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.143,0.006,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,15.370903471648356,0.6922378809996064,100*(exp(b - se^2/2) - 1),reported,0.19257394934842662,0.008080025846787131,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.833333333333332,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.1,0.008,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,10.513555317211075,0.8841367344605182,100*(exp(b - se^2/2) - 1),reported,0.10238595385961516,0.008190876308769213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.143,0.006,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,15.370903471648356,0.6922378809996064,100*(exp(b - se^2/2) - 1),reported,0.19257394934842662,0.008080025846787131,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.833333333333332,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.242,0.005,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,27.37782704882994,0.6368970964080974,100*(exp(b - se^2/2) - 1),reported,0.3702154250256378,0.007649079029455326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.4,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.181,0.009,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,19.83666444612753,1.0785736613638797,100*(exp(b - se^2/2) - 1),reported,0.16337297139003842,0.0081235179144218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.11111111111111,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.119,0.006,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,12.634964381229462,0.6758219509730118,100*(exp(b - se^2/2) - 1),reported,0.21844572425099582,0.011014070130302311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.833333333333332,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.112,0.007,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,11.848545741565042,0.7829590024515317,100*(exp(b - se^2/2) - 1),reported,0.17771197071360278,0.011106998169600174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.143,0.006,,,***,,,low,bedroom-count category dummy; reference = 1 bedroom; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,15.370903471648356,0.6922378809996064,100*(exp(b - se^2/2) - 1),reported,0.2597643867738408,0.010899205039461853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.833333333333332,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.12,0.006,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,12.747655681870063,0.6764981109476255,100*(exp(b - se^2/2) - 1),reported,0.22019275302527214,0.011009637651263606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Number of bedrooms: 2 (ref. 1),bedrooms,A_structural,dummy,binary,0.092,0.008,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,9.632973896784325,0.8770918576646535,100*(exp(b - se^2/2) - 1),reported,0.12871678318227486,0.011192763754980423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.5,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.182,0.008,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,19.95758068398601,0.9596913551038947,100*(exp(b - se^2/2) - 1),reported,0.18412415679466407,0.008093369529435783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.75,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.122,0.011,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,12.968575372471047,1.2427295119583506,100*(exp(b - se^2/2) - 1),reported,0.09094573485417047,0.00820002527373668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.090909090909092,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.183,0.008,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,20.077598263458274,0.9606515264646646,100*(exp(b - se^2/2) - 1),reported,0.185101257104707,0.008091858234085552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.875,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.375,0.006,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,45.49652250084453,0.8729948487709208,100*(exp(b - se^2/2) - 1),reported,0.45759311420585436,0.00732148982729367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),62.5,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.32,0.01,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,37.70589096691212,1.3771277643359572,100*(exp(b - se^2/2) - 1),reported,0.2547961295766584,0.007962379049270574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.0,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.165,0.01,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,17.933415052967195,1.1793931187113906,100*(exp(b - se^2/2) - 1),reported,0.18308205049265489,0.01109588184803969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.5,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.146,0.011,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,15.712617979919008,1.2729158068558757,100*(exp(b - se^2/2) - 1),reported,0.14815152320497843,0.011162101063388786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.272727272727273,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.183,0.008,,,***,,,low,bedroom-count category dummy; reference = 1 bedroom; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,20.077598263458274,0.9606515264646646,100*(exp(b - se^2/2) - 1),reported,0.24998483781829273,0.010928298921018261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.875,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.166,0.009,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,18.052528948086067,1.0625157915509484,100*(exp(b - se^2/2) - 1),reported,0.20380666878430775,0.011049759150956443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.444444444444446,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Number of bedrooms: 3 (ref. 1),bedrooms,A_structural,dummy,binary,0.154,0.013,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,16.63923224629078,1.5164381528119715,100*(exp(b - se^2/2) - 1),reported,0.1325241335717461,0.011187102184627918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.846153846153847,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,0.226,0.01,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,25.35129880618665,1.2535756652781869,100*(exp(b - se^2/2) - 1),reported,0.18295091569150435,0.008095173260686034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.6,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,0.15,0.013,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,16.173607188254913,1.510384515546768,100*(exp(b - se^2/2) - 1),reported,0.09458347606654768,0.008197234592434132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.538461538461538,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,0.224,0.01,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,25.100846744120364,1.2510710194283623,100*(exp(b - se^2/2) - 1),reported,0.18138537824958534,0.008097561528999346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.4,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,0.419,0.008,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,52.039170147730005,1.216352283832157,100*(exp(b - se^2/2) - 1),reported,0.39600672018691796,0.007560987497602252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.375,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,0.372,0.013,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,45.051040798503486,1.8858228756811064,100*(exp(b - se^2/2) - 1),reported,0.22934295532672716,0.008014673170020034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.615384615384617,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,-0.137,0.009,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-12.806508794693395,0.7847732035188983,100*(exp(b - se^2/2) - 1),reported,-0.169327019503196,0.011123672814078568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.222222222222225,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,-0.135,0.01,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-12.63277730154233,0.8737159116880345,100*(exp(b - se^2/2) - 1),reported,-0.1506312776851143,0.01115787242111958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.5,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,0.224,0.01,,,***,,,low,bedroom-count category dummy; reference = 1 bedroom; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,25.100846744120364,1.2510710194283623,100*(exp(b - se^2/2) - 1),reported,0.24510886304251522,0.010942359957255145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.4,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,-0.145,0.009,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-13.501273958271288,0.7785200637996671,100*(exp(b - se^2/2) - 1),reported,-0.17890671255793433,0.011104554572561442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.11111111111111,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Number of bedrooms: 4+ (ref. 1),bedrooms,A_structural,dummy,binary,-0.109,0.012,,,***,,,high,bedroom-count category dummy; reference = 1 bedroom,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-10.333414476755499,1.0760765009978825,100*(exp(b - se^2/2) - 1),reported,-0.10198587384459472,0.011227802625092995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.083333333333334,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.063,0.019,,,***,,,high,energy passport class E-G vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,6.483461923520073,2.0235509945394803,100*(exp(b - se^2/2) - 1),reported,0.02729253345060239,0.00823108151684834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3157894736842106,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.083,0.024,,,***,,,high,energy passport class E-G vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,8.622892956411254,2.6077003405157715,100*(exp(b - se^2/2) - 1),reported,0.028464892497306646,0.008230812288377826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4583333333333335,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.068,0.018,,,***,,,high,energy passport class E-G vs H-I,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,7.019192334337587,1.9266575552617935,100*(exp(b - se^2/2) - 1),reported,0.031091745400222855,0.008230167900058991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.777777777777778,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.032,0.006,,,***,,,high,energy passport class E-G vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,3.2498920157289835,0.619510503183071,100*(exp(b - se^2/2) - 1),reported,0.043873174581596494,0.008226220234049344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333333,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.155,0.01,,,***,,,high,energy passport class E-G vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,16.759957966661787,1.167657961105125,100*(exp(b - se^2/2) - 1),reported,0.1266023440146963,0.008167893162238472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.5,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.317,0.045,,,***,,,high,energy passport class E-G vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,37.16131103550055,6.178511573664564,100*(exp(b - se^2/2) - 1),reported,0.07925807730502528,0.011251146620587182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.044444444444445,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.25,0.048,,,***,,,high,energy passport class E-G vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,28.254707109827002,6.163322000101159,100*(exp(b - se^2/2) - 1),reported,0.05868334416172844,0.011267202079051861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.208333333333333,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.068,0.018,,,***,,,low,energy passport class E-G vs H-I; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,7.019192334337587,1.9266575552617935,100*(exp(b - se^2/2) - 1),reported,0.04259976031142777,0.011276407141260292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.777777777777778,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.032,0.01,,,**,,,high,energy passport class E-G vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,3.2465880720478513,1.0325175053051183,100*(exp(b - se^2/2) - 1),reported,0.03609375573809582,0.011279298668154943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Energy Efficiency Class: medium (E-G) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.243,0.012,,,***,,,high,energy passport class E-G vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,27.49768224824216,1.5300823489421518,100*(exp(b - se^2/2) - 1),reported,0.22280932391732255,0.011002929576164076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.25,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.157,0.061,,,*,,,high,energy passport class A-D vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,16.782086075268545,7.1369732447955725,100*(exp(b - se^2/2) - 1),reported,0.02118805165382723,0.008232300324098479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5737704918032787,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.268,0.077,,,***,,,high,energy passport class A-D vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,30.34772483993431,10.066572978115008,100*(exp(b - se^2/2) - 1),reported,0.02864735312473659,0.00823076936792805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.480519480519481,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.141,0.06,,,*,,,high,energy passport class A-D vs H-I,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,14.935394780762001,6.9085478879084645,100*(exp(b - se^2/2) - 1),reported,0.019346628076281472,0.008232607692034669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3499999999999996,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.132,0.007,,,***,,,high,energy passport class A-D vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,14.10803624558854,0.7987758234870646,100*(exp(b - se^2/2) - 1),reported,0.15343392474270154,0.008136647524234172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.857142857142858,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.217,0.011,,,***,,,high,energy passport class A-D vs H-I,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,24.22689425931839,1.3665785123515402,100*(exp(b - se^2/2) - 1),reported,0.16033577201651952,0.00812761977963924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.727272727272727,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.358,0.106,,,***,,,high,energy passport class A-D vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,42.24517965331391,15.162935577086918,100*(exp(b - se^2/2) - 1),reported,0.0380914086269604,0.011278461772228498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3773584905660377,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,True
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.285,0.116,,,*,,,high,energy passport class A-D vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,32.08454183065908,15.425239526209095,100*(exp(b - se^2/2) - 1),reported,0.027719483189996105,0.011282315965051049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4568965517241375,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.141,0.06,,,*,,,low,energy passport class A-D vs H-I; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,14.935394780762001,6.9085478879084645,100*(exp(b - se^2/2) - 1),reported,0.026514309644288303,0.011282684955016302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3499999999999996,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.123,0.013,,,***,,,high,energy passport class A-D vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,13.078886525120437,1.470149747231736,100*(exp(b - se^2/2) - 1),reported,0.10618535325422836,0.011222842213861535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.461538461538462,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Energy Efficiency Class: high (A-D) (ref. low H-I),energy_efficiency,A_structural,dummy,binary,0.29,0.016,,,***,,,high,energy passport class A-D vs H-I,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,33.62564362545517,2.138283980840755,100*(exp(b - se^2/2) - 1),reported,0.20041986782221133,0.011057647879846142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.125,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,0.009,0.014,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,0.8941740638188866,1.412656870483415,100*(exp(b - se^2/2) - 1),reported,0.00529330722866528,0.008234033466812658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6428571428571428,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,-0.043,0.018,,,*,,,high,state of dwelling: new-build vs planned (off-plan),log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-4.224377900912568,1.724240502120655,100*(exp(b - se^2/2) - 1),reported,-0.019666662220070415,0.00823255627816901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.388888888888889,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,0.003,0.014,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,0.29062148782059705,1.4042063063047279,100*(exp(b - se^2/2) - 1),reported,0.001764457715700655,0.008234136006603056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21428571428571427,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,0.016,0.015,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,1.601437735893474,1.5241930281091425,100*(exp(b - se^2/2) - 1),reported,0.008782753323154825,0.008233831240457649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0666666666666667,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,-0.021,0.024,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-2.1063009886065798,2.350125514966703,100*(exp(b - se^2/2) - 1),reported,-0.007204693224839902,0.008233935114102746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.875,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,-0.006,0.019,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-0.6161439996873797,1.8886341317024768,100*(exp(b - se^2/2) - 1),reported,-0.0035641835589396826,0.01128658126997566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31578947368421056,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,-0.029,0.022,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-2.8818589875073575,2.1371162218265303,100*(exp(b - se^2/2) - 1),reported,-0.014876214380126363,0.011285404012509654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3181818181818183,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,-0.005,0.014,,,,,,low,state of dwelling: new-build vs planned (off-plan); column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,-0.5085027252384688,1.3930174708697554,100*(exp(b - se^2/2) - 1),reported,-0.0040309147374917,0.011286561264976758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35714285714285715,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,0.004,0.02,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,0.3807229154028091,2.008016021354684,100*(exp(b - se^2/2) - 1),reported,0.0022573248407936368,0.011286624203968181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Dwelling State: new-build (ref. planned),condition,A_structural,dummy,binary,0.003,0.026,,,,,,high,state of dwelling: new-build vs planned (off-plan),log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,0.2665546268023622,2.6078117117087802,100*(exp(b - se^2/2) - 1),reported,0.0013023050063752691,0.011286643388585664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11538461538461539,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.032,0.007,,,***,,,high,state of dwelling: good vs planned,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-3.1517146041392663,0.6779546074554383,100*(exp(b - se^2/2) - 1),reported,-0.03761518402395636,0.008228321505240456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.571428571428571,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.085,0.009,,,***,,,high,state of dwelling: good vs planned,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-8.152491459277622,0.8266610559613116,100*(exp(b - se^2/2) - 1),reported,-0.07753286674996972,0.008209362361761499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.444444444444446,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.038,0.007,,,***,,,high,state of dwelling: good vs planned,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-3.7310645286920407,0.6738990586238397,100*(exp(b - se^2/2) - 1),reported,-0.044655075547102845,0.008225934969203156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.428571428571428,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.044,0.007,,,***,,,high,state of dwelling: good vs planned,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-4.306948761170792,0.6698677702311328,100*(exp(b - se^2/2) - 1),reported,-0.05168832084118772,0.008223141952007138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.285714285714286,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.065,0.011,,,***,,,high,state of dwelling: good vs planned,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-6.298922748921488,1.0307742097151438,100*(exp(b - se^2/2) - 1),reported,-0.04859884057869502,0.00822441917485608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.90909090909091,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.033,0.01,,,**,,,high,state of dwelling: good vs planned,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-3.25098161295444,0.967538559589032,100*(exp(b - se^2/2) - 1),reported,-0.03722014668274072,0.011278832328103247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3000000000000003,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.02,0.011,,,.,,,high,state of dwelling: good vs planned,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-1.986062691912982,1.0782185406374307,100*(exp(b - se^2/2) - 1),reported,-0.020516867631437938,0.011284277197290866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8181818181818183,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.038,0.007,,,***,,,low,state of dwelling: good vs planned; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,-3.7310645286920407,0.6738990586238397,100*(exp(b - se^2/2) - 1),reported,-0.06115571812971836,0.011265527023895489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.428571428571428,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,-0.039,0.01,,,***,,,high,state of dwelling: good vs planned,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-3.8297377186922277,0.9617507091463668,100*(exp(b - se^2/2) - 1),reported,-0.043975364274581104,0.011275734429379772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Dwelling State: good (ref. planned),condition,A_structural,dummy,binary,0.04,0.013,,,**,,,high,state of dwelling: good vs planned,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,4.07228293976889,1.3530540064501047,100*(exp(b - se^2/2) - 1),reported,0.034707240007530636,0.011279853002447457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.076923076923077,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.054,0.005,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-5.257973632551516,0.47371605325089916,100*(exp(b - se^2/2) - 1),reported,-0.0885792400123301,0.008201781482623157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.799999999999999,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.078,0.006,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-7.505222266629518,0.5549786559261236,100*(exp(b - se^2/2) - 1),reported,-0.10643587894401649,0.008187375303385884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.059,0.005,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-5.730501460386606,0.47135338457854986,100*(exp(b - se^2/2) - 1),reported,-0.09670753793459366,0.008195554062253701,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.799999999999999,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c4,A.2,4 (S.Alt.3),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.058,0.005,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised sales price (EUR),log,OLS,14759.0,0.809,se,"Sales adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-5.636184811382206,0.4718249737183993,100*(exp(b - se^2/2) - 1),reported,-0.09508337264825192,0.008196842469676889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.6,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c5,A.2,5 (S.Alt.4),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.073,0.008,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised sales price (EUR),log,OLS,14759.0,0.507,se,"Sales adverts 2018, Luxembourg; physical characteristics only; n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",pct_premium,-7.042891672481755,0.7436806640206342,100*(exp(b - se^2/2) - 1),reported,-0.07492540961375312,0.008211003793288013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.125,14749.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.059,0.008,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-5.732339697685251,0.7541654153256798,100*(exp(b - se^2/2) - 1),reported,-0.08295218450695516,0.01124775383145155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.374999999999999,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.052,0.009,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-5.070958019765969,0.8543959801586005,100*(exp(b - se^2/2) - 1),reported,-0.06507355435114054,0.011262730560774323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.777777777777778,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.059,0.005,,,***,,,low,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use,log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",pct_premium,-5.730501460386606,0.47135338457854986,100*(exp(b - se^2/2) - 1),reported,-0.13201682185799632,0.011187866259152232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.799999999999999,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c9,A.2,9 (R.Alt.3),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.063,0.008,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised monthly rent (EUR),log,OLS,7860.0,0.742,se,"Rent adverts 2018, Luxembourg; physical characteristics + locality dummies (no interaction); n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-6.108657202129086,0.7511547789513066,100*(exp(b - se^2/2) - 1),reported,-0.08853336969267049,0.01124233265938673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.875,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c10,A.2,10 (R.Alt.4),52,Dwelling State: average (ref. planned),condition,A_structural,dummy,binary,-0.038,0.011,,,***,,,high,state of dwelling: average vs planned; 'average' assigned when advert mentions neither improvements nor repairs,log advertised monthly rent (EUR),log,OLS,7860.0,0.526,se,"Rent adverts 2018, Luxembourg; physical characteristics only; n from Table 2; r2 is adjusted R2 from Table 5",pct_premium,-3.7345301479874915,1.0589842349803193,100*(exp(b - se^2/2) - 1),reported,-0.03896065215402182,0.011278083518269475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4545454545454546,7850.0,binary,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,True,True,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c1,A.2,1 (S.Main),52,Dwelling Type x Surface,other,other,other,m2,-0.003,0.0,,,***,,,high,interaction term house dummy x surface (m2); only in models with interactions; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,OLS,14759.0,0.824,se,"Sales adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",other,-0.003,,not harmonised,,,,,,14749.0,m2,nonstandard_x_transform,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Dwelling Type x Surface,other,other,other,m2,-0.004,0.0,,,***,,,high,interaction term house dummy x surface (m2); only in models with interactions; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",other,-0.004,,not harmonised,,,,,,14749.0,m2,nonstandard_x_transform,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c3,A.2,3 (S.Alt.2),52,Dwelling Type x Surface,other,other,other,m2,-0.003,0.0,,,***,,,high,interaction term house dummy x surface (m2); only in models with interactions; SE printed as 0.000 (rounded),log advertised sales price (EUR),log,other,14759.0,0.819,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",other,-0.003,,not harmonised,,,,,,14749.0,m2,nonstandard_x_transform,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c6,A.2,6 (R.Main),52,Dwelling Type x Surface,other,other,other,m2,0.0,0.0,,,,,,high,interaction term house dummy x surface (m2); only in models with interactions; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,OLS,7860.0,0.744,se,"Rent adverts 2018, Luxembourg; physical characteristics + dwelling type x surface interaction + locality dummies (composite geography); n from Table 2; r2 is adjusted R2 from Table 5",other,0.0,,not harmonised,,,,,,7850.0,m2,nonstandard_x_transform,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Dwelling Type x Surface,other,other,other,m2,0.0,0.0,,,,,,high,interaction term house dummy x surface (m2); only in models with interactions; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",other,0.0,,not harmonised,,,,,,7850.0,m2,nonstandard_x_transform,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c8,A.2,8 (R.Alt.2),52,Dwelling Type x Surface,other,other,other,m2,-0.003,0.0,,,***,,,low,interaction term house dummy x surface (m2); only in models with interactions; column appears to be a duplicate of S.Alt.2 (typesetting error) - do not use; SE printed as 0.000 (rounded),log advertised monthly rent (EUR),log,other,7860.0,0.728,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + linked-neighbour Markov random field spline (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5; NOTE: printed R.Alt.2 column is numerically identical to S.Alt.2 (intercept 12.252) - apparent typesetting error",other,-0.003,,not harmonised,,,,,,7850.0,m2,nonstandard_x_transform,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,other,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c2,A.2,2 (S.Alt.1),52,Distance to Capital,cbd_distance,D_location,level,km,-0.014,0.0,,,***,,,medium,"approximate travel distance from dwelling to Luxembourg City (Glaesener and Caruso 2015); unit not printed in table, assumed km; SE printed as 0.000 (rounded)",log advertised sales price (EUR),log,OLS,14759.0,0.703,se,"Sales adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n is the number of adverts in Table 2 (5 Cook's-distance outliers dropped for sales per text, not reported in table); r2 is adjusted R2 from Table 5",semi_elasticity,-1.4000000000000001,,100*b/1 (X per km->km),,,,,,14749.0,km,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43,17f8f1569fa95f6eb5e22b566b1a1962d7ce1a43:TA2-c7,A.2,7 (R.Alt.1),52,Distance to Capital,cbd_distance,D_location,level,km,-0.013,0.0,,,***,,,medium,"approximate travel distance from dwelling to Luxembourg City (Glaesener and Caruso 2015); unit not printed in table, assumed km; SE printed as 0.000 (rounded)",log advertised monthly rent (EUR),log,OLS,7860.0,0.679,se,"Rent adverts 2018, Luxembourg; physical characteristics + interaction + travel distance to Luxembourg City (no locality dummies); n from Table 2; r2 is adjusted R2 from Table 5",semi_elasticity,-1.3,,100*b/1 (X per km->km),,,,,,7850.0,km,,2024.0,Luxembourg,,,2018.0,2018.0,mixed,listing,journal,False,True,unknown,,2018.0,ols,journal,False,False,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,C(GSHP)[T.True],heating,A_structural,binary,binary,0.0519,0.006,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,5.325145150906163,0.63196224612349,100*(exp(b - se^2/2) - 1),reported,0.06263378020388717,0.007240899445536088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.65,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0126,0.0046,,,***,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,1.2669000354148308,0.46583266864647077,100*(exp(b - se^2/2) - 1),reported,0.019868863732984937,0.007253712156486565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.739130434782609,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.0474,0.0062,,,***,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,4.85211889733097,0.650095631881422,100*(exp(b - se^2/2) - 1),reported,0.05538162180019943,0.00724400960255773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.645161290322581,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.0228,0.0041,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,-2.25502596830085,0.40075776188479967,100*(exp(b - se^2/2) - 1),reported,-0.040312884045480404,0.00724924669238902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.560975609756097,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,Sqm,living_area,A_structural,level,m2,0.0039,0.0,,,***,,,high,Floor area in square metres; SE printed as 0.0000,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",semi_elasticity,0.38999999999999996,,100*b/1 (X per m2->m2),,,,,,18998.0,m2,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,False,False,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,Age,age,A_structural,level,years,-0.0051,0.0001,,,***,,,high,Building age in years,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",semi_elasticity,-0.51,0.01,100*b/1 (per year),reported,-0.3470190798363288,0.00680429568306527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-51.0,18998.0,years,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,Distance to CBD,cbd_distance,D_location,level,km,-0.0161,0.0015,,,***,,,high,Kilometres to CBD (Table 1),log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",semi_elasticity,-1.6099999999999999,0.15,100*b/1 (X per km->km),reported,-0.0776368425146061,0.007233246197012992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.733333333333333,18998.0,km,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,C(condition)[T.excellent],condition,A_structural,dummy,ordinal,0.3189,0.02,,,***,,,high,Condition excellent vs decent,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,37.533866141118686,2.751227513304152,100*(exp(b - se^2/2) - 1),reported,0.11491688783380577,0.007207079826516511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.945,18998.0,ordinal,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,C(condition)[T.good],condition,A_structural,dummy,ordinal,0.18,0.0037,,,***,,,high,Condition good vs decent,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,19.720916819700673,0.4429704243550697,100*(exp(b - se^2/2) - 1),reported,0.33283000606237884,0.006841505680171121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.648648648648646,18998.0,ordinal,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,C(condition)[T.poor],condition,A_structural,dummy,ordinal,-0.2987,0.008,,,***,,,high,Condition poor vs decent,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,-25.82418260100002,0.5934255285050813,100*(exp(b - se^2/2) - 1),reported,-0.26146549109358697,0.007002758382151643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.3375,18998.0,ordinal,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-A,4,GSHP (A),8,C(lot_ownership)[T.rented],other,other,binary,binary,-0.0561,0.004,,,***,,,high,Leasehold (rented lot) vs owned lot,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec A: GSHP true/false; full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,-5.456297682209421,0.3781778346817381,100*(exp(b - se^2/2) - 1),reported,-0.10123069032802465,0.00721787453319249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.024999999999999,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,False,False,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,Sqm,living_area,A_structural,level,m2,0.0039,0.0,,,***,,,high,Floor area in square metres; SE printed as 0.0000,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",semi_elasticity,0.38999999999999996,,100*b/1 (X per m2->m2),,,,,,18998.0,m2,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,False,False,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,Age,age,A_structural,level,years,-0.0049,0.0001,,,***,,,high,Building age in years,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",semi_elasticity,-0.49,0.01,100*b/1 (per year),reported,-0.33496493061694127,0.006836018992182475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-48.99999999999999,18998.0,years,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,Distance to CBD,cbd_distance,D_location,level,km,-0.016,0.0015,,,***,,,high,Kilometres to CBD (Table 1),log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",semi_elasticity,-1.6,0.15,100*b/1 (X per km->km),reported,-0.07715750578855231,0.0072335161676767784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.666666666666666,18998.0,km,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(condition)[T.excellent],condition,A_structural,dummy,ordinal,0.317,0.02,,,***,,,high,Condition excellent vs decent,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,37.27279988692946,2.7460051438509177,100*(exp(b - se^2/2) - 1),reported,0.11424117737141508,0.0072076452600261885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.85,18998.0,ordinal,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(condition)[T.good],condition,A_structural,dummy,ordinal,0.1791,0.0037,,,***,,,high,Condition good vs decent,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,19.613216466991435,0.44257193032236336,100*(exp(b - se^2/2) - 1),reported,0.3313489750744137,0.00684528870896332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.4054054054054,18998.0,ordinal,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(condition)[T.poor],condition,A_structural,dummy,ordinal,-0.2995,0.008,,,***,,,high,Condition poor vs decent,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,-25.883499524986053,0.5929509779278176,100*(exp(b - se^2/2) - 1),reported,-0.2621177138467971,0.007001474827293412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.4375,18998.0,ordinal,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T4-B,4,Heating type (B),8,C(lot_ownership)[T.rented],other,other,binary,binary,-0.0593,0.0041,,,***,,,high,Leasehold (rented lot) vs owned lot,log sales price (EUR),log,OLS,19008.0,0.851,se,"Spec B: heating type categories (direct electricity omitted); full controls, year+month FE, postcode FE, neighbourhood characteristics; adj. R2",pct_premium,-5.758392149651259,0.38639383981300535,100*(exp(b - se^2/2) - 1),reported,-0.10436116445738293,0.007215527390813999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.46341463414634,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,False,False,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-A-c1,5,A-1,9,C(GSHP)[T.True],heating,A_structural,binary,binary,0.2175,0.014,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.1309,se,"Spec A (GSHP true/false), sensitivity column 1: year+month FE only; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,24.284362486416434,1.7401516013108234,100*(exp(b - se^2/2) - 1),reported,0.11200461896270085,0.007209492714840515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.535714285714285,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c1,5,B-1,9,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0661,0.0089,,,***,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.148,se,"Spec B (heating type categories), sensitivity column 1: year+month FE only; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,6.82912346668243,0.9508168552093291,100*(exp(b - se^2/2) - 1),reported,0.05380565808885141,0.007244634750238691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.426966292134832,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c1,5,B-1,9,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.2023,0.0142,,,***,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.148,se,"Spec B (heating type categories), sensitivity column 1: year+month FE only; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,22.40917984358861,1.7383856089812928,100*(exp(b - se^2/2) - 1),reported,0.10281252909093981,0.0072166975437041305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.246478873239436,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c1,5,B-1,9,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.1354,0.0089,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.148,se,"Spec B (heating type categories), sensitivity column 1: year+month FE only; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,-12.66680937837763,0.777296180738069,100*(exp(b - se^2/2) - 1),reported,-0.10970975003400231,0.007211349891452146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.213483146067414,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-A-c2,5,A-2,9,C(GSHP)[T.True],heating,A_structural,binary,binary,0.0976,0.0096,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.6179,se,"Spec A (GSHP true/false), sensitivity column 2: + postcode FE; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,10.247088519084091,1.0584208206909342,100*(exp(b - se^2/2) - 1),reported,0.07356079719335024,0.007235488248526251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.166666666666668,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c2,5,B-2,9,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0394,0.0071,,,***,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.6228,se,"Spec B (heating type categories), sensitivity column 2: + postcode FE; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,4.016025746665353,0.7385323972758076,100*(exp(b - se^2/2) - 1),reported,0.040228351206604465,0.007249271410327202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.549295774647887,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c2,5,B-2,9,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.081,0.0097,,,***,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.6228,se,"Spec B (heating type categories), sensitivity column 2: + postcode FE; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,8.431988353789466,1.0518397696697577,100*(exp(b - se^2/2) - 1),reported,0.060473314850933005,0.007241866099432718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.350515463917526,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c2,5,B-2,9,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.0832,0.0064,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.6228,se,"Spec B (heating type categories), sensitivity column 2: + postcode FE; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,-7.985174943266548,0.588906941053745,100*(exp(b - se^2/2) - 1),reported,-0.09390014888741624,0.007223088375955095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.999999999999998,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-A-c3,5,A-3,9,C(GSHP)[T.True],heating,A_structural,binary,binary,0.0898,0.0094,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.6332,se,"Spec A (GSHP true/false), sensitivity column 3: + neighbourhood characteristics; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,9.39071408454144,1.0283181424866663,100*(exp(b - se^2/2) - 1),reported,0.06914390400249462,0.007237780597143089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.553191489361703,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c3,5,B-3,9,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0353,0.007,,,***,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.6368,se,"Spec B (heating type categories), sensitivity column 3: + neighbourhood characteristics; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,3.5905061334904342,0.7251513089238676,100*(exp(b - se^2/2) - 1),reported,0.036562193972489596,0.007250293422306718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.042857142857143,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c3,5,B-3,9,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.0762,0.0096,,,***,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.6368,se,"Spec B (heating type categories), sensitivity column 3: + neighbourhood characteristics; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,7.9128660858402755,1.036011252722693,100*(exp(b - se^2/2) - 1),reported,0.05749245488887264,0.007243143923007575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.937500000000001,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c3,5,B-3,9,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.071,0.0063,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.6368,se,"Spec B (heating type categories), sensitivity column 3: + neighbourhood characteristics; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,-6.8556592550242375,0.5868209920403831,100*(exp(b - se^2/2) - 1),reported,-0.08149237434578006,0.007231013498287527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.269841269841269,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-A-c4a,5,A-4a,9,C(GSHP)[T.True],heating,A_structural,binary,binary,0.0555,0.0064,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.8282,se,"Spec A (GSHP true/false), sensitivity column 4a: full model excluding age; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,5.704736852063519,0.6765241709263506,100*(exp(b - se^2/2) - 1),reported,0.062791551148654,0.007240827519844785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.671875,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4a,5,B-4a,9,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0262,0.0049,,,***,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.8303,se,"Spec B (heating type categories), sensitivity column 4a: full model excluding age; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,2.6533913577719392,0.5030076562237489,100*(exp(b - se^2/2) - 1),reported,0.038763656299617845,0.0072496914453483744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.346938775510204,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4a,5,B-4a,9,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.0436,0.0066,,,***,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.8303,se,"Spec B (heating type categories), sensitivity column 4a: full model excluding age; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,4.454169516806017,0.6894125340523948,100*(exp(b - se^2/2) - 1),reported,0.047872970731610204,0.007246825844693288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.606060606060606,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4a,5,B-4a,9,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.0564,0.0043,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.8303,se,"Spec B (heating type categories), sensitivity column 4a: full model excluding age; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,-5.484774210392917,0.4064192282237071,100*(exp(b - se^2/2) - 1),reported,-0.09473253857602182,0.007222516238952018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.116279069767442,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-A-c4b,5,A-4b,9,C(GSHP)[T.True],heating,A_structural,binary,binary,0.0363,0.0067,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.8119,se,"Spec A (GSHP true/false), sensitivity column 4b: full model excluding condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,3.6943615438404187,0.6947678162323632,100*(exp(b - se^2/2) - 1),reported,0.039277390454697594,0.007249545896597077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.4179104477611935,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4b,5,B-4b,9,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0121,0.0051,,,**,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.8124,se,"Spec B (heating type categories), sensitivity column 4b: full model excluding condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,1.2160337924706166,0.5162084855893022,100*(exp(b - se^2/2) - 1),reported,0.017210636127247107,0.007254069772641345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3725490196078427,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4b,5,B-4b,9,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.0297,0.0069,,,***,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.8124,se,"Spec B (heating type categories), sensitivity column 4b: full model excluding condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,3.012092163873281,0.7108003563318123,100*(exp(b - se^2/2) - 1),reported,0.031213448428566933,0.007251609230879185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.304347826086957,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4b,5,B-4b,9,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.0305,0.0046,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.8124,se,"Spec B (heating type categories), sensitivity column 4b: full model excluding condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,-3.004983005987394,0.44618179875091685,100*(exp(b - se^2/2) - 1),reported,-0.04804919908931362,0.0072467644528145135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.630434782608695,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-A-c4c,5,A-4c,9,C(GSHP)[T.True],heating,A_structural,binary,binary,0.0374,0.0076,,,***,,,high,Ground source heat pump vs all other heating types,log sales price (EUR),log,OLS,19008.0,0.7609,se,"Spec A (GSHP true/false), sensitivity column 4c: full model excluding age and condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,3.80782009416496,0.7889622176154829,100*(exp(b - se^2/2) - 1),reported,0.03568021369967563,0.007250524709024995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.921052631578948,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4c,5,B-4c,9,C(heating_type)[T.district_heating],heating,A_structural,binary,binary,0.0338,0.0057,,,***,,,high,District heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.7658,se,"Spec B (heating type categories), sensitivity column 4c: full model excluding age and condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,3.436090716766338,0.5895952949833385,100*(exp(b - se^2/2) - 1),reported,0.04298197460245034,0.00724843950396352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.929824561403508,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4c,5,B-4c,9,C(heating_type)[T.GSHP],heating,A_structural,binary,binary,0.0184,0.0077,,,**,,,high,Ground source heat pump vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.7658,se,"Spec B (heating type categories), sensitivity column 4c: full model excluding age and condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,1.854012797459137,0.784299148744072,100*(exp(b - se^2/2) - 1),reported,0.01733436343964642,0.007254054265504207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3896103896103895,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25,18a3ce8a193301208557e0d6f2d027166917cb25:T5-B-c4c,5,B-4c,9,C(heating_type)[T.oil],heating,A_structural,binary,binary,-0.0874,0.0051,,,***,,,high,Oil heating vs direct electricity,log sales price (EUR),log,OLS,19008.0,0.7658,se,"Spec B (heating type categories), sensitivity column 4c: full model excluding age and condition; adj. R2; column 4 = Table 4 (not duplicated)",pct_premium,-8.37014184212508,0.4673183540408378,100*(exp(b - se^2/2) - 1),reported,-0.12338323981706109,0.007199708501910888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.137254901960784,18998.0,binary,,2023.0,Finland,Europe,High income,1999.0,2018.0,single_family,transaction,journal,True,True,unknown,,2008.5,ols,journal,True,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T2-c1,2,1 Binary,8,Treated × Construction,transit_distance,D_location,other,binary,-0.003,0.002,,0.063,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 2 km of Elizabeth Line station x construction period (2007-2015); p=0.063 reported in text,"ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Construction window 2000-2015, Post=1 from 2007; treated = within 2 km of an Elizabeth Line station; property and year FE; macro controls (inflation, interest rate, GDP growth) not extracted",other,-0.003,0.002,not harmonised,reported,-0.0020378699625795854,0.001358579975053057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T2-c2,2,2 Distance bands,8,Within 1 km × Construction,transit_distance,D_location,other,binary,0.049,0.003,,,,,,high,"DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 1 km band x construction period, ref > 10 km; text: significant at 1%","ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Construction window 2000-2015, Post=1 from 2007; distance-band indicators x Post, reference > 10 km; property and year FE",other,0.049,0.003,not harmonised,reported,0.022184724414458745,0.0013582484335382906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.333333333333332,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T2-c2,2,2 Distance bands,8,1–2 km × Construction,transit_distance,D_location,other,binary,0.053,0.002,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; 1-2 km band x construction period; text: significant at 1%,"ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Construction window 2000-2015, Post=1 from 2007; distance-band indicators x Post, reference > 10 km; property and year FE",other,0.053,0.002,not harmonised,reported,0.03597913400290571,0.0013577031699209701,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.5,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T2-c2,2,2 Distance bands,8,2–5 km × Construction,transit_distance,D_location,other,binary,0.081,0.002,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; 2-5 km band x construction period; text: significant at 1%,"ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Construction window 2000-2015, Post=1 from 2007; distance-band indicators x Post, reference > 10 km; property and year FE",other,0.081,0.002,not harmonised,reported,0.05493950178137328,0.0013565309081820564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.5,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T2-c2,2,2 Distance bands,8,5–10 km × Construction,transit_distance,D_location,other,binary,0.077,0.002,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; 5-10 km band x construction period; text: significant at 1%,"ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Construction window 2000-2015, Post=1 from 2007; distance-band indicators x Post, reference > 10 km; property and year FE",other,0.077,0.002,not harmonised,reported,0.05223403398290978,0.001356728155400254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.5,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T3-c1,3,1 Binary,10,Treated × Opening,transit_distance,D_location,other,binary,-0.014,0.004,,0.001,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 2 km x post-opening (2022-2024); p=0.001 reported in text,"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Opening window 2016-2024, Post=1 from 2022; treated = within 2 km; property and year FE",other,-0.014,0.004,not harmonised,reported,-0.008486369777660319,0.0024246770793315197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T3-c2,3,2 Distance bands,10,Within 1 km × Opening,transit_distance,D_location,other,binary,-0.02,0.007,,0.005,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 1 km band x post-opening; p=0.005 in text,"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Opening window 2016-2024, Post=1 from 2022; distance bands x Post, reference > 10 km; property and year FE",other,-0.02,0.007,not harmonised,reported,-0.006927732021968624,0.0024247062076890188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.857142857142857,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T3-c2,3,2 Distance bands,10,1–2 km × Opening,transit_distance,D_location,other,binary,-0.012,0.005,,0.024,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; 1-2 km band x post-opening; p=0.024 in text,"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Opening window 2016-2024, Post=1 from 2022; distance bands x Post, reference > 10 km; property and year FE",other,-0.012,0.005,not harmonised,reported,-0.005819336010106567,0.002424723337544403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T3-c2,3,2 Distance bands,10,2–5 km × Opening,transit_distance,D_location,other,binary,-0.001,0.004,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; 2-5 km band x post-opening; text: not significant,"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Opening window 2016-2024, Post=1 from 2022; distance bands x Post, reference > 10 km; property and year FE",other,-0.001,0.004,not harmonised,reported,-0.0006061909873264648,0.002424763949305859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T3-c2,3,2 Distance bands,10,5–10 km × Opening,transit_distance,D_location,other,binary,-0.003,0.004,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; 5-10 km band x post-opening; text: not significant,"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Opening window 2016-2024, Post=1 from 2022; distance bands x Post, reference > 10 km; property and year FE",other,-0.003,0.004,not harmonised,reported,-0.0018185702889193529,0.002424760385225804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T4-c1,4,1 Construction,11,Treated × Post,transit_distance,D_location,other,binary,-0.038,0.004,,,,,,high,"DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 2 km x post, for properties without prior TfL access (TfL=0); text: p<0.001","ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Heterogeneity by pre-existing TfL access, construction phase 2007-2015; property and year FE, macro and amenity controls",other,-0.038,0.004,not harmonised,reported,-0.012905461720231454,0.0013584696547612055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.5,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T4-c1,4,1 Construction,11,TfL × Post,transit_distance,D_location,other,binary,0.105,0.001,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 1 km of a non-Elizabeth TfL station x post (construction),"ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Heterogeneity by pre-existing TfL access, construction phase 2007-2015; property and year FE, macro and amenity controls",other,0.105,0.001,not harmonised,reported,0.14122154581442575,0.0013449671029945307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),105.0,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T4-c1,4,1 Construction,11,Treated × TfL × Post,transit_distance,D_location,other,binary,0.021,0.006,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; triple interaction treated x prior TfL access x post; text: p<0.001,"ln(nominal sale price), GBP",log,FE_panel,541796.0,,se,"Heterogeneity by pre-existing TfL access, construction phase 2007-2015; property and year FE, macro and amenity controls",other,0.021,0.006,not harmonised,reported,0.004754986030559336,0.0013585674373026676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,541786.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T4-c2,4,2 Opening,11,Treated × Post,transit_distance,D_location,other,binary,-0.021,0.01,,,,,,high,"DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 2 km x post-opening, properties without prior TfL access","ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Heterogeneity by pre-existing TfL access, opening phase 2022-2024; property and year FE, macro and amenity controls",other,-0.021,0.01,not harmonised,reported,-0.005091939216326629,0.0024247329601555373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T4-c2,4,2 Opening,11,TfL × Post,transit_distance,D_location,other,binary,-0.034,0.003,,,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; within 1 km of a non-Elizabeth TfL station x post (opening),"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Heterogeneity by pre-existing TfL access, opening phase 2022-2024; property and year FE, macro and amenity controls",other,-0.034,0.003,not harmonised,reported,-0.02747029249704886,0.0024238493379748994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.333333333333334,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6,1908d2ee0b0995f38129f51d7446a3626531b8d6:T4-c2,4,2 Opening,11,Treated × TfL × Post,transit_distance,D_location,other,binary,0.015,0.013,,0.25,,,,high,DiD interaction (treatment-by-post); not a cross-sectional hedonic characteristic; triple interaction; p=0.25 in text,"ln(nominal sale price), GBP",log,FE_panel,170093.0,,se,"Heterogeneity by pre-existing TfL access, opening phase 2022-2024; property and year FE, macro and amenity controls",other,0.015,0.013,not harmonised,reported,0.002797794120799082,0.0024247549046925373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.153846153846154,170083.0,binary,nonstandard_x_transform,2026.0,United Kingdom,Europe,High income,2000.0,2024.0,mixed,transaction,journal,True,True,clustered,,2012.0,panel,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDHOS: Shortest distance to the nearest hospital,hospital_distance,D_location,level,m,-0.031,0.01,-3.258,0.001,***,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,-0.031,0.01,"b (currency per unit, as reported)",reported,-0.08287059924799695,0.025436034146100964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.258,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDPRK: Shortest distance to the nearest park or green space,green_space,E_environment,level,m,0.036,0.012,3.086,0.0,***,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,0.036,0.012,"b (currency per unit, as reported)",reported,0.0785233240905555,0.025445017527723754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.086,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDSCH: Shortest distance to the nearest school,school_distance,D_location,level,m,0.036,0.015,2.4,0.002,**,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,0.036,0.015,"b (currency per unit, as reported)",reported,0.0611425777038917,0.02547607404328821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDUNI: Shortest distance to the nearest university,university_distance,D_location,level,m,-0.014,0.005,-3.034,0.0,***,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,-0.014,0.005,"b (currency per unit, as reported)",reported,-0.0772081377311811,0.025447639331305574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.034,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDSHP: Shortest distance to the nearest shopping center,commercial_distance,D_location,level,m,0.008,0.008,1.024,0.257,,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,0.008,0.008,"b (currency per unit, as reported)",reported,0.02612747745221636,0.02551511469943004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.024,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDCENL: Shortest distance to the nearest local People's committee location,public_services,F_neighbourhood,level,m,-0.015,0.004,-4.385,0.0,***,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre; distance to district-level People's Committee (local government office)",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,-0.015,0.004,"b (currency per unit, as reported)",reported,-0.11122750665878361,0.025365451917624543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.385,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931,1956ccee53765fb417ccdc3f95db189441def931:T2-OLS,2,OLS,15,SDCEN: Shortest distance to Hanoi People's committee location,cbd_distance,D_location,level,m,-0.02,0.002,-10.977,0.0,***,,,high,"Euclidean distance in metres; dependent variable is level price per m2 (million VND/m2), so beta = million VND/m2 per metre; Hanoi People's Committee HQ used as city centre",Advertised price per m2 (million VND/m2),per_m2,OLS,1545.0,0.163853,se,"All 1,545 listings, 12 central districts; SE, t and p in separate columns; r2 is adjusted R2",monetary,-0.02,0.002,"b (currency per unit, as reported)",reported,-0.2697862361786122,0.02457741060204174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.977,1535.0,m,,2025.0,Vietnam,Asia,Lower middle income,2024.0,2025.0,mixed,listing,journal,False,False,robust,,2024.5,ols,journal,False,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,BATHS,bathrooms,A_structural,level,count,0.146,,3.37,,,,,high,total number of baths (full plus half),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",semi_elasticity,14.6,4.332344213649851,100*b/1 (per unit count),|b/t|,0.138231253548581,0.04101817612717537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.37,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,FIREPLACES,fireplace,B_interior,level,count,0.061,,4.7,,,,,high,number of fireplaces,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",semi_elasticity,6.1,1.2978723404255317,100*b/1 (per unit count),|b/t|,0.19106796148464633,0.040652757762690714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,ROOMS,bedrooms,A_structural,level,count,0.0336,,2.1,,,,,high,total listed number of rooms (not bedrooms per se),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",semi_elasticity,3.36,1.5999999999999996,100*b/1 (per unit count),|b/t|,0.08664602165648265,0.041260010312610784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,SQFT,living_area,A_structural,level,sqft,1.2e-05,,2.67,,,,,medium,"finished living space in square feet; printed SQFT coefficient inconsistent across columns (0.000012 vs 0.000128), transcribed as printed",ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",semi_elasticity,0.012916692500051667,0.004837712546835831,100*b/0.092903 (X per sqft->m2),|b/t|,0.10991015480906013,0.041164851988412035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.67,583.0,sqft,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,APT,other,other,binary,binary,-0.152,,-1.97,,,,,high,rental apartment on the premises (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,-14.356480711679032,6.627726836175165,100*(exp(b - se^2/2) - 1),|b/t|,-0.08131885188288798,0.04127860501669441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.97,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,False,False,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,BRICK,roof_materials,A_structural,binary,binary,0.25,,4.04,,,,,high,brick exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,28.156932135788626,7.945701835938994,100*(exp(b - se^2/2) - 1),|b/t|,0.16502563022318165,0.040847928273064764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.04,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,DECK,terrace_balcony,C_exterior,binary,binary,0.144,,3.32,,,,,high,exterior deck (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,15.3798300649431,5.009135892397215,100*(exp(b - se^2/2) - 1),|b/t|,0.13621868160828807,0.04102972337599038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.32,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,CTYD,garden,C_exterior,binary,binary,0.21,,4.6,,,,,high,private courtyard (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,23.23931623780129,5.632008534585132,100*(exp(b - se^2/2) - 1),|b/t|,0.18714655953493428,0.04068403468150746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,SPRINKLER,landscaping,C_exterior,binary,binary,0.216,,2.99,,,,,high,underground sprinkler system (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,23.786810920857903,8.965823206158698,100*(exp(b - se^2/2) - 1),|b/t|,0.12289446054826032,0.041101826270321176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.99,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,STORIES,storeys,A_structural,level,count,0.195,,3.1,,,,,high,number of stories/floors,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",semi_elasticity,19.5,6.290322580645161,100*b/1 (per unit count),|b/t|,0.12734362427420484,0.041078588475549946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,STUCCO,roof_materials,A_structural,binary,binary,0.35,,5.6,,,,,high,stucco exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,41.62986371826736,8.869172178707856,100*(exp(b - se^2/2) - 1),|b/t|,0.22593138318568567,0.04034488985458674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,GARAGESP,garage,A_structural,level,count,0.106,,3.26,,,,,high,number of garage spaces,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",semi_elasticity,10.6,3.2515337423312882,100*b/1 (per unit count),|b/t|,0.13380136922282077,0.04104336479227631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.26,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,BALCNY,terrace_balcony,C_exterior,binary,binary,0.011,,0.21,,,,,high,balcony (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,0.9674617355335036,5.296032355662816,100*(exp(b - se^2/2) - 1),|b/t|,0.008696982421229535,0.041414202005854925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,POOLTUB,pool,C_exterior,binary,binary,0.229,,1.64,,,,,high,hot-tub and/or swimming pool (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,24.51439585477633,17.55678822763739,100*(exp(b - se^2/2) - 1),|b/t|,0.06776572543813854,0.041320564291547894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.64,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,NEW,age,A_structural,binary,binary,0.296,,4.2,,,,,high,"house new at time of sale (=1); new-construction dummy, not years",ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,34.113537942659164,9.475313486244943,100*(exp(b - se^2/2) - 1),|b/t|,0.17137290747088343,0.0408030732073532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,PARKSQ,green_space,E_environment,binary,binary,0.088,,0.76,,,,,high,located across from or adjacent to a park or square (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,8.46923505359305,12.644072991905443,100*(exp(b - se^2/2) - 1),|b/t|,0.03146040328584296,0.041395267481372316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.76,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-ab,1,(a)-(b),7,CORNER,other,other,binary,binary,0.127,,1.86,,,,,high,corner lot (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, full specification",pct_premium,13.27733865476899,7.75257856227144,100*(exp(b - se^2/2) - 1),|b/t|,0.07680577828808507,0.041293429187142516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.86,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,False,False,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,BATHS,bathrooms,A_structural,level,count,0.144,,3.37,,,,,high,total number of baths (full plus half),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",semi_elasticity,14.399999999999999,4.272997032640949,100*b/1 (per unit count),|b/t|,0.138231253548581,0.04101817612717537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.37,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,FIREPLACES,fireplace,B_interior,level,count,0.062,,4.82,,,,,high,number of fireplaces,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",semi_elasticity,6.2,1.2863070539419086,100*b/1 (per unit count),|b/t|,0.1957615822285687,0.04061443614700595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.82,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,ROOMS,bedrooms,A_structural,level,count,0.0385,,2.47,,,,,high,total listed number of rooms (not bedrooms per se),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",semi_elasticity,3.85,1.5587044534412955,100*b/1 (per unit count),|b/t|,0.10176586071947681,0.041200753327723404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.47,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,SQFT,living_area,A_structural,level,sqft,0.000128,,2.9,,,,,medium,"finished living space in square feet; printed SQFT coefficient inconsistent across columns (0.000012 vs 0.000128), transcribed as printed",ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",semi_elasticity,0.13777805333388443,0.047509673563408426,100*b/0.092903 (X per sqft->m2),|b/t|,0.11924870352745057,0.0411202425956726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9,583.0,sqft,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,APT,other,other,binary,binary,-0.155,,-1.96,,,,,high,rental apartment on the premises (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,-14.62586093546343,6.772671046426536,100*(exp(b - se^2/2) - 1),|b/t|,-0.08090877487307833,0.04127998718014201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.96,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,False,False,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,BRICK,roof_materials,A_structural,binary,binary,0.254,,4.16,,,,,high,brick exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,28.677099935784867,7.871385535673612,100*(exp(b - se^2/2) - 1),|b/t|,0.1697880549793755,0.04081443629311911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.16,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,DECK,terrace_balcony,C_exterior,binary,binary,0.146,,3.36,,,,,high,exterior deck (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,15.610424705172687,5.028292959982626,100*(exp(b - se^2/2) - 1),|b/t|,0.13782887530225418,0.04102049860186136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.36,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,CTYD,garden,C_exterior,binary,binary,0.23,,5.02,,,,,high,private courtyard (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,25.727969334635546,5.766494069398007,100*(exp(b - se^2/2) - 1),|b/t|,0.20355433999201003,0.04054867330518128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.02,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,True
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,SPRINKLER,landscaping,C_exterior,binary,binary,0.206,,2.79,,,,,high,underground sprinkler system (=1),ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,22.540841339934282,9.072514696166103,100*(exp(b - se^2/2) - 1),|b/t|,0.11478623310408428,0.041142019033721965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.79,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,STORIES,storeys,A_structural,level,count,0.188,,3.17,,,,,high,number of stories/floors,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",semi_elasticity,18.8,5.930599369085174,100*b/1 (per unit count),|b/t|,0.13017093066224605,0.04106338506695459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.17,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,STUCCO,roof_materials,A_structural,binary,binary,0.366,,5.82,,,,,high,stucco exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,43.910679214477796,9.06796595896139,100*(exp(b - se^2/2) - 1),|b/t|,0.23432860919509355,0.040262647628022945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.82,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,GARAGESP,garage,A_structural,level,count,0.098,,3.11,,,,,high,number of garage spaces,ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",semi_elasticity,9.8,3.1511254019292605,100*b/1 (per unit count),|b/t|,0.12774771694976383,0.041076436318252044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.11,583.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T1-cd,1,(c)-(d),7,NEW,age,A_structural,binary,binary,0.289,,4.09,,,,,high,"house new at time of sale (=1); new-construction dummy, not years",ln real sales price (2005 USD),log,OLS,593.0,0.62,t,"Inside Savannah Historic Landmark District, restricted specification",pct_premium,33.17629247221001,9.433777739480169,100*(exp(b - se^2/2) - 1),|b/t|,0.16701139708693138,0.04083408241734265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.09,583.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,BATHS,bathrooms,A_structural,level,count,0.121,,3.07,,,,,high,total number of baths (full plus half),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",semi_elasticity,12.1,3.9413680781758957,100*b/1 (per unit count),|b/t|,0.0702934164338514,0.022896878317215437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.07,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,FIREPLACES,fireplace,B_interior,level,count,0.06,,3.82,,,,,high,number of fireplaces,ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",semi_elasticity,6.0,1.5706806282722512,100*b/1 (per unit count),|b/t|,0.08734783600838444,0.022865925656645143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.82,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,True
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,ROOMS,bedrooms,A_structural,level,count,0.032,,2.14,,,,,high,total listed number of rooms (not bedrooms per se),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",semi_elasticity,3.2,1.4953271028037383,100*b/1 (per unit count),|b/t|,0.04906167321556152,0.02292601552129043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.14,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,SQFT,living_area,A_structural,level,sqft,1.1e-05,,2.65,,,,,medium,"finished living space in square feet; printed SQFT coefficient inconsistent across columns (0.000012 vs 0.000128), transcribed as printed",ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",semi_elasticity,0.011840301458380694,0.004468038286181393,100*b/0.092903 (X per sqft->m2),|b/t|,0.0607149748164644,0.022911311251496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,1898.0,sqft,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,True
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,APT,other,other,binary,binary,0.0002,,0.156,,,,,high,rental apartment on the premises (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,0.019919800954437683,0.12823077179504275,100*(exp(b - se^2/2) - 1),|b/t|,0.0035807476047259763,0.022953510286704972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.156,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,False,False,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,BRICK,roof_materials,A_structural,binary,binary,0.2,,3.98,,,,,high,brick exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,21.986159503999005,6.1377023023124115,100*(exp(b - se^2/2) - 1),|b/t|,0.09097670700742888,0.022858469097343938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.98,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,DECK,terrace_balcony,C_exterior,binary,binary,0.11,,2.99,,,,,high,exterior deck (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,11.552291137648908,4.106708620417252,100*(exp(b - se^2/2) - 1),|b/t|,0.06847036809533213,0.022899788660646198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.99,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,CTYD,garden,C_exterior,binary,binary,0.09,,2.11,,,,,high,private courtyard (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,9.317938447280127,4.667094101112272,100*(exp(b - se^2/2) - 1),|b/t|,0.04837551370216429,0.02292678374510156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.11,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,SPRINKLER,landscaping,C_exterior,binary,binary,0.24,,3.15,,,,,high,underground sprinkler system (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,26.756470649450947,9.685707811972607,100*(exp(b - se^2/2) - 1),|b/t|,0.07211576070309567,0.02289389228669704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.15,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,STORIES,storeys,A_structural,level,count,0.18,,2.09,,,,,high,number of stories/floors,ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",semi_elasticity,18.0,8.61244019138756,100*b/1 (per unit count),|b/t|,0.047918035914461775,0.02292728991122573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.09,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,STUCCO,roof_materials,A_structural,binary,binary,0.28,,3.04,,,,,high,stucco exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,31.752939219283526,12.186721955739552,100*(exp(b - se^2/2) - 1),|b/t|,0.06960985485375422,0.022897978570313886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.04,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,GARAGESP,garage,A_structural,level,count,0.11,,3.05,,,,,high,number of garage spaces,ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",semi_elasticity,11.0,3.606557377049181,100*b/1 (per unit count),|b/t|,0.06983771965665282,0.022897613002181252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,BALCNY,terrace_balcony,C_exterior,binary,binary,0.01,,0.56,,,,,high,balcony (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,0.9889138759744842,1.8036610126502997,100*(exp(b - se^2/2) - 1),|b/t|,0.012852986383067717,0.022951761398335203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,POOLTUB,pool,C_exterior,binary,binary,0.16,,2.18,,,,,high,hot-tub and/or swimming pool (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,17.035441766735595,8.612923823793102,100*(exp(b - se^2/2) - 1),|b/t|,0.04997644435620161,0.022924974475321838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.18,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,NEW,age,A_structural,binary,binary,0.302,,3.97,,,,,high,"house new at time of sale (=1); new-construction dummy, not years",ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,34.865343258102044,10.289004797638881,100*(exp(b - se^2/2) - 1),|b/t|,0.09075000719162483,0.022858943876983586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.97,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,True
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,PARKSQ,green_space,E_environment,binary,binary,0.066,,1.81,,,,,high,located across from or adjacent to a park or square (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,6.751678017294105,3.8951913443621855,100*(exp(b - se^2/2) - 1),|b/t|,0.041510310352749026,0.02293387312306576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.81,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-ab,2,(a)-(b),7,CORNER,other,other,binary,binary,0.09,,1.12,,,,,high,corner lot (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.63,t,"Outside the district (metro Savannah), full specification",pct_premium,9.064729084390288,8.792471922631155,100*(exp(b - se^2/2) - 1),|b/t|,0.025699605221365638,0.02294607609050503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.12,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,False,False,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,BATHS,bathrooms,A_structural,level,count,0.122,,3.09,,,,,high,total number of baths (full plus half),ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",semi_elasticity,12.2,3.9482200647249193,100*b/1 (per unit count),|b/t|,0.07074906920654925,0.022896138901795874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.09,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,FIREPLACES,fireplace,B_interior,level,count,0.061,,3.69,,,,,high,number of fireplaces,ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",semi_elasticity,6.1,1.6531165311653118,100*b/1 (per unit count),|b/t|,0.08439680897788568,0.022871763950646527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.69,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,ROOMS,bedrooms,A_structural,level,count,0.033,,2.55,,,,,high,total listed number of rooms (not bedrooms per se),ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",semi_elasticity,3.3000000000000003,1.2941176470588238,100*b/1 (per unit count),|b/t|,0.058431819087844325,0.022914438857978166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.55,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,SQFT,living_area,A_structural,level,sqft,0.00012,,2.71,,,,,medium,"finished living space in square feet; printed SQFT coefficient inconsistent across columns (0.000012 vs 0.000128), transcribed as printed",ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",semi_elasticity,0.12916692500051666,0.04766307195591021,100*b/0.092903 (X per sqft->m2),|b/t|,0.062084413263495855,0.02290937758800585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.71,1898.0,sqft,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,BRICK,roof_materials,A_structural,binary,binary,0.21,,4.01,,,,,high,brick exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,23.19875257670938,6.460658169349528,100*(exp(b - se^2/2) - 1),|b/t|,0.09165672146165825,0.0228570377709871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.01,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,DECK,terrace_balcony,C_exterior,binary,binary,0.114,,3.01,,,,,high,exterior deck (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,11.994859773371092,4.244709044411743,100*(exp(b - se^2/2) - 1),|b/t|,0.06892619520752961,0.022899068175259007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.01,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,CTYD,garden,C_exterior,binary,binary,0.11,,2.23,,,,,high,private courtyard (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,11.49208390910761,5.50630438342941,100*(exp(b - se^2/2) - 1),|b/t|,0.05111973116695662,0.022923646263209245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.23,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,SPRINKLER,landscaping,C_exterior,binary,binary,0.242,,2.87,,,,,high,underground sprinkler system (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,26.92739217126665,10.740703646742828,100*(exp(b - se^2/2) - 1),|b/t|,0.06573451464772291,0.02290401207237732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.87,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,True
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,STORIES,storeys,A_structural,level,count,0.19,,2.19,,,,,high,number of stories/floors,ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",semi_elasticity,19.0,8.675799086757992,100*b/1 (per unit count),|b/t|,0.050205117580583235,0.022924711224010613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.19,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,STUCCO,roof_materials,A_structural,binary,binary,0.29,,3.12,,,,,high,stucco exterior (=1); exterior material,ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,33.06669303179828,12.421922164339321,100*(exp(b - se^2/2) - 1),|b/t|,0.0714324652457378,0.02289502091209545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.12,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,GARAGESP,garage,A_structural,level,count,0.11,,3.14,,,,,high,number of garage spaces,ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",semi_elasticity,11.0,3.5031847133757963,100*b/1 (per unit count),|b/t|,0.07188800678457123,0.022894269676615044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.14,1898.0,count,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,POOLTUB,pool,C_exterior,binary,binary,0.17,,2.21,,,,,high,hot-tub and/or swimming pool (=1),ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,18.18032147236215,9.117729625541273,100*(exp(b - se^2/2) - 1),|b/t|,0.05066244030718153,0.022924181134471283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.21,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,False
1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3,1aa52b387c00d728ed5f6e7b8aeacbd09246f0d3:T2-cd,2,(c)-(d),7,NEW,age,A_structural,binary,binary,0.31,,4.01,,,,,high,"house new at time of sale (=1); new-construction dummy, not years",ln real sales price (2005 USD),log,OLS,1908.0,0.61,t,"Outside the district (metro Savannah), restricted specification",pct_premium,35.93570552926397,10.5401941491515,100*(exp(b - se^2/2) - 1),|b/t|,0.09165672146165825,0.0228570377709871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.01,1898.0,binary,,2011.0,United States,North America,High income,2000.0,2005.0,single_family,transaction,journal,False,False,robust,,2002.5,ols,journal,True,True,True
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,area,living_area,A_structural,level,sqft (unit not printed; sqft in source dataset),124.558,65.678,1.897,0.061,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,124.558,65.678,"b (currency per unit, as reported)",reported,0.19607969991629037,0.10336304687205607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.897,90.0,sqft,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,bathrooms,bathrooms,A_structural,level,count,650566.462,230693.362,2.82,0.006,**,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,650566.462,230693.362,"b (currency per unit, as reported)",reported,0.2849322237941364,0.10103979567167957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.82,90.0,count,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,stories,storeys,A_structural,level,count,87504.025,144525.352,0.605,0.546,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,87504.025,144525.352,"b (currency per unit, as reported)",reported,0.06364331388510164,0.10519556014066386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.605,90.0,count,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,bedrooms,bedrooms,A_structural,level,count,372422.35,220797.72,1.687,0.095,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,372422.35,220797.72,"b (currency per unit, as reported)",reported,0.17507879787850006,0.10378114871280383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.687,90.0,count,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,guestroom,other,other,binary,binary (guest room present),81401.929,304786.009,0.267,0.79,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,81401.929,304786.009,"b (currency per unit, as reported)",reported,0.028133131254411477,0.10536753278805795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.267,90.0,binary,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,False,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,basement,basement,A_structural,binary,binary,403708.567,333873.702,1.209,0.23,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,403708.567,333873.702,"b (currency per unit, as reported)",reported,0.12641735844761323,0.10456357191696711,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.209,90.0,binary,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,5,hotwaterheating,heating,A_structural,binary,binary (hot water heating),486872.335,510848.994,0.953,0.343,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,486872.335,510848.994,"b (currency per unit, as reported)",reported,0.09995196804881226,0.10488139354544834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.953,90.0,binary,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,6,airconditioning,air_conditioning,A_structural,binary,binary,530970.1,339127.011,1.566,0.121,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,530970.1,339127.011,"b (currency per unit, as reported)",reported,0.16286687605116718,0.10400183655885516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.566,90.0,binary,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,6,parking,parking,A_structural,level,count (parking spaces),660661.38,159352.363,4.146,0.0,**,,,medium,Level-level model; currency and data provenance unverified (see study notes); p printed as 0.000 recorded as 0.0,"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,660661.38,159352.363,"b (currency per unit, as reported)",reported,0.40045484086133043,0.0965882394745129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.146,90.0,count,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,True,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,6,prefarea,other,other,binary,binary (located in a preferred area),434783.946,276309.617,1.574,0.119,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,434783.946,276309.617,"b (currency per unit, as reported)",reported,0.16367665616502153,0.10398771039709118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.574,90.0,binary,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,False,False
1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b,1ad007ac2e8575311162da739431607dfdaeeb1b:T3-c1,3,1,6,furnishingstatus,other,other,level,ordinal (furnishing status category),78390.114,172544.131,0.454,0.651,,,,medium,Level-level model; currency and data provenance unverified (see study notes),"house price (level, currency not stated)",level,OLS,100.0,0.368,se,"Full sample; N inferred from F(11,88) = 100 (abstract claims 158); SE, t and p in separate columns; adjusted R2 0.289",monetary,78390.114,172544.131,"b (currency per unit, as reported)",reported,0.0478010967223949,0.10528875930042929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.454,90.0,ordinal,,2025.0,United States,North America,High income,,,unknown,unknown,conference,False,False,OLS,,2022.0,ols,other,False,False,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,AGE,age,A_structural,level,years,-2.737,609.503,,0.996,,,,high,,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,-2.737,609.503,"b (currency per unit, as reported)",reported,-0.0002702989713414089,0.06019292434399079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.00449054393497653,276.0,years,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,AGE,age,A_structural,level,years,-1489.234,607.064,,0.015,**,,,high,,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,-1489.234,607.064,"b (currency per unit, as reported)",reported,-0.1208565893947301,0.049265383804239254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4531746240923527,406.0,years,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,AREA,living_area,A_structural,level,m2,1665.921,342.579,,0.0,***,,,high,Area of property (m2),PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,1665.921,342.579,"b (currency per unit, as reported)",reported,0.280923618237615,0.05776896516234798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.862881262424142,276.0,m2,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,AREA,living_area,A_structural,level,m2,2816.495,394.776,,0.0,***,,,high,Area of property (m2),PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,2816.495,394.776,"b (currency per unit, as reported)",reported,0.3337703356987022,0.04678315354573358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.1344129329037225,406.0,m2,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,BDRM,bedrooms,A_structural,level,count,69979.407,21476.228,,0.001,***,,,high,,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,69979.407,21476.228,"b (currency per unit, as reported)",reported,0.19246903056720238,0.05906750229249882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.258458934222528,276.0,count,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,BDRM,bedrooms,A_structural,level,count,82793.058,19860.177,,0.0,***,,,high,,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,82793.058,19860.177,"b (currency per unit, as reported)",reported,0.2026031613122139,0.04859990368298898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.168797589266199,406.0,count,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,BTHR,bathrooms,A_structural,level,count,73740.179,30429.466,,0.016,**,,,high,,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,73740.179,30429.466,"b (currency per unit, as reported)",reported,0.14433894852200288,0.05956260462191224,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.423314921135981,276.0,count,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,BTHR,bathrooms,A_structural,level,count,-15883.716,26759.403,,0.553,,,,high,,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,-15883.716,26759.403,"b (currency per unit, as reported)",reported,-0.029445864904690706,0.04960764632584561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5935751257230963,406.0,count,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,PRKG,parking,A_structural,level,count,54604.633,21856.738,,0.013,**,,,high,Number of parking spaces,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,54604.633,21856.738,"b (currency per unit, as reported)",reported,0.1487077894843431,0.05952365238529196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4982974586601165,276.0,count,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,PRKG,parking,A_structural,level,count,103929.702,20881.198,,0.0,***,,,high,Number of parking spaces,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,103929.702,20881.198,"b (currency per unit, as reported)",reported,0.23980615319504467,0.04818102688761736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.977190580732006,406.0,count,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,LOT_SZ,lot_size,A_structural,level,m2,-0.535,16.841,,0.975,,,,high,,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,-0.535,16.841,"b (currency per unit, as reported)",reported,-0.001912187923844287,0.060192816496189974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.031767709755952736,276.0,m2,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,LOT_SZ,lot_size,A_structural,level,m2,-19.36,20.237,,0.339,,,,high,,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,-19.36,20.237,"b (currency per unit, as reported)",reported,-0.04742499154232237,0.04957332406208563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9566635370855364,406.0,m2,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,P_TYPE,dwelling_type,A_structural,binary,binary,598145.437,60749.289,,0.0,***,,,high,"1 = house, 0 = unit",PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,598145.437,60749.289,"b (currency per unit, as reported)",reported,0.5098500305121452,0.0517817656615194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.846130660064187,276.0,binary,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,P_TYPE,dwelling_type,A_structural,binary,binary,981064.7,51472.085,,0.0,***,,,high,"1 = house, 0 = unit",PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,981064.7,51472.085,"b (currency per unit, as reported)",reported,0.6871969742299397,0.03605415735507175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.060131331380884,406.0,binary,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,D400,transit_distance,D_location,dummy,binary,64979.197,45392.838,,0.153,,,,high,1 = within 0-400 m of light-rail stop,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,64979.197,45392.838,"b (currency per unit, as reported)",reported,0.0858472163036569,0.05997071312566168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4314856674085898,276.0,binary,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,D400,transit_distance,D_location,dummy,binary,154805.107,48003.912,,0.001,***,,,high,1 = within 0-400 m of light-rail stop,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,154805.107,48003.912,"b (currency per unit, as reported)",reported,0.15803507714250006,0.04900550171165725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2248435710822903,406.0,binary,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T5-T1,5,T1 (Announcement Phase),24,D800,transit_distance,D_location,dummy,binary,22742.752,67041.932,,0.735,,,,high,1 = within 401-800 m of light-rail stop,PRICE (AUD),level,OLS,286.0,0.808,se,Dec 2012 - Feb 2015,monetary,22742.752,67041.932,"b (currency per unit, as reported)",reported,0.02041509629146244,0.06018038174736625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33923175125681043,276.0,binary,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666,1b06d5abfe32d650517ed54e21f8d196f135c666:T6-T2,6,T2 (Construction Phase),24,D800,transit_distance,D_location,dummy,binary,120083.533,65190.065,,0.066,*,,,high,1 = within 401-800 m of light-rail stop,PRICE (AUD),level,OLS,416.0,0.867,se,Mar 2015 - Mar 2020,monetary,120083.533,65190.065,"b (currency per unit, as reported)",reported,0.09103989594922328,0.049423068977518365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8420526655403702,406.0,binary,,2021.0,Australia,Oceania,High income,2012.0,2020.0,mixed,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Bedrooms,bedrooms,A_structural,level,count,1.062,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.212,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,1.062,,not harmonised,,,,,,433.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Bathrooms,bathrooms,A_structural,level,count,2.147,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.412,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,2.147,,not harmonised,,,,,,433.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,WES,other,other,binary,binary,8.101,,,,,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.787","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,8.101,,not harmonised,,,,,,433.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Severe Problems,condition,A_structural,dummy,binary,-2.607,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.253,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,-2.607,,not harmonised,,,,,,433.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Medium Problems,condition,A_structural,dummy,binary,-3.108,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.302,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,-3.108,,not harmonised,,,,,,433.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Small Problems,condition,A_structural,dummy,binary,-2.473,,,,**,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.24,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,-2.473,,not harmonised,,,,,,433.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Years of construction,age,A_structural,other,index,0.009,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.004","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,0.009,,not harmonised,,,,,,433.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,House,dwelling_type,A_structural,dummy,binary,1.244,,,,*,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.121,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,1.244,,not harmonised,,,,,,433.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Construction Type,condition,A_structural,other,index,-1.37,,,,,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.398","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,-1.37,,not harmonised,,,,,,433.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.24,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.223","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,0.24,,not harmonised,,,,,,433.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m1,18,1,64,m2 avg. Price,other,other,level,currency,0.0,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.14","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,443.0,,none,"Market 1: Montevideo neighbourhood group 1 (Table 15); Box-Cox with common lambda=0.261 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,433.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Bedrooms,bedrooms,A_structural,level,count,1.065,,,,***,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.182,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,1.065,,not harmonised,,,,,,583.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Bathrooms,bathrooms,A_structural,level,count,1.203,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.156,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,1.203,,not harmonised,,,,,,583.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,WES,other,other,binary,binary,-0.452,,,,,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.042","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,-0.452,,not harmonised,,,,,,583.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Severe Problems,condition,A_structural,dummy,binary,-2.36,,,,*,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.217,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,-2.36,,not harmonised,,,,,,583.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Medium Problems,condition,A_structural,dummy,binary,-1.168,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.107,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,-1.168,,not harmonised,,,,,,583.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Small Problems,condition,A_structural,dummy,binary,-1.313,,,,**,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.121,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,-1.313,,not harmonised,,,,,,583.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Years of construction,age,A_structural,other,index,-0.569,,,,**,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.226","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,-0.569,,not harmonised,,,,,,583.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,House,dwelling_type,A_structural,dummy,binary,1.234,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.114,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,1.234,,not harmonised,,,,,,583.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Construction Type,condition,A_structural,other,index,4.262,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 1.163","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,4.262,,not harmonised,,,,,,583.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.796,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.651","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,0.796,,not harmonised,,,,,,583.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m2,18,2,64,m2 avg. Price,other,other,level,currency,0.001,,,,***,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.621","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,593.0,,none,"Market 2: Montevideo neighbourhood group 2 (Table 15); Box-Cox with common lambda=0.282 for dependent variable and transformed regressors (income, age, education of head)",other,0.001,,not harmonised,,,,,,583.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Bedrooms,bedrooms,A_structural,level,count,0.604,,,,,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.059,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,0.604,,not harmonised,,,,,,273.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Bathrooms,bathrooms,A_structural,level,count,0.762,,,,*,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.061,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,0.762,,not harmonised,,,,,,273.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,WES,other,other,binary,binary,-2.102,,,,,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.122","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,-2.102,,not harmonised,,,,,,273.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Severe Problems,condition,A_structural,dummy,binary,-10.108,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.587,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,-10.108,,not harmonised,,,,,,273.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Medium Problems,condition,A_structural,dummy,binary,0.441,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.026,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,0.441,,not harmonised,,,,,,273.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Small Problems,condition,A_structural,dummy,binary,-0.03,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.002,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,-0.03,,not harmonised,,,,,,273.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Years of construction,age,A_structural,other,index,-1.029,,,,**,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.262","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,-1.029,,not harmonised,,,,,,273.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,House,dwelling_type,A_structural,dummy,binary,0.666,,,,,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.039,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,0.666,,not harmonised,,,,,,273.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.634,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.37","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,0.634,,not harmonised,,,,,,273.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m3,18,3,64,m2 avg. Price,other,other,level,currency,0.0,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.026","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,283.0,,none,"Market 3: Montevideo neighbourhood group 3 (Table 15); Box-Cox with common lambda=0.346 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,273.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Bedrooms,bedrooms,A_structural,level,count,0.442,,,,*,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.103,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,0.442,,not harmonised,,,,,,234.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Bathrooms,bathrooms,A_structural,level,count,0.466,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.077,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,0.466,,not harmonised,,,,,,234.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,WES,other,other,binary,binary,3.535,,,,*,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.44","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,3.535,,not harmonised,,,,,,234.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Severe Problems,condition,A_structural,dummy,binary,-2.567,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.32,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,-2.567,,not harmonised,,,,,,234.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Medium Problems,condition,A_structural,dummy,binary,-1.229,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.153,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,-1.229,,not harmonised,,,,,,234.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Small Problems,condition,A_structural,dummy,binary,-0.459,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.057,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,-0.459,,not harmonised,,,,,,234.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Years of construction,age,A_structural,other,index,-0.281,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.158","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,-0.281,,not harmonised,,,,,,234.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,House,dwelling_type,A_structural,dummy,binary,1.625,,,,***,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.202,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,1.625,,not harmonised,,,,,,234.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Construction Type,condition,A_structural,other,index,1.366,,,,,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.501","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,1.366,,not harmonised,,,,,,234.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.336,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.355","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,0.336,,not harmonised,,,,,,234.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m4,18,4,64,m2 avg. Price,other,other,level,currency,0.0,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.075","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,244.0,,none,"Market 4: Montevideo neighbourhood group 4 (Table 15); Box-Cox with common lambda=0.255 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,234.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Bedrooms,bedrooms,A_structural,level,count,1.252,,,,***,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.139,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,1.252,,not harmonised,,,,,,279.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Bathrooms,bathrooms,A_structural,level,count,3.863,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.335,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,3.863,,not harmonised,,,,,,279.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,WES,other,other,binary,binary,-6.687,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.433","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,-6.687,,not harmonised,,,,,,279.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Severe Problems,condition,A_structural,dummy,binary,-1.823,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.118,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,-1.823,,not harmonised,,,,,,279.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Medium Problems,condition,A_structural,dummy,binary,0.24,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.016,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,0.24,,not harmonised,,,,,,279.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Small Problems,condition,A_structural,dummy,binary,-0.601,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.039,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,-0.601,,not harmonised,,,,,,279.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Years of construction,age,A_structural,other,index,-0.748,,,,**,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.216","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,-0.748,,not harmonised,,,,,,279.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,House,dwelling_type,A_structural,dummy,binary,0.138,,,,,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.009,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,0.138,,not harmonised,,,,,,279.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Construction Type,condition,A_structural,other,index,12.069,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 2.34","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,12.069,,not harmonised,,,,,,279.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.401,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.251","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,0.401,,not harmonised,,,,,,279.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m5,18,5,64,m2 avg. Price,other,other,level,currency,0.0,,,,**,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.164","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,289.0,,none,"Market 5: Montevideo neighbourhood group 5 (Table 15); Box-Cox with common lambda=0.329 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,279.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Bedrooms,bedrooms,A_structural,level,count,0.925,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.116,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,0.925,,not harmonised,,,,,,302.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Bathrooms,bathrooms,A_structural,level,count,0.511,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.048,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,0.511,,not harmonised,,,,,,302.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,WES,other,other,binary,binary,14.109,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 1.019","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,14.109,,not harmonised,,,,,,302.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Severe Problems,condition,A_structural,dummy,binary,1.258,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.091,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,1.258,,not harmonised,,,,,,302.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Medium Problems,condition,A_structural,dummy,binary,-1.05,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.076,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,-1.05,,not harmonised,,,,,,302.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Small Problems,condition,A_structural,dummy,binary,-0.706,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.051,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,-0.706,,not harmonised,,,,,,302.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Years of construction,age,A_structural,other,index,-0.475,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.158","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,-0.475,,not harmonised,,,,,,302.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,House,dwelling_type,A_structural,dummy,binary,1.79,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.129,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,1.79,,not harmonised,,,,,,302.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Construction Type,condition,A_structural,other,index,-1.516,,,,,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.326","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,-1.516,,not harmonised,,,,,,302.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.345,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.209","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,0.345,,not harmonised,,,,,,302.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m6,18,6,64,m2 avg. Price,other,other,level,currency,0.0,,,,**,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.187","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,312.0,,none,"Market 6: Montevideo neighbourhood group 6 (Table 15); Box-Cox with common lambda=0.323 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,302.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Bedrooms,bedrooms,A_structural,level,count,0.165,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.169,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,0.165,,not harmonised,,,,,,223.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Bathrooms,bathrooms,A_structural,level,count,0.047,,,,,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.037,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,0.047,,not harmonised,,,,,,223.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,WES,other,other,binary,binary,0.889,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.546","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,0.889,,not harmonised,,,,,,223.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Severe Problems,condition,A_structural,dummy,binary,-0.775,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.476,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,-0.775,,not harmonised,,,,,,223.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Medium Problems,condition,A_structural,dummy,binary,-0.299,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.184,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,-0.299,,not harmonised,,,,,,223.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Small Problems,condition,A_structural,dummy,binary,-0.043,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.026,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,-0.043,,not harmonised,,,,,,223.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Years of construction,age,A_structural,other,index,-0.061,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.17","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,-0.061,,not harmonised,,,,,,223.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,House,dwelling_type,A_structural,dummy,binary,0.308,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.189,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,0.308,,not harmonised,,,,,,223.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Construction Type,condition,A_structural,other,index,-0.071,,,,,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.13","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,-0.071,,not harmonised,,,,,,223.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.074,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.44","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,0.074,,not harmonised,,,,,,223.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T18-m7,18,7,64,m2 avg. Price,other,other,level,currency,0.0,,,,**,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.501","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,233.0,,none,"Market 7: Montevideo neighbourhood group 7 (Table 15); Box-Cox with common lambda=0.061 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,223.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Bedrooms,bedrooms,A_structural,level,count,1.483,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.103,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,1.483,,not harmonised,,,,,,285.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Bathrooms,bathrooms,A_structural,level,count,2.594,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.116,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,2.594,,not harmonised,,,,,,285.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,WES,other,other,binary,binary,9.041,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.347","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,9.041,,not harmonised,,,,,,285.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Severe Problems,condition,A_structural,dummy,binary,-6.485,,,,*,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.249,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,-6.485,,not harmonised,,,,,,285.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Medium Problems,condition,A_structural,dummy,binary,-6.609,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.254,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,-6.609,,not harmonised,,,,,,285.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Small Problems,condition,A_structural,dummy,binary,-2.183,,,,*,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.084,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,-2.183,,not harmonised,,,,,,285.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Years of construction,age,A_structural,other,index,0.547,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.093","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,0.547,,not harmonised,,,,,,285.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,House,dwelling_type,A_structural,dummy,binary,5.034,,,,***,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.193,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,5.034,,not harmonised,,,,,,285.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Construction Type,condition,A_structural,other,index,3.945,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.436","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,3.945,,not harmonised,,,,,,285.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,1.445,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.438","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,1.445,,not harmonised,,,,,,285.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,Type Neigh.,other,other,level,index,0.161,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.02,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,0.161,,not harmonised,,,,,,285.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m8,19,8,65,m2 avg. Price,other,other,level,currency,-0.006,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = -1.44","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,295.0,,none,"Market 8: Montevideo neighbourhood group 8 (Table 15); Box-Cox with common lambda=0.405 for dependent variable and transformed regressors (income, age, education of head)",other,-0.006,,not harmonised,,,,,,285.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Bedrooms,bedrooms,A_structural,level,count,2.048,,,,,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.068,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,2.048,,not harmonised,,,,,,246.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Bathrooms,bathrooms,A_structural,level,count,1.983,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.045,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,1.983,,not harmonised,,,,,,246.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,WES,other,other,binary,binary,20.899,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.37","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,20.899,,not harmonised,,,,,,246.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Severe Problems,condition,A_structural,dummy,binary,-12.802,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.227,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,-12.802,,not harmonised,,,,,,246.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Medium Problems,condition,A_structural,dummy,binary,-8.072,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.143,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,-8.072,,not harmonised,,,,,,246.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Small Problems,condition,A_structural,dummy,binary,-5.056,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.09,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,-5.056,,not harmonised,,,,,,246.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Years of construction,age,A_structural,other,index,0.11,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.008","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,0.11,,not harmonised,,,,,,246.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,House,dwelling_type,A_structural,dummy,binary,7.615,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.135,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,7.615,,not harmonised,,,,,,246.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Construction Type,condition,A_structural,other,index,10.989,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.543","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,10.989,,not harmonised,,,,,,246.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,1.916,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.215","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,1.916,,not harmonised,,,,,,246.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,Type Neigh.,other,other,level,index,-1.909,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.141,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,-1.909,,not harmonised,,,,,,246.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m9,19,9,65,m2 avg. Price,other,other,level,currency,0.003,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.328","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,256.0,,none,"Market 9: Montevideo neighbourhood group 9 (Table 15); Box-Cox with common lambda=0.513 for dependent variable and transformed regressors (income, age, education of head)",other,0.003,,not harmonised,,,,,,246.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Bedrooms,bedrooms,A_structural,level,count,-0.034,,,,,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.004,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,-0.034,,not harmonised,,,,,,218.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Bathrooms,bathrooms,A_structural,level,count,5.897,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.367,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,5.897,,not harmonised,,,,,,218.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,WES,other,other,binary,binary,3.104,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.184","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,3.104,,not harmonised,,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Severe Problems,condition,A_structural,dummy,binary,-2.678,,,,*,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.159,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,-2.678,,not harmonised,,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Medium Problems,condition,A_structural,dummy,binary,-1.369,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.081,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,-1.369,,not harmonised,,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Small Problems,condition,A_structural,dummy,binary,-0.172,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.01,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,-0.172,,not harmonised,,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Years of construction,age,A_structural,other,index,-0.403,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.083","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,-0.403,,not harmonised,,,,,,218.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,House,dwelling_type,A_structural,dummy,binary,2.334,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.138,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,2.334,,not harmonised,,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Construction Type,condition,A_structural,other,index,2.323,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.357","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,2.323,,not harmonised,,,,,,218.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,1.143,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.321","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,1.143,,not harmonised,,,,,,218.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,Type Neigh.,other,other,level,index,1.189,,,,***,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.332,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,1.189,,not harmonised,,,,,,218.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m10,19,10,65,m2 avg. Price,other,other,level,currency,8.95e-05,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.118","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,228.0,,none,"Market 10: Montevideo neighbourhood group 10 (Table 15); Box-Cox with common lambda=0.366 for dependent variable and transformed regressors (income, age, education of head)",other,8.95e-05,,not harmonised,,,,,,218.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Bedrooms,bedrooms,A_structural,level,count,0.124,,,,***,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,12.4,,100*b/1 (per unit count),,,,,,201.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Bathrooms,bathrooms,A_structural,level,count,0.011,,,,,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,1.0999999999999999,,100*b/1 (per unit count),,,,,,201.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,WES,other,other,binary,binary,0.304,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),pct_premium,35.52690560896716,,100*(exp(b) - 1),,,,,,201.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Severe Problems,condition,A_structural,dummy,binary,-0.238,,,,*,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),pct_premium,-21.17973089062296,,100*(exp(b) - 1),,,,,,201.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Medium Problems,condition,A_structural,dummy,binary,-0.2,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),pct_premium,-18.12692469220182,,100*(exp(b) - 1),,,,,,201.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Small Problems,condition,A_structural,dummy,binary,-0.095,,,,*,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),pct_premium,-9.062706553176858,,100*(exp(b) - 1),,,,,,201.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Years of construction,age,A_structural,other,index,0.001,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),other,0.001,,not harmonised,,,,,,201.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,House,dwelling_type,A_structural,dummy,binary,0.128,,,,**,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),pct_premium,13.655300269706029,,100*(exp(b) - 1),,,,,,201.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Construction Type,condition,A_structural,other,index,0.421,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),other,0.421,,not harmonised,,,,,,201.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m11,19,11†,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.044,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,211.0,,none,Market 11: Montevideo neighbourhood group 11 (Table 15); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,4.3999999999999995,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,201.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Bedrooms,bedrooms,A_structural,level,count,1.174,,,,***,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.242,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,1.174,,not harmonised,,,,,,450.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Bathrooms,bathrooms,A_structural,level,count,0.652,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.079,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,0.652,,not harmonised,,,,,,450.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,WES,other,other,binary,binary,1.922,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.209","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,1.922,,not harmonised,,,,,,450.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Severe Problems,condition,A_structural,dummy,binary,-1.198,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.13,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,-1.198,,not harmonised,,,,,,450.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Medium Problems,condition,A_structural,dummy,binary,-1.045,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.114,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,-1.045,,not harmonised,,,,,,450.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Small Problems,condition,A_structural,dummy,binary,-0.087,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.009,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,-0.087,,not harmonised,,,,,,450.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Years of construction,age,A_structural,other,index,0.236,,,,*,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.105","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,0.236,,not harmonised,,,,,,450.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,House,dwelling_type,A_structural,dummy,binary,1.17,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.127,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,1.17,,not harmonised,,,,,,450.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Construction Type,condition,A_structural,other,index,1.151,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.338","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,1.151,,not harmonised,,,,,,450.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.594,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.401","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,0.594,,not harmonised,,,,,,450.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m12,19,12,65,Type Neigh.,other,other,level,index,-0.766,,,,***,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.443,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,460.0,,none,"Market 12: Montevideo neighbourhood group 12 (Table 15); Box-Cox with common lambda=0.287 for dependent variable and transformed regressors (income, age, education of head)",other,-0.766,,not harmonised,,,,,,450.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Bedrooms,bedrooms,A_structural,level,count,4.088,,,,***,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.177,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,4.088,,not harmonised,,,,,,321.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Bathrooms,bathrooms,A_structural,level,count,0.683,,,,,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.019,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,0.683,,not harmonised,,,,,,321.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,WES,other,other,binary,binary,12.218,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.296","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,12.218,,not harmonised,,,,,,321.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Severe Problems,condition,A_structural,dummy,binary,-8.904,,,,*,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.215,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,-8.904,,not harmonised,,,,,,321.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Medium Problems,condition,A_structural,dummy,binary,-7.321,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.177,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,-7.321,,not harmonised,,,,,,321.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Small Problems,condition,A_structural,dummy,binary,-3.151,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.076,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,-3.151,,not harmonised,,,,,,321.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Years of construction,age,A_structural,other,index,-0.474,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.049","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,-0.474,,not harmonised,,,,,,321.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,House,dwelling_type,A_structural,dummy,binary,4.78,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.116,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,4.78,,not harmonised,,,,,,321.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Construction Type,condition,A_structural,other,index,6.972,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.473","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,6.972,,not harmonised,,,,,,321.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,1.533,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.279","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,1.533,,not harmonised,,,,,,321.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,Type Neigh.,other,other,level,index,-3.629,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.45,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,-3.629,,not harmonised,,,,,,321.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m13,19,13,65,m2 avg. Price,other,other,level,currency,0.002,,,,**,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.231","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,331.0,,none,"Market 13: Montevideo neighbourhood group 13 (Table 15); Box-Cox with common lambda=0.474 for dependent variable and transformed regressors (income, age, education of head)",other,0.002,,not harmonised,,,,,,321.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Bedrooms,bedrooms,A_structural,level,count,0.104,,,,,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.035,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,0.104,,not harmonised,,,,,,238.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Bathrooms,bathrooms,A_structural,level,count,0.285,,,,*,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.055,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,0.285,,not harmonised,,,,,,238.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,WES,other,other,binary,binary,1.134,,,,,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.194","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,1.134,,not harmonised,,,,,,238.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Severe Problems,condition,A_structural,dummy,binary,-1.49,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.255,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,-1.49,,not harmonised,,,,,,238.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Medium Problems,condition,A_structural,dummy,binary,-0.58,,,,*,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.099,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,-0.58,,not harmonised,,,,,,238.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Small Problems,condition,A_structural,dummy,binary,-0.879,,,,**,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.151,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,-0.879,,not harmonised,,,,,,238.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Years of construction,age,A_structural,other,index,0.013,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.009","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,0.013,,not harmonised,,,,,,238.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,House,dwelling_type,A_structural,dummy,binary,-0.081,,,,,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.014,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,-0.081,,not harmonised,,,,,,238.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Construction Type,condition,A_structural,other,index,2.301,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 1.095","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,2.301,,not harmonised,,,,,,238.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Neigh. Serv.,public_services,F_neighbourhood,level,index,-0.079,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.083","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,-0.079,,not harmonised,,,,,,238.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T19-m14,19,14,65,Type Neigh.,other,other,level,index,0.543,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.567,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,248.0,,none,"Market 14: Montevideo neighbourhood group 14 (Table 15); Box-Cox with common lambda=0.228 for dependent variable and transformed regressors (income, age, education of head)",other,0.543,,not harmonised,,,,,,238.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Bedrooms,bedrooms,A_structural,level,count,0.775,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.124,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,0.775,,not harmonised,,,,,,517.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Bathrooms,bathrooms,A_structural,level,count,1.607,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.152,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,1.607,,not harmonised,,,,,,517.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,WES,other,other,binary,binary,1.693,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.149","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,1.693,,not harmonised,,,,,,517.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Severe Problems,condition,A_structural,dummy,binary,-1.859,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.164,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,-1.859,,not harmonised,,,,,,517.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Medium Problems,condition,A_structural,dummy,binary,-1.42,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.125,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,-1.42,,not harmonised,,,,,,517.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Small Problems,condition,A_structural,dummy,binary,-0.573,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.051,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,-0.573,,not harmonised,,,,,,517.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Years of construction,age,A_structural,other,index,-0.082,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.03","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,-0.082,,not harmonised,,,,,,517.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,House,dwelling_type,A_structural,dummy,binary,0.896,,,,*,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.079,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,0.896,,not harmonised,,,,,,517.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Construction Type,condition,A_structural,other,index,2.585,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.614","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,2.585,,not harmonised,,,,,,517.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.377,,,,*,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.194","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,0.377,,not harmonised,,,,,,517.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,Type Neigh.,other,other,level,index,-0.942,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.513,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,-0.942,,not harmonised,,,,,,517.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m15,20,15,66,m2 avg. Price,other,other,level,currency,0.001,,,,**,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.374","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,527.0,,none,"Market 15: Montevideo neighbourhood group 15 (Table 15); Box-Cox with common lambda=0.315 for dependent variable and transformed regressors (income, age, education of head)",other,0.001,,not harmonised,,,,,,517.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Bedrooms,bedrooms,A_structural,level,count,0.27,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.129,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.27,,not harmonised,,,,,,441.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Bathrooms,bathrooms,A_structural,level,count,0.285,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.08,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.285,,not harmonised,,,,,,441.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,WES,other,other,binary,binary,0.716,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.185","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.716,,not harmonised,,,,,,441.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Severe Problems,condition,A_structural,dummy,binary,-1.055,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.273,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,-1.055,,not harmonised,,,,,,441.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Medium Problems,condition,A_structural,dummy,binary,-0.574,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.148,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,-0.574,,not harmonised,,,,,,441.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Small Problems,condition,A_structural,dummy,binary,-0.31,,,,*,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.08,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,-0.31,,not harmonised,,,,,,441.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Years of construction,age,A_structural,other,index,-0.036,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.037","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,-0.036,,not harmonised,,,,,,441.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,House,dwelling_type,A_structural,dummy,binary,0.635,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.164,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.635,,not harmonised,,,,,,441.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Construction Type,condition,A_structural,other,index,0.613,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.417","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.613,,not harmonised,,,,,,441.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.202,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.246","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.202,,not harmonised,,,,,,441.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,Type Neigh.,other,other,level,index,-0.511,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.806,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,-0.511,,not harmonised,,,,,,441.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m16,20,16,66,m2 avg. Price,other,other,level,currency,0.0,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.082","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,451.0,,none,"Market 16: Montevideo neighbourhood group 16 (Table 15); Box-Cox with common lambda=0.178 for dependent variable and transformed regressors (income, age, education of head)",other,0.0,,not harmonised,,,,,,441.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Bedrooms,bedrooms,A_structural,level,count,0.441,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.099,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,0.441,,not harmonised,,,,,,477.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Bathrooms,bathrooms,A_structural,level,count,0.192,,,,,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.025,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,0.192,,not harmonised,,,,,,477.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,WES,other,other,binary,binary,1.394,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.168","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,1.394,,not harmonised,,,,,,477.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Severe Problems,condition,A_structural,dummy,binary,-2.542,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.306,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,-2.542,,not harmonised,,,,,,477.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Medium Problems,condition,A_structural,dummy,binary,-1.501,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.181,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,-1.501,,not harmonised,,,,,,477.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Small Problems,condition,A_structural,dummy,binary,-0.954,,,,*,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.115,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,-0.954,,not harmonised,,,,,,477.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Years of construction,age,A_structural,other,index,-0.158,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.072","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,-0.158,,not harmonised,,,,,,477.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,House,dwelling_type,A_structural,dummy,binary,1.742,,,,***,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.21,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,1.742,,not harmonised,,,,,,477.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Construction Type,condition,A_structural,other,index,0.819,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.24","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,0.819,,not harmonised,,,,,,477.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.426,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.268","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,0.426,,not harmonised,,,,,,477.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,Type Neigh.,other,other,level,index,-0.509,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.442,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,-0.509,,not harmonised,,,,,,477.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m17,20,17,66,m2 avg. Price,other,other,level,currency,0.001,,,,**,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.252","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,487.0,,none,"Market 17: Montevideo neighbourhood group 17 (Table 15); Box-Cox with common lambda=0.285 for dependent variable and transformed regressors (income, age, education of head)",other,0.001,,not harmonised,,,,,,477.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Bedrooms,bedrooms,A_structural,level,count,0.356,,,,,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.058,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,0.356,,not harmonised,,,,,,431.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Bathrooms,bathrooms,A_structural,level,count,2.993,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.27,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,2.993,,not harmonised,,,,,,431.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,WES,other,other,binary,binary,2.098,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.187","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,2.098,,not harmonised,,,,,,431.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Severe Problems,condition,A_structural,dummy,binary,-1.967,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.175,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,-1.967,,not harmonised,,,,,,431.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Medium Problems,condition,A_structural,dummy,binary,-1.623,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.144,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,-1.623,,not harmonised,,,,,,431.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Small Problems,condition,A_structural,dummy,binary,-0.453,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.04,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,-0.453,,not harmonised,,,,,,431.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Years of construction,age,A_structural,other,index,0.157,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.047","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,0.157,,not harmonised,,,,,,431.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,House,dwelling_type,A_structural,dummy,binary,1.537,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.137,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,1.537,,not harmonised,,,,,,431.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Construction Type,condition,A_structural,other,index,1.131,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.236","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,1.131,,not harmonised,,,,,,431.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.652,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.248","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,0.652,,not harmonised,,,,,,431.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,Type Neigh.,other,other,level,index,-1.684,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -1.086,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,-1.684,,not harmonised,,,,,,431.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m18,20,18,66,m2 avg. Price,other,other,level,currency,-0.001,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.306","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,441.0,,none,"Market 18: Montevideo neighbourhood group 18 (Table 15); Box-Cox with common lambda=0.325 for dependent variable and transformed regressors (income, age, education of head)",other,-0.001,,not harmonised,,,,,,431.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Bedrooms,bedrooms,A_structural,level,count,2.182,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.131,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,2.182,,not harmonised,,,,,,505.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Bathrooms,bathrooms,A_structural,level,count,1.049,,,,,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.035,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,1.049,,not harmonised,,,,,,505.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,WES,other,other,binary,binary,5.275,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.169","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,5.275,,not harmonised,,,,,,505.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Severe Problems,condition,A_structural,dummy,binary,-5.948,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.191,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,-5.948,,not harmonised,,,,,,505.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Medium Problems,condition,A_structural,dummy,binary,-2.487,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.08,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,-2.487,,not harmonised,,,,,,505.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Small Problems,condition,A_structural,dummy,binary,-2.556,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.082,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,-2.556,,not harmonised,,,,,,505.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Years of construction,age,A_structural,other,index,-0.573,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.062","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,-0.573,,not harmonised,,,,,,505.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,House,dwelling_type,A_structural,dummy,binary,5.428,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.174,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,5.428,,not harmonised,,,,,,505.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Construction Type,condition,A_structural,other,index,3.207,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.252","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,3.207,,not harmonised,,,,,,505.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.529,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.068","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,0.529,,not harmonised,,,,,,505.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,Type Neigh.,other,other,level,index,-7.093,,,,***,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -1.714,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,-7.093,,not harmonised,,,,,,505.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m19,20,19,66,m2 avg. Price,other,other,level,currency,-0.002,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.205","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,515.0,,none,"Market 19: Montevideo neighbourhood group 19 (Table 15); Box-Cox with common lambda=0.466 for dependent variable and transformed regressors (income, age, education of head)",other,-0.002,,not harmonised,,,,,,505.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Bedrooms,bedrooms,A_structural,level,count,1.905,,,,*,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.107,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,1.905,,not harmonised,,,,,,278.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Bathrooms,bathrooms,A_structural,level,count,1.787,,,,*,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.057,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,1.787,,not harmonised,,,,,,278.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,WES,other,other,binary,binary,2.164,,,,,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.066","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,2.164,,not harmonised,,,,,,278.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Severe Problems,condition,A_structural,dummy,binary,-6.32,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.193,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,-6.32,,not harmonised,,,,,,278.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Medium Problems,condition,A_structural,dummy,binary,-3.704,,,,*,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.113,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,-3.704,,not harmonised,,,,,,278.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Small Problems,condition,A_structural,dummy,binary,-1.245,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.038,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,-1.245,,not harmonised,,,,,,278.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Years of construction,age,A_structural,other,index,0.721,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.088","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,0.721,,not harmonised,,,,,,278.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,House,dwelling_type,A_structural,dummy,binary,9.651,,,,***,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.295,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,9.651,,not harmonised,,,,,,278.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Construction Type,condition,A_structural,other,index,6.114,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.486","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,6.114,,not harmonised,,,,,,278.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,2.712,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.445","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,2.712,,not harmonised,,,,,,278.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,Type Neigh.,other,other,level,index,-3.835,,,,*,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.838,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,-3.835,,not harmonised,,,,,,278.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m20,20,20,66,m2 avg. Price,other,other,level,currency,0.004,,,,,,,medium,"Printed name: m2 avg. Price = average price of a constructed square metre in the neighbourhood (Montevideo only), UYU; coefficient printed with 3 decimals (often 0.000); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.383","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,288.0,,none,"Market 20: Montevideo neighbourhood group 20 (Table 15); Box-Cox with common lambda=0.461 for dependent variable and transformed regressors (income, age, education of head)",other,0.004,,not harmonised,,,,,,278.0,currency,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Bedrooms,bedrooms,A_structural,level,count,0.067,,,,**,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,6.7,,100*b/1 (per unit count),,,,,,462.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Bathrooms,bathrooms,A_structural,level,count,0.251,,,,**,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,25.1,,100*b/1 (per unit count),,,,,,462.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,WES,other,other,binary,binary,0.254,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,28.91718042678042,,100*(exp(b) - 1),,,,,,462.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Severe Problems,condition,A_structural,dummy,binary,-0.099,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-9.42572919764515,,100*(exp(b) - 1),,,,,,462.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Medium Problems,condition,A_structural,dummy,binary,-0.024,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-2.3714290242090708,,100*(exp(b) - 1),,,,,,462.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Small Problems,condition,A_structural,dummy,binary,0.055,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,5.654061467549432,,100*(exp(b) - 1),,,,,,462.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Years of construction,age,A_structural,other,index,0.008,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.008,,not harmonised,,,,,,462.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,House,dwelling_type,A_structural,dummy,binary,0.144,,,,,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,15.488410852491352,,100*(exp(b) - 1),,,,,,462.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Construction Type,condition,A_structural,other,index,0.214,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.214,,not harmonised,,,,,,462.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T20-m21,20,21†,66,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.065,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,472.0,,none,Market 21: upcountry region group 21 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,6.5,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,462.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Bedrooms,bedrooms,A_structural,level,count,0.075,,,,***,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,7.5,,100*b/1 (per unit count),,,,,,915.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Bathrooms,bathrooms,A_structural,level,count,0.277,,,,***,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,27.700000000000003,,100*b/1 (per unit count),,,,,,915.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,WES,other,other,binary,binary,0.197,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,21.774404070590837,,100*(exp(b) - 1),,,,,,915.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Severe Problems,condition,A_structural,dummy,binary,-0.195,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-17.716534194398157,,100*(exp(b) - 1),,,,,,915.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Medium Problems,condition,A_structural,dummy,binary,-0.089,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-8.515442642554804,,100*(exp(b) - 1),,,,,,915.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Small Problems,condition,A_structural,dummy,binary,-0.013,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-1.2915864979712421,,100*(exp(b) - 1),,,,,,915.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Years of construction,age,A_structural,other,index,0.0,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.0,,not harmonised,,,,,,915.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,House,dwelling_type,A_structural,dummy,binary,0.073,,,,,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,7.573053691470344,,100*(exp(b) - 1),,,,,,915.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Construction Type,condition,A_structural,other,index,0.172,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.172,,not harmonised,,,,,,915.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.065,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,6.5,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,915.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m22,21,22†,68,Type Neigh.,other,other,level,index,-0.066,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,925.0,,none,Market 22: upcountry region group 22 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,-6.6000000000000005,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,915.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Bedrooms,bedrooms,A_structural,level,count,0.022,,,,,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,2.1999999999999997,,100*b/1 (per unit count),,,,,,344.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Bathrooms,bathrooms,A_structural,level,count,0.272,,,,**,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,27.200000000000003,,100*b/1 (per unit count),,,,,,344.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,WES,other,other,binary,binary,0.297,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,34.581529944809766,,100*(exp(b) - 1),,,,,,344.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Severe Problems,condition,A_structural,dummy,binary,-0.285,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-24.79857456806174,,100*(exp(b) - 1),,,,,,344.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Medium Problems,condition,A_structural,dummy,binary,-0.071,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-6.853810787240788,,100*(exp(b) - 1),,,,,,344.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Small Problems,condition,A_structural,dummy,binary,-0.12,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-11.307956328284252,,100*(exp(b) - 1),,,,,,344.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Years of construction,age,A_structural,other,index,0.003,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.003,,not harmonised,,,,,,344.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,House,dwelling_type,A_structural,dummy,binary,-0.011,,,,,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-1.0939721224631271,,100*(exp(b) - 1),,,,,,344.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Construction Type,condition,A_structural,other,index,0.177,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.177,,not harmonised,,,,,,344.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.016,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,1.6,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,344.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m24,21,24†,68,Type Neigh.,other,other,level,index,-0.009,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,354.0,,none,Market 24: upcountry region group 24 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,-0.8999999999999999,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,344.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Bedrooms,bedrooms,A_structural,level,count,0.092,,,,***,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,9.2,,100*b/1 (per unit count),,,,,,1491.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Bathrooms,bathrooms,A_structural,level,count,0.052,,,,***,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,5.2,,100*b/1 (per unit count),,,,,,1491.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,WES,other,other,binary,binary,0.22,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,24.60767305873808,,100*(exp(b) - 1),,,,,,1491.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Severe Problems,condition,A_structural,dummy,binary,-0.245,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-21.729546175813187,,100*(exp(b) - 1),,,,,,1491.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Medium Problems,condition,A_structural,dummy,binary,-0.081,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-7.7806308555391945,,100*(exp(b) - 1),,,,,,1491.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Small Problems,condition,A_structural,dummy,binary,-0.024,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-2.3714290242090708,,100*(exp(b) - 1),,,,,,1491.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Years of construction,age,A_structural,other,index,0.022,,,,**,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.022,,not harmonised,,,,,,1491.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,House,dwelling_type,A_structural,dummy,binary,0.084,,,,**,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,8.76288938088261,,100*(exp(b) - 1),,,,,,1491.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Construction Type,condition,A_structural,other,index,0.17,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.17,,not harmonised,,,,,,1491.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.096,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,9.6,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1491.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m26,21,26†,68,Type Neigh.,other,other,level,index,-0.028,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,1501.0,,none,Market 26: upcountry region group 26 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,-2.8000000000000003,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1491.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Bedrooms,bedrooms,A_structural,level,count,0.889,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.137,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,0.889,,not harmonised,,,,,,403.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Bathrooms,bathrooms,A_structural,level,count,0.241,,,,,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.021,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,0.241,,not harmonised,,,,,,403.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,WES,other,other,binary,binary,3.474,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.284","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,3.474,,not harmonised,,,,,,403.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Severe Problems,condition,A_structural,dummy,binary,-0.52,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.042,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,-0.52,,not harmonised,,,,,,403.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Medium Problems,condition,A_structural,dummy,binary,-1.066,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.087,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,-1.066,,not harmonised,,,,,,403.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Small Problems,condition,A_structural,dummy,binary,0.004,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.0,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,0.004,,not harmonised,,,,,,403.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Years of construction,age,A_structural,other,index,0.361,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.118","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,0.361,,not harmonised,,,,,,403.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,House,dwelling_type,A_structural,dummy,binary,-4.307,,,,,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.352,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,-4.307,,not harmonised,,,,,,403.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Construction Type,condition,A_structural,other,index,1.557,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.27","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,1.557,,not harmonised,,,,,,403.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m28,21,28,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,-0.19,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.062","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,413.0,,none,"Market 28: upcountry region group 28 (Tables 15-16); Box-Cox with common lambda=0.355 for dependent variable and transformed regressors (income, age, education of head)",other,-0.19,,not harmonised,,,,,,403.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Bedrooms,bedrooms,A_structural,level,count,0.116,,,,***,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.12,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,0.116,,not harmonised,,,,,,872.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Bathrooms,bathrooms,A_structural,level,count,0.107,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.064,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,0.107,,not harmonised,,,,,,872.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,WES,other,other,binary,binary,0.465,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.244","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,0.465,,not harmonised,,,,,,872.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Severe Problems,condition,A_structural,dummy,binary,-0.371,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.195,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,-0.371,,not harmonised,,,,,,872.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Medium Problems,condition,A_structural,dummy,binary,-0.076,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.04,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,-0.076,,not harmonised,,,,,,872.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Small Problems,condition,A_structural,dummy,binary,-0.009,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.004,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,-0.009,,not harmonised,,,,,,872.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Years of construction,age,A_structural,other,index,-0.011,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.024","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,-0.011,,not harmonised,,,,,,872.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,House,dwelling_type,A_structural,dummy,binary,0.236,,,,**,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.124,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,0.236,,not harmonised,,,,,,872.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Construction Type,condition,A_structural,other,index,0.397,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.535","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,0.397,,not harmonised,,,,,,872.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.149,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.442","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,0.149,,not harmonised,,,,,,872.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m29,21,29,68,Type Neigh.,other,other,level,index,-0.217,,,,***,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.254,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,882.0,,none,"Market 29: upcountry region group 29 (Tables 15-16); Box-Cox with common lambda=0.085 for dependent variable and transformed regressors (income, age, education of head)",other,-0.217,,not harmonised,,,,,,872.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Bedrooms,bedrooms,A_structural,level,count,0.096,,,,**,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,9.6,,100*b/1 (per unit count),,,,,,434.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Bathrooms,bathrooms,A_structural,level,count,0.083,,,,*,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,8.3,,100*b/1 (per unit count),,,,,,434.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,WES,other,other,binary,binary,0.174,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,19.005556582036263,,100*(exp(b) - 1),,,,,,434.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Severe Problems,condition,A_structural,dummy,binary,-0.068,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-6.5739526422786465,,100*(exp(b) - 1),,,,,,434.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Medium Problems,condition,A_structural,dummy,binary,0.039,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,3.9770483650157784,,100*(exp(b) - 1),,,,,,434.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Small Problems,condition,A_structural,dummy,binary,0.025,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,2.531512052442886,,100*(exp(b) - 1),,,,,,434.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Years of construction,age,A_structural,other,index,-0.012,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,-0.012,,not harmonised,,,,,,434.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,House,dwelling_type,A_structural,dummy,binary,0.167,,,,*,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,18.17542653083615,,100*(exp(b) - 1),,,,,,434.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Construction Type,condition,A_structural,other,index,0.101,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.101,,not harmonised,,,,,,434.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m31,21,31†,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.075,,,,**,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,444.0,,none,Market 31: upcountry region group 31 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,7.5,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,434.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Bedrooms,bedrooms,A_structural,level,count,0.411,,,,***,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.167,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,0.411,,not harmonised,,,,,,1588.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Bathrooms,bathrooms,A_structural,level,count,0.365,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.085,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,0.365,,not harmonised,,,,,,1588.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,WES,other,other,binary,binary,0.991,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.199","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,0.991,,not harmonised,,,,,,1588.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Severe Problems,condition,A_structural,dummy,binary,-1.002,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.201,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,-1.002,,not harmonised,,,,,,1588.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Medium Problems,condition,A_structural,dummy,binary,-0.686,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.138,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,-0.686,,not harmonised,,,,,,1588.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Small Problems,condition,A_structural,dummy,binary,-0.352,,,,**,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.071,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,-0.352,,not harmonised,,,,,,1588.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Years of construction,age,A_structural,other,index,0.038,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.029","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,0.038,,not harmonised,,,,,,1588.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,House,dwelling_type,A_structural,dummy,binary,1.283,,,,***,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.258,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,1.283,,not harmonised,,,,,,1588.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Construction Type,condition,A_structural,other,index,0.604,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.307","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,0.604,,not harmonised,,,,,,1588.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.364,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.431","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,0.364,,not harmonised,,,,,,1588.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T21-m32,21,32,68,Type Neigh.,other,other,level,index,-0.263,,,,***,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.127,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,1598.0,,none,"Market 32: upcountry region group 32 (Tables 15-16); Box-Cox with common lambda=0.21 for dependent variable and transformed regressors (income, age, education of head)",other,-0.263,,not harmonised,,,,,,1588.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Bedrooms,bedrooms,A_structural,level,count,0.044,,,,,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,4.3999999999999995,,100*b/1 (per unit count),,,,,,218.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Bathrooms,bathrooms,A_structural,level,count,0.339,,,,***,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,33.900000000000006,,100*b/1 (per unit count),,,,,,218.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,WES,other,other,binary,binary,0.247,,,,*,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,28.017911277827,,100*(exp(b) - 1),,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Severe Problems,condition,A_structural,dummy,binary,-0.171,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-15.71784265283801,,100*(exp(b) - 1),,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Medium Problems,condition,A_structural,dummy,binary,-0.171,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-15.71784265283801,,100*(exp(b) - 1),,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Small Problems,condition,A_structural,dummy,binary,0.042,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Years of construction,age,A_structural,other,index,0.0,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.0,,not harmonised,,,,,,218.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,House,dwelling_type,A_structural,dummy,binary,0.217,,,,,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,24.234410213776393,,100*(exp(b) - 1),,,,,,218.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Construction Type,condition,A_structural,other,index,0.195,,,,**,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.195,,not harmonised,,,,,,218.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.037,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,3.6999999999999997,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,218.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m34,22,34†,70,Type Neigh.,other,other,level,index,-0.148,,,,***,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,228.0,,none,Market 34: upcountry region group 34 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,-14.799999999999999,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,218.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Bedrooms,bedrooms,A_structural,level,count,6.852,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.111,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,6.852,,not harmonised,,,,,,474.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Bathrooms,bathrooms,A_structural,level,count,24.946,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.215,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,24.946,,not harmonised,,,,,,474.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,WES,other,other,binary,binary,9.928,,,,,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.082","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,9.928,,not harmonised,,,,,,474.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Severe Problems,condition,A_structural,dummy,binary,-5.351,,,,,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.044,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,-5.351,,not harmonised,,,,,,474.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Medium Problems,condition,A_structural,dummy,binary,-20.104,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.166,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,-20.104,,not harmonised,,,,,,474.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Small Problems,condition,A_structural,dummy,binary,-17.392,,,,**,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.144,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,-17.392,,not harmonised,,,,,,474.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Years of construction,age,A_structural,other,index,4.796,,,,**,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.146","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,4.796,,not harmonised,,,,,,474.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,House,dwelling_type,A_structural,dummy,binary,1.718,,,,,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.014,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,1.718,,not harmonised,,,,,,474.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Construction Type,condition,A_structural,other,index,17.097,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.358","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,17.097,,not harmonised,,,,,,474.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Neigh. Serv.,public_services,F_neighbourhood,level,index,6.963,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.248","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,6.963,,not harmonised,,,,,,474.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m35,22,35,70,Type Neigh.,other,other,level,index,-5.019,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.1,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,484.0,,none,"Market 35: upcountry region group 35 (Tables 15-16); Box-Cox with common lambda=0.632 for dependent variable and transformed regressors (income, age, education of head)",other,-5.019,,not harmonised,,,,,,474.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Bedrooms,bedrooms,A_structural,level,count,0.219,,,,**,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.141,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.219,,not harmonised,,,,,,709.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Bathrooms,bathrooms,A_structural,level,count,0.514,,,,***,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.193,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.514,,not harmonised,,,,,,709.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,WES,other,other,binary,binary,0.499,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.167","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.499,,not harmonised,,,,,,709.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Severe Problems,condition,A_structural,dummy,binary,-1.203,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.402,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,-1.203,,not harmonised,,,,,,709.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Medium Problems,condition,A_structural,dummy,binary,-0.342,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.114,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,-0.342,,not harmonised,,,,,,709.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Small Problems,condition,A_structural,dummy,binary,-0.262,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.088,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,-0.262,,not harmonised,,,,,,709.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Years of construction,age,A_structural,other,index,0.021,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.024","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.021,,not harmonised,,,,,,709.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,House,dwelling_type,A_structural,dummy,binary,-0.063,,,,,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.021,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,-0.063,,not harmonised,,,,,,709.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Construction Type,condition,A_structural,other,index,0.636,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.5","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.636,,not harmonised,,,,,,709.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.003,,,,,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.004","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.003,,not harmonised,,,,,,709.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m36,22,36,70,Type Neigh.,other,other,level,index,0.005,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = n/a,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,719.0,,none,"Market 36: upcountry region group 36 (Tables 15-16); Box-Cox with common lambda=0.147 for dependent variable and transformed regressors (income, age, education of head)",other,0.005,,not harmonised,,,,,,709.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Bedrooms,bedrooms,A_structural,level,count,0.074,,,,***,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,7.3999999999999995,,100*b/1 (per unit count),,,,,,670.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Bathrooms,bathrooms,A_structural,level,count,0.126,,,,***,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,12.6,,100*b/1 (per unit count),,,,,,670.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,WES,other,other,binary,binary,0.279,,,,***,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,32.18073438695392,,100*(exp(b) - 1),,,,,,670.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Severe Problems,condition,A_structural,dummy,binary,-0.271,,,,***,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-23.738350359493467,,100*(exp(b) - 1),,,,,,670.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Medium Problems,condition,A_structural,dummy,binary,-0.127,,,,***,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-11.926632740284305,,100*(exp(b) - 1),,,,,,670.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Small Problems,condition,A_structural,dummy,binary,-0.117,,,,**,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-11.041480683658866,,100*(exp(b) - 1),,,,,,670.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Years of construction,age,A_structural,other,index,0.015,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.015,,not harmonised,,,,,,670.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,House,dwelling_type,A_structural,dummy,binary,0.249,,,,***,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,28.27420330698114,,100*(exp(b) - 1),,,,,,670.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Construction Type,condition,A_structural,other,index,0.104,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.104,,not harmonised,,,,,,670.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.039,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,3.9,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,670.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m37,22,37†,70,Type Neigh.,other,other,level,index,-0.035,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,680.0,,none,Market 37: upcountry region group 37 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,-3.5000000000000004,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,670.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Bedrooms,bedrooms,A_structural,level,count,0.058,,,,**,,,medium,Number of bedrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,5.800000000000001,,100*b/1 (per unit count),,,,,,718.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Bathrooms,bathrooms,A_structural,level,count,0.093,,,,***,,,medium,Number of full bathrooms; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,9.3,,100*b/1 (per unit count),,,,,,718.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,WES,other,other,binary,binary,0.186,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,20.44222603776298,,100*(exp(b) - 1),,,,,,718.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Severe Problems,condition,A_structural,dummy,binary,-0.152,,,,*,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-14.101171925887657,,100*(exp(b) - 1),,,,,,718.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Medium Problems,condition,A_structural,dummy,binary,-0.072,,,,,,,medium,Dummy: housing unit has medium (average) construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-6.946910418879426,,100*(exp(b) - 1),,,,,,718.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Small Problems,condition,A_structural,dummy,binary,-0.045,,,,,,,medium,Dummy: housing unit has small construction problems; log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-4.4002518166900035,,100*(exp(b) - 1),,,,,,718.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Years of construction,age,A_structural,other,index,0.016,,,,,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.016,,not harmonised,,,,,,718.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,House,dwelling_type,A_structural,dummy,binary,-0.035,,,,,,,medium,Dummy: unit is a house (vs apartment); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),pct_premium,-3.4394583742433538,,100*(exp(b) - 1),,,,,,718.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Construction Type,condition,A_structural,other,index,0.168,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),other,0.168,,not harmonised,,,,,,718.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Neigh. Serv.,public_services,F_neighbourhood,level,index,0.064,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); log-linear model: coefficient is the semi-elasticity/elasticity directly","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,6.4,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,718.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m38,22,38†,70,Type Neigh.,other,other,level,index,-0.034,,,,**,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); log-linear model: coefficient is the semi-elasticity/elasticity directly,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",log,OLS,728.0,,none,Market 38: upcountry region group 38 (Tables 15-16); log-linear specification (theta=lambda=0 not rejected),semi_elasticity,-3.4000000000000004,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,718.0,index,unit_not_harmonisable,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,ols,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Bedrooms,bedrooms,A_structural,level,count,0.449,,,,,,,medium,Number of bedrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.084,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,0.449,,not harmonised,,,,,,366.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Bathrooms,bathrooms,A_structural,level,count,2.208,,,,**,,,medium,Number of full bathrooms; Box-Cox coefficient (not directly interpretable); printed elasticity = 0.245,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,2.208,,not harmonised,,,,,,366.0,count,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,WES,other,other,binary,binary,1.913,,,,**,,,medium,"Printed name: WES = dummy for access to basic services (water, electricity, sewerage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.196","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,1.913,,not harmonised,,,,,,366.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Severe Problems,condition,A_structural,dummy,binary,-2.666,,,,**,,,medium,Dummy: housing unit has severe construction/structural problems (omitted: no problems); Box-Cox coefficient (not directly interpretable); printed elasticity = -0.273,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,-2.666,,not harmonised,,,,,,366.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Medium Problems,condition,A_structural,dummy,binary,-1.24,,,,**,,,medium,Dummy: housing unit has medium (average) construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.127,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,-1.24,,not harmonised,,,,,,366.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Small Problems,condition,A_structural,dummy,binary,-0.835,,,,,,,medium,Dummy: housing unit has small construction problems; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.085,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,-0.835,,not harmonised,,,,,,366.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Years of construction,age,A_structural,other,index,-0.359,,,,*,,,medium,"Categorical construction-period variable (edadvivienda 1-5, construction era of the dwelling), NOT age in years; Box-Cox coefficient (not directly interpretable); printed elasticity = -0.149","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,-0.359,,not harmonised,,,,,,366.0,index,year_built_not_age;nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,House,dwelling_type,A_structural,dummy,binary,3.468,,,,***,,,medium,Dummy: unit is a house (vs apartment); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.354,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,3.468,,not harmonised,,,,,,366.0,binary,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Construction Type,condition,A_structural,other,index,1.559,,,,***,,,medium,"Ordinal construction quality 1=poor, 2=medium, 3=good/very good (tipo_construccion); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.388","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,1.559,,not harmonised,,,,,,366.0,index,nonstandard_x_transform,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Neigh. Serv.,public_services,F_neighbourhood,level,index,1.074,,,,***,,,medium,"Neighbourhood services index 0-10 (count of: electricity, water, sanitation, gas, waste collection, bins, paved street, sidewalks, lighting, drainage); Box-Cox coefficient (not directly interpretable); printed elasticity = 0.549","monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,1.074,,not harmonised,,,,,,366.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06,1b4b730b1b7e5c68c755a7ae46919b1120fc8c06:T22-m39,22,39,70,Type Neigh.,other,other,level,index,-0.285,,,,,,,medium,Printed name: Type Neigh. = neighbourhood socioeconomic/safety type 1 (best) to 8 (irregular settlement); Box-Cox coefficient (not directly interpretable); printed elasticity = n/a,"monthly rent (actual rent for tenants, self-estimated rental value for owners/occupants), UYU",boxcox,other,376.0,,none,"Market 39: upcountry region group 39 (Tables 15-16); Box-Cox with common lambda=0.303 for dependent variable and transformed regressors (income, age, education of head)",other,-0.285,,not harmonised,,,,,,366.0,index,,2011.0,Uruguay,South America,High income,2006.0,2006.0,mixed,rent,working_paper,False,False,unknown,,2006.0,other,other,False,False,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,beta1 (ln house plot area),lot_size,A_structural,log,m2,0.0785,,,,,0.0633,0.0936,high,ln plot area (m2); lower-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,0.0785,0.007729733811437355,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.27567249359523277,0.027144904390144182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.155589042904289,1254.0,m2,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,beta1 (ln house plot area),lot_size,A_structural,log,m2,0.072,,,,,0.0559,0.088,high,ln plot area (m2); lower-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,0.072,0.008188925919047492,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.24097204887607834,0.027406975788990245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.792361869158878,1254.0,m2,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,beta1 (ln house plot area),lot_size,A_structural,log,m2,0.0696,,,,,0.0538,0.0854,high,ln plot area (m2); lower-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,0.0696,0.008061372555822455,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.2368713952267552,0.027435467884200267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.63376546835443,1254.0,m2,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,beta2 (ln travel time to CBD),cbd_distance,D_location,log,minutes,0.613,,,,,0.426,0.801,high,ln estimated travel time to CBD San Jose (min); quadratic in logs with beta3. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,0.613,0.09566502241877914,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.17805838132854845,0.02778786140562335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.407775637333332,1254.0,minutes,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,beta2 (ln travel time to CBD),cbd_distance,D_location,log,minutes,0.506,,,,,0.264,0.756,high,ln estimated travel time to CBD San Jose (min); quadratic in logs with beta3. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,0.506,0.1255125094134382,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.11311453937217979,0.028057884758394148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.031470666666667,1254.0,minutes,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,beta2 (ln travel time to CBD),cbd_distance,D_location,log,minutes,0.383,,,,,0.118,0.647,high,ln estimated travel time to CBD San Jose (min); quadratic in logs with beta3. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,0.383,0.13495145829209107,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.07988810602187686,0.028148867905601737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.838057512287335,1254.0,minutes,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,beta3 (ln travel time to CBD squared),cbd_distance,D_location,other,minutes,-0.113,,,,,-0.148,-0.0779,high,Square of ln travel time to CBD. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",other,-0.113,0.01788298152415044,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.17566428335957662,0.027800010033384396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.3188568330955786,1254.0,minutes,nonstandard_x_transform,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,False,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,beta3 (ln travel time to CBD squared),cbd_distance,D_location,other,minutes,-0.0945,,,,,-0.142,-0.0491,high,Square of ln travel time to CBD. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,other,-0.0945,0.02369941488721221,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.11189469010575634,0.02806178502108316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.987440215285254,1254.0,minutes,nonstandard_x_transform,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,False,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,beta3 (ln travel time to CBD squared),cbd_distance,D_location,other,minutes,-0.0723,,,,,-0.125,-0.0198,high,Square of ln travel time to CBD. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,other,-0.0723,0.026837227622548167,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.07585753492344127,0.028157758390398205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6940189581749054,1254.0,minutes,nonstandard_x_transform,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,False,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,beta4 (ln distance to major road),highway_distance,D_location,log,m,-0.0111,,,,,-0.0207,-0.00147,high,ln Euclidean distance to nearest major road (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,-0.0111,0.004905702349634993,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.06376586819417203,0.028181654903303148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.262672948517941,1254.0,m,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,beta4 (ln distance to major road),highway_distance,D_location,log,m,-0.012,,,,,-0.0224,-0.00165,high,ln Euclidean distance to nearest major road (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,-0.012,0.005293464573839111,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.06388580762123373,0.028181438284508455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2669463132530123,1254.0,m,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,beta4 (ln distance to major road),highway_distance,D_location,log,m,-0.0113,,,,,-0.0218,-0.000723,high,ln Euclidean distance to nearest major road (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,-0.0113,0.005376884473388286,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.05924279036412947,0.0281895256344323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1015887650045073,1254.0,m,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,beta5 (ln distance to bus route),transit_distance,D_location,log,m,0.0112,,,,,0.00463,0.0178,high,ln Euclidean distance to nearest bus route (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,0.0112,0.003359755587347523,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.09372290153620519,0.02811482518738278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.333575823842065,1254.0,m,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,beta5 (ln distance to bus route),transit_distance,D_location,log,m,0.0084,,,,,0.00183,0.015,high,ln Euclidean distance to nearest bus route (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,0.0084,0.0033597555873475225,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.0704276328405674,0.02816900392139955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.500181867881549,1254.0,m,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,beta5 (ln distance to bus route),transit_distance,D_location,log,m,0.00806,,,,,0.0016,0.0145,high,ln Euclidean distance to nearest bus route (m). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,0.00806,0.0032908767712060017,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.06899830111515441,0.028171824614458912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.449195324031008,1254.0,m,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,gamma2 (ln crime rate),crime,F_neighbourhood,log,count,0.000414,,,,,-0.00336,0.00419,high,"ln homicides per 100,000 residents in district (2018), 0.001 added; district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.","ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,0.000414,0.0019260557846980863,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.00606980460729523,0.028238604531082307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21494704529801323,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,gamma2 (ln crime rate),crime,F_neighbourhood,log,count,-6.36e-05,,,,,-0.00642,0.00641,high,"ln homicides per 100,000 residents in district (2018), 0.001 added; district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.","ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,-6.36e-05,0.0032730193003544963,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.000548731277764295,0.028239120484759773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.019431599438815277,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,gamma2 (ln crime rate),crime,F_neighbourhood,log,count,0.000859,,,,,-0.00455,0.00622,high,"ln homicides per 100,000 residents in district (2018), 0.001 added; district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.","ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,0.000859,0.002747499443867336,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.008828559667563997,0.02823802418717308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3126479249767874,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,gamma3 (ln number of jobs in district),employment_access,D_location,log,count,0.0188,,,,,0.00934,0.0283,high,ln number of jobs in district (2018); district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,0.0188,0.004836823533493471,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.10910593753869006,0.028070540762287488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8868484388185665,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,gamma3 (ln number of jobs in district),employment_access,D_location,log,count,0.0203,,,,,0.00406,0.0367,high,ln number of jobs in district (2018); district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,0.0203,0.008326683551330535,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.06868286919853864,0.02817243927160879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4379454166666665,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,gamma3 (ln number of jobs in district),employment_access,D_location,log,count,0.0108,,,,,-0.00437,0.0261,high,ln number of jobs in district (2018); district-level covariate. Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.,"ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,0.0108,0.0077731019549338665,b (log-log),(ci_hi-ci_lo)/(2*1.96),0.03920546382433876,0.02821741365713487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3894067082376107,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c1,3,1,10,gamma4 (ln poverty proxy),income,F_neighbourhood,log,count,-0.0195,,,,,-0.0346,-0.00427,high,"ln number of households with unsatisfied housing needs (Census 2011), poverty proxy; district-level covariate; closest leaf income (sign negative = poorer). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.","ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,"Linear model without multilevel effects (Bayesian, WinBUGS); posterior means; dependent variable is log of price per m2",elasticity,-0.0195,0.007737387013230855,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.07098955259457293,0.028167879093349602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5202306627101883,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c2,3,2,10,gamma4 (ln poverty proxy),income,F_neighbourhood,log,count,-0.0259,,,,,-0.0542,0.00214,high,"ln number of households with unsatisfied housing needs (Census 2011), poverty proxy; district-level covariate; closest leaf income (sign negative = poorer). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.","ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random-intercept model (district level); posterior means; dependent variable is log of price per m2,elasticity,-0.0259,0.014372712968197376,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.050821871787894844,0.02820263226694751,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8020258288959885,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,True
1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c,1cd473df8f393883694de4530b0d1c667382890c:T3-c3,3,3,10,gamma4 (ln poverty proxy),income,F_neighbourhood,log,count,-0.0117,,,,,-0.0349,0.0114,high,"ln number of households with unsatisfied housing needs (Census 2011), poverty proxy; district-level covariate; closest leaf income (sign negative = poorer). Bayesian posterior mean with 2.5% and 97.5% percentiles (95% posterior interval) recorded as ci_lo/ci_hi; no SE or stars printed.","ln housing price per m2 of living area (US$/m2), from listings",log,other,1264.0,,none,Multilevel random intercepts with intrinsic CAR spatial effect at district level; posterior means; dependent variable is log of price per m2,elasticity,-0.0117,0.011811441434638595,b (log-log),(ci_hi-ci_lo)/(2*1.96),-0.027961749942060028,0.02822808306415821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.990564958963283,1254.0,count,,2021.0,Costa Rica,,,,,single_family,listing,journal,False,True,unknown,,2018.0,other,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Number of rooms (ln),bedrooms,A_structural,log,count,0.64,0.052,,0.0,***,,,high,number of rooms (log),ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,elasticity,0.64,0.052,b (log-log),reported,0.3016736878466599,0.024510987137541112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.307692307692308,1513.0,count,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Number of rooms (ln),bedrooms,A_structural,log,count,0.556,0.052,,0.0,***,,,high,number of rooms (log),ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,elasticity,0.556,0.052,b (log-log),reported,0.2676990327819065,0.025036600188235857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.692307692307693,1481.0,count,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Number of toilets (0/1/2),bathrooms,A_structural,level,"count (0 = none, 1 = one, 2 = two or more sealed toilets)",0.467,0.052,,0.0,***,,,high,,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,semi_elasticity,46.7,5.2,100*b/1 (per unit count),reported,0.22496579409640255,0.0250497243961733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.980769230769232,1513.0,count,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,True
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Number of toilets (0/1/2),bathrooms,A_structural,level,"count (0 = none, 1 = one, 2 = two or more sealed toilets)",0.334,0.052,,0.0,***,,,high,,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,semi_elasticity,33.4,5.2,100*b/1 (per unit count),reported,0.16462632663858204,0.025630446063491817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.423076923076923,1481.0,count,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,True
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Wall is made of red bricks or cement (otherwise reference),roof_materials,A_structural,binary,binary,0.929,0.113,,0.0,***,,,high,wall material,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,pct_premium,151.58620292662155,28.611328068579404,100*(exp(b - se^2/2) - 1),reported,0.2067891864268558,0.025153044204773625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.221238938053098,1513.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,True
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Wall is made of red bricks or cement (otherwise reference),roof_materials,A_structural,binary,binary,0.847,0.111,,0.0,***,,,high,wall material,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,pct_premium,131.8312383328244,25.892286564791856,100*(exp(b - se^2/2) - 1),reported,0.19449532296836514,0.025488761333516564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.63063063063063,1481.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Roof is made of tiles (otherwise reference case),roof_materials,A_structural,binary,binary,0.143,0.054,,0.008,**,,,high,roof material,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,pct_premium,15.204888929806625,6.230140928996457,100*(exp(b - se^2/2) - 1),reported,0.06792328332438545,0.02564935174487283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.648148148148148,1513.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Roof is made of tiles (otherwise reference case),roof_materials,A_structural,binary,binary,0.138,0.053,,0.009,**,,,high,roof material,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,pct_premium,14.636435034382679,6.084270416453147,100*(exp(b - se^2/2) - 1),reported,0.06750468447641196,0.025925712153984298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6037735849056607,1481.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Overhead storage tank (not available reference case),other,other,binary,binary (water storage tank),0.323,0.048,,0.0,***,,,high,water storage tank present,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,pct_premium,37.96750494109222,6.630073684347025,100*(exp(b - se^2/2) - 1),reported,0.1704662085935036,0.02533243966714604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.729166666666667,1513.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,False,False,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Overhead storage tank (not available reference case),other,other,binary,binary (water storage tank),0.219,0.048,,0.0,***,,,high,water storage tank present,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,pct_premium,24.339805674997383,5.97519012810015,100*(exp(b - se^2/2) - 1),reported,0.11773198197794836,0.025804270022564024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5625,1481.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,False,False,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Mean household income by municipality (ln),income,F_neighbourhood,log,"currency (LKR, mean household income by municipality), log",0.355,0.112,,0.002,***,,,high,,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,elasticity,0.355,0.112,b (log-log),reported,0.08121826907175965,0.02562379193249882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1696428571428568,1513.0,currency log,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Mean household income by municipality (ln),income,F_neighbourhood,log,"currency (LKR, mean household income by municipality), log",0.31,0.109,,0.005,***,,,high,,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,elasticity,0.31,0.109,b (log-log),reported,0.07370126371713216,0.02591431530699163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8440366972477062,1481.0,currency log,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Percentage of Samurdhi recipients by municipality,other,other,level,share (proportion of welfare (Samurdhi) recipients in municipality),-1.856,0.414,,0.0,***,,,high,neighbourhood poverty proxy,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,semi_elasticity,-185.60000000000002,41.4,100*b (unit as reported: share recipients in municipality); no harmonised unit defined for this characteristic),reported,-0.11449661738848935,0.025539654956268636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.483091787439614,1513.0,share recipients in municipality),unit_not_harmonisable,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,False,False,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Percentage of Samurdhi recipients by municipality,other,other,level,share (proportion of welfare (Samurdhi) recipients in municipality),-1.553,0.406,,0.0,***,,,high,neighbourhood poverty proxy,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,semi_elasticity,-155.29999999999998,40.6,100*b (unit as reported: share recipients in municipality); no harmonised unit defined for this characteristic),reported,-0.09890838501100045,0.025857568779437342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.825123152709359,1481.0,share recipients in municipality),unit_not_harmonisable,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,False,False,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Distance to road in km (ln),highway_distance,D_location,log,km,-0.071,0.022,,0.001,***,,,high,distance to paved road,ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,elasticity,-0.071,0.022,b (log-log),reported,-0.08268495853207729,0.025620691376136626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.227272727272727,1513.0,km,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Distance to road in km (ln),highway_distance,D_location,log,km,-0.071,0.021,,0.0,***,,,high,distance to paved road,ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,elasticity,-0.071,0.021,b (log-log),reported,-0.08751690421358357,0.02588528152796134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3809523809523805,1481.0,km,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,True,True,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T2-c1,2,1,10,Household has access to private piped water connection (0/1),other,other,binary,binary,0.195,0.048,,0.0,***,,,high,key variable: private piped water connection (reference: non-piped sources only),ln of house appraisal value (LKR),log,OLS,1523.0,0.41,none,conventional hedonic model; SE and p value in separate columns; adj. R2,pct_premium,21.391175434575338,5.833492735150707,100*(exp(b - se^2/2) - 1),reported,0.10387668431550709,0.02556964536997098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0625,1513.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,False,False,False
1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de,1ce55be799df6cb17762fb0541fe95340687f3de:T3-c1,3,1,11,Household has access to private piped water connection (0/1),other,other,binary,binary,0.152,0.047,,0.001,***,,,high,key variable: private piped water connection (reference: non-piped sources only),ln of house appraisal value (LKR),log,OLS,1491.0,0.45,none,adjusted hedonic model with household socio-economic characteristics; SE and p value in separate columns; adj. R2,pct_premium,16.287513128093668,5.471553111230929,100*(exp(b - se^2/2) - 1),reported,0.08374137081992669,0.02589371334563523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.234042553191489,1481.0,binary,,2012.0,Sri Lanka,Asia,Lower middle income,2003.0,2004.0,single_family,appraisal,journal,False,True,unknown,,2003.5,ols,journal,False,False,False
1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868:T3-c1,3,1,14,treat × post,neighbourhood_amenities,F_neighbourhood,other,binary,0.0399,0.002,,,***,,,high,DiD interaction: house within 1 km of a religious heritage site (treatment ring) x after start of adaptive reuse project; control ring 1-2 km. Interpreted by authors as premium of 100*beta % (e.g. 3.99%).,"lnp, natural log of house transaction price (EUR)",log,OLS,342439.0,0.585,se,"Full sample, no controls; parametric DiD with year and neighbourhood fixed effects; robust SEs; adjusted R2",other,0.0399,0.002,not harmonised,reported,0.034072608980611555,0.001707900199529401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.95,342429.0,binary,nonstandard_x_transform,2020.0,Netherlands,Europe,High income,1995.0,2017.0,mixed,transaction,journal,True,True,robust,,2006.0,ols,journal,False,True,False
1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868:T3-c2,3,2,14,treat × post,neighbourhood_amenities,F_neighbourhood,other,binary,0.0217,0.001,,,***,,,high,DiD interaction: house within 1 km of a religious heritage site (treatment ring) x after start of adaptive reuse project; control ring 1-2 km. Interpreted by authors as premium of 100*beta % (e.g. 3.99%).,"lnp, natural log of house transaction price (EUR)",log,OLS,342439.0,0.866,se,"Full sample, housing and neighbourhood controls; parametric DiD with year and neighbourhood fixed effects; robust SEs; adjusted R2",other,0.0217,0.001,not harmonised,reported,0.03705749519341318,0.00170771867250752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.7,342429.0,binary,nonstandard_x_transform,2020.0,Netherlands,Europe,High income,1995.0,2017.0,mixed,transaction,journal,True,True,robust,,2006.0,ols,journal,False,True,False
1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868:T3-c3,3,3,14,treat × post,neighbourhood_amenities,F_neighbourhood,other,binary,0.0715,0.007,,,***,,,high,DiD interaction: house within 1 km of a religious heritage site (treatment ring) x after start of adaptive reuse project; control ring 1-2 km. Interpreted by authors as premium of 100*beta % (e.g. 3.99%).,"lnp, natural log of house transaction price (EUR)",log,OLS,64337.0,0.544,se,"Projects with size >= 5000 m2, no controls; parametric DiD with year and neighbourhood fixed effects; robust SEs; adjusted R2",other,0.0715,0.007,not harmonised,reported,0.04024013677304944,0.003939593809948897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.214285714285714,64327.0,binary,nonstandard_x_transform,2020.0,Netherlands,Europe,High income,1995.0,2017.0,mixed,transaction,journal,True,True,robust,,2006.0,ols,journal,False,True,False
1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868:T3-c4,3,4,14,treat × post,neighbourhood_amenities,F_neighbourhood,other,binary,0.0338,0.003,,,***,,,high,DiD interaction: house within 1 km of a religious heritage site (treatment ring) x after start of adaptive reuse project; control ring 1-2 km. Interpreted by authors as premium of 100*beta % (e.g. 3.99%).,"lnp, natural log of house transaction price (EUR)",log,OLS,64337.0,0.872,se,"Projects with size >= 5000 m2, with controls; parametric DiD with year and neighbourhood fixed effects; robust SEs; adjusted R2",other,0.0338,0.003,not harmonised,reported,0.04437830573519502,0.003938902875904884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.266666666666666,64327.0,binary,nonstandard_x_transform,2020.0,Netherlands,Europe,High income,1995.0,2017.0,mixed,transaction,journal,True,True,robust,,2006.0,ols,journal,False,True,False
1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868:T3-c5,3,5,14,treat × post,neighbourhood_amenities,F_neighbourhood,other,binary,0.0459,0.004,,,***,,,high,DiD interaction: house within 1 km of a religious heritage site (treatment ring) x after start of adaptive reuse project; control ring 1-2 km. Interpreted by authors as premium of 100*beta % (e.g. 3.99%).,"lnp, natural log of house transaction price (EUR)",log,OLS,159980.0,0.588,se,"National monuments only, no controls; parametric DiD with year and neighbourhood fixed effects; robust SEs; adjusted R2",other,0.0459,0.004,not harmonised,reported,0.028678389281066616,0.002499206037565718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.475000000000001,159970.0,binary,nonstandard_x_transform,2020.0,Netherlands,Europe,High income,1995.0,2017.0,mixed,transaction,journal,True,True,robust,,2006.0,ols,journal,False,True,False
1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868,1d57857811e3f735e7c7dfe3dfb0c766f1aba868:T3-c6,3,6,14,treat × post,neighbourhood_amenities,F_neighbourhood,other,binary,0.0228,0.002,,,***,,,high,DiD interaction: house within 1 km of a religious heritage site (treatment ring) x after start of adaptive reuse project; control ring 1-2 km. Interpreted by authors as premium of 100*beta % (e.g. 3.99%).,"lnp, natural log of house transaction price (EUR)",log,OLS,159980.0,0.866,se,"National monuments only, with controls; parametric DiD with year and neighbourhood fixed effects; robust SEs; adjusted R2",other,0.0228,0.002,not harmonised,reported,0.02849110148179493,0.0024992194282276252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.4,159970.0,binary,nonstandard_x_transform,2020.0,Netherlands,Europe,High income,1995.0,2017.0,mixed,transaction,journal,True,True,robust,,2006.0,ols,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Floor low,floor_level,A_structural,binary,binary,-282.06,148.31,,,*,,,high,ground or first floor (reference: second-third floor); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-282.06,148.31,"b (currency per unit, as reported)",reported,-0.03030010044446408,0.015932099187826943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9018272537253051,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Floor low,floor_level,A_structural,binary,binary,-278.25,103.94,,,***,,,high,ground or first floor (reference: second-third floor); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-278.25,103.94,"b (currency per unit, as reported)",reported,-0.04263143040936305,0.01592492678076979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.677025206850106,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Floor low,floor_level,A_structural,binary,binary,-245.67,100.43,,,**,,,high,ground or first floor (reference: second-third floor); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-245.67,100.43,"b (currency per unit, as reported)",reported,-0.038961103177613414,0.015927315472494465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4461814198944536,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Floor low,floor_level,A_structural,binary,binary,-181.39,139.4,,,,,,high,ground or first floor (reference: second-third floor); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-181.39,139.4,"b (currency per unit, as reported)",reported,-0.020736221857411277,0.01593599055583622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3012195121951218,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Floor low,floor_level,A_structural,binary,binary,-59.53,273.26,,,,,,high,ground or first floor (reference: second-third floor); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-59.53,273.26,"b (currency per unit, as reported)",reported,-0.003472399263159709,0.01593932173107714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2178511307911879,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Floor high,floor_level,A_structural,binary,binary,-218.79,145.36,,,,,,high,fourth floor or higher (reference: second-third floor); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-218.79,145.36,"b (currency per unit, as reported)",reported,-0.023984466254455966,0.015934832555179485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5051596037424324,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Floor high,floor_level,A_structural,binary,binary,-258.78,102.9,,,***,,,high,fourth floor or higher (reference: second-third floor); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-258.78,102.9,"b (currency per unit, as reported)",reported,-0.04005337767050535,0.01592662710524384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5148688046647227,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Floor high,floor_level,A_structural,binary,binary,-227.98,100.03,,,***,,,high,fourth floor or higher (reference: second-third floor); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-227.98,100.03,"b (currency per unit, as reported)",reported,-0.03630383908873963,0.015928910536216447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.279116265120464,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Floor high,floor_level,A_structural,binary,binary,-101.96,139.94,,,,,,high,fourth floor or higher (reference: second-third floor); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-101.96,139.94,"b (currency per unit, as reported)",reported,-0.011612644398207448,0.0159383430471278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7285979705588109,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Floor high,floor_level,A_structural,binary,binary,124.35,276.12,,,,,,high,fourth floor or higher (reference: second-third floor); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,124.35,276.12,"b (currency per unit, as reported)",reported,0.007178094824866055,0.015939007181680863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.45034767492394606,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Lift,accessibility,B_interior,binary,binary,562.95,170.38,,,***,,,high,at least one elevator; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,562.95,170.38,"b (currency per unit, as reported)",reported,0.052592306040387414,0.015917358740849465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3040849865007633,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Lift,accessibility,B_interior,binary,binary,579.63,119.65,,,***,,,high,at least one elevator; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,579.63,119.65,"b (currency per unit, as reported)",reported,0.07698741416867894,0.015892110665911764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.84437944003343,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Lift,accessibility,B_interior,binary,binary,532.38,114.56,,,***,,,high,at least one elevator; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,532.38,114.56,"b (currency per unit, as reported)",reported,0.07387083131743852,0.015895868431807653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.647171787709497,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Lift,accessibility,B_interior,binary,binary,575.58,156.44,,,***,,,high,at least one elevator; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,575.58,156.44,"b (currency per unit, as reported)",reported,0.058544325342452665,0.015912078697267615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.679238046535413,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Lift,accessibility,B_interior,binary,binary,862.57,302.79,,,***,,,high,at least one elevator; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,862.57,302.79,"b (currency per unit, as reported)",reported,0.04536051943706635,0.015923011095156706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.848740050860332,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Heating System,heating,A_structural,binary,binary,125.97,195.78,,,,,,high,autonomous (independent) heating system; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,125.97,195.78,"b (currency per unit, as reported)",reported,0.010255301230031136,0.015938579620667585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6434262948207171,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Heating System,heating,A_structural,binary,binary,184.53,135.17,,,,,,high,autonomous (independent) heating system; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,184.53,135.17,"b (currency per unit, as reported)",reported,0.021754861793001803,0.015935645524088514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3651697861951617,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Heating System,heating,A_structural,binary,binary,-1.57,30.98,,,,,,high,autonomous (independent) heating system; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-1.57,30.98,"b (currency per unit, as reported)",reported,-0.0008077752686686435,0.015939412626340493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05067785668173015,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Heating System,heating,A_structural,binary,binary,-25.19,181.81,,,,,,high,autonomous (independent) heating system; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-25.19,181.81,"b (currency per unit, as reported)",reported,-0.0022084206365671805,0.015939378957295715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13855123480556625,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Heating System,heating,A_structural,binary,binary,-567.16,345.35,,,,,,high,autonomous (independent) heating system; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-567.16,345.35,"b (currency per unit, as reported)",reported,-0.026167958567930697,0.015933959537758068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6422759519328216,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Parking area,parking,A_structural,binary,binary,1227.97,721.31,,,*,,,high,parking place or garage; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,1227.97,721.31,"b (currency per unit, as reported)",reported,0.0271255419704451,0.015933552675311085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7024164367608936,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Parking area,parking,A_structural,binary,binary,1940.22,462.08,,,***,,,high,parking place or garage; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,1940.22,462.08,"b (currency per unit, as reported)",reported,0.06677836162198131,0.01590383839888524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.1988833102493075,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Parking area,parking,A_structural,binary,binary,2325.26,441.55,,,***,,,high,parking place or garage; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,2325.26,441.55,"b (currency per unit, as reported)",reported,0.08364490322093335,0.01588356012540667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.266130676027631,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Parking area,parking,A_structural,binary,binary,1913.61,602.36,,,***,,,high,parking place or garage; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,1913.61,602.36,"b (currency per unit, as reported)",reported,0.05057241368704736,0.015919021696442767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1768543728003187,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Parking area,parking,A_structural,binary,binary,1508.37,1103.27,,,,,,high,parking place or garage; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,1508.37,1103.27,"b (currency per unit, as reported)",reported,0.02178689973049558,0.01593563440380269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3671811977122552,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Bathroom,bathrooms,A_structural,binary,binary,841.78,178.24,,,***,,,high,two or more bathrooms; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,841.78,178.24,"b (currency per unit, as reported)",reported,0.07506523445575133,0.015894446754963435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.722733393177737,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Bathroom,bathrooms,A_structural,binary,binary,924.98,126.16,,,***,,,high,two or more bathrooms; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,924.98,126.16,"b (currency per unit, as reported)",reported,0.11607468981361076,0.015831675135554425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.331800887761573,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Bathroom,bathrooms,A_structural,binary,binary,896.58,123.13,,,***,,,high,two or more bathrooms; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,896.58,123.13,"b (currency per unit, as reported)",reported,0.1152900931189425,0.015833131639937752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.281572321936165,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Bathroom,bathrooms,A_structural,binary,binary,742.37,174.75,,,***,,,high,two or more bathrooms; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,742.37,174.75,"b (currency per unit, as reported)",reported,0.06755885989136255,0.015903000883677416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.248183118741059,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Bathroom,bathrooms,A_structural,binary,binary,824.39,355.76,,,**,,,high,two or more bathrooms; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,824.39,355.76,"b (currency per unit, as reported)",reported,0.0369106770119285,0.015928556209759557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.317264447942433,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Housing Area,living_area,A_structural,other,m2,13793.34,3749.06,,,***,,,medium,"total floor area, cubic polynomial (likely orthogonal polynomial basis - scale not interpretable per m2); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1","annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,13793.34,3749.06,not harmonised,reported,0.05854286950564359,0.015912080058117044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6791462393239907,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Housing Area,living_area,A_structural,other,m2,2229.88,2575.02,,,,,,medium,"total floor area, cubic polynomial (likely orthogonal polynomial basis - scale not interpretable per m2); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1","annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,2229.88,2575.02,not harmonised,reported,0.013801680621555602,0.01593789963321708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8659660895837703,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Housing Area,living_area,A_structural,other,m2,-18234.58,2402.27,,,***,,,medium,"total floor area, cubic polynomial (likely orthogonal polynomial basis - scale not interpretable per m2); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1","annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,-18234.58,2402.27,not harmonised,reported,-0.12011320637800055,0.015824019653081087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.590562259862548,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Housing Area,living_area,A_structural,other,m2,-38649.82,3718.76,,,***,,,medium,"total floor area, cubic polynomial (likely orthogonal polynomial basis - scale not interpretable per m2); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1","annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,-38649.82,3718.76,not harmonised,reported,-0.1634341283479756,0.015725100379130302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.393200959459604,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Housing Area,living_area,A_structural,other,m2,-59740.72,7237.17,,,***,,,medium,"total floor area, cubic polynomial (likely orthogonal polynomial basis - scale not interpretable per m2); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1","annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,-59740.72,7237.17,not harmonised,reported,-0.1304508883498392,0.015803211873556357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.254707295807615,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Housing Area2,living_area,A_structural,squared,m2,26441.33,12661.3,,,**,,,medium,second polynomial term of floor area (likely orthogonal basis); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,26441.33,12661.3,not harmonised,reported,0.03326878787886032,0.015930594413012283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0883582254586814,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Housing Area2,living_area,A_structural,squared,m2,88902.99,8845.19,,,***,,,medium,second polynomial term of floor area (likely orthogonal basis); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,88902.99,8845.19,not harmonised,reported,0.15818984941301814,0.01573872008275013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.05099833921035,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Housing Area2,living_area,A_structural,squared,m2,201195.88,8073.78,,,***,,,medium,second polynomial term of floor area (likely orthogonal basis); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,201195.88,8073.78,not harmonised,reported,0.36915023738949465,0.014813612503549052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.919663404254266,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Housing Area2,living_area,A_structural,squared,m2,312160.71,13545.17,,,***,,,medium,second polynomial term of floor area (likely orthogonal basis); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,312160.71,13545.17,not harmonised,reported,0.34481033400385885,0.01496189123813515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.04590566231358,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Housing Area2,living_area,A_structural,squared,m2,450974.91,25960.66,,,***,,,medium,second polynomial term of floor area (likely orthogonal basis); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,450974.91,25960.66,not harmonised,reported,0.2668505230431905,0.01536142154681384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.371473221405,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Housing Area3,living_area,A_structural,other,m2,59418.29,11060.53,,,***,,,medium,third polynomial term of floor area (likely orthogonal basis); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,59418.29,11060.53,not harmonised,reported,0.08531596577555649,0.01588130185065097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.372101517739204,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Housing Area3,living_area,A_structural,other,m2,21609.05,7778.6,,,***,,,medium,third polynomial term of floor area (likely orthogonal basis); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,21609.05,7778.6,not harmonised,reported,0.04423655969947987,0.01592381447950623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7780127529375465,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Housing Area3,living_area,A_structural,other,m2,-70502.26,6841.9,,,***,,,medium,third polynomial term of floor area (likely orthogonal basis); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,-70502.26,6841.9,not harmonised,reported,-0.1620758701293871,0.015728671617594295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.304485596106344,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Housing Area3,living_area,A_structural,other,m2,-146289.17,13132.12,,,***,,,medium,third polynomial term of floor area (likely orthogonal basis); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,-146289.17,13132.12,not harmonised,reported,-0.1748272916164625,0.015693936692527406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.139798448384571,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Housing Area3,living_area,A_structural,other,m2,-281400.47,24340.79,,,***,,,medium,third polynomial term of floor area (likely orthogonal basis); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",other,-281400.47,24340.79,not harmonised,reported,-0.18122221430457125,0.0156754957151371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.560860185721168,3936.0,m2,nonstandard_x_transform,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Age,age,A_structural,binary,binary,385.45,162.86,,,**,,,high,1 = building constructed before 1950; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,385.45,162.86,"b (currency per unit, as reported)",reported,0.03769790886760203,0.015928087788760325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3667567235662528,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Age,age,A_structural,binary,binary,467.61,115.82,,,***,,,high,1 = building constructed before 1950; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,467.61,115.82,"b (currency per unit, as reported)",reported,0.0642207320605847,0.015906514375776653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.037385598342255,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Age,age,A_structural,binary,binary,536.03,112.99,,,***,,,high,1 = building constructed before 1950; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,536.03,112.99,"b (currency per unit, as reported)",reported,0.07540209879884213,0.015894041645581725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.744048145853616,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Age,age,A_structural,binary,binary,662.14,159.36,,,***,,,high,1 = building constructed before 1950; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,662.14,159.36,"b (currency per unit, as reported)",reported,0.0660834328853371,0.015904575867048243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.154994979919678,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Age,age,A_structural,binary,binary,1622.88,316.73,,,***,,,high,1 = building constructed before 1950; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,1622.88,316.73,"b (currency per unit, as reported)",reported,0.08140030921361548,0.015886522686352923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.123859438638588,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Abandoned area,vacancy,F_neighbourhood,binary,binary,-191.62,184.95,,,,,,high,1 = at least one abandoned/ruined site within 200 m; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-191.62,184.95,"b (currency per unit, as reported)",reported,-0.016512002391636513,0.015937244767420797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0360638010273047,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Abandoned area,vacancy,F_neighbourhood,binary,binary,-356.68,132.17,,,***,,,high,1 = at least one abandoned/ruined site within 200 m; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-356.68,132.17,"b (currency per unit, as reported)",reported,-0.04297510157894674,0.015924692092882668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.698645683589317,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Abandoned area,vacancy,F_neighbourhood,binary,binary,-506.7,127.0,,,***,,,high,1 = at least one abandoned/ruined site within 200 m; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-506.7,127.0,"b (currency per unit, as reported)",reported,-0.06346630422733093,0.015907283672530154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.989763779527559,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Abandoned area,vacancy,F_neighbourhood,binary,binary,-667.02,174.07,,,***,,,high,1 = at least one abandoned/ruined site within 200 m; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-667.02,174.07,"b (currency per unit, as reported)",reported,-0.06096475136639135,0.015909769227830863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.83190670419946,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Abandoned area,vacancy,F_neighbourhood,binary,binary,-1056.59,325.83,,,***,,,high,1 = at least one abandoned/ruined site within 200 m; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-1056.59,325.83,"b (currency per unit, as reported)",reported,-0.051618873195010496,0.015918168308549457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2427646318632415,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,University,university_distance,D_location,binary,binary,808.94,146.14,,,***,,,high,1 = nearest university site within 441 m (first quartile); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,808.94,146.14,"b (currency per unit, as reported)",reported,0.08788925685529067,0.01587773629296632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.535377035719175,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,University,university_distance,D_location,binary,binary,896.77,107.38,,,***,,,high,1 = nearest university site within 441 m (first quartile); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,896.77,107.38,"b (currency per unit, as reported)",reported,0.1319520075985424,0.015800045246753887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.351368970013038,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,University,university_distance,D_location,binary,binary,927.38,106.5,,,***,,,high,1 = nearest university site within 441 m (first quartile); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,927.38,106.5,"b (currency per unit, as reported)",reported,0.13747923016413507,0.015788067472320286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.707793427230047,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,University,university_distance,D_location,binary,binary,763.42,154.85,,,***,,,high,1 = nearest university site within 441 m (first quartile); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,763.42,154.85,"b (currency per unit, as reported)",reported,0.07834079581228169,0.015890430210803778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9300613496932515,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,University,university_distance,D_location,binary,binary,513.88,297.03,,,*,,,high,1 = nearest university site within 441 m (first quartile); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,513.88,297.03,"b (currency per unit, as reported)",reported,0.027565685034512556,0.015933360756988528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7300609366057302,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Metro,transit_distance,D_location,binary,binary,140.45,143.41,,,,,,high,1 = nearest metro station within 680 m (first quartile); M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,140.45,143.41,"b (currency per unit, as reported)",reported,0.015608524617470608,0.015937476079682877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9793598772749459,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Metro,transit_distance,D_location,binary,binary,-39.91,103.14,,,,,,high,1 = nearest metro station within 680 m (first quartile); M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-39.91,103.14,"b (currency per unit, as reported)",reported,-0.00616763686252831,0.01593911465800977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.386949777002133,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Metro,transit_distance,D_location,binary,binary,-242.94,101.0,,,**,,,high,1 = nearest metro station within 680 m (first quartile); M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-242.94,101.0,"b (currency per unit, as reported)",reported,-0.03831167580177212,0.015927715715728097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4053465346534653,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Metro,transit_distance,D_location,binary,binary,-402.89,141.32,,,***,,,high,1 = nearest metro station within 680 m (first quartile); M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-402.89,141.32,"b (currency per unit, as reported)",reported,-0.0453949327869849,0.015922986178502088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8509057458250777,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Metro,transit_distance,D_location,binary,binary,-304.77,275.28,,,,,,high,1 = nearest metro station within 680 m (first quartile); M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-304.77,275.28,"b (currency per unit, as reported)",reported,-0.017644217313639347,0.01593693651638494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1071272885789016,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q10,3,beta_0.10,16,Year,transaction_timing,G_market,binary,binary,-440.49,130.27,,,***,,,high,1 = sold in post-crisis period 2009-2010; M-quantile q=0.10; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.10, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-440.49,130.27,"b (currency per unit, as reported)",reported,-0.05381882641681435,0.01591631709532204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3813617870576493,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q25,3,beta_0.25,16,Year,transaction_timing,G_market,binary,binary,-428.92,92.56,,,***,,,high,1 = sold in post-crisis period 2009-2010; M-quantile q=0.25; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.25, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-428.92,92.56,"b (currency per unit, as reported)",reported,-0.07366207278356465,0.01589611455946737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.633967156439066,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q50,3,beta_0.50,16,Year,transaction_timing,G_market,binary,binary,-377.85,89.97,,,***,,,high,1 = sold in post-crisis period 2009-2010; M-quantile q=0.50; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.50, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-377.85,89.97,"b (currency per unit, as reported)",reported,-0.06679181854138756,0.015903824041732538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.199733244414805,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q75,3,beta_0.75,16,Year,transaction_timing,G_market,binary,binary,-356.1,125.08,,,***,,,high,1 = sold in post-crisis period 2009-2010; M-quantile q=0.75; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.75, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-356.1,125.08,"b (currency per unit, as reported)",reported,-0.04533251891115688,0.01592303135469672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.846977934122162,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d,1daf1d20ba429ff68e39043313dece9a679c8c5d:T3-q90,3,beta_0.90,16,Year,transaction_timing,G_market,binary,binary,-283.69,246.91,,,,,,high,1 = sold in post-crisis period 2009-2010; M-quantile q=0.90; stars *** p<0.01 ** p<0.05 * p<0.1,"annual market value of the dwelling (EUR, level)",level,quantile,3946.0,,se,"spatial semiparametric M-quantile regression (SSPMQ), q=0.90, Huber c=1.345, 20 knots Cultural Catalyst spline + 40 knots spatial thin-plate spline",monetary,-283.69,246.91,"b (currency per unit, as reported)",reported,-0.01831070160020918,0.015936745504274555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.148961159936819,3936.0,binary,,2023.0,Italy,Europe,High income,2004.0,2010.0,condo_apartment,transaction,journal,False,False,unknown,,2007.0,quantile,journal,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Domestic gardens,garden,C_exterior,level,share (fraction of census ward),2.03,0.32,,,***,,,high,ward share of domestic gardens; coefficient ~ % change for 1 pp increase,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,202.99999999999997,32.0,100*b (unit as reported: share; area unit not harmonisable),reported,0.006302430445669498,0.0009934865727163743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.343749999999999,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Domestic gardens,garden,C_exterior,level,share (fraction of census ward),1.35,0.23,,,***,,,high,ward share of domestic gardens; coefficient ~ % change for 1 pp increase,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,135.0,23.0,100*b (unit as reported: share; area unit not harmonisable),reported,0.005831350897683446,0.0009934894121979203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.869565217391305,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Domestic gardens,garden,C_exterior,level,share (fraction of census ward),1.01,0.119,,,***,,,high,ward share of domestic gardens; coefficient ~ % change for 1 pp increase,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,101.0,11.899999999999999,100*b (unit as reported: share; area unit not harmonisable),reported,0.008431980631784894,0.0009934709853291115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.487394957983193,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Domestic gardens,garden,C_exterior,level,share (fraction of census ward),1.2,0.22,,,***,,,high,ward share of domestic gardens; coefficient ~ % change for 1 pp increase,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,120.0,22.0,100*b (unit as reported: share; area unit not harmonisable),reported,0.008140607114631146,0.0014924446376823768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.454545454545454,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Green space,green_space,E_environment,level,share (fraction of census ward),1.5,0.16,,,***,,,high,ward share of green space,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,150.0,16.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.00931371761537537,0.000993463212306706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.375,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Green space,green_space,E_environment,level,share (fraction of census ward),1.0,0.13,,,***,,,high,ward share of green space,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,100.0,13.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.007642133018162716,0.000993477292361153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.692307692307692,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Green space,green_space,E_environment,level,share (fraction of census ward),1.04,0.08,,,***,,,high,ward share of green space,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,104.0,8.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.012914504851446953,0.0009934234501113041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Green space,green_space,E_environment,level,share (fraction of census ward),1.2,0.13,,,***,,,high,ward share of green space,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,120.0,13.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.013775561287443804,0.0014923524728064123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.23076923076923,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Water,water_proximity,E_environment,level,share (fraction of census ward),1.24,0.19,,,***,,,high,"ward share of water (share, not distance)",ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,124.0,19.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.00648379958778225,0.0009934854207085707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.526315789473684,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Water,water_proximity,E_environment,level,share (fraction of census ward),0.75,0.14,,,***,,,high,"ward share of water (share, not distance)",ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,75.0,14.000000000000002,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.005322279818580608,0.0009934922328017136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.357142857142857,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Water,water_proximity,E_environment,level,share (fraction of census ward),0.97,0.08,,,***,,,high,"ward share of water (share, not distance)",ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,97.0,8.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.012045390002681788,0.0009934342270253023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.125,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Water,water_proximity,E_environment,level,share (fraction of census ward),1.09,0.15,,,***,,,high,"ward share of water (share, not distance)",ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,109.00000000000001,15.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.010844819281441999,0.0014924063231342196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.2666666666666675,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Domestic buildings,other,other,level,share (fraction of census ward),2.31,0.92,,,**,,,high,ward share of domestic buildings (land use control),ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,231.0,92.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0024945569805996937,0.000993503213052692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.510869565217391,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Domestic buildings,other,other,level,share (fraction of census ward),1.21,0.45,,,***,,,high,ward share of domestic buildings (land use control),ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,121.0,45.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.002671418530256984,0.0009935027591864817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6888888888888887,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Domestic buildings,other,other,level,share (fraction of census ward),2.16,0.3,,,***,,,high,ward share of domestic buildings (land use control),ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,216.0,30.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007153062385777569,0.0009934808869135512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.200000000000001,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Domestic buildings,other,other,level,share (fraction of census ward),2.3,0.16,,,***,,,high,ward share of domestic buildings (land use control),ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,229.99999999999997,16.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02144966649869945,0.0014921507129530055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.374999999999998,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Other buildings,other,other,level,share (fraction of census ward),3.6,0.44,,,***,,,high,ward share of non-domestic buildings (land use control),ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,360.0,44.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.008128419403420031,0.000993473482640226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.181818181818182,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Other buildings,other,other,level,share (fraction of census ward),2.89,0.35,,,***,,,high,ward share of non-domestic buildings (land use control),ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,289.0,35.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.008203247458149349,0.0009934728755544191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.257142857142858,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Other buildings,other,other,level,share (fraction of census ward),2.67,0.23,,,***,,,high,ward share of non-domestic buildings (land use control),ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,267.0,23.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.011532545326197935,0.0009934402340919571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.608695652173912,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Other buildings,other,other,level,share (fraction of census ward),3.02,0.29,,,***,,,high,ward share of non-domestic buildings (land use control),ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,302.0,28.999999999999996,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0155406477168968,0.0014923138536092953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.413793103448276,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Green Belt,other,other,level,share (fraction of census ward),-0.01,0.03,,,,,,high,ward share designated Green Belt,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-1.0,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0003311687499266508,0.0009935062497799523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33333333333333337,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Green Belt,other,other,level,share (fraction of census ward),-0.03,0.04,,,,,,high,ward share designated Green Belt,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-3.0,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0007451295213404264,0.000993506028453902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Green Belt,other,other,level,share (fraction of census ward),0.02,0.02,,,,,,high,ward share designated Green Belt,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,2.0,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0009935058139380248,0.0009935058139380248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Green Belt,other,other,level,share (fraction of census ward),0.03,0.02,,,**,,,high,ward share designated Green Belt,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,3.0,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.002238735527641266,0.0014924903517608442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,National Park,other,other,level,share (fraction of census ward),-0.14,0.06,,,**,,,high,"ward share within National Park designation; Model 1 SE printed as (-0.06), sign dropped",ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-14.000000000000002,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.002318175147719648,0.000993503634736992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3333333333333335,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,National Park,other,other,level,share (fraction of census ward),-0.02,0.05,,,,,,high,"ward share within National Park designation; Model 1 SE printed as (-0.06), sign dropped",ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-2.0,5.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0003974024903234525,0.0009935062258086312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39999999999999997,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,National Park,other,other,level,share (fraction of census ward),0.05,0.04,,,,,,high,"ward share within National Park designation; Model 1 SE printed as (-0.06), sign dropped",ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,5.0,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.001241881922665127,0.0009935055381321016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,National Park,other,other,level,share (fraction of census ward),0.01,0.04,,,,,,high,"ward share within National Park designation; Model 1 SE printed as (-0.06), sign dropped",ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,1.0,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0003731234970037908,0.0014924939880151633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Ward area (km2),other,other,level,km2,2e-06,5e-07,,,***,,,high,census ward area control,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,0.00019999999999999998,4.9999999999999996e-05,100*b (unit as reported: km2; no harmonised unit defined for this characteristic),reported,0.00397399383676804,0.00099349845919201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,1013115.0,km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Ward area (km2),other,other,level,km2,7e-07,4e-07,,,*,,,high,census ward area control,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,7e-05,3.9999999999999996e-05,100*b (unit as reported: km2; no harmonised unit defined for this characteristic),reported,0.001738633404638834,0.0009935048026507623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,1013115.0,km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Ward area (km2),other,other,level,km2,9e-07,2e-07,,,***,,,high,census ward area control,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,8.999999999999999e-05,1.9999999999999998e-05,100*b (unit as reported: km2; no harmonised unit defined for this characteristic),reported,0.004470733689196505,0.0009934963753770011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,1013115.0,km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Ward area (km2),other,other,level,km2,1e-06,5e-07,,,**,,,high,census ward area control,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,9.999999999999999e-05,4.9999999999999996e-05,100*b (unit as reported: km2; no harmonised unit defined for this characteristic),reported,0.0029849748855530498,0.0014924874427765249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,448926.0,km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Distance (100kms) to: Coastline,water_proximity,E_environment,level,100 km,-0.15,0.11,,,,,,high,distance to coastline in 100 km,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-0.15,0.11,100*b/100 (X per 100km->km),reported,-0.0013547800806881461,0.0009935053925046405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3636363636363635,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Distance (100kms) to: Coastline,water_proximity,E_environment,level,100 km,-0.15,0.08,,,**,,,high,distance to coastline in 100 km,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-0.15,0.08,100*b/100 (X per 100km->km),reported,-0.0018628210883895545,0.000993504580474429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.875,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Distance (100kms) to: Coastline,water_proximity,E_environment,level,100 km,-0.14,0.13,,,,,,high,distance to coastline in 100 km,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.14,0.13,100*b/100 (X per 100km->km),reported,-0.0010699292537258676,0.0009935057356025914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.076923076923077,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Distance (100kms) to: Coastline,water_proximity,E_environment,level,100 km,-0.53,0.24,,,***,,,high,distance to coastline in 100 km,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-0.53,0.24,100*b/100 (X per 100km->km),reported,-0.003295906551102407,0.0014924859854048636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2083333333333335,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Distance (100kms) to: Coastline,water_proximity,E_environment,level,100 km,-0.2,0.12,,,*,,,high,distance to coastline in 100 km,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-0.2,0.12,100*b/100 (X per 100km->km),reported,-0.0015642545836930218,0.0009385527502158131,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6666666666666667,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Distance (100kms) to: Rivers,water_proximity,E_environment,level,100 km,1.35,0.97,,,,,,high,distance to rivers in 100 km,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,1.35,0.97,100*b/100 (X per 100km->km),reported,0.0013827136377325125,0.0009935053545189162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3917525773195878,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,True
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Distance (100kms) to: Rivers,water_proximity,E_environment,level,100 km,0.92,1.01,,,,,,high,distance to rivers in 100 km,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,0.92,1.01,100*b/100 (X per 100km->km),reported,0.0009049756689450239,0.000993505897428776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9108910891089109,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Distance (100kms) to: Rivers,water_proximity,E_environment,level,100 km,-0.91,0.69,,,*,,,high,distance to rivers in 100 km,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.91,0.69,100*b/100 (X per 100km->km),reported,-0.0013102753055004752,0.0009935054514234373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.318840579710145,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Distance (100kms) to: Rivers,water_proximity,E_environment,level,100 km,-2.16,0.48,,,***,,,high,distance to rivers in 100 km,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-2.16,0.48,100*b/100 (X per 100km->km),reported,-0.006716071942161937,0.001492460431591541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.500000000000001,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Distance (100kms) to: Rivers,water_proximity,E_environment,level,100 km,-1.05,0.62,,,*,,,high,distance to rivers in 100 km,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-1.05,0.62,100*b/100 (X per 100km->km),reported,-0.0015894844330967383,0.0009385527128761692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6935483870967742,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Distance (100kms) to: National Parks,green_space,E_environment,level,100 km,0.22,0.09,,,**,,,high,distance to National Parks in 100 km,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,0.22,0.09,100*b/100 (X per 100km->km),reported,0.002428563804197652,0.000993503374444494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4444444444444446,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Distance (100kms) to: National Parks,green_space,E_environment,level,100 km,0.17,0.06,,,**,,,high,distance to National Parks in 100 km,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,0.17,0.06,100*b/100 (X per 100km->km),reported,0.0028149233762351147,0.0009935023680829815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8333333333333335,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Distance (100kms) to: National Parks,green_space,E_environment,level,100 km,-0.24,0.09,,,***,,,high,distance to National Parks in 100 km,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.24,0.09,100*b/100 (X per 100km->km),reported,-0.002649340846774138,0.0009935028175403018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6666666666666665,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Distance (100kms) to: National Parks,green_space,E_environment,level,100 km,-0.4,0.14,,,***,,,high,distance to National Parks in 100 km,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-0.4,0.14,100*b/100 (X per 100km->km),reported,-0.004264230063845008,0.0014924805223457528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.857142857142857,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Distance (100kms) to: Nature Reserves,green_space,E_environment,level,100 km,-0.54,0.2,,,***,,,high,distance to nature reserves in 100 km,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-0.54,0.2,100*b/100 (X per 100km->km),reported,-0.0026824573705355372,0.0009935027298279768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Distance (100kms) to: Nature Reserves,green_space,E_environment,level,100 km,-0.42,0.19,,,***,,,high,distance to nature reserves in 100 km,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-0.42,0.19,100*b/100 (X per 100km->km),reported,-0.002196166534233326,0.0009935039083436474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2105263157894735,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Distance (100kms) to: Nature Reserves,green_space,E_environment,level,100 km,-0.07,0.23,,,,,,high,distance to nature reserves in 100 km,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.07,0.23,100*b/100 (X per 100km->km),reported,-0.00030237147008260165,0.000993506258842834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.30434782608695654,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Distance (100kms) to: Nature Reserves,green_space,E_environment,level,100 km,-0.28,0.51,,,,,,high,distance to nature reserves in 100 km,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-0.28,0.51,100*b/100 (X per 100km->km),reported,-0.0008194082459600968,0.0014924935908558904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5490196078431373,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Distance (100kms) to: National Trust properties,neighbourhood_amenities,F_neighbourhood,level,100 km,-1.85,0.33,,,***,,,high,distance to National Trust (heritage) properties in 100 km,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-185.0,33.0,100*b (unit as reported: 100km; no harmonised unit defined for this characteristic),reported,-0.0055695701678315735,0.0009934908948023887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.606060606060606,1013115.0,100km,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Distance (100kms) to: National Trust properties,neighbourhood_amenities,F_neighbourhood,level,100 km,-1.67,0.25,,,***,,,high,distance to National Trust (heritage) properties in 100 km,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-167.0,25.0,100*b (unit as reported: 100km; no harmonised unit defined for this characteristic),reported,-0.006636475963095116,0.0009934844256130415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.68,1013115.0,100km,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Distance (100kms) to: National Trust properties,neighbourhood_amenities,F_neighbourhood,level,100 km,-0.7,0.17,,,***,,,high,distance to National Trust (heritage) properties in 100 km,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-70.0,17.0,100*b (unit as reported: 100km; no harmonised unit defined for this characteristic),reported,-0.004090874080328389,0.0009934979909368947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.117647058823529,1013115.0,100km,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Distance (100kms) to: National Trust properties,neighbourhood_amenities,F_neighbourhood,level,100 km,-0.38,0.33,,,,,,high,distance to National Trust (heritage) properties in 100 km,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-38.0,33.0,100*b (unit as reported: 100km; no harmonised unit defined for this characteristic),reported,-0.0017186270222377654,0.0014924918877327964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1515151515151514,448926.0,100km,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Land in 1km x 1km square: Marine and coastal margins,other,other,level,share (fraction of 1 km2 square),-0.36,0.23,,,,,,high,"landcover share, omitted = urban",ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-36.0,23.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.001555051465594105,0.000993505103018456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5652173913043477,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Land in 1km x 1km square: Marine and coastal margins,other,other,level,share (fraction of 1 km2 square),-0.26,0.12,,,**,,,high,"landcover share, omitted = urban",ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-26.0,12.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0021525920053650145,0.0009935040024761606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.166666666666667,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Land in 1km x 1km square: Marine and coastal margins,other,other,level,share (fraction of 1 km2 square),0.04,0.04,,,,,,high,"landcover share, omitted = urban",ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,4.0,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0009935058139380248,0.0009935058139380248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Land in 1km x 1km square: Marine and coastal margins,other,other,level,share (fraction of 1 km2 square),-0.15,0.12,,,,,,high,"landcover share, omitted = urban",ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-15.0,12.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0018656143682259578,0.0014924914945807662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Land in 1km x 1km square: Marine and coastal margins,other,other,level,share (fraction of 1 km2 square),0.04,0.04,,,,,,high,"landcover share, omitted = urban",ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,4.0,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0009385534851087062,0.0009385534851087063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,"Land in 1km x 1km square: Freshwater, wetlands, floodplains",water_proximity,E_environment,level,share (fraction of 1 km2 square),1.05,0.27,,,***,,,high,"landcover share of freshwater/wetland/floodplain (share, not distance)",ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,105.0,27.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0038636067904428877,0.0009934988889710283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.888888888888889,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,"Land in 1km x 1km square: Freshwater, wetlands, floodplains",water_proximity,E_environment,level,share (fraction of 1 km2 square),1.09,0.21,,,***,,,high,"landcover share of freshwater/wetland/floodplain (share, not distance)",ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,109.00000000000001,21.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0051567022535578795,0.0009934930947221603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.190476190476191,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,"Land in 1km x 1km square: Freshwater, wetlands, floodplains",water_proximity,E_environment,level,share (fraction of 1 km2 square),0.4,0.15,,,***,,,high,"landcover share of freshwater/wetland/floodplain (share, not distance)",ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,40.0,15.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0026493408467741382,0.0009935028175403018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.666666666666667,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,"Land in 1km x 1km square: Freshwater, wetlands, floodplains",water_proximity,E_environment,level,share (fraction of 1 km2 square),0.47,0.02,,,***,,,high,"landcover share of freshwater/wetland/floodplain (share, not distance)",ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,47.0,2.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.035052057997980085,0.0014915769360842593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.499999999999996,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,"Land in 1km x 1km square: Freshwater, wetlands, floodplains",water_proximity,E_environment,level,share (fraction of 1 km2 square),0.32,0.14,,,**,,,high,"landcover share of freshwater/wetland/floodplain (share, not distance)",ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,32.0,14.000000000000002,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.002145261117265244,0.0009385517388035444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2857142857142856,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,"Land in 1km x 1km square: Mountains, moors and heathland",green_space,E_environment,level,share (fraction of 1 km2 square),0.09,0.22,,,,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,9.0,22.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.00040643436362814296,0.0009935062222021273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.40909090909090906,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,"Land in 1km x 1km square: Mountains, moors and heathland",green_space,E_environment,level,share (fraction of 1 km2 square),0.19,0.21,,,,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,19.0,21.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0008988862930867095,0.0009935059028853103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9047619047619048,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,"Land in 1km x 1km square: Mountains, moors and heathland",green_space,E_environment,level,share (fraction of 1 km2 square),0.09,0.1,,,,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,9.0,10.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0008941553163892697,0.0009935059070991887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8999999999999999,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,"Land in 1km x 1km square: Mountains, moors and heathland",green_space,E_environment,level,share (fraction of 1 km2 square),0.08,0.21,,,,,,high,landcover share,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,8.0,21.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0005685690859690024,0.0014924938506686311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.380952380952381,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,"Land in 1km x 1km square: Mountains, moors and heathland",green_space,E_environment,level,share (fraction of 1 km2 square),-0.07,0.08,,,,,,high,landcover share,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-7.000000000000001,8.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.0008212343842448626,0.0009385535819941285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8750000000000001,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Land in 1km x 1km square: Semi-natural grassland,green_space,E_environment,level,share (fraction of 1 km2 square),-0.18,0.06,,,***,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-18.0,6.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.002980505674159471,0.0009935018913864904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Land in 1km x 1km square: Semi-natural grassland,green_space,E_environment,level,share (fraction of 1 km2 square),-0.25,0.06,,,***,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-25.0,6.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.004139574132604132,0.0009934977918249915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.166666666666667,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Land in 1km x 1km square: Semi-natural grassland,green_space,E_environment,level,share (fraction of 1 km2 square),-0.01,0.02,,,,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-1.0,2.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.0004967530908398067,0.0009935061816796133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Land in 1km x 1km square: Semi-natural grassland,green_space,E_environment,level,share (fraction of 1 km2 square),-0.02,0.04,,,,,,high,landcover share,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-2.0,4.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.00074624683816759,0.00149249367633518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Land in 1km x 1km square: Semi-natural grassland,green_space,E_environment,level,share (fraction of 1 km2 square),-0.02,0.03,,,,,,high,landcover share,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-2.0,3.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.0006257024765087264,0.0009385537147630895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666667,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Land in 1km x 1km square: Enclosed farmland,other,other,level,share (fraction of 1 km2 square),0.16,0.07,,,,,,high,landcover share of farmland,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,16.0,7.000000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0022708656973368436,0.0009935037425848692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2857142857142856,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Land in 1km x 1km square: Enclosed farmland,other,other,level,share (fraction of 1 km2 square),0.08,0.03,,,,,,high,landcover share of farmland,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,8.0,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0026493408467741382,0.0009935028175403018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.666666666666667,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Land in 1km x 1km square: Enclosed farmland,other,other,level,share (fraction of 1 km2 square),0.06,0.01,,,***,,,high,landcover share of farmland,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,6.0,1.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.005960931918708394,0.0009934886531180658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Land in 1km x 1km square: Enclosed farmland,other,other,level,share (fraction of 1 km2 square),0.07,0.02,,,***,,,high,landcover share of farmland,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,7.000000000000001,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.005223658052278434,0.0014924737292224097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5000000000000004,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Land in 1km x 1km square: Enclosed farmland,other,other,level,share (fraction of 1 km2 square),0.09,0.02,,,***,,,high,landcover share of farmland,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,9.0,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.00422345487459803,0.0009385455276884513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Land in 1km x 1km square: Coniferous woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.53,0.22,,,**,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,53.0,22.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0023934401502107108,0.0009935034585780308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.409090909090909,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Land in 1km x 1km square: Coniferous woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.33,0.15,,,*,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,33.0,15.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0021857086484364783,0.00099350393110749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Land in 1km x 1km square: Coniferous woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.12,0.06,,,*,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,12.0,6.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0019870086859515097,0.0009935043429757549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Land in 1km x 1km square: Coniferous woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.09,0.12,,,,,,high,landcover share,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,9.0,12.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0011193698676517318,0.0014924931568689756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Land in 1km x 1km square: Coniferous woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.15,0.07,,,**,,,high,landcover share,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,15.0,7.000000000000001,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0020111828578433253,0.0009385520003268852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.142857142857143,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Land in 1km x 1km square: Broadleaved woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.82,0.08,,,***,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,82.0,8.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.010182911635951275,0.0009934547937513441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.25,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Land in 1km x 1km square: Broadleaved woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.6,0.07,,,***,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,60.0,7.000000000000001,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.008515459564459373,0.0009934702825202602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.571428571428571,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Land in 1km x 1km square: Broadleaved woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.19,0.04,,,***,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,19.0,4.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.004719102397324452,0.0009934952415419898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Land in 1km x 1km square: Broadleaved woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.17,0.08,,,***,,,high,landcover share,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,17.0,8.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.003171533994545175,0.001492486585668318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.125,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Land in 1km x 1km square: Broadleaved woodland,green_space,E_environment,level,share (fraction of 1 km2 square),0.25,0.04,,,***,,,high,landcover share,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,25.0,4.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0058658609457137734,0.0009385377513142038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M1,2,1,29,Land in 1km x 1km square: Inland bare ground,other,other,level,share (fraction of 1 km2 square),-0.87,0.31,,,**,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.516,se,England 1996-2008,semi_elasticity,-87.0,31.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0027882165319486948,0.0009935024424185003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.806451612903226,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,29,Land in 1km x 1km square: Inland bare ground,other,other,level,share (fraction of 1 km2 square),-0.73,0.27,,,**,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-73.0,27.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.002686136983744003,0.0009935027200149054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7037037037037033,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,29,Land in 1km x 1km square: Inland bare ground,other,other,level,share (fraction of 1 km2 square),-0.38,0.1,,,***,,,high,landcover share,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-38.0,10.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.003775297051496344,0.0009934992240779852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8,1013115.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,29,Land in 1km x 1km square: Inland bare ground,other,other,level,share (fraction of 1 km2 square),-0.42,0.12,,,***,,,high,landcover share,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-42.0,12.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.005223658052278433,0.0014924737292224097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,448926.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,29,Land in 1km x 1km square: Inland bare ground,other,other,level,share (fraction of 1 km2 square),-0.45,0.12,,,***,,,high,landcover share,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-45.0,12.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0035195553202822627,0.0009385480854086032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7500000000000004,1135224.0,share,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Distance to station,transit_distance,D_location,level,100 km,-1.15,0.25,,,***,,,high,distance to railway station in 100 km (table unit header),ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-1.15,0.25,100*b/100 (X per 100km->km),reported,-0.004570081274306839,0.000993495929197139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.6,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Distance to station,transit_distance,D_location,level,100 km,-0.14,0.21,,,,,,high,distance to railway station in 100 km (table unit header),ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.14,0.21,100*b/100 (X per 100km->km),reported,-0.0006623373908927749,0.0009935060863391623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666667,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to station,transit_distance,D_location,level,100 km,-0.15,0.58,,,,,,high,distance to railway station in 100 km (table unit header),ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-0.15,0.58,100*b/100 (X per 100km->km),reported,-0.0003859898226016988,0.0014924939807265684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25862068965517243,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,30,Distance to station,transit_distance,D_location,level,100 km,0.06,0.2,,,,,,high,distance to railway station in 100 km (table unit header),ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,0.06,0.2,100*b/100 (X per 100km->km),reported,0.00028156615838480215,0.0009385538612826739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Distance to motorways,highway_distance,D_location,level,100 km,-0.27,0.07,,,***,,,high,,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-0.27,0.07,100*b/100 (X per 100km->km),reported,-0.0038320676082314997,0.0009934990095414999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.857142857142857,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Distance to motorways,highway_distance,D_location,level,100 km,-0.17,0.11,,,,,,high,,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.17,0.11,100*b/100 (X per 100km->km),reported,-0.001535417023976533,0.000993505133161286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5454545454545456,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to motorways,highway_distance,D_location,level,100 km,-0.38,0.41,,,,,,high,,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-0.38,0.41,100*b/100 (X per 100km->km),reported,-0.0013832858836880168,0.001492492663979176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.926829268292683,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,30,Distance to motorways,highway_distance,D_location,level,100 km,-0.06,0.1,,,,,,high,,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-0.06,0.1,100*b/100 (X per 100km->km),reported,-0.0005631322498023422,0.0009385537496705704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Distance to primary road,highway_distance,D_location,level,100 km,0.69,0.38,,,,,,high,,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,0.69,0.38,100*b/100 (X per 100km->km),reported,0.0018039953538469182,0.0009935046876258392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8157894736842104,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Distance to primary road,highway_distance,D_location,level,100 km,-0.17,0.17,,,,,,high,,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.17,0.17,100*b/100 (X per 100km->km),reported,-0.0009935058139380248,0.0009935058139380248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to primary road,highway_distance,D_location,level,100 km,0.06,0.46,,,,,,high,,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,0.06,0.46,100*b/100 (X per 100km->km),reported,0.0001946731387340268,0.001492494063627539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13043478260869565,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,30,Distance to primary road,highway_distance,D_location,level,100 km,0.1,0.18,,,,,,high,,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,0.1,0.18,100*b/100 (X per 100km->km),reported,0.0005214187616115852,0.0009385537709008532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5555555555555556,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Distance to A-road,highway_distance,D_location,level,100 km,-0.64,0.24,,,***,,,high,,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-0.64,0.24,100*b/100 (X per 100km->km),reported,-0.0026493408467741382,0.0009935028175403018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.666666666666667,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Distance to A-road,highway_distance,D_location,level,100 km,0.16,0.2,,,,,,high,,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,0.16,0.2,100*b/100 (X per 100km->km),reported,0.000794804792363149,0.0009935059904539363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7999999999999999,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to A-road,highway_distance,D_location,level,100 km,0.33,0.58,,,,,,high,,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,0.33,0.58,100*b/100 (X per 100km->km),reported,0.0008491773668104426,0.0014924935537880505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5689655172413793,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,30,Distance to A-road,highway_distance,D_location,level,100 km,0.51,0.26,,,**,,,high,,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,0.51,0.26,100*b/100 (X per 100km->km),reported,0.0018410064502346769,0.0009385523079627765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9615384615384615,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Population (1000s/km2),density,F_neighbourhood,level,1000 persons per km2,0.03,0.008,,,***,,,high,,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,3.0,0.8,100*b (unit as reported: 1000 persons per km2; no harmonised unit defined for this characteristic),reported,0.003725622784390689,0.0009934994091708505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.75,1013115.0,1000 persons per km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Population (1000s/km2),density,F_neighbourhood,level,1000 persons per km2,0.002,0.005,,,,,,high,,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,0.2,0.5,100*b (unit as reported: 1000 persons per km2; no harmonised unit defined for this characteristic),reported,0.00039740249032345256,0.0009935062258086312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,1013115.0,1000 persons per km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Population (1000s/km2),density,F_neighbourhood,level,1000 persons per km2,0.004,0.003,,,,,,high,,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,0.4,0.3,100*b (unit as reported: 1000 persons per km2; no harmonised unit defined for this characteristic),reported,0.001989988182303708,0.001492491136727781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,448926.0,1000 persons per km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,30,Population (1000s/km2),density,F_neighbourhood,level,1000 persons per km2,0.002,0.007,,,,,,high,,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,0.2,0.7000000000000001,100*b (unit as reported: 1000 persons per km2; no harmonised unit defined for this characteristic),reported,0.0002681582470690152,0.0009385538647415531,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2857142857142857,1135224.0,1000 persons per km2,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Age7-11 Value Added (standard deviation),school_quality,F_neighbourhood,level,standard deviation,0.035,0.006,,,***,,,high,"primary school value-added (Key Stage 1-2), in SD units",ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,3.5000000000000004,0.6,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.005795356117209215,0.0009934896200930084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.833333333333334,1013115.0,standard deviation,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Age7-11 Value Added (standard deviation),school_quality,F_neighbourhood,level,standard deviation,0.022,0.004,,,***,,,high,"primary school value-added (Key Stage 1-2), in SD units",ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,2.1999999999999997,0.4,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.005464203097840218,0.0009934914723345848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,1013115.0,standard deviation,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Age7-11 Value Added (standard deviation),school_quality,F_neighbourhood,level,standard deviation,0.032,0.004,,,***,,,high,"primary school value-added (Key Stage 1-2), in SD units",ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,3.2,0.4,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.01193910173167338,0.0014923877164591723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,448926.0,standard deviation,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Distance to School,school_distance,D_location,level,100 km,-0.17,0.27,,,,,,high,distance to nearest primary school; interacted with value added (interaction not extracted),ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,-0.17,0.27,100*b/100 (X per 100km->km),reported,-0.0006255408839985259,0.000993506109880012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6296296296296297,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Distance to School,school_distance,D_location,level,100 km,0.85,0.33,,,***,,,high,distance to nearest primary school; interacted with value added (interaction not extracted),ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,0.85,0.33,100*b/100 (X per 100km->km),reported,0.0025590230107125846,0.000993503051217827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5757575757575757,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to School,school_distance,D_location,level,100 km,4.49,1.34,,,***,,,high,distance to nearest primary school; interacted with value added (interaction not extracted),ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,4.49,1.34,100*b/100 (X per 100km->km),reported,0.005000906474282805,0.0014924754288505476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3507462686567164,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M2,2,2,30,Distance to TTWA centre,cbd_distance,D_location,level,100 km,0.98,0.14,,,***,,,high,distance to Travel-to-Work-Area centre,ln house sale price,log,OLS,1013125.0,0.766,se,England 1996-2008,semi_elasticity,0.98,0.14,100*b/100 (X per 100km->km),reported,0.006954375955279956,0.0009934822793257081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.999999999999999,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M3,2,3,30,Distance to TTWA centre,cbd_distance,D_location,level,100 km,-0.61,0.27,,,**,,,high,distance to Travel-to-Work-Area centre,ln house sale price,log,OLS,1013125.0,0.865,se,England 1996-2008,semi_elasticity,-0.61,0.27,100*b/100 (X per 100km->km),reported,-0.00224458266273745,0.0009935038015395272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.259259259259259,1013115.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to TTWA centre,cbd_distance,D_location,level,100 km,-1.09,0.49,,,**,,,high,distance to Travel-to-Work-Area centre,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-1.09,0.49,100*b/100 (X per 100km->km),reported,-0.0033200195802599855,0.0014924858663554062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2244897959183674,448926.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M5,2,5,30,Distance to TTWA centre,cbd_distance,D_location,level,100 km,-0.6,0.26,,,**,,,high,distance to Travel-to-Work-Area centre,ln house sale price,log,OLS,1135234.0,0.854,se,All Great Britain,semi_elasticity,-0.6,0.26,100*b/100 (X per 100km->km),reported,-0.0021658885317038016,0.0009385516970716476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3076923076923075,1135224.0,100km,,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,True,True,False
22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6,22e62fc82c4ed1c605cd41b5c6124856f4afd7c6:T2-M4,2,4,30,Distance to nearest church,other,other,level,100 km,-4.21,0.95,,,***,,,high,Model 4 only,ln house sale price,log,OLS,448936.0,0.854,se,"Metropolitan TTWAs (North West, West Midlands, London), sales within 2 km of nearest church",semi_elasticity,-421.0,95.0,100*b (unit as reported: 100km; no harmonised unit defined for this characteristic),reported,-0.006613960729902328,0.0014924614473651335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.431578947368421,448926.0,100km,unit_not_harmonisable,2014.0,United Kingdom,Europe,High income,1996.0,2008.0,mixed,transaction,working_paper,True,True,clustered,,2002.0,ols,other,False,False,False
2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88:T5-c1,5,1,9,Ln_S***,living_area,A_structural,log,m2,1.245,,32.311,0.0,***,,,high,Log of gross floor area (m2),"Ln(P), total price of new residence in 10,000 RMB",log,OLS,378.0,0.782,t,Final model after collinearity elimination; t-ratio and p-value in separate columns,elasticity,1.245,0.038531769366469626,b (log-log),|b/t|,0.8598702830185788,0.026612307976187015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.311,368.0,m2,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88:T5-c1,5,1,9,Ln_DC***,cbd_distance,D_location,log,km,-0.268,,-0.924,0.0,***,,,medium,"Log straight-line distance to CBD (km); printed t-ratio -0.924 inconsistent with p=0.000 and ***, as printed","Ln(P), total price of new residence in 10,000 RMB",log,OLS,378.0,0.782,t,Final model after collinearity elimination; t-ratio and p-value in separate columns,elasticity,-0.268,0.29004329004329005,b (log-log),|b/t|,-0.04811105211367316,0.05206823821826099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.924,368.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88:T5-c1,5,1,9,HB***,floor_level,A_structural,dummy,binary,-0.142,,-4.538,0.0,***,,,high,"High-rise building above 12 floors dummy (building height, not unit floor)","Ln(P), total price of new residence in 10,000 RMB",log,OLS,378.0,0.782,t,Final model after collinearity elimination; t-ratio and p-value in separate columns,pct_premium,-13.2803403741924,2.7149012433010085,100*(exp(b - se^2/2) - 1),|b/t|,-0.23020606264099502,0.050728528567870204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.538,368.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88:T5-c1,5,1,9,Ln_DS***,transit_distance,D_location,log,km,-0.061,,-3.174,0.002,***,,,high,Log straight-line distance to nearest subway station (km),"Ln(P), total price of new residence in 10,000 RMB",log,OLS,378.0,0.782,t,Final model after collinearity elimination; t-ratio and p-value in separate columns,elasticity,-0.061,0.01921865154379332,b (log-log),|b/t|,-0.1632369072374484,0.051429397365295657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.174,368.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88:T5-c1,5,1,9,E**,school_quality,F_neighbourhood,dummy,binary,0.09,,2.097,0.037,**,,,high,Within 825 m buffer of a Key Primary School (attendance zone proxy),"Ln(P), total price of new residence in 10,000 RMB",log,OLS,378.0,0.782,t,Final model after collinearity elimination; t-ratio and p-value in separate columns,pct_premium,9.31670165122236,4.69602696869189,100*(exp(b - se^2/2) - 1),|b/t|,0.1086663563477336,0.051819912421427555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.097,368.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88,2344ebd24eeebb31c33011f3e72a12383c3c1e88:T5-c1,5,1,9,SN*,sunlight,E_environment,dummy,binary,0.044,,1.69,0.092,*,,,high,South-facing residence dummy,"Ln(P), total price of new residence in 10,000 RMB",log,OLS,378.0,0.782,t,Final model after collinearity elimination; t-ratio and p-value in separate columns,pct_premium,4.462824560622036,2.720664119236185,100*(exp(b - se^2/2) - 1),|b/t|,0.08775744916779234,0.05192748471467002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.69,368.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0:T2-c1,2,1,5,Geographic Position,other,other,other,index,58080.342,8630.594,6.73,0.0,,,,high,"District code: Tianqiao=1, Huaiyin=2, Shizhong=3, Lixia=4 (categorical district entered as a numeric code); standardized coefficient printed = 0.239; p printed as 0.000","total housing sales price, RMB",level,OLS,226.0,0.731,none,"Second-step multivariate linear regression (SPSS 18) after dropping rooms, halls, toilets, housing years and floors (insignificant); R2 0.731, adj. R2 0.723, F=98.952; B, Std. Error, t and Sig. printed in columns",other,58080.342,8630.594,not harmonised,reported,0.41634299280303694,0.06186374335854932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.73,216.0,index,nonstandard_x_transform,2013.0,China,Asia,Upper middle income,2012.0,2012.0,condo_apartment,transaction,conference,False,False,OLS,,2012.0,ols,other,False,False,False
25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0:T2-c1,2,1,5,The Number of Verandas,terrace_balcony,C_exterior,level,count,-35112.641,14006.309,-2.507,0.013,,,,high,Number of verandas (balconies) in the dwelling; standardized coefficient printed = -0.092; p printed as 0.013,"total housing sales price, RMB",level,OLS,226.0,0.731,none,"Second-step multivariate linear regression (SPSS 18) after dropping rooms, halls, toilets, housing years and floors (insignificant); R2 0.731, adj. R2 0.723, F=98.952; B, Std. Error, t and Sig. printed in columns",monetary,-35112.641,14006.309,"b (currency per unit, as reported)",reported,-0.16815090441369235,0.06707255860139302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.507,216.0,count,,2013.0,China,Asia,Upper middle income,2012.0,2012.0,condo_apartment,transaction,conference,False,False,OLS,,2012.0,ols,other,False,True,False
25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0:T2-c1,2,1,5,Building area,living_area,A_structural,level,m2,6972.19,363.181,19.198,0.0,,,,high,Building area of the property in m2; standardized coefficient printed = 0.713; p printed as 0.000,"total housing sales price, RMB",level,OLS,226.0,0.731,none,"Second-step multivariate linear regression (SPSS 18) after dropping rooms, halls, toilets, housing years and floors (insignificant); R2 0.731, adj. R2 0.723, F=98.952; B, Std. Error, t and Sig. printed in columns",monetary,6972.19,363.181,"b (currency per unit, as reported)",reported,0.7940361043339873,0.041360355471090085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.198,216.0,m2,,2013.0,China,Asia,Upper middle income,2012.0,2012.0,condo_apartment,transaction,conference,False,False,OLS,,2012.0,ols,other,False,True,False
25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0:T2-c1,2,1,5,Housing type,storeys,A_structural,other,index,29134.01,11631.539,2.505,0.013,,,,high,"Building height class: multi-storey (<=7 floors)=0, small high-rise (8-12)=1, high-rise (>13)=2, entered linearly; standardized coefficient printed = 0.099; p printed as 0.013","total housing sales price, RMB",level,OLS,226.0,0.731,none,"Second-step multivariate linear regression (SPSS 18) after dropping rooms, halls, toilets, housing years and floors (insignificant); R2 0.731, adj. R2 0.723, F=98.952; B, Std. Error, t and Sig. printed in columns",other,29134.01,11631.539,not harmonised,reported,0.16802054780355954,0.0670740709794649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.505,216.0,index,nonstandard_x_transform,2013.0,China,Asia,Upper middle income,2012.0,2012.0,condo_apartment,transaction,conference,False,False,OLS,,2012.0,ols,other,False,True,False
25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0:T2-c1,2,1,5,Decoration,condition,A_structural,other,index,25098.127,8085.78,3.104,0.002,,,,high,"Decoration standard ordinal 1 (blank/clear-water) to 5 (luxury decoration), entered linearly (interior finish quality); standardized coefficient printed = 0.11; p printed as 0.002","total housing sales price, RMB",level,OLS,226.0,0.731,none,"Second-step multivariate linear regression (SPSS 18) after dropping rooms, halls, toilets, housing years and floors (insignificant); R2 0.731, adj. R2 0.723, F=98.952; B, Std. Error, t and Sig. printed in columns",other,25098.127,8085.78,not harmonised,reported,0.20664202829242492,0.06657281839317813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.104,216.0,index,nonstandard_x_transform,2013.0,China,Asia,Upper middle income,2012.0,2012.0,condo_apartment,transaction,conference,False,False,OLS,,2012.0,ols,other,False,True,False
25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0,25220943ab063e0fe03168460d2005c02cf25de0:T2-c1,2,1,5,Facilities,heating,A_structural,binary,binary,105639.321,26027.895,4.059,0.0,,,,high,"Double-gas-source facilities = 1 (central heating and piped natural gas available), 0 otherwise; standardized coefficient printed = 0.158; p printed as 0.000","total housing sales price, RMB",level,OLS,226.0,0.731,none,"Second-step multivariate linear regression (SPSS 18) after dropping rooms, halls, toilets, housing years and floors (insignificant); R2 0.731, adj. R2 0.723, F=98.952; B, Std. Error, t and Sig. printed in columns",monetary,105639.321,26027.895,"b (currency per unit, as reported)",reported,0.266213744307921,0.06558604195809829,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.059,216.0,binary,,2013.0,China,Asia,Upper middle income,2012.0,2012.0,condo_apartment,transaction,conference,False,False,OLS,,2012.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,LandPrice,other,other,level,currency,0.138,,,,,,,medium,"Mean of coefficients across 10 CV folds; SD across folds = 0.005 (not an SE). p printed as <0.000. Average land price in zip-code area, Yen/m2",Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,0.138,,"b (currency per unit, as reported)",,,,,,15313.0,currency,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,Area,living_area,A_structural,level,m2,1044.0,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 50.83 (not an SE). p printed as <0.000.,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,1044.0,,"b (currency per unit, as reported)",,,,,,15313.0,m2,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,Year,age,A_structural,level,years,929.8,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 57.14 (not an SE). p printed as <0.000. Year of building completion (higher = newer),Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,929.8,,"b (currency per unit, as reported)",,,,,,15313.0,years,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,FPV,multimodal_embedding,H_ai_derived,level,index,599.0,,,,,,,medium,"Mean of coefficients across 10 CV folds; SD across folds = 90.81 (not an SE). p printed as <0.000. Floor-plan value learned by GCN from access graph of floor-plan image (deviation score, mean 50, SD 10)",Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,599.0,,"b (currency per unit, as reported)",,,,,,15313.0,index,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,L_Building,storeys,A_structural,level,count,1073.0,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 236.5 (not an SE). p printed as <0.000. Number of building floors,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,1073.0,,"b (currency per unit, as reported)",,,,,,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,Passenger,other,other,level,count,0.071,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 0.011 (not an SE). p printed as <0.000. Daily passengers at nearest station (2016),Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,0.071,,"b (currency per unit, as reported)",,,,,,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,L_Room,floor_level,A_structural,level,count,838.3,,,0.001,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 368.1 (not an SE). p = mean across folds (SD 0.001). Floor of housing unit,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,838.3,254.76164407876502,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.026581641413664616,0.008078232934340195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,Distance,transit_distance,D_location,level,m,-2.929,,,0.025,,,,medium,"Mean of coefficients across 10 CV folds; SD across folds = 0.963 (not an SE). p = mean across folds (SD 0.08). Distance to nearest station/bus stop, metres",Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,-2.929,1.3067709626327564,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.018110003110045382,0.00807976312645825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.241402727604945,15313.0,m,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T5-FPV,5,FPV model (mean over 10 folds),12,Structure_RC,roof_materials,A_structural,binary,binary,3032.0,,,0.299,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 2746 (not an SE). p = mean across folds (SD 0.189). Reinforced-concrete structure,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + GCN floor-plan value; mean adj. R2 across folds 0.79 (Fig. 11),monetary,3032.0,2919.370006713519,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.008392563164768965,0.008080803820143537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.038580239239107,15313.0,binary,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Land price,other,other,level,currency,0.141,,,,,,,medium,"Mean of coefficients across 10 CV folds; SD across folds = 0.007 (not an SE). p printed as <0.000. Average land price in zip-code area, Yen/m2",Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,0.141,,"b (currency per unit, as reported)",,,,,,15313.0,currency,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Area,living_area,A_structural,level,m2,1098.0,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 86.27 (not an SE). p printed as <0.000.,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,1098.0,,"b (currency per unit, as reported)",,,,,,15313.0,m2,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Year,age,A_structural,level,years,926.6,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 87.12 (not an SE). p printed as <0.000. Year of building completion (higher = newer),Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,926.6,,"b (currency per unit, as reported)",,,,,,15313.0,years,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,F_Building,storeys,A_structural,level,count,1299.0,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 329.8 (not an SE). p printed as <0.000. Number of building floors,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,1299.0,,"b (currency per unit, as reported)",,,,,,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Passenger,other,other,level,count,0.072,,,,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 0.013 (not an SE). p printed as <0.000. Daily passengers at nearest station,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,0.072,,"b (currency per unit, as reported)",,,,,,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Distance,transit_distance,D_location,level,m,-3.322,,,0.019,,,,medium,"Mean of coefficients across 10 CV folds; SD across folds = 1.1 (not an SE). p = mean across folds (SD 0.061). Distance to nearest station/bus stop, metres",Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,-3.322,1.4163104394471069,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.018951039197225482,0.008079637162372599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3455309708066743,15313.0,m,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,F_Dwelling,floor_level,A_structural,level,count,808.1,,,0.058,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 471.1 (not an SE). p = mean across folds (SD 0.155). Floor of housing unit,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,808.1,426.28099644607676,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.015317505285438748,0.008080140349145993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8956979239918386,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,RRA_jp,other,other,level,index,-3987.0,,,0.02,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 1469 (not an SE). p = mean across folds (SD 0.038). Access-graph feature: real relative asymmetry of Japanese room,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,-3987.0,1713.8451409137813,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.01879610171921319,0.008079660797490519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,15313.0,index,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Num_cl,storage,B_interior,level,count,1211.0,,,0.102,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 614.0 (not an SE). p = mean across folds (SD 0.224). Number of closets (graph nodes),Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,1211.0,740.5667865294366,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.013213317048848717,0.008080382944888945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6352340153886502,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Num_bl,terrace_balcony,C_exterior,level,count,1533.0,,,0.112,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 690.4 (not an SE). p = mean across folds (SD 0.168). Number of balconies (graph nodes),Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,1533.0,964.5952899486688,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.012841952602633263,0.008080422044516696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.589267557051392,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,RRA_wc,other,other,level,index,4082.0,,,0.195,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 2343 (not an SE). p = mean across folds (SD 0.231). Access-graph feature: relative asymmetry of toilet,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,4082.0,3149.8642936910833,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.010471941362497583,0.008080645316844166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2959288462604268,15313.0,index,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Num_edge,other,other,level,count,-568.9,,,0.148,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 250.1 (not an SE). p = mean across folds (SD 0.187). Number of access-graph edges,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,-568.9,393.258253370019,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.011689562900712972,0.00808053627877194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4466320671589785,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Depth_en,other,other,level,count,631.6,,,0.428,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 1077 (not an SE). p = mean across folds (SD 0.324). Depth of entrance in access graph,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,631.6,796.8522391590697,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.006405090553115174,0.008080922655583328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7926187177017123,15313.0,count,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,RRA_bt,other,other,level,index,-2354.0,,,0.45,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 3229 (not an SE). p = mean across folds (SD 0.333). Access-graph feature: relative asymmetry of bathroom,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,-2354.0,3116.1678254421126,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.006104461879939867,0.008080937851277184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7554150263604693,15313.0,index,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,Structure_RC,roof_materials,A_structural,binary,binary,2871.0,,,0.382,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 3362 (not an SE). p = mean across folds (SD 0.259). Reinforced-concrete structure,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,2871.0,3284.0810217201342,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.007064449921337933,0.008080886769612747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8742171648664835,15313.0,binary,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,True,False
2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015,2580ac3af513b0ee5f2e19ed10bd991f2a8c8015:T6-GF,6,Graph-feature (GF) model (mean over 10 folds),13,H*,other,other,level,index,-5471.0,,,0.593,,,,medium,Mean of coefficients across 10 CV folds; SD across folds = 8045 (not an SE). p = mean across folds (SD 0.383). Access-graph integration/entropy measure,Rent (Yen/month),level,OLS,15323.0,,none,baseline attributes + explicit access-graph features,monetary,-5471.0,10235.854783484745,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.0043192506421798555,0.008081013040911394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5344937101713576,15313.0,index,,,Japan,Asia,High income,2015.0,2015.0,rental,rent,working_paper,False,False,unknown,graph convolutional network (ResGatedGraphConv) on floor-plan access graphs producing a floor-plan value (FPV);semantic segmentation of floor-plan images;integrated gradients,2015.0,ols,other,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c1,7,1,14,No_rooms,bedrooms,A_structural,level,count,0.31,,22.75,,***,,,high,"Number of rooms (all rooms, not only bedrooms)",ln(Price): log sale price of the apartment,log,OLS,765.0,0.4,t,"Bivariate regression: single regressor No_rooms, full sample of apartment transactions 2014-2018",semi_elasticity,31.0,1.3626373626373625,100*b/1 (per unit count),|b/t|,0.6377373317266176,0.02803241018578539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.75,755.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c1,8,1,15,No_rooms,bedrooms,A_structural,level,count,0.3,,22.31,,***,,,high,"Number of rooms (all rooms, not only bedrooms)",ln(Price): log sale price of the apartment,log,OLS,765.0,0.41,t,Bivariate regression: single regressor No_rooms plus Duration control,semi_elasticity,30.0,1.3446884805020172,100*b/1 (per unit count),|b/t|,0.6303326074052893,0.028253366535423104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.31,755.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c1,8,1,15,Duration,time_on_market,G_market,level,days,0.0,,2.45,,*,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.41,t,Bivariate regression: single regressor No_rooms plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.0888122847345473,0.03624991213654991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c2,7,2,14,S_useful,living_area,A_structural,level,m2,0.01,,36.13,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,760.0,0.63,t,"Bivariate regression: single regressor S_useful, full sample of apartment transactions 2014-2018",semi_elasticity,1.0,0.02767783005812344,100*b/1 (X per m2->m2),|b/t|,0.7969337617966491,0.022057397226588688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.13,750.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c2,8,2,15,S_useful,living_area,A_structural,level,m2,0.01,,35.48,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,760.0,0.63,t,Bivariate regression: single regressor S_useful plus Duration control,semi_elasticity,1.0,0.028184892897406996,100*b/1 (X per m2->m2),|b/t|,0.7916112820618978,0.022311479201293623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.48,750.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c2,8,2,15,Duration,time_on_market,G_market,level,days,-0.0,,-0.26,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,760.0,0.63,t,Bivariate regression: single regressor S_useful plus Duration control,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.00949342983582511,0.036513191676250424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26,750.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c3,7,3,14,S_constructed,living_area,A_structural,level,m2,0.01,,35.2,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,762.0,0.62,t,"Bivariate regression: single regressor S_constructed, full sample of apartment transactions 2014-2018",semi_elasticity,1.0,0.028409090909090908,100*b/1 (X per m2->m2),|b/t|,0.7888649691609271,0.022410936623889967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.2,752.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c3,8,3,15,S_constructed,living_area,A_structural,level,m2,0.01,,34.56,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,762.0,0.62,t,Bivariate regression: single regressor S_constructed plus Duration control,semi_elasticity,1.0,0.028935185185185182,100*b/1 (X per m2->m2),|b/t|,0.7833546032972407,0.02266651051207294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.56,752.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c3,8,3,15,Duration,time_on_market,G_market,level,days,-0.0,,-0.79,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,762.0,0.62,t,Bivariate regression: single regressor S_constructed plus Duration control,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.028796388946437577,0.03645112524865516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.79,752.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c4,7,4,14,Comfort,condition,A_structural,binary,binary,0.52,,11.05,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,734.0,0.14,t,"Bivariate regression: single regressor Comfort, full sample of apartment transactions 2014-2018",pct_premium,68.01662276732216,7.915424233877113,100*(exp(b - se^2/2) - 1),|b/t|,0.3798838133462677,0.034378625642196174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.05,724.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c4,8,4,15,Comfort,condition,A_structural,binary,binary,0.51,,10.72,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,734.0,0.15,t,Bivariate regression: single regressor Comfort plus Duration control,pct_premium,66.34076965477256,7.922560722223899,100*(exp(b - se^2/2) - 1),|b/t|,0.3701134991568745,0.03452551298105172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.72,724.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c4,8,4,15,Duration,time_on_market,G_market,level,days,0.0,,1.56,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,734.0,0.15,t,Bivariate regression: single regressor Comfort plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.057879748663494664,0.03710240298941966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.56,724.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c5,7,5,14,Floor,floor_level,A_structural,level,count,-0.02,,-4.26,,***,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,"Bivariate regression: single regressor Floor, full sample of apartment transactions 2014-2018",semi_elasticity,-2.0,0.4694835680751174,100*b/1 (per unit count),|b/t|,-0.15320692396775779,0.03596406665909808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.26,755.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c5,8,5,15,Floor,floor_level,A_structural,level,count,-0.02,,-4.23,,***,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,765.0,0.05,t,Bivariate regression: single regressor Floor plus Duration control,semi_elasticity,-2.0,0.4728132387706856,100*b/1 (per unit count),|b/t|,-0.15215306612011023,0.03596999199056979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.23,755.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c5,8,5,15,Duration,time_on_market,G_market,level,days,0.0,,4.19,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.05,t,Bivariate regression: single regressor Floor plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15074711520953293,0.035977831792251294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.19,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c6,7,6,14,No_bathrooms,bathrooms,A_structural,level,count,0.49,,17.23,,***,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,765.0,0.28,t,"Bivariate regression: single regressor No_bathrooms, full sample of apartment transactions 2014-2018",semi_elasticity,49.0,2.8438769587928032,100*b/1 (per unit count),|b/t|,0.5312557508279308,0.030833183449096385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.23,755.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c6,8,6,15,No_bathrooms,bathrooms,A_structural,level,count,0.48,,17.01,,***,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,765.0,0.29,t,Bivariate regression: single regressor No_bathrooms plus Duration control,semi_elasticity,48.0,2.8218694885361546,100*b/1 (per unit count),|b/t|,0.5263605554522475,0.03094418315415916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.01,755.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c6,8,6,15,Duration,time_on_market,G_market,level,days,0.0,,3.55,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.29,t,Bivariate regression: single regressor No_bathrooms plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.1281327546236428,0.03609373369680079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.55,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c7,7,7,14,No_balconies,terrace_balcony,C_exterior,level,count,0.29,,8.71,,***,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,706.0,0.1,t,"Bivariate regression: single regressor No_balconies, full sample of apartment transactions 2014-2018",semi_elasticity,28.999999999999996,3.3295063145809407,100*b/1 (per unit count),|b/t|,0.3135073549981706,0.035993955797723375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.71,696.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c7,8,7,15,No_balconies,terrace_balcony,C_exterior,level,count,0.28,,8.47,,***,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,706.0,0.11,t,Bivariate regression: single regressor No_balconies plus Duration control,semi_elasticity,28.000000000000004,3.3057851239669422,100*b/1 (per unit count),|b/t|,0.30568636777518854,0.03609048025681092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.47,696.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c7,8,7,15,Duration,time_on_market,G_market,level,days,0.0,,2.73,,**,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,706.0,0.11,t,Bivariate regression: single regressor No_balconies plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.10293074943232373,0.0377035712206314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.73,696.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c8,7,8,14,No_parking,parking,A_structural,level,count,0.3,,4.74,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,317.0,0.07,t,"Bivariate regression: single regressor No_parking, full sample of apartment transactions 2014-2018",semi_elasticity,30.0,6.329113924050632,100*b/1 (per unit count),|b/t|,0.2611391496139819,0.055092647597886485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.74,307.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c8,8,8,15,No_parking,parking,A_structural,level,count,0.28,,4.39,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,317.0,0.07,t,Bivariate regression: single regressor No_parking plus Duration control,semi_elasticity,28.000000000000004,6.378132118451026,100*b/1 (per unit count),|b/t|,0.2430382062058144,0.05536177817900101,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.39,307.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c8,8,8,15,Duration,time_on_market,G_market,level,days,0.0,,1.67,,†,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,317.0,0.07,t,Bivariate regression: single regressor No_parking plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.09488193759032558,0.056815531491212934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.67,307.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c9,7,9,14,Seniority_construction,age,A_structural,level,years,-0.01,,-7.6,,***,,,high,Years since construction (age),ln(Price): log sale price of the apartment,log,OLS,685.0,0.08,t,"Bivariate regression: single regressor Seniority_construction, full sample of apartment transactions 2014-2018",semi_elasticity,-1.0,0.13157894736842107,100*b/1 (per year),|b/t|,-0.2807583718925217,0.036941891038489695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.6,675.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c9,8,9,15,Seniority_construction,age,A_structural,level,years,-0.01,,-6.98,,***,,,high,Years since construction (age),ln(Price): log sale price of the apartment,log,OLS,685.0,0.09,t,Bivariate regression: single regressor Seniority_construction plus Duration control,semi_elasticity,-1.0,0.14326647564469913,100*b/1 (per year),|b/t|,-0.25945976198141935,0.03717188567069045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.98,675.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c9,8,9,15,Duration,time_on_market,G_market,level,days,0.0,,3.05,,**,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,685.0,0.09,t,Bivariate regression: single regressor Seniority_construction plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.1165938856592312,0.03822750349482991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,675.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c10,7,10,14,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.23,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,451.0,0.0,t,"Bivariate regression: single regressor Seniority_renovation, full sample of apartment transactions 2014-2018",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.05847121840448346,0.04753757593860443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.23,441.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c10,8,10,15,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.32,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,451.0,0.02,t,Bivariate regression: single regressor Seniority_renovation plus Duration control,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06273333468593087,0.047525253549947626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.32,441.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c10,8,10,15,Duration,time_on_market,G_market,level,days,0.0,,3.05,,**,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,451.0,0.02,t,Bivariate regression: single regressor Seniority_renovation plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.14373007768106982,0.04712461563313764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,441.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T7-c11,7,11,14,Structure,roof_materials,A_structural,binary,binary,0.08,,1.65,,†,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,669.0,0.0,t,"Bivariate regression: single regressor Structure, full sample of apartment transactions 2014-2018",pct_premium,8.201453061035036,5.252300934181618,100*(exp(b - se^2/2) - 1),|b/t|,0.06414251630948829,0.038874252308780784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.65,659.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c11,8,11,15,Structure,roof_materials,A_structural,binary,binary,0.1,,1.97,,*,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,669.0,0.03,t,Bivariate regression: single regressor Structure plus Duration control,pct_premium,10.37479758532347,5.610004660282476,100*(exp(b - se^2/2) - 1),|b/t|,0.07651533312171237,0.03884027062015856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.97,659.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T8-c11,8,11,15,Duration,time_on_market,G_market,level,days,0.0,,4.2,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,669.0,0.03,t,Bivariate regression: single regressor Structure plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.16146204788161328,0.038443344733717445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2,659.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c1,9,1,16,Height_regime_1,storeys,A_structural,dummy,binary,-0.23,,-7.75,,***,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.07,t,Bivariate regression: single height-regime dummy Height_regime_1 (base = all other height regimes),pct_premium,-20.58162128912644,2.3579706913002174,100*(exp(b - se^2/2) - 1),|b/t|,-0.27146025103791244,0.03502712916618225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c1,10,1,16,Height_regime_1,storeys,A_structural,dummy,binary,-0.22,,-7.35,,***,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.09,t,Bivariate regression: single height-regime dummy Height_regime_1 plus Duration control,pct_premium,-19.78406190515458,2.4020970823366703,100*(exp(b - se^2/2) - 1),|b/t|,-0.2584086451506488,0.03515763879600665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.35,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c1,10,1,16,Duration,time_on_market,G_market,level,days,0.0,,3.48,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.09,t,Bivariate regression: single height-regime dummy Height_regime_1 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.12564647488404584,0.036105308874725815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.48,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c2,9,2,16,Height_regime_2,storeys,A_structural,dummy,binary,0.18,,2.06,,*,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.01,t,Bivariate regression: single height-regime dummy Height_regime_2 (base = all other height regimes),pct_premium,19.265568243566,10.461122590384747,100*(exp(b - se^2/2) - 1),|b/t|,0.07476126658991274,0.03629187698539453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.06,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c2,10,2,16,Height_regime_2,storeys,A_structural,dummy,binary,0.18,,2.04,,*,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_2 plus Duration control,pct_premium,19.256597409174937,10.563682615780676,100*(exp(b - se^2/2) - 1),|b/t|,0.07403942736970325,0.03629383694593297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.04,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c2,10,2,16,Duration,time_on_market,G_market,level,days,0.0,,4.21,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_2 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15145020556451988,0.03597392056164368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.21,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c3,9,3,16,Height_regime_3,storeys,A_structural,dummy,binary,0.3,,3.09,,**,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.01,t,Bivariate regression: single height-regime dummy Height_regime_3 (base = all other height regimes),pct_premium,34.3511918753137,13.105425316271875,100*(exp(b - se^2/2) - 1),|b/t|,0.11175219772321075,0.03616575978097436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.09,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c3,10,3,16,Height_regime_3,storeys,A_structural,dummy,binary,0.27,,2.87,,**,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_3 plus Duration control,pct_premium,30.41803921657007,12.323707376236126,100*(exp(b - se^2/2) - 1),|b/t|,0.10388485005727441,0.03619681186664613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.87,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c3,10,3,16,Duration,time_on_market,G_market,level,days,0.0,,4.06,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_3 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.1461714935071232,0.03600283091308453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.06,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c4,9,4,16,Height_regime_4,storeys,A_structural,dummy,binary,-0.01,,-0.23,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.0,t,Bivariate regression: single height-regime dummy Height_regime_4 (base = all other height regimes),pct_premium,-1.0885499042591729,4.3045644945616,100*(exp(b - se^2/2) - 1),|b/t|,-0.008370263809080588,0.036392451343828645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c4,10,4,16,Height_regime_4,storeys,A_structural,dummy,binary,-0.05,,-0.99,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_4 plus Duration control,pct_premium,-4.998298123948352,4.80418901262987,100*(exp(b - se^2/2) - 1),|b/t|,-0.03600642578615333,0.03637012705672053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.99,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c4,10,4,16,Duration,time_on_market,G_market,level,days,0.0,,4.32,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_4 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15531306900139957,0.035952099305879524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.32,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c5,9,5,16,Height_regime_5,storeys,A_structural,dummy,binary,0.04,,1.04,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.0,t,Bivariate regression: single height-regime dummy Height_regime_5 (base = all other height regimes),pct_premium,4.00412283685363,4.003118362278417,100*(exp(b - se^2/2) - 1),|b/t|,0.037822393154129844,0.036367685725124846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.04,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c5,10,5,16,Height_regime_5,storeys,A_structural,dummy,binary,0.04,,1.09,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_5 plus Duration control,pct_premium,4.011018640281749,3.819489079605094,100*(exp(b - se^2/2) - 1),|b/t|,0.03963798587870104,0.03636512465935875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.09,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c5,10,5,16,Duration,time_on_market,G_market,level,days,0.0,,4.23,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_5 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15215306612011023,0.03596999199056979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.23,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c6,9,6,16,Height_regime_6,storeys,A_structural,dummy,binary,0.23,,3.37,,***,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.01,t,Bivariate regression: single height-regime dummy Height_regime_6 (base = all other height regimes),pct_premium,25.567216566807694,8.589851699815428,100*(exp(b - se^2/2) - 1),|b/t|,0.1217346930087746,0.03612305430527436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.37,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c6,10,6,16,Height_regime_6,storeys,A_structural,dummy,binary,0.19,,2.78,,**,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_6 plus Duration control,pct_premium,20.642864038431274,8.264655523556755,100*(exp(b - se^2/2) - 1),|b/t|,0.10066067472932196,0.03620887580191438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.78,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c6,10,6,16,Duration,time_on_market,G_market,level,days,0.0,,3.76,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_6 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.1355769387454866,0.03605769647486346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.76,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c7,9,7,16,Height_regime_7,storeys,A_structural,dummy,binary,0.17,,2.19,,*,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.01,t,Bivariate regression: single height-regime dummy Height_regime_7 (base = all other height regimes),pct_premium,18.173906258757498,9.200996562760828,100*(exp(b - se^2/2) - 1),|b/t|,0.0794503079426398,0.036278679425862916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.19,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c7,10,7,16,Height_regime_7,storeys,A_structural,dummy,binary,0.17,,2.21,,*,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_7 plus Duration control,pct_premium,18.18032147236215,9.117729625541273,100*(exp(b - se^2/2) - 1),|b/t|,0.08017123892720256,0.036276578700091655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.21,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c7,10,7,16,Duration,time_on_market,G_market,level,days,0.0,,4.23,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.03,t,Bivariate regression: single height-regime dummy Height_regime_7 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15215306612011023,0.03596999199056979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.23,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c8,9,8,16,Height_regime_8,storeys,A_structural,dummy,binary,0.06,,0.71,,,,,high,1 if building height regime is Ground Floor +7 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.0,t,Bivariate regression: single height-regime dummy Height_regime_8 (base = all other height regimes),pct_premium,5.805178663123223,8.973266590524165,100*(exp(b - se^2/2) - 1),|b/t|,0.025830923662282835,0.036381582622933575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.71,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c8,10,8,16,Height_regime_8,storeys,A_structural,dummy,binary,0.07,,0.86,,,,,high,1 if building height regime is Ground Floor +7 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_8 plus Duration control,pct_premium,6.896126714465689,8.729717754394787,100*(exp(b - se^2/2) - 1),|b/t|,0.03128328574149982,0.03637591365290677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.86,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c8,10,8,16,Duration,time_on_market,G_market,level,days,0.0,,4.24,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_8 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15250440992130374,0.03596802120785465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.24,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c9,9,9,16,Height_regime_9,storeys,A_structural,dummy,binary,0.13,,3.38,,***,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.01,t,Bivariate regression: single height-regime dummy Height_regime_9 (base = all other height regimes),pct_premium,13.798636607403015,4.380109166633161,100*(exp(b - se^2/2) - 1),|b/t|,0.1220905468595488,0.03612146356791385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.38,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c9,10,9,16,Height_regime_9,storeys,A_structural,dummy,binary,0.14,,3.63,,***,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.04,t,Bivariate regression: single height-regime dummy Height_regime_9 plus Duration control,pct_premium,14.9418628644556,4.436317681543026,100*(exp(b - se^2/2) - 1),|b/t|,0.1309712593264584,0.036080236729051904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.63,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c9,10,9,16,Duration,time_on_market,G_market,level,days,0.0,,4.42,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.04,t,Bivariate regression: single height-regime dummy Height_regime_9 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15881859622747505,0.03593180910123869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.42,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T9-c10,9,10,16,Height_regime_10,storeys,A_structural,dummy,binary,-0.05,,-0.66,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.0,t,Bivariate regression: single height-regime dummy Height_regime_10 (base = all other height regimes),pct_premium,-5.149631557804213,7.206283518944804,100*(exp(b - se^2/2) - 1),|b/t|,-0.02401293315739747,0.036383232056662836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.66,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c10,10,10,16,Height_regime_10,storeys,A_structural,dummy,binary,-0.03,,-0.48,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_10 plus Duration control,pct_premium,-3.1448018110717735,6.065284584678176,100*(exp(b - se^2/2) - 1),|b/t|,-0.017466323748954828,0.03638817447698923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48,755.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T10-c10,10,10,16,Duration,time_on_market,G_market,level,days,0.0,,4.19,,***,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,765.0,0.02,t,Bivariate regression: single height-regime dummy Height_regime_10 plus Duration control,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.15074711520953293,0.035977831792251294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.19,755.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,No_rooms,bedrooms,A_structural,level,count,0.23,,9.19,,***,,,high,"Number of rooms (all rooms, not only bedrooms)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,23.0,2.50272034820457,100*b/1 (per unit count),|b/t|,0.5842065600926486,0.06356981067384641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.19,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,No_rooms,bedrooms,A_structural,level,count,0.24,,9.95,,***,,,high,"Number of rooms (all rooms, not only bedrooms)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,24.0,2.412060301507538,100*b/1 (per unit count),|b/t|,0.6147101201288447,0.06177991157073817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.95,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,Comfort,condition,A_structural,binary,binary,0.34,,5.79,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",pct_premium,40.252736135501024,8.25012402058069,100*(exp(b - se^2/2) - 1),|b/t|,0.4130195942405717,0.07133326325398476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.79,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Comfort,condition,A_structural,binary,binary,0.31,,5.21,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,36.101373831430415,8.112510276026393,100*(exp(b - se^2/2) - 1),|b/t|,0.37782987639681886,0.0725201298266447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.21,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,Comfort,condition,A_structural,binary,binary,0.26,,4.33,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,pct_premium,29.459412310079045,7.787570959194011,100*(exp(b - se^2/2) - 1),|b/t|,0.32118259363543356,0.07417611862250198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.33,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,Comfort,condition,A_structural,binary,binary,0.14,,2.54,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,pct_premium,14.852785584430617,6.340091804724876,100*(exp(b - se^2/2) - 1),|b/t|,0.1951240850641541,0.07682050593076933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.54,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Comfort,condition,A_structural,binary,binary,0.2,,3.32,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,21.91885483357334,7.357847940723915,100*(exp(b - se^2/2) - 1),|b/t|,0.2516723513898991,0.07580492511743948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.32,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Comfort,condition,A_structural,binary,binary,0.12,,2.07,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,12.560388774052722,6.536213632344207,100*(exp(b - se^2/2) - 1),|b/t|,0.16004494992527651,0.07731640093008528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.07,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,Comfort,condition,A_structural,binary,binary,0.26,,4.28,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,pct_premium,29.45392763433925,7.878547255446278,100*(exp(b - se^2/2) - 1),|b/t|,0.31785044952466784,0.0742641237207168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.28,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,Comfort,condition,A_structural,binary,binary,0.15,,2.51,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,pct_premium,15.97614229080997,6.94323252626464,100*(exp(b - se^2/2) - 1),|b/t|,0.187449141439015,0.07468093284422908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.51,173.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Comfort,condition,A_structural,binary,binary,0.21,,3.26,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,23.112109498487676,7.947005907696812,100*(exp(b - se^2/2) - 1),|b/t|,0.2474048556487383,0.0758910600149504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.26,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Comfort,condition,A_structural,binary,binary,0.12,,2.06,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,12.558548046768125,6.567942824734227,100*(exp(b - se^2/2) - 1),|b/t|,0.1592914504015278,0.07732594679685816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.06,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,Floor,floor_level,A_structural,level,count,-0.01,,-2.03,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,-1.0,0.4926108374384237,100*b/1 (per unit count),|b/t|,-0.1570292856479949,0.07735432790541621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.03,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Floor,floor_level,A_structural,level,count,-0.01,,-1.49,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,-1.0,0.6711409395973155,100*b/1 (per unit count),|b/t|,-0.11591905394981178,0.07779802278510858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.49,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,Floor,floor_level,A_structural,level,count,-0.01,,-1.45,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,-1.0,0.6896551724137931,100*b/1 (per unit count),|b/t|,-0.11284730128956229,0.07782572502728434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.45,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,Floor,floor_level,A_structural,level,count,-0.01,,-2.66,,**,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,-1.0,0.37593984962406013,100*b/1 (per unit count),|b/t|,-0.2039673393366316,0.07667945087843293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.66,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Floor,floor_level,A_structural,level,count,-0.01,,-1.37,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-1.0,0.7299270072992701,100*b/1 (per unit count),|b/t|,-0.10669416293216134,0.07787895104537323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.37,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Floor,floor_level,A_structural,level,count,-0.01,,-2.42,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-1.0,0.4132231404958678,100*b/1 (per unit count),|b/t|,-0.18623298443757855,0.07695577869321428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.42,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,Floor,floor_level,A_structural,level,count,-0.01,,-1.2,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,-1.0,0.8333333333333334,100*b/1 (per unit count),|b/t|,-0.09357880867084666,0.07798234055903888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,Floor,floor_level,A_structural,level,count,-0.01,,-2.09,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,-1.0,0.47846889952153115,100*b/1 (per unit count),|b/t|,-0.15693092304356518,0.0750865660495527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.09,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Floor,floor_level,A_structural,level,count,-0.01,,-1.35,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-1.0,0.7407407407407407,100*b/1 (per unit count),|b/t|,-0.10515393275111268,0.07789180203786124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.35,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Floor,floor_level,A_structural,level,count,-0.01,,-2.19,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-1.0,0.45662100456621013,100*b/1 (per unit count),|b/t|,-0.16906479552348647,0.07719853676871528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.19,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,No_bathrooms,bathrooms,A_structural,level,count,0.09,,1.92,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,9.0,4.6875,100*b/1 (per unit count),|b/t|,0.14871375690053495,0.07745508171902862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,No_bathrooms,bathrooms,A_structural,level,count,0.03,,0.7,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,3.0,4.285714285714286,100*b/1 (per unit count),|b/t|,0.054746006305799545,0.07820858043685651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,No_bathrooms,bathrooms,A_structural,level,count,0.07,,1.52,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,7.000000000000001,4.605263157894737,100*b/1 (per unit count),|b/t|,0.11822069266068613,0.0777767714872935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,No_bathrooms,bathrooms,A_structural,level,count,0.06,,1.41,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,6.0,4.25531914893617,100*b/1 (per unit count),|b/t|,0.10977230965416428,0.07785270188238601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.41,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,No_bathrooms,bathrooms,A_structural,level,count,0.04,,0.9,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,4.0,4.444444444444445,100*b/1 (per unit count),|b/t|,0.07031893818579951,0.07813215353977723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,No_bathrooms,bathrooms,A_structural,level,count,0.03,,0.8,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,3.0,3.75,100*b/1 (per unit count),|b/t|,0.06253818193281793,0.07817272741602241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,No_bathrooms,bathrooms,A_structural,level,count,0.09,,1.8,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,9.0,5.0,100*b/1 (per unit count),|b/t|,0.13960621092548198,0.07755900606971221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,No_bathrooms,bathrooms,A_structural,level,count,0.08,,1.85,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,8.0,4.324324324324324,100*b/1 (per unit count),|b/t|,0.13928191920566704,0.07528752389495516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.85,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,No_bathrooms,bathrooms,A_structural,level,count,0.06,,1.14,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,6.0,5.2631578947368425,100*b/1 (per unit count),|b/t|,0.08893784421580979,0.07801565282088578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.14,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,No_bathrooms,bathrooms,A_structural,level,count,0.05,,1.19,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,5.0,4.201680672268908,100*b/1 (per unit count),|b/t|,0.09280572978466087,0.0779880082224041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.19,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.71,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,-3.0,4.225352112676056,100*b/1 (per unit count),|b/t|,-0.05552569778801017,0.07820520815212702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.71,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.04,,-0.89,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,-4.0,4.49438202247191,100*b/1 (per unit count),|b/t|,-0.06954141625164179,0.07813642275465368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.89,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-1.12,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,-5.0,4.464285714285714,100*b/1 (per unit count),|b/t|,-0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.6,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,-2.0,3.3333333333333335,100*b/1 (per unit count),|b/t|,-0.04694381579433043,0.07823969299055072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-1.41,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-6.0,4.25531914893617,100*b/1 (per unit count),|b/t|,-0.10977230965416428,0.07785270188238601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.41,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.82,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-3.0,3.6585365853658534,100*b/1 (per unit count),|b/t|,-0.06409529150234007,0.0781649896370001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.82,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.08,,-1.81,,†,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,-8.0,4.41988950276243,100*b/1 (per unit count),|b/t|,-0.14036656109869525,0.07755058624237307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.81,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-1.21,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,-5.0,4.132231404958678,100*b/1 (per unit count),|b/t|,-0.09160777460527436,0.07570890463245815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.21,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.09,,-1.97,,†,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-9.0,4.568527918781726,100*b/1 (per unit count),|b/t|,-0.15249756513080967,0.07740993153848208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.97,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-1.3,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-5.0,3.8461538461538463,100*b/1 (per unit count),|b/t|,-0.10130006814979362,0.0779231293459951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,No_parking,parking,A_structural,level,count,0.21,,3.05,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,21.0,6.885245901639345,100*b/1 (per unit count),|b/t|,0.23235608476109681,0.07618232287249077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,No_parking,parking,A_structural,level,count,0.23,,3.15,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,23.0,7.301587301587302,100*b/1 (per unit count),|b/t|,0.23954372623574144,0.07604562737642585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.15,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,No_parking,parking,A_structural,level,count,0.09,,1.35,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,9.0,6.666666666666667,100*b/1 (per unit count),|b/t|,0.10515393275111268,0.07789180203786124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.35,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,No_parking,parking,A_structural,level,count,0.16,,2.53,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,16.0,6.32411067193676,100*b/1 (per unit count),|b/t|,0.19438496221732968,0.0768320008764149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.53,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,No_parking,parking,A_structural,level,count,0.13,,1.71,,†,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,13.0,7.602339181286551,100*b/1 (per unit count),|b/t|,0.1327520924133248,0.07763280258089171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.71,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,No_parking,parking,A_structural,level,count,0.19,,2.83,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,19.0,6.713780918727916,100*b/1 (per unit count),|b/t|,0.21640987730547695,0.07646992130935581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.83,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,No_parking,parking,A_structural,level,count,0.06,,0.82,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,6.0,7.317073170731707,100*b/1 (per unit count),|b/t|,0.06409529150234007,0.0781649896370001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.82,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,No_parking,parking,A_structural,level,count,0.16,,2.43,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,16.0,6.584362139917695,100*b/1 (per unit count),|b/t|,0.18167499454258063,0.07476337223974511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.43,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,No_parking,parking,A_structural,level,count,0.09,,1.12,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,9.0,8.035714285714285,100*b/1 (per unit count),|b/t|,0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,No_parking,parking,A_structural,level,count,0.16,,2.23,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,16.0,7.174887892376682,100*b/1 (per unit count),|b/t|,0.17206211339621486,0.07715789838395284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.23,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,Seniority_construction,age,A_structural,level,years,-0.0,,-3.76,,***,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.28250911640950094,0.0751354033003992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.76,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Seniority_construction,age,A_structural,level,years,-0.01,,-4.57,,***,,,high,Years since construction (age),ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,-1.0,0.2188183807439825,100*b/1 (per year),|b/t|,-0.3370102935989432,0.07374404673937487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.57,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,Seniority_construction,age,A_structural,level,years,-0.0,,-0.43,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.03366111292083537,0.0782816579554311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,Seniority_construction,age,A_structural,level,years,-0.0,,-1.2,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.09357880867084666,0.07798234055903888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Seniority_construction,age,A_structural,level,years,-0.0,,-0.89,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.06954141625164179,0.07813642275465368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.89,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Seniority_construction,age,A_structural,level,years,-0.0,,-1.83,,†,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.14188651541975236,0.07753361498347124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.83,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,Seniority_construction,age,A_structural,level,years,-0.0,,-1.03,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.08041455812861766,0.0780723865326385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.03,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,Seniority_construction,age,A_structural,level,years,-0.0,,-1.55,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.11703447428760615,0.07550611244361687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.55,173.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Seniority_construction,age,A_structural,level,years,-0.0,,-1.34,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.10438353308113031,0.0778981590157689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.34,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Seniority_construction,age,A_structural,level,years,-0.0,,-2.02,,*,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.15627467928229707,0.07736370261499854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.02,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.27,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09898554713693115,0.077941375698371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.27,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.76,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.059422603574431083,0.07818763628214616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.76,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.12,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.12,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.99,,*,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.15400922248106896,0.07739156908596431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.99,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.04,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.08119016016629894,0.07806746169836437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.04,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.5,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.1166864762376621,0.07779098415844139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.81,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06331679451827393,0.07816888212132585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.81,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.55,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.11703447428760615,0.07550611244361687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.55,173.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.85,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06643007487109245,0.07815302926010877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.85,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.26,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09821368109222837,0.077947365946213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.26,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c1,11,1,17,Structure,roof_materials,A_structural,binary,binary,0.07,,0.92,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration not included",pct_premium,6.940817451536296,8.160388335629909,100*(exp(b - se^2/2) - 1),|b/t|,0.07187359623253212,0.07812347416579578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.92,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Structure,roof_materials,A_structural,binary,binary,0.09,,1.09,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,9.0450807965053,9.034466562703571,100*(exp(b - se^2/2) - 1),|b/t|,0.08506593097263847,0.07804213850700777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.09,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,Structure,roof_materials,A_structural,binary,binary,0.14,,1.9,,†,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,pct_premium,14.71554065599694,8.475701675790097,100*(exp(b - se^2/2) - 1),|b/t|,0.14719839301007198,0.07747283842635369,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,Structure,roof_materials,A_structural,binary,binary,0.04,,0.6,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,pct_premium,3.850042936142084,6.938738494615921,100*(exp(b - se^2/2) - 1),|b/t|,0.04694381579433043,0.07823969299055072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Structure,roof_materials,A_structural,binary,binary,0.12,,1.51,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,12.394210682465223,8.960239880101,100*(exp(b - se^2/2) - 1),|b/t|,0.11745368957370614,0.07778390037993785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.51,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Structure,roof_materials,A_structural,binary,binary,0.04,,0.61,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,3.8575476745496218,6.824988683228776,100*(exp(b - se^2/2) - 1),|b/t|,0.047724445293116674,0.07823679556248635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.61,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,Structure,roof_materials,A_structural,binary,binary,0.24,,3.14,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,pct_premium,26.75412356429363,9.71655401519545,100*(exp(b - se^2/2) - 1),|b/t|,0.2388267101342657,0.07605946182619927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.14,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Structure,roof_materials,A_structural,binary,binary,0.2,,2.45,,*,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,21.733988371508307,9.970634760491182,100*(exp(b - se^2/2) - 1),|b/t|,0.18846014453071327,0.07692250797171969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Structure,roof_materials,A_structural,binary,binary,0.14,,1.83,,†,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,14.69126338515152,8.799908843716494,100*(exp(b - se^2/2) - 1),|b/t|,0.14188651541975236,0.07753361498347124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.83,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_1,storeys,A_structural,dummy,binary,0.05,,0.43,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,4.418804081737182,12.224082516000282,100*(exp(b - se^2/2) - 1),|b/t|,0.03366111292083537,0.0782816579554311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_1,storeys,A_structural,dummy,binary,0.05,,0.39,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,4.266686324688607,13.477834568923388,100*(exp(b - se^2/2) - 1),|b/t|,0.030532915299387793,0.07828952640868665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_1,storeys,A_structural,dummy,binary,0.09,,0.85,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,8.805800852102251,11.585374768643403,100*(exp(b - se^2/2) - 1),|b/t|,0.06643007487109245,0.07815302926010877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.85,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_1,storeys,A_structural,dummy,binary,-0.0,,-0.04,,,,,medium,"1 if building height regime is Ground Floor +10 Floors ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,0.0,0.0,100*(exp(b - se^2/2) - 1),|b/t|,-0.003133026423172763,0.07832566057931907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.04,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_1,storeys,A_structural,dummy,binary,0.02,,0.21,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,1.558505226477136,9.716203238350056,100*(exp(b - se^2/2) - 1),|b/t|,0.016446244816782134,0.07831545150848634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_2,storeys,A_structural,dummy,binary,0.02,,0.11,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,0.34771055350184543,18.54911527321374,100*(exp(b - se^2/2) - 1),|b/t|,0.00861554517636434,0.07832313796694854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_2,storeys,A_structural,dummy,binary,-0.02,,-0.14,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-2.9752496352559166,14.002838190096503,100*(exp(b - se^2/2) - 1),|b/t|,-0.010964987075026307,0.0783213362501879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_2,storeys,A_structural,dummy,binary,0.05,,0.35,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,4.059838550336048,15.01815851965749,100*(exp(b - se^2/2) - 1),|b/t|,0.027403820235783725,0.07829662924509635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_2,storeys,A_structural,dummy,binary,-0.04,,-0.24,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-5.2462617415583,16.013157319205387,100*(exp(b - se^2/2) - 1),|b/t|,-0.018794930270996775,0.07831220946248657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_2,storeys,A_structural,dummy,binary,0.01,,0.05,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-0.9950166250831893,20.201003341683357,100*(exp(b - se^2/2) - 1),|b/t|,0.00391627221729254,0.0783254443458508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_3,storeys,A_structural,dummy,binary,0.27,,1.86,,†,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,29.623524789608503,19.015612994514882,100*(exp(b - se^2/2) - 1),|b/t|,0.14416456333874586,0.0775078297520139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.86,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_3,storeys,A_structural,dummy,binary,0.09,,0.65,,,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,8.373585939900142,15.150105466687526,100*(exp(b - se^2/2) - 1),|b/t|,0.05084607475894782,0.07822473039838125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.65,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_3,storeys,A_structural,dummy,binary,0.22,,1.67,,†,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,23.531097960946656,16.415382079594238,100*(exp(b - se^2/2) - 1),|b/t|,0.129699632680199,0.077664450706706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.67,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_3,storeys,A_structural,dummy,binary,-0.05,,-0.34,,,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-5.900094983755255,13.988668007363442,100*(exp(b - se^2/2) - 1),|b/t|,-0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_3,storeys,A_structural,dummy,binary,0.08,,0.54,,,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,7.146413446146438,16.048697298888275,100*(exp(b - se^2/2) - 1),|b/t|,0.042258282063579004,0.07825607789551667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.54,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_4,storeys,A_structural,dummy,binary,0.03,,0.2,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,1.8926885052026332,15.456818009302754,100*(exp(b - se^2/2) - 1),|b/t|,0.015663287241915964,0.07831643620957981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_4,storeys,A_structural,dummy,binary,-0.01,,-0.07,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-2.000134682790544,14.143569053559542,100*(exp(b - se^2/2) - 1),|b/t|,-0.005482740741241676,0.07832486773202393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_4,storeys,A_structural,dummy,binary,0.04,,0.36,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,3.440580508803559,11.564564157693203,100*(exp(b - se^2/2) - 1),|b/t|,0.028186173125265654,0.07829492534796015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.36,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_4,storeys,A_structural,dummy,binary,-0.09,,-0.61,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-9.59622593201913,13.484230602362384,100*(exp(b - se^2/2) - 1),|b/t|,-0.047724445293116674,0.07823679556248635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.61,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_4,storeys,A_structural,dummy,binary,-0.04,,-0.34,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-4.583666855712954,11.30340516649792,100*(exp(b - se^2/2) - 1),|b/t|,-0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_5,storeys,A_structural,dummy,binary,0.09,,0.69,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,8.490603954297903,14.27183848311144,100*(exp(b - se^2/2) - 1),|b/t|,0.053966214667216864,0.07821190531480704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.69,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_5,storeys,A_structural,dummy,binary,0.09,,0.74,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,8.611172155118464,13.307525072090396,100*(exp(b - se^2/2) - 1),|b/t|,0.05786415721199271,0.0781948070432334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.74,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_5,storeys,A_structural,dummy,binary,0.09,,0.81,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,8.74409248126231,12.157492041169004,100*(exp(b - se^2/2) - 1),|b/t|,0.06331679451827393,0.07816888212132585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.81,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_5,storeys,A_structural,dummy,binary,0.04,,0.33,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,3.3192797170577437,12.61588817202895,100*(exp(b - se^2/2) - 1),|b/t|,0.025838964808374536,0.07829989335871071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_5,storeys,A_structural,dummy,binary,0.03,,0.25,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,2.306190670327446,12.365454407442204,100*(exp(b - se^2/2) - 1),|b/t|,0.01957775820486337,0.0783110328194535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_6,storeys,A_structural,dummy,binary,0.12,,0.75,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,11.315686359039056,18.039949625270012,100*(exp(b - se^2/2) - 1),|b/t|,0.05864343398897361,0.07819124531863149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_6,storeys,A_structural,dummy,binary,0.03,,0.18,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,1.6241594865993747,17.174242232558615,100*(exp(b - se^2/2) - 1),|b/t|,0.01409728708933399,0.07831826160741105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_6,storeys,A_structural,dummy,binary,0.08,,0.62,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,7.430650114507498,13.97789764741882,100*(exp(b - se^2/2) - 1),|b/t|,0.04850498735522779,0.07823385057294804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.62,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_6,storeys,A_structural,dummy,binary,-0.0,,-0.02,,,,,medium,"1 if building height regime is Ground Floor +5 Floors ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,0.0,0.0,100*(exp(b - se^2/2) - 1),|b/t|,-0.0015665189778679186,0.07832594889339593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_6,storeys,A_structural,dummy,binary,0.02,,0.17,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,1.3165496699512946,12.002368706197126,100*(exp(b - se^2/2) - 1),|b/t|,0.013314247389992906,0.0783191022940759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_7,storeys,A_structural,dummy,binary,0.24,,1.44,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,25.37149592016259,21.18748583869008,100*(exp(b - se^2/2) - 1),|b/t|,0.11207885376546127,0.07783253733712589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.44,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_7,storeys,A_structural,dummy,binary,-0.09,,-0.56,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-9.779592818942673,14.688179763287595,100*(exp(b - se^2/2) - 1),|b/t|,-0.04382045179337803,0.07825080677388933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.56,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_7,storeys,A_structural,dummy,binary,0.11,,0.74,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,10.401307339431408,16.593322668983223,100*(exp(b - se^2/2) - 1),|b/t|,0.05786415721199271,0.0781948070432334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.74,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_7,storeys,A_structural,dummy,binary,-0.1,,-0.58,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,-10.851196059331036,15.600645138551027,100*(exp(b - se^2/2) - 1),|b/t|,-0.045382300157365094,0.07824534509890534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.58,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_7,storeys,A_structural,dummy,binary,0.03,,0.16,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,1.249934693917143,19.321022511628442,100*(exp(b - se^2/2) - 1),|b/t|,0.0125311831946079,0.07831989496629937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_9,storeys,A_structural,dummy,binary,0.19,,1.59,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,20.064660693071357,14.45015242483508,100*(exp(b - se^2/2) - 1),|b/t|,0.12358371871647124,0.07772560925564229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_9,storeys,A_structural,dummy,binary,0.18,,1.5,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,18.862835563875247,14.36660835746172,100*(exp(b - se^2/2) - 1),|b/t|,0.1166864762376621,0.07779098415844139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_9,storeys,A_structural,dummy,binary,0.19,,1.76,,†,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,20.22236792872667,13.054399065618055,100*(exp(b - se^2/2) - 1),|b/t|,0.1365623523826234,0.07759224567194511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.76,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_9,storeys,A_structural,dummy,binary,0.13,,1.06,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,13.029598277098353,13.966763191717058,100*(exp(b - se^2/2) - 1),|b/t|,0.08274092047284654,0.07805747214419485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.06,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_9,storeys,A_structural,dummy,binary,0.13,,1.13,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,13.131696575274576,13.10156547187618,100*(exp(b - se^2/2) - 1),|b/t|,0.08816377835032914,0.07802104278790191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.13,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c2,11,2,17,Height_regime_10,storeys,A_structural,dummy,binary,0.25,,1.82,,†,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration not included,pct_premium,27.196856673769588,17.637711767688753,100*(exp(b - se^2/2) - 1),|b/t|,0.14112666300859447,0.07754212253219477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.82,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,Height_regime_10,storeys,A_structural,dummy,binary,0.22,,1.6,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,23.43529121588619,17.133555045576482,100*(exp(b - se^2/2) - 1),|b/t|,0.12434899333339904,0.0777181208333744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,Height_regime_10,storeys,A_structural,dummy,binary,0.27,,2.16,,*,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,29.97702265007551,16.374555634165592,100*(exp(b - se^2/2) - 1),|b/t|,0.16681372002476,0.07722857408553703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.16,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,Height_regime_10,storeys,A_structural,dummy,binary,0.16,,1.12,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,pct_premium,16.159715789230788,16.764441014168717,100*(exp(b - se^2/2) - 1),|b/t|,0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.12,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,Height_regime_10,storeys,A_structural,dummy,binary,0.19,,1.45,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,pct_premium,19.891260342171368,15.845339555508813,100*(exp(b - se^2/2) - 1),|b/t|,0.11284730128956229,0.07782572502728434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.45,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c3,11,3,17,S_useful,living_area,A_structural,level,m2,0.01,,9.63,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration not included,semi_elasticity,1.0,0.10384215991692627,100*b/1 (X per m2->m2),|b/t|,0.6021845224140387,0.06253214147601648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.63,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,S_useful,living_area,A_structural,level,m2,0.03,,9.82,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,semi_elasticity,3.0,0.30549898167006106,100*b/1 (X per m2->m2),|b/t|,0.6096763956663303,0.062085172674779046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.82,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c5,11,5,17,S_useful,living_area,A_structural,level,m2,0.01,,10.24,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,1.0,0.09765625,100*b/1 (X per m2->m2),|b/t|,0.6256738009765551,0.061100957126616694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.24,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,S_useful,living_area,A_structural,level,m2,0.03,,9.54,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,3.0,0.3144654088050315,100*b/1 (X per m2->m2),|b/t|,0.5985791699146055,0.06274414778979095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.54,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c4,11,4,17,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-6.58,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.75,t,Multivariate specification: useful surface + square; complete-case subsample; Duration not included,other,-0.0,0.0,not harmonised,|b/t|,-0.4581208617176179,0.06962323126407567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.58,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c6,11,6,17,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-6.15,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration not included,other,-0.0,0.0,not harmonised,|b/t|,-0.43397921625226327,0.07056572621988019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.15,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c7,11,7,17,S_constructed,living_area,A_structural,level,m2,0.01,,8.86,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration not included,semi_elasticity,1.0,0.11286681715575622,100*b/1 (X per m2->m2),|b/t|,0.5701321267090858,0.06434899849989682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.86,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,S_constructed,living_area,A_structural,level,m2,0.03,,9.93,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,semi_elasticity,3.0,0.3021148036253776,100*b/1 (X per m2->m2),|b/t|,0.6025324595345515,0.06067799189673228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.93,173.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c9,11,9,17,S_constructed,living_area,A_structural,level,m2,0.01,,9.37,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,1.0,0.10672358591248667,100*b/1 (X per m2->m2),|b/t|,0.5916685824113874,0.06314499278670092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.37,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,S_constructed,living_area,A_structural,level,m2,0.02,,9.28,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,semi_elasticity,2.0,0.2155172413793104,100*b/1 (X per m2->m2),|b/t|,0.5879563338913402,0.06335736356587718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.28,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c8,11,8,17,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-6.82,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.72,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration not included,other,-0.0,0.0,not harmonised,|b/t|,-0.46031470154511983,0.06749482427347799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.82,173.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T11-c10,11,10,17,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-5.77,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration not included,other,-0.0,0.0,not harmonised,|b/t|,-0.41183525124229003,0.07137526018063953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.77,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,No_rooms,bedrooms,A_structural,level,count,0.23,,9.05,,***,,,high,"Number of rooms (all rooms, not only bedrooms)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,23.0,2.5414364640883975,100*b/1 (per unit count),|b/t|,0.5782983723495626,0.06390037263531077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.05,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,No_rooms,bedrooms,A_structural,level,count,0.25,,9.7,,***,,,high,"Number of rooms (all rooms, not only bedrooms)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,25.0,2.577319587628866,100*b/1 (per unit count),|b/t|,0.60496345752378,0.062367366755028866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.7,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,Comfort,condition,A_structural,binary,binary,0.34,,5.81,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",pct_premium,40.25439809465801,8.221724282127745,100*(exp(b - se^2/2) - 1),|b/t|,0.4142018455012256,0.0712911954391094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.81,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Comfort,condition,A_structural,binary,binary,0.31,,5.23,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,36.103212953285535,8.081487292179256,100*(exp(b - se^2/2) - 1),|b/t|,0.37907220375186784,0.07248034488563437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.23,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,Comfort,condition,A_structural,binary,binary,0.26,,4.32,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,pct_premium,29.458330573942206,7.8055977438217745,100*(exp(b - se^2/2) - 1),|b/t|,0.32051711359830654,0.07419377629590429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.32,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,Comfort,condition,A_structural,binary,binary,0.14,,2.47,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,pct_premium,14.842757382416515,6.519770519838536,100*(exp(b - se^2/2) - 1),|b/t|,0.18994330645012425,0.0769001240688762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.47,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Comfort,condition,A_structural,binary,binary,0.2,,3.3,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,21.91616528112361,7.402440958546484,100*(exp(b - se^2/2) - 1),|b/t|,0.2502514693627675,0.07583377859477804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Comfort,condition,A_structural,binary,binary,0.12,,2.05,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,12.5566803471304,6.599981570220737,100*(exp(b - se^2/2) - 1),|b/t|,0.15853767239265218,0.07733544994763521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.05,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,Comfort,condition,A_structural,binary,binary,0.26,,4.26,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,pct_premium,29.451679567130217,7.915535740213632,100*(exp(b - se^2/2) - 1),|b/t|,0.3165142787122256,0.07429912645826893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.26,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,Comfort,condition,A_structural,binary,binary,0.14,,2.5,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,pct_premium,14.84715828472185,6.4415332736004745,100*(exp(b - se^2/2) - 1),|b/t|,0.18672842174331777,0.0746913686973271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,173.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Comfort,condition,A_structural,binary,binary,0.21,,3.25,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,23.110535205154115,7.971458233566648,100*(exp(b - se^2/2) - 1),|b/t|,0.24669219681861024,0.07590529132880315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Comfort,condition,A_structural,binary,binary,0.12,,2.03,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,12.552861900135536,6.665006019188429,100*(exp(b - se^2/2) - 1),|b/t|,0.1570292856479949,0.07735432790541621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.03,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,Floor,floor_level,A_structural,level,count,-0.01,,-2.01,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,-1.0,0.49751243781094534,100*b/1 (per unit count),|b/t|,-0.15551979917162387,0.07737303441374323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.01,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Floor,floor_level,A_structural,level,count,-0.01,,-1.47,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,-1.0,0.6802721088435375,100*b/1 (per unit count),|b/t|,-0.11438358771689541,0.07781196443326219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.47,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,Floor,floor_level,A_structural,level,count,-0.01,,-1.44,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,-1.0,0.6944444444444445,100*b/1 (per unit count),|b/t|,-0.11207885376546127,0.07783253733712589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.44,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,Floor,floor_level,A_structural,level,count,-0.01,,-2.64,,**,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,-1.0,0.3787878787878788,100*b/1 (per unit count),|b/t|,-0.2024968634671151,0.07670335737390722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.64,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Floor,floor_level,A_structural,level,count,-0.01,,-1.36,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-1.0,0.7352941176470588,100*b/1 (per unit count),|b/t|,-0.10592414314269169,0.07788539936962624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.36,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Floor,floor_level,A_structural,level,count,-0.01,,-2.41,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-1.0,0.4149377593360996,100*b/1 (per unit count),|b/t|,-0.1854899574949399,0.07696678734229871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.41,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,Floor,floor_level,A_structural,level,count,-0.01,,-1.2,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,-1.0,0.8333333333333334,100*b/1 (per unit count),|b/t|,-0.09357880867084666,0.07798234055903888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,Floor,floor_level,A_structural,level,count,-0.01,,-1.98,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,-1.0,0.5050505050505051,100*b/1 (per unit count),|b/t|,-0.14885938964601347,0.07518150992222902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.98,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Floor,floor_level,A_structural,level,count,-0.01,,-1.35,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-1.0,0.7407407407407407,100*b/1 (per unit count),|b/t|,-0.10515393275111268,0.07789180203786124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.35,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Floor,floor_level,A_structural,level,count,-0.01,,-2.18,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-1.0,0.45871559633027514,100*b/1 (per unit count),|b/t|,-0.1683147291176314,0.07720859133836303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.18,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,No_bathrooms,bathrooms,A_structural,level,count,0.08,,1.65,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,8.0,4.8484848484848495,100*b/1 (per unit count),|b/t|,0.12817201327964664,0.07768000804827069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.65,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,No_bathrooms,bathrooms,A_structural,level,count,0.03,,0.55,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,3.0,5.454545454545453,100*b/1 (per unit count),|b/t|,0.04303940638866546,0.07825346616120993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.55,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,No_bathrooms,bathrooms,A_structural,level,count,0.07,,1.48,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,7.000000000000001,4.72972972972973,100*b/1 (per unit count),|b/t|,0.11515142400924942,0.07780501622246584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,No_bathrooms,bathrooms,A_structural,level,count,0.05,,1.14,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,5.0,4.385964912280702,100*b/1 (per unit count),|b/t|,0.08893784421580979,0.07801565282088578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.14,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,No_bathrooms,bathrooms,A_structural,level,count,0.04,,0.9,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,4.0,4.444444444444445,100*b/1 (per unit count),|b/t|,0.07031893818579951,0.07813215353977723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,No_bathrooms,bathrooms,A_structural,level,count,0.03,,0.64,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,3.0,4.6875,100*b/1 (per unit count),|b/t|,0.050065803503583264,0.07822781797434884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.64,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,No_bathrooms,bathrooms,A_structural,level,count,0.09,,1.79,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,9.0,5.027932960893854,100*b/1 (per unit count),|b/t|,0.13884561372651844,0.07756738197012203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.79,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,No_bathrooms,bathrooms,A_structural,level,count,0.07,,1.59,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,7.000000000000001,4.402515723270441,100*b/1 (per unit count),|b/t|,0.12001175822494643,0.07547909322323675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,No_bathrooms,bathrooms,A_structural,level,count,0.06,,1.15,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,6.0,5.217391304347826,100*b/1 (per unit count),|b/t|,0.08971174897143021,0.07801021649689584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.15,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,No_bathrooms,bathrooms,A_structural,level,count,0.05,,1.04,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,5.0,4.807692307692308,100*b/1 (per unit count),|b/t|,0.08119016016629894,0.07806746169836437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.04,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.04,,-0.82,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,-4.0,4.878048780487805,100*b/1 (per unit count),|b/t|,-0.06409529150234007,0.0781649896370001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.82,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.04,,-0.97,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,-4.0,4.123711340206185,100*b/1 (per unit count),|b/t|,-0.07575792603081245,0.07810095467094068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.97,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-1.12,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,-5.0,4.464285714285714,100*b/1 (per unit count),|b/t|,-0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.73,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,-3.0,4.10958904109589,100*b/1 (per unit count),|b/t|,-0.057084774648618365,0.07819832143646352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.73,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-1.39,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-6.0,4.316546762589928,100*b/1 (per unit count),|b/t|,-0.1082336254184039,0.07786591756719705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.39,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.04,,-0.92,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-4.0,4.3478260869565215,100*b/1 (per unit count),|b/t|,-0.07187359623253212,0.07812347416579578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.08,,-1.78,,†,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,-8.0,4.49438202247191,100*b/1 (per unit count),|b/t|,-0.13808477074149605,0.07757571389971687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.78,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-1.38,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,-6.0,4.3478260869565215,100*b/1 (per unit count),|b/t|,-0.10434669999987986,0.07561355072455063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.38,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.09,,-1.92,,†,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-9.0,4.6875,100*b/1 (per unit count),|b/t|,-0.14871375690053495,0.07745508171902862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-1.4,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-6.0,4.285714285714286,100*b/1 (per unit count),|b/t|,-0.10900306547625242,0.07785933248303746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,No_parking,parking,A_structural,level,count,0.21,,3.11,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,21.0,6.752411575562702,100*b/1 (per unit count),|b/t|,0.2366733203280875,0.07610074608620178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.11,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,No_parking,parking,A_structural,level,count,0.23,,3.19,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,23.0,7.210031347962382,100*b/1 (per unit count),|b/t|,0.24240786447110518,0.07598992616649065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.19,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,No_parking,parking,A_structural,level,count,0.09,,1.35,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,9.0,6.666666666666667,100*b/1 (per unit count),|b/t|,0.10515393275111268,0.07789180203786124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.35,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,No_parking,parking,A_structural,level,count,0.16,,2.63,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,16.0,6.083650190114069,100*b/1 (per unit count),|b/t|,0.20176111088380483,0.0767152512866178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.63,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,No_parking,parking,A_structural,level,count,0.13,,1.69,,†,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,13.0,7.6923076923076925,100*b/1 (per unit count),|b/t|,0.1312263290940617,0.0776487154402732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.69,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,No_parking,parking,A_structural,level,count,0.2,,2.92,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,20.0,6.849315068493151,100*b/1 (per unit count),|b/t|,0.2229550763794247,0.07635447821213175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,No_parking,parking,A_structural,level,count,0.06,,0.81,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,6.0,7.4074074074074066,100*b/1 (per unit count),|b/t|,0.06331679451827393,0.07816888212132585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.81,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,No_parking,parking,A_structural,level,count,0.16,,2.52,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,16.0,6.349206349206349,100*b/1 (per unit count),|b/t|,0.1881695585203947,0.07467045973031534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.52,173.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,No_parking,parking,A_structural,level,count,0.09,,1.1,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,9.0,8.18181818181818,100*b/1 (per unit count),|b/t|,0.08584062768258548,0.07803693425689588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,No_parking,parking,A_structural,level,count,0.17,,2.3,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,17.0,7.391304347826089,100*b/1 (per unit count),|b/t|,0.17729589519341188,0.07708517182322257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,Seniority_construction,age,A_structural,level,years,-0.0,,-3.87,,***,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.29008758735119816,0.07495803290728634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.87,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Seniority_construction,age,A_structural,level,years,-0.01,,-4.61,,***,,,high,Years since construction (age),ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,-1.0,0.21691973969631237,100*b/1 (per year),|b/t|,-0.339621129643022,0.07367052703753188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.61,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,Seniority_construction,age,A_structural,level,years,-0.0,,-0.43,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.03366111292083537,0.0782816579554311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,Seniority_construction,age,A_structural,level,years,-0.0,,-1.37,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.10669416293216134,0.07787895104537323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.37,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Seniority_construction,age,A_structural,level,years,-0.0,,-0.88,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.06876376756217446,0.07814064495701643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.88,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Seniority_construction,age,A_structural,level,years,-0.0,,-1.91,,†,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.14795620521528294,0.07746398178810625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.91,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,Seniority_construction,age,A_structural,level,years,-0.0,,-1.01,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.07886291714378159,0.07808209618196196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.01,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,Seniority_construction,age,A_structural,level,years,-0.0,,-1.73,,†,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.1304062869104936,0.07537935659566103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.73,173.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Seniority_construction,age,A_structural,level,years,-0.0,,-1.32,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.10284217120915926,0.07791073576451459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.32,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Seniority_construction,age,A_structural,level,years,-0.0,,-2.09,,*,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.1615511088030177,0.07729718124546302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.09,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.18,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09203248302014484,0.07799362967808886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.18,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.7,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.054746006305799545,0.07820858043685651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.1,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.08584062768258548,0.07803693425689588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.89,,†,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.14644032150069775,0.07748165158767077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.89,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.03,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.08041455812861766,0.0780723865326385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.03,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.43,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.11131020511130388,0.0778393042736391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.43,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.81,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06331679451827393,0.07816888212132585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.81,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.4,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.10584214937952004,0.07560153527108575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,173.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.86,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06720809547985139,0.07814894823238534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.19,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09280572978466087,0.0779880082224041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.19,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,Structure,roof_materials,A_structural,binary,binary,0.06,,0.72,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",pct_premium,5.81560076144243,8.848637887877995,100*(exp(b - se^2/2) - 1),|b/t|,0.056305287704943245,0.07820178847908785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.72,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Structure,roof_materials,A_structural,binary,binary,0.08,,0.91,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,7.910903427587801,9.523402792746888,100*(exp(b - se^2/2) - 1),|b/t|,0.07109633197544415,0.07812783733565291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.91,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,Structure,roof_materials,A_structural,binary,binary,0.14,,1.85,,†,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,pct_premium,14.698481142136322,8.704774694054693,100*(exp(b - se^2/2) - 1),|b/t|,0.14340546681487304,0.07751646854858002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.85,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,Structure,roof_materials,A_structural,binary,binary,0.02,,0.32,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,pct_premium,1.821070389791668,6.3762583751672235,100*(exp(b - se^2/2) - 1),|b/t|,0.02505646513830408,0.07830145355720024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.32,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Structure,roof_materials,A_structural,binary,binary,0.12,,1.48,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,12.379677407986666,9.141866364157101,100*(exp(b - se^2/2) - 1),|b/t|,0.11515142400924942,0.07780501622246584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Structure,roof_materials,A_structural,binary,binary,0.03,,0.34,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,2.6451046313887394,9.092245887825149,100*(exp(b - se^2/2) - 1),|b/t|,0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,Structure,roof_materials,A_structural,binary,binary,0.25,,3.11,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,pct_premium,27.98834990113248,10.321747722570269,100*(exp(b - se^2/2) - 1),|b/t|,0.2366733203280875,0.07610074608620178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.11,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Structure,roof_materials,A_structural,binary,binary,0.21,,2.44,,*,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,22.911740942384064,10.617721007824429,100*(exp(b - se^2/2) - 1),|b/t|,0.18771807904379892,0.0769336389523766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.44,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Structure,roof_materials,A_structural,binary,binary,0.12,,1.57,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,12.420822627343853,8.617810330543,100*(exp(b - se^2/2) - 1),|b/t|,0.12205250908544199,0.07774045164677833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.57,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c1,12,1,18,Duration,time_on_market,G_market,level,days,-0.0,,-0.93,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.68,t,"Multivariate specification: rooms, no surface, no height regimes; complete-case subsample; Duration control included",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.07265072957019178,0.07811906405396965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.93,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Duration,time_on_market,G_market,level,days,-0.0,,-0.69,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.053966214667216864,0.07821190531480704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.69,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,Duration,time_on_market,G_market,level,days,-0.0,,-0.11,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.00861554517636434,0.07832313796694854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,Duration,time_on_market,G_market,level,days,-0.0,,-1.37,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.10669416293216134,0.07787895104537323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.37,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Duration,time_on_market,G_market,level,days,0.0,,0.05,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.00391627221729254,0.0783254443458508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Duration,time_on_market,G_market,level,days,-0.0,,-1.08,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.08429107995739295,0.07804729625684532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.08,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,Duration,time_on_market,G_market,level,days,0.0,,0.09,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.007049168904005755,0.07832409893339728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,Duration,time_on_market,G_market,level,days,-0.0,,-1.73,,†,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.1304062869104936,0.07537935659566103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.73,173.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Duration,time_on_market,G_market,level,days,0.0,,0.19,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.014880300853079419,0.0783173729109443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Duration,time_on_market,G_market,level,days,-0.0,,-0.97,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.07575792603081245,0.07810095467094068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.97,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_1,storeys,A_structural,dummy,binary,0.06,,0.51,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,5.451356500483562,12.492194665239523,100*(exp(b - se^2/2) - 1),|b/t|,0.039914449789453355,0.07826362703814382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.51,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_1,storeys,A_structural,dummy,binary,0.05,,0.38,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,4.2210033111480305,13.832514426000317,100*(exp(b - se^2/2) - 1),|b/t|,0.029750722089589628,0.07829137391997269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.38,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_1,storeys,A_structural,dummy,binary,0.11,,1.0,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,10.954497627981418,12.279058775047584,100*(exp(b - se^2/2) - 1),|b/t|,0.07808688094430304,0.07808688094430304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_1,storeys,A_structural,dummy,binary,-0.01,,-0.06,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-2.3605808025439434,16.500830562486136,100*(exp(b - se^2/2) - 1),|b/t|,-0.004699510803775961,0.0783251800629327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_1,storeys,A_structural,dummy,binary,0.04,,0.34,,,,,high,1 if building height regime is Ground Floor +10 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,3.3632797401716763,12.244832637557508,100*(exp(b - se^2/2) - 1),|b/t|,0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_2,storeys,A_structural,dummy,binary,0.03,,0.18,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,1.6241594865993747,17.174242232558615,100*(exp(b - se^2/2) - 1),|b/t|,0.01409728708933399,0.07831826160741105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_2,storeys,A_structural,dummy,binary,-0.02,,-0.15,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-2.8475594374056667,13.06931564409007,100*(exp(b - se^2/2) - 1),|b/t|,-0.011748095942956003,0.0783206396197067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_2,storeys,A_structural,dummy,binary,0.06,,0.47,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,5.321934523235283,13.555360168664164,100*(exp(b - se^2/2) - 1),|b/t|,0.03678832155190899,0.07827302457852976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_2,storeys,A_structural,dummy,binary,-0.04,,-0.26,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-5.0513834870797965,14.78137598695882,100*(exp(b - se^2/2) - 1),|b/t|,-0.020360550133690272,0.07830980820650105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_2,storeys,A_structural,dummy,binary,0.02,,0.15,,,,,high,1 if building height regime is Ground Floor +11 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,1.1173068766337835,13.602684533690077,100*(exp(b - se^2/2) - 1),|b/t|,0.011748095942956003,0.0783206396197067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_3,storeys,A_structural,dummy,binary,0.27,,1.89,,†,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,29.66654340625343,18.71377786761782,100*(exp(b - se^2/2) - 1),|b/t|,0.14644032150069775,0.07748165158767077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.89,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_3,storeys,A_structural,dummy,binary,0.09,,0.65,,,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,8.373585939900142,15.150105466687526,100*(exp(b - se^2/2) - 1),|b/t|,0.05084607475894782,0.07822473039838125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.65,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_3,storeys,A_structural,dummy,binary,0.22,,1.72,,†,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,23.59252781663521,15.93819074007115,100*(exp(b - se^2/2) - 1),|b/t|,0.1335146210126104,0.07762477965849443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.72,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_3,storeys,A_structural,dummy,binary,-0.05,,-0.34,,,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-5.900094983755255,13.988668007363442,100*(exp(b - se^2/2) - 1),|b/t|,-0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_3,storeys,A_structural,dummy,binary,0.08,,0.56,,,,,high,1 if building height regime is Ground Floor +2 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,7.228932436664737,15.475529538213694,100*(exp(b - se^2/2) - 1),|b/t|,0.04382045179337803,0.07825080677388933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_4,storeys,A_structural,dummy,binary,0.04,,0.28,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,3.0244258567254656,14.868725345605545,100*(exp(b - se^2/2) - 1),|b/t|,0.021926020227473576,0.0783072150981199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.28,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_4,storeys,A_structural,dummy,binary,-0.01,,-0.08,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-1.7654795182503569,12.3756229218646,100*(exp(b - se^2/2) - 1),|b/t|,-0.006265960588387915,0.07832450735484893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_4,storeys,A_structural,dummy,binary,0.06,,0.49,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,5.3905854157488164,13.002080161779913,100*(exp(b - se^2/2) - 1),|b/t|,0.03835152642653928,0.07826842127865159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_4,storeys,A_structural,dummy,binary,-0.09,,-0.62,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-9.564735452408723,13.2667430120017,100*(exp(b - se^2/2) - 1),|b/t|,-0.04850498735522779,0.07823385057294804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.62,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_4,storeys,A_structural,dummy,binary,-0.03,,-0.23,,,,,high,1 if building height regime is Ground Floor +3 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-3.777466161145937,12.657985220197933,100*(exp(b - se^2/2) - 1),|b/t|,-0.018012067769695272,0.07831333812910987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_5,storeys,A_structural,dummy,binary,0.1,,0.78,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,9.612553648050405,14.168857924046767,100*(exp(b - se^2/2) - 1),|b/t|,0.060980615555143815,0.07818027635274846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.78,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_5,storeys,A_structural,dummy,binary,0.09,,0.72,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,8.565935187536255,13.67717854631513,100*(exp(b - se^2/2) - 1),|b/t|,0.056305287704943245,0.07820178847908785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.72,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_5,storeys,A_structural,dummy,binary,0.11,,0.95,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,10.882001339798485,12.925325026365877,100*(exp(b - se^2/2) - 1),|b/t|,0.07420459794567237,0.07811010310070776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.95,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_5,storeys,A_structural,dummy,binary,0.04,,0.3,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,3.1600119754384925,13.877476989231843,100*(exp(b - se^2/2) - 1),|b/t|,0.023491329058363677,0.0783044301945456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_5,storeys,A_structural,dummy,binary,0.04,,0.37,,,,,high,1 if building height regime is Ground Floor +4 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,3.474634406287591,11.252008369647442,100*(exp(b - se^2/2) - 1),|b/t|,0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_6,storeys,A_structural,dummy,binary,0.12,,0.78,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,11.423233663544362,17.34610540891347,100*(exp(b - se^2/2) - 1),|b/t|,0.060980615555143815,0.07818027635274846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.78,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_6,storeys,A_structural,dummy,binary,0.03,,0.18,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,1.6241594865993747,17.174242232558615,100*(exp(b - se^2/2) - 1),|b/t|,0.01409728708933399,0.07831826160741105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_6,storeys,A_structural,dummy,binary,0.09,,0.67,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,8.434698221278492,14.69786351245805,100*(exp(b - se^2/2) - 1),|b/t|,0.05240633656110709,0.07821841277777176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.67,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_6,storeys,A_structural,dummy,binary,-0.0,,-0.02,,,,,medium,"1 if building height regime is Ground Floor +5 Floors ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,0.0,0.0,100*(exp(b - se^2/2) - 1),|b/t|,-0.0015665189778679186,0.07832594889339593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_6,storeys,A_structural,dummy,binary,0.03,,0.2,,,,,high,1 if building height regime is Ground Floor +5 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,1.8926885052026332,15.456818009302754,100*(exp(b - se^2/2) - 1),|b/t|,0.015663287241915964,0.07831643620957981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_7,storeys,A_structural,dummy,binary,0.25,,1.52,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,26.677487696860737,21.11883909025891,100*(exp(b - se^2/2) - 1),|b/t|,0.11822069266068613,0.0777767714872935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_7,storeys,A_structural,dummy,binary,-0.09,,-0.56,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-9.779592818942673,14.688179763287595,100*(exp(b - se^2/2) - 1),|b/t|,-0.04382045179337803,0.07825080677388933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.56,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_7,storeys,A_structural,dummy,binary,0.13,,0.87,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,12.618525578572616,17.016975842781704,100*(exp(b - se^2/2) - 1),|b/t|,0.06798599350774875,0.07814482012384914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.87,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_7,storeys,A_structural,dummy,binary,-0.1,,-0.59,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,-10.80664891580938,15.336227424338299,100*(exp(b - se^2/2) - 1),|b/t|,-0.046163100276330755,0.07824254284123856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_7,storeys,A_structural,dummy,binary,0.04,,0.27,,,,,high,1 if building height regime is Ground Floor +6 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,2.945142482105978,15.419418876924269,100*(exp(b - se^2/2) - 1),|b/t|,0.021143304620203605,0.07830853563038372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_9,storeys,A_structural,dummy,binary,0.2,,1.65,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,21.246297931010915,14.804881917092969,100*(exp(b - se^2/2) - 1),|b/t|,0.12817201327964664,0.07768000804827069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.65,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_9,storeys,A_structural,dummy,binary,0.18,,1.48,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,18.83955149521004,14.560751713643635,100*(exp(b - se^2/2) - 1),|b/t|,0.11515142400924942,0.07780501622246584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_9,storeys,A_structural,dummy,binary,0.2,,1.87,,†,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,21.443706516507554,13.063131103317323,100*(exp(b - se^2/2) - 1),|b/t|,0.1449234054437153,0.07749914729610444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.87,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_9,storeys,A_structural,dummy,binary,0.13,,1.03,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,12.979371681417827,14.373562119631147,100*(exp(b - se^2/2) - 1),|b/t|,0.08041455812861766,0.0780723865326385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.03,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_9,storeys,A_structural,dummy,binary,0.14,,1.22,,,,,high,1 if building height regime is Ground Floor +8 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,14.272498573339787,13.199863265574741,100*(exp(b - se^2/2) - 1),|b/t|,0.09512445741279114,0.07797086673179603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.22,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c2,12,2,18,Height_regime_10,storeys,A_structural,dummy,binary,0.26,,1.86,,†,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.72,t,Multivariate specification: rooms + height-regime dummies (base GF+7); complete-case subsample; Duration control included,pct_premium,28.432088489895712,18.12913024371509,100*(exp(b - se^2/2) - 1),|b/t|,0.14416456333874586,0.0775078297520139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.86,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,Height_regime_10,storeys,A_structural,dummy,binary,0.22,,1.59,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,23.420568589887814,17.2413132534103,100*(exp(b - se^2/2) - 1),|b/t|,0.12358371871647124,0.07772560925564229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,Height_regime_10,storeys,A_structural,dummy,binary,0.28,,2.25,,*,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,31.292410396968705,16.465615442421438,100*(exp(b - se^2/2) - 1),|b/t|,0.17355898767151737,0.07713732785400773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,Height_regime_10,storeys,A_structural,dummy,binary,0.16,,1.1,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,pct_premium,16.116230159783807,17.06924903260815,100*(exp(b - se^2/2) - 1),|b/t|,0.08584062768258548,0.07803693425689588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,Height_regime_10,storeys,A_structural,dummy,binary,0.2,,1.52,,,,,high,1 if building height regime is Ground Floor +9 Floors,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,pct_premium,21.08753045838958,16.07108892316013,100*(exp(b - se^2/2) - 1),|b/t|,0.11822069266068613,0.0777767714872935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c3,12,3,18,S_useful,living_area,A_structural,level,m2,0.01,,9.42,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.69,t,Multivariate specification: useful surface (linear); complete-case subsample; Duration control included,semi_elasticity,1.0,0.10615711252653928,100*b/1 (X per m2->m2),|b/t|,0.5937148261491466,0.06302705160818965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.42,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,S_useful,living_area,A_structural,level,m2,0.03,,9.89,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,semi_elasticity,3.0,0.3033367037411526,100*b/1 (X per m2->m2),|b/t|,0.6123961103490635,0.06192073916572938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.89,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c5,12,5,18,S_useful,living_area,A_structural,level,m2,0.01,,9.96,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.73,t,Multivariate specification: useful surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,1.0,0.1004016064257028,100*b/1 (X per m2->m2),|b/t|,0.6150942535183284,0.061756451156458655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.96,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,S_useful,living_area,A_structural,level,m2,0.03,,9.53,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,3.0,0.3147953830010493,100*b/1 (X per m2->m2),|b/t|,0.5981763119611426,0.06276771374198768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.53,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c4,12,4,18,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-6.74,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: useful surface + square; complete-case subsample; Duration control included,other,-0.0,0.0,not harmonised,|b/t|,-0.4668552883253223,0.06926636325301518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.74,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c6,12,6,18,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-6.25,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.78,t,Multivariate specification: useful surface + square + height-regime dummies; complete-case subsample; Duration control included,other,-0.0,0.0,not harmonised,|b/t|,-0.43968045084532936,0.0703488721352527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c7,12,7,18,S_constructed,living_area,A_structural,level,m2,0.01,,8.65,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.67,t,Multivariate specification: constructed surface (linear); complete-case subsample; Duration control included,semi_elasticity,1.0,0.11560693641618498,100*b/1 (X per m2->m2),|b/t|,0.5609054210453405,0.06484455734628214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.65,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,S_constructed,living_area,A_structural,level,m2,0.03,,10.13,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,semi_elasticity,3.0,0.2961500493583415,100*b/1 (X per m2->m2),|b/t|,0.6101779697082571,0.06023474528215764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.13,173.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c9,12,9,18,S_constructed,living_area,A_structural,level,m2,0.01,,9.09,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.71,t,Multivariate specification: constructed surface + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,1.0,0.11001100110011,100*b/1 (X per m2->m2),|b/t|,0.5799958135325068,0.06380592008058383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.09,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,S_constructed,living_area,A_structural,level,m2,0.03,,9.2,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,semi_elasticity,3.0,0.32608695652173914,100*b/1 (X per m2->m2),|b/t|,0.5846250629439848,0.06354620249391138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.2,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c8,12,8,18,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-7.03,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,183.0,0.73,t,Multivariate specification: constructed surface + square (Structure omitted); complete-case subsample; Duration control included,other,-0.0,0.0,not harmonised,|b/t|,-0.4713760666453294,0.06705207206903689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.03,173.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T12-c10,12,10,18,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-5.84,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,OLS,173.0,0.76,t,Multivariate specification: constructed surface + square + height-regime dummies; complete-case subsample; Duration control included,other,-0.0,0.0,not harmonised,|b/t|,-0.4159712981120024,0.07122796200547987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.84,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,S_useful,living_area,A_structural,level,m2,0.01,,6.85,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,1.0,0.14598540145985403,100*b/1 (X per m2->m2),|b/t|,0.4727821557643431,0.06901929281231285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.85,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,S_useful,living_area,A_structural,level,m2,0.01,,6.34,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,1.0,0.1577287066246057,100*b/1 (X per m2->m2),|b/t|,0.4447665405253948,0.0701524511869708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.34,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,S_useful,living_area,A_structural,level,m2,0.01,,9.78,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,1.0,0.10224948875255625,100*b/1 (X per m2->m2),|b/t|,0.6081125365183934,0.062179195963025916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.78,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,S_useful,living_area,A_structural,level,m2,0.01,,14.08,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,1.0,0.07102272727272728,100*b/1 (X per m2->m2),|b/t|,0.7407998587965198,0.05261362633498009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.08,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,S_useful,living_area,A_structural,level,m2,0.01,,14.41,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,1.0,0.06939625260235947,100*b/1 (X per m2->m2),|b/t|,0.7484849834846347,0.051942052982972546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.41,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,S_useful,living_area,A_structural,level,m2,0.01,,9.2,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,1.0,0.10869565217391304,100*b/1 (X per m2->m2),|b/t|,0.5846250629439848,0.06354620249391138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.2,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,S_useful,living_area,A_structural,level,m2,0.01,,6.82,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,1.0,0.1466275659824047,100*b/1 (X per m2->m2),|b/t|,0.471172037664912,0.06908680904177594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.82,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,S_useful,living_area,A_structural,level,m2,0.01,,6.47,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,1.0,0.15455950540958271,100*b/1 (X per m2->m2),|b/t|,0.4520377894762579,0.06986673716789148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.47,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,S_useful,living_area,A_structural,level,m2,0.01,,5.62,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,1.0,0.1779359430604982,100*b/1 (X per m2->m2),|b/t|,0.40288612018947934,0.07168792174190024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.62,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,Comfort,condition,A_structural,binary,binary,0.14,,1.51,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",pct_premium,14.534046585398318,10.664790188080254,100*(exp(b - se^2/2) - 1),|b/t|,0.11745368957370614,0.07778390037993785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.51,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,Comfort,condition,A_structural,binary,binary,0.12,,1.26,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",pct_premium,12.239506459536198,10.738065253136911,100*(exp(b - se^2/2) - 1),|b/t|,0.09821368109222837,0.077947365946213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.26,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,Comfort,condition,A_structural,binary,binary,0.18,,2.73,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",pct_premium,19.461785720376824,7.893740855748199,100*(exp(b - se^2/2) - 1),|b/t|,0.20910308960733248,0.0765945383177042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.73,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,Comfort,condition,A_structural,binary,binary,0.18,,3.91,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",pct_premium,19.594940640911474,5.511486582146439,100*(exp(b - se^2/2) - 1),|b/t|,0.2928300077679118,0.07489258510688282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.91,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,Comfort,condition,A_structural,binary,binary,0.2,,4.09,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",pct_premium,21.994333013338863,5.972629624255109,100*(exp(b - se^2/2) - 1),|b/t|,0.3050811089962229,0.07459195818978555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.09,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,Comfort,condition,A_structural,binary,binary,0.24,,3.86,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",pct_premium,26.879427811133482,7.904139794744485,100*(exp(b - se^2/2) - 1),|b/t|,0.2894008610108357,0.0749743163240507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.86,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,Comfort,condition,A_structural,binary,binary,0.26,,3.25,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",pct_premium,29.278654359318026,10.375440693326174,100*(exp(b - se^2/2) - 1),|b/t|,0.24669219681861024,0.07590529132880315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,Comfort,condition,A_structural,binary,binary,0.29,,2.91,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",pct_premium,32.9807646677611,13.318349537023604,100*(exp(b - se^2/2) - 1),|b/t|,0.22222930149559103,0.07636745755862234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.91,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,Comfort,condition,A_structural,binary,binary,0.3,,2.57,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",pct_premium,34.06933109755457,15.757106703221826,100*(exp(b - se^2/2) - 1),|b/t|,0.1973394555861255,0.07678578038370643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.57,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,Floor,floor_level,A_structural,level,count,0.0,,0.3,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,0.0,0.0,100*b/1 (per unit count),|b/t|,0.023491329058363677,0.0783044301945456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,Floor,floor_level,A_structural,level,count,-0.0,,-0.42,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.032879152659595456,0.07828369680856062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,Floor,floor_level,A_structural,level,count,-0.01,,-0.95,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,-1.0,1.0526315789473686,100*b/1 (per unit count),|b/t|,-0.07420459794567237,0.07811010310070776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.95,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,Floor,floor_level,A_structural,level,count,-0.0,,-0.65,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.05084607475894782,0.07822473039838125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.65,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,Floor,floor_level,A_structural,level,count,-0.01,,-1.3,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,-1.0,0.7692307692307692,100*b/1 (per unit count),|b/t|,-0.10130006814979362,0.0779231293459951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,Floor,floor_level,A_structural,level,count,-0.0,,-0.55,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.04303940638866546,0.07825346616120993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.55,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,Floor,floor_level,A_structural,level,count,-0.01,,-1.04,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,-1.0,0.9615384615384616,100*b/1 (per unit count),|b/t|,-0.08119016016629894,0.07806746169836437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.04,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,Floor,floor_level,A_structural,level,count,-0.01,,-1.52,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,-1.0,0.6578947368421052,100*b/1 (per unit count),|b/t|,-0.11822069266068613,0.0777767714872935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.52,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,Floor,floor_level,A_structural,level,count,-0.02,,-1.6,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,-2.0,1.25,100*b/1 (per unit count),|b/t|,-0.12434899333339904,0.0777181208333744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,No_bathrooms,bathrooms,A_structural,level,count,0.03,,0.35,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,3.0,8.571428571428571,100*b/1 (per unit count),|b/t|,0.027403820235783725,0.07829662924509635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,No_bathrooms,bathrooms,A_structural,level,count,0.1,,1.25,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,10.0,8.0,100*b/1 (per unit count),|b/t|,0.09744163775668438,0.0779533102053475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,No_bathrooms,bathrooms,A_structural,level,count,0.07,,1.31,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,7.000000000000001,5.34351145038168,100*b/1 (per unit count),|b/t|,0.10207121166292979,0.07791695546788532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.31,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,No_bathrooms,bathrooms,A_structural,level,count,0.07,,2.07,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,7.000000000000001,3.381642512077295,100*b/1 (per unit count),|b/t|,0.16004494992527651,0.07731640093008528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.07,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,No_bathrooms,bathrooms,A_structural,level,count,0.05,,1.3,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,5.0,3.8461538461538463,100*b/1 (per unit count),|b/t|,0.10130006814979362,0.0779231293459951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,No_bathrooms,bathrooms,A_structural,level,count,0.12,,2.32,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,12.0,5.172413793103448,100*b/1 (per unit count),|b/t|,0.17878852324638245,0.07706401864068209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.32,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,No_bathrooms,bathrooms,A_structural,level,count,0.13,,2.1,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,13.0,6.190476190476191,100*b/1 (per unit count),|b/t|,0.16230376580572478,0.0772875075265356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,No_bathrooms,bathrooms,A_structural,level,count,0.07,,0.96,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,7.000000000000001,7.291666666666667,100*b/1 (per unit count),|b/t|,0.07498133021571891,0.07810555230804055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.96,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,No_bathrooms,bathrooms,A_structural,level,count,0.07,,0.72,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,7.000000000000001,9.722222222222223,100*b/1 (per unit count),|b/t|,0.056305287704943245,0.07820178847908785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.72,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,No_balconies,terrace_balcony,C_exterior,level,count,0.01,,0.11,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,1.0,9.090909090909092,100*b/1 (per unit count),|b/t|,0.00861554517636434,0.07832313796694854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,No_balconies,terrace_balcony,C_exterior,level,count,0.0,,0.05,,,,,medium,"Number of balconies ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,0.0,0.0,100*b/1 (per unit count),|b/t|,0.00391627221729254,0.0783254443458508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,No_balconies,terrace_balcony,C_exterior,level,count,0.01,,0.12,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,1.0,8.333333333333334,100*b/1 (per unit count),|b/t|,0.009398710251270781,0.07832258542725651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.69,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,-2.0,2.8985507246376816,100*b/1 (per unit count),|b/t|,-0.053966214667216864,0.07821190531480704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.69,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.59,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,-2.0,3.389830508474576,100*b/1 (per unit count),|b/t|,-0.046163100276330755,0.07824254284123856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,No_balconies,terrace_balcony,C_exterior,level,count,-0.04,,-0.95,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,-4.0,4.210526315789474,100*b/1 (per unit count),|b/t|,-0.07420459794567237,0.07811010310070776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.95,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-0.92,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,-5.0,5.434782608695652,100*b/1 (per unit count),|b/t|,-0.07187359623253212,0.07812347416579578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-0.88,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,-6.0,6.8181818181818175,100*b/1 (per unit count),|b/t|,-0.06876376756217446,0.07814064495701643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.88,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.23,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,-2.0,8.695652173913043,100*b/1 (per unit count),|b/t|,-0.018012067769695272,0.07831333812910987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,No_parking,parking,A_structural,level,count,-0.07,,-0.59,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,-7.000000000000001,11.864406779661019,100*b/1 (per unit count),|b/t|,-0.046163100276330755,0.07824254284123856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,No_parking,parking,A_structural,level,count,0.06,,0.5,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,6.0,12.0,100*b/1 (per unit count),|b/t|,0.03913302400974161,0.07826604801948324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,No_parking,parking,A_structural,level,count,0.04,,0.53,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,4.0,7.547169811320755,100*b/1 (per unit count),|b/t|,0.04147708024010317,0.07825864196245881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.53,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,No_parking,parking,A_structural,level,count,0.03,,0.65,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,3.0,4.615384615384615,100*b/1 (per unit count),|b/t|,0.05084607475894782,0.07822473039838125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.65,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,No_parking,parking,A_structural,level,count,0.02,,0.27,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,2.0,7.4074074074074066,100*b/1 (per unit count),|b/t|,0.021143304620203605,0.07830853563038372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,No_parking,parking,A_structural,level,count,0.1,,1.37,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,10.0,7.2992700729927,100*b/1 (per unit count),|b/t|,0.10669416293216134,0.07787895104537323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.37,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,No_parking,parking,A_structural,level,count,0.09,,0.92,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,9.0,9.782608695652172,100*b/1 (per unit count),|b/t|,0.07187359623253212,0.07812347416579578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,No_parking,parking,A_structural,level,count,0.06,,0.51,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,6.0,11.76470588235294,100*b/1 (per unit count),|b/t|,0.039914449789453355,0.07826362703814382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.51,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,No_parking,parking,A_structural,level,count,0.1,,0.76,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,10.0,13.157894736842104,100*b/1 (per unit count),|b/t|,0.059422603574431083,0.07818763628214616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.76,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,Seniority_construction,age,A_structural,level,years,0.0,,0.13,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.010181858031006727,0.0783219848538979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,Seniority_construction,age,A_structural,level,years,-0.0,,-0.45,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.035224846576422025,0.07827743683649338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,Seniority_construction,age,A_structural,level,years,0.0,,0.02,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.0015665189778679186,0.07832594889339593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.02,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,Seniority_construction,age,A_structural,level,years,-0.0,,-0.46,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.03600661711320239,0.07827525459391824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.46,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,Seniority_construction,age,A_structural,level,years,-0.0,,-0.44,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.03444301136946857,0.07827957129424674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.44,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,Seniority_construction,age,A_structural,level,years,0.0,,0.16,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.0125311831946079,0.07831989496629937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,Seniority_construction,age,A_structural,level,years,-0.0,,-0.37,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,Seniority_construction,age,A_structural,level,years,0.0,,0.53,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.04147708024010317,0.07825864196245881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.53,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,Seniority_construction,age,A_structural,level,years,-0.0,,-0.11,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.00861554517636434,0.07832313796694854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.96,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07498133021571891,0.07810555230804055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.96,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.27,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09898554713693115,0.077941375698371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.27,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.98,,*,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.15325352828981126,0.07740077186354105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.98,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,Seniority_renovation,renovation,A_structural,level,years,-0.01,,-3.34,,**,,,high,Years since renovation,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,-1.0,0.29940119760479045,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.2530916078945275,0.07577593050734355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.34,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-2.44,,*,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.18771807904379892,0.0769336389523766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.44,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.91,,†,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.14795620521528294,0.07746398178810625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.91,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.0,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07808688094430304,0.07808688094430304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.05,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.08196561473788967,0.07806249022656159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.05,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,Seniority_renovation,renovation,A_structural,level,years,0.0,,0.13,,,,,medium,"Years since renovation ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,0.010181858031006727,0.0783219848538979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,Structure,roof_materials,A_structural,binary,binary,0.87,,7.22,,***,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",pct_premium,136.96447322266724,28.76194518789641,100*(exp(b - se^2/2) - 1),|b/t|,0.4922529460177041,0.0681790783958039,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.22,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,Structure,roof_materials,A_structural,binary,binary,-0.02,,-0.16,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",pct_premium,-2.7429293286145295,12.25248341633444,100*(exp(b - se^2/2) - 1),|b/t|,-0.0125311831946079,0.07831989496629937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,Structure,roof_materials,A_structural,binary,binary,0.06,,0.76,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",pct_premium,5.853264984809026,8.38292010430547,100*(exp(b - se^2/2) - 1),|b/t|,0.059422603574431083,0.07818763628214616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.76,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,Structure,roof_materials,A_structural,binary,binary,0.04,,0.74,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",pct_premium,3.9291342675290775,5.626004184823721,100*(exp(b - se^2/2) - 1),|b/t|,0.05786415721199271,0.0781948070432334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.74,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,Structure,roof_materials,A_structural,binary,binary,0.07,,1.12,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",pct_premium,7.041548302856615,6.703176132838854,100*(exp(b - se^2/2) - 1),|b/t|,0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.12,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,Structure,roof_materials,A_structural,binary,binary,0.14,,1.78,,†,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",pct_premium,14.672144822677469,9.047097294382688,100*(exp(b - se^2/2) - 1),|b/t|,0.13808477074149605,0.07757571389971687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.78,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,Structure,roof_materials,A_structural,binary,binary,0.08,,0.82,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",pct_premium,7.814387466396622,10.568654318780084,100*(exp(b - se^2/2) - 1),|b/t|,0.06409529150234007,0.0781649896370001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.82,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,Structure,roof_materials,A_structural,binary,binary,-0.09,,-0.75,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",pct_premium,-9.262548691804383,10.96717422325474,100*(exp(b - se^2/2) - 1),|b/t|,-0.05864343398897361,0.07819124531863149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,Structure,roof_materials,A_structural,binary,binary,-0.02,,-0.16,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",pct_premium,-2.7429293286145295,12.25248341633444,100*(exp(b - se^2/2) - 1),|b/t|,-0.0125311831946079,0.07831989496629937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c1,13,1,19,Duration,time_on_market,G_market,level,days,-0.0,,-1.05,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4887",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.08196561473788967,0.07806249022656159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.05,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c2,13,2,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.34,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5165",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c3,13,3,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.02,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.527",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.0015665189778679186,0.07832594889339593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c4,13,4,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.24,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5213",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.018794930270996775,0.07831220946248657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c5,13,5,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.86,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5132",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.06720809547985139,0.07814894823238534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c6,13,6,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.06,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.5084",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.004699510803775961,0.0783251800629327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c7,13,7,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.13,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4991",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.010181858031006727,0.0783219848538979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c8,13,8,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.62,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4787",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.04850498735522779,0.07823385057294804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.62,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T13-c9,13,9,19,Duration,time_on_market,G_market,level,days,-0.0,,-0.61,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface (linear), Duration control, no height regimes; pseudo-R2 = 0.4593",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.047724445293116674,0.07823679556248635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.61,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,S_useful,living_area,A_structural,level,m2,0.03,,5.56,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,3.0,0.539568345323741,100*b/1 (X per m2->m2),|b/t|,0.3992736565154539,0.07181180872580108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.56,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,S_useful,living_area,A_structural,level,m2,0.03,,9.94,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,3.0,0.3018108651911469,100*b/1 (X per m2->m2),|b/t|,0.6143255488460344,0.06180337513541593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.94,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,S_useful,living_area,A_structural,level,m2,0.03,,10.65,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,3.0,0.2816901408450704,100*b/1 (X per m2->m2),|b/t|,0.6405645679788211,0.0601469077914386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.65,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,S_useful,living_area,A_structural,level,m2,0.03,,10.89,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,3.0,0.2754820936639118,100*b/1 (X per m2->m2),|b/t|,0.6489595778177493,0.05959224773349395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.89,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,S_useful,living_area,A_structural,level,m2,0.03,,8.98,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,3.0,0.3340757238307349,100*b/1 (X per m2->m2),|b/t|,0.5753096844287285,0.06406566641745307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.98,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,S_useful,living_area,A_structural,level,m2,0.03,,8.31,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,3.0,0.3610108303249097,100*b/1 (X per m2->m2),|b/t|,0.5455122772628948,0.06564528005570333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.31,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,S_useful,living_area,A_structural,level,m2,0.03,,6.46,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,3.0,0.46439628482972134,100*b/1 (X per m2->m2),|b/t|,0.4514816231577851,0.06988879615445591,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.46,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,S_useful,living_area,A_structural,level,m2,0.04,,6.84,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,4.0,0.584795321637427,100*b/1 (X per m2->m2),|b/t|,0.4722459744779502,0.06904180913420324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.84,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,S_useful,living_area,A_structural,level,m2,0.04,,7.5,,***,,,high,Useful (interior usable) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,4.0,0.5333333333333333,100*b/1 (X per m2->m2),|b/t|,0.5065140100898807,0.06753520134531743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-3.73,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",other,-0.0,0.0,not harmonised,|b/t|,-0.2804329761226666,0.07518310351814118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.73,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-6.99,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",other,-0.0,0.0,not harmonised,|b/t|,-0.48023365160304293,0.06870295444964848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.99,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-7.35,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",other,-0.0,0.0,not harmonised,|b/t|,-0.49892464623843935,0.0678809042501278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.35,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-7.54,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",other,-0.0,0.0,not harmonised,|b/t|,-0.5085182345615921,0.06744273667925624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.54,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-6.15,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",other,-0.0,0.0,not harmonised,|b/t|,-0.43397921625226327,0.07056572621988019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.15,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-5.83,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",other,-0.0,0.0,not harmonised,|b/t|,-0.4153820039807677,0.07124905728658108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.83,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-4.57,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",other,-0.0,0.0,not harmonised,|b/t|,-0.3370102935989432,0.07374404673937487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.57,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-4.92,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",other,-0.0,0.0,not harmonised,|b/t|,-0.35958764117113284,0.07308691893722212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.92,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,S_useful_sq,living_area,A_structural,squared,m2,-0.0,,-5.69,,***,,,medium,"Square of useful surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",other,-0.0,0.0,not harmonised,|b/t|,-0.4070769840916536,0.07154252795986882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.69,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,Comfort,condition,A_structural,binary,binary,0.11,,1.14,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",pct_premium,11.10935621329514,10.771104188638231,100*(exp(b - se^2/2) - 1),|b/t|,0.08893784421580979,0.07801565282088578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.14,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,Comfort,condition,A_structural,binary,binary,0.1,,1.82,,†,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",pct_premium,10.35039436444627,6.0723676817343275,100*(exp(b - se^2/2) - 1),|b/t|,0.14112666300859447,0.07754212253219477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.82,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,Comfort,condition,A_structural,binary,binary,0.11,,2.15,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",pct_premium,11.481802400731222,5.711190127929108,100*(exp(b - se^2/2) - 1),|b/t|,0.166062779645818,0.07723850216084559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.15,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,Comfort,condition,A_structural,binary,binary,0.1,,1.98,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",pct_premium,10.376230357060567,5.581671303412363,100*(exp(b - se^2/2) - 1),|b/t|,0.15325352828981126,0.07740077186354105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.98,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,Comfort,condition,A_structural,binary,binary,0.13,,2.27,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",pct_premium,13.696240224322697,6.52192466221149,100*(exp(b - se^2/2) - 1),|b/t|,0.1750546600335824,0.07711659032316404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.27,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,Comfort,condition,A_structural,binary,binary,0.16,,2.46,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",pct_premium,17.103135076161546,7.632591030841043,100*(exp(b - se^2/2) - 1),|b/t|,0.1892018873600817,0.07691133632523646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.46,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,Comfort,condition,A_structural,binary,binary,0.18,,2.15,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",pct_premium,19.30289422980951,10.023215133112828,100*(exp(b - se^2/2) - 1),|b/t|,0.166062779645818,0.07723850216084559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.15,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,Comfort,condition,A_structural,binary,binary,0.16,,1.65,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",pct_premium,16.800648770761818,11.3794993550721,100*(exp(b - se^2/2) - 1),|b/t|,0.12817201327964664,0.07768000804827069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.65,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,Comfort,condition,A_structural,binary,binary,0.03,,0.24,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",pct_premium,2.2435473150278495,12.880681674418963,100*(exp(b - se^2/2) - 1),|b/t|,0.018794930270996775,0.07831220946248657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,Floor,floor_level,A_structural,level,count,-0.01,,-1.24,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,-1.0,0.8064516129032258,100*b/1 (per unit count),|b/t|,-0.09666941847029474,0.07795920844378608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.24,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,Floor,floor_level,A_structural,level,count,-0.01,,-2.02,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,-1.0,0.49504950495049505,100*b/1 (per unit count),|b/t|,-0.15627467928229707,0.07736370261499854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.02,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,Floor,floor_level,A_structural,level,count,-0.01,,-2.73,,**,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,-1.0,0.3663003663003663,100*b/1 (per unit count),|b/t|,-0.20910308960733248,0.0765945383177042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.73,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,Floor,floor_level,A_structural,level,count,-0.01,,-1.5,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,-1.0,0.6666666666666667,100*b/1 (per unit count),|b/t|,-0.1166864762376621,0.07779098415844139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,Floor,floor_level,A_structural,level,count,-0.0,,-0.15,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.011748095942956003,0.0783206396197067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,Floor,floor_level,A_structural,level,count,-0.0,,-0.68,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.05318632428208009,0.07821518276776482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.68,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,Floor,floor_level,A_structural,level,count,-0.01,,-1.37,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,-1.0,0.7299270072992701,100*b/1 (per unit count),|b/t|,-0.10669416293216134,0.07787895104537323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.37,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,Floor,floor_level,A_structural,level,count,-0.02,,-1.88,,†,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,-2.0,1.0638297872340428,100*b/1 (per unit count),|b/t|,-0.14568199190517223,0.07749042122615545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.88,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,Floor,floor_level,A_structural,level,count,-0.03,,-3.32,,**,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,-3.0,0.9036144578313252,100*b/1 (per unit count),|b/t|,-0.2516723513898991,0.07580492511743948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.32,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,No_bathrooms,bathrooms,A_structural,level,count,0.08,,1.17,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,8.0,6.837606837606838,100*b/1 (per unit count),|b/t|,0.09125906972803648,0.07799920489575768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.17,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,No_bathrooms,bathrooms,A_structural,level,count,0.08,,2.09,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,8.0,3.8277511961722492,100*b/1 (per unit count),|b/t|,0.1615511088030177,0.07729718124546302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.09,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,No_bathrooms,bathrooms,A_structural,level,count,0.08,,2.19,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,8.0,3.652968036529681,100*b/1 (per unit count),|b/t|,0.16906479552348647,0.07719853676871528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.19,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,No_bathrooms,bathrooms,A_structural,level,count,0.08,,2.13,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,8.0,3.755868544600939,100*b/1 (per unit count),|b/t|,0.1645600330113241,0.07725823146071555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.13,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,No_bathrooms,bathrooms,A_structural,level,count,0.1,,2.38,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,10.0,4.201680672268908,100*b/1 (per unit count),|b/t|,0.183258971223675,0.07699956774103991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.38,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,No_bathrooms,bathrooms,A_structural,level,count,0.12,,2.47,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,12.0,4.8582995951417,100*b/1 (per unit count),|b/t|,0.18994330645012425,0.0769001240688762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.47,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,No_bathrooms,bathrooms,A_structural,level,count,0.11,,1.75,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,11.0,6.2857142857142865,100*b/1 (per unit count),|b/t|,0.1358007794987115,0.07760044542783513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,No_bathrooms,bathrooms,A_structural,level,count,0.03,,0.48,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,3.0,6.25,100*b/1 (per unit count),|b/t|,0.03756995846524677,0.07827074680259744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,No_bathrooms,bathrooms,A_structural,level,count,0.04,,0.47,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,4.0,8.510638297872342,100*b/1 (per unit count),|b/t|,0.03678832155190899,0.07827302457852976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-0.86,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,-5.0,5.813953488372094,100*b/1 (per unit count),|b/t|,-0.06720809547985139,0.07814894823238534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.0,,-0.02,,,,,medium,"Number of balconies ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.0015665189778679186,0.07832594889339593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.54,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,-2.0,3.7037037037037033,100*b/1 (per unit count),|b/t|,-0.042258282063579004,0.07825607789551667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.54,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.01,,-0.2,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,-1.0,5.0,100*b/1 (per unit count),|b/t|,-0.015663287241915964,0.07831643620957981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.01,,-0.37,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,-1.0,2.7027027027027026,100*b/1 (per unit count),|b/t|,-0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.56,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,-2.0,3.571428571428571,100*b/1 (per unit count),|b/t|,-0.04382045179337803,0.07825080677388933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.56,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.45,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,-3.0,6.666666666666667,100*b/1 (per unit count),|b/t|,-0.035224846576422025,0.07827743683649338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-0.72,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,-5.0,6.944444444444445,100*b/1 (per unit count),|b/t|,-0.056305287704943245,0.07820178847908785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.72,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.3,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,-2.0,6.666666666666667,100*b/1 (per unit count),|b/t|,-0.023491329058363677,0.0783044301945456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,No_parking,parking,A_structural,level,count,0.11,,1.05,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,11.0,10.476190476190476,100*b/1 (per unit count),|b/t|,0.08196561473788967,0.07806249022656159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.05,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,No_parking,parking,A_structural,level,count,0.23,,3.9,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,23.0,5.897435897435898,100*b/1 (per unit count),|b/t|,0.2921450767369252,0.07490899403510903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,No_parking,parking,A_structural,level,count,0.18,,3.38,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,18.0,5.325443786982249,100*b/1 (per unit count),|b/t|,0.25592521771509946,0.0757175200340531,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.38,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,No_parking,parking,A_structural,level,count,0.25,,4.65,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,25.0,5.376344086021505,100*b/1 (per unit count),|b/t|,0.3422241386894086,0.0735965889654642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.65,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,No_parking,parking,A_structural,level,count,0.23,,3.59,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,23.0,6.406685236768802,100*b/1 (per unit count),|b/t|,0.27069249797073924,0.07540181001970454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.59,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,No_parking,parking,A_structural,level,count,0.22,,3.01,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,22.0,7.308970099667775,100*b/1 (per unit count),|b/t|,0.22947026126280803,0.0762359671969462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.01,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,No_parking,parking,A_structural,level,count,0.29,,3.2,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,28.999999999999996,9.062499999999998,100*b/1 (per unit count),|b/t|,0.24312291308276734,0.07597591033836479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,No_parking,parking,A_structural,level,count,0.27,,2.56,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,27.0,10.546875,100*b/1 (per unit count),|b/t|,0.19660133280507944,0.07679739562698415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.56,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,No_parking,parking,A_structural,level,count,0.12,,1.0,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,12.0,12.0,100*b/1 (per unit count),|b/t|,0.07808688094430304,0.07808688094430304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,Seniority_construction,age,A_structural,level,years,-0.0,,-1.0,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.07808688094430304,0.07808688094430304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,Seniority_construction,age,A_structural,level,years,-0.0,,-1.87,,†,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.1449234054437153,0.07749914729610444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.87,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,Seniority_construction,age,A_structural,level,years,-0.0,,-1.74,,†,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.13503896580859895,0.07760860103942469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.74,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,Seniority_construction,age,A_structural,level,years,-0.0,,-1.73,,†,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.13427691256247734,0.07761671246385972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.73,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,Seniority_construction,age,A_structural,level,years,-0.0,,-0.6,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.04694381579433043,0.07823969299055072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,Seniority_construction,age,A_structural,level,years,-0.0,,-0.19,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.014880300853079419,0.0783173729109443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.19,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,Seniority_construction,age,A_structural,level,years,0.0,,0.43,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.03366111292083537,0.0782816579554311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,Seniority_construction,age,A_structural,level,years,0.0,,1.29,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.1005287420017387,0.07792925736568891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.29,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,Seniority_construction,age,A_structural,level,years,0.0,,0.26,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.020360550133690272,0.07830980820650105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,Seniority_renovation,renovation,A_structural,level,years,-0.01,,-1.83,,†,,,high,Years since renovation,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,-1.0,0.546448087431694,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.14188651541975236,0.07753361498347124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.83,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,Seniority_renovation,renovation,A_structural,level,years,-0.01,,-2.97,,**,,,high,Years since renovation,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,-1.0,0.33670033670033667,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.22657837332761346,0.07628901458842204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.97,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,Seniority_renovation,renovation,A_structural,level,years,-0.01,,-3.05,,**,,,high,Years since renovation,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,-1.0,0.3278688524590164,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.23235608476109681,0.07618232287249077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.05,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-2.93,,**,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.22368048069859853,0.07634146098928277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.93,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-2.25,,*,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.17355898767151737,0.07713732785400773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.66,,†,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.12893593772025744,0.07767225163870932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.66,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.94,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07342773060273564,0.07811460702418685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.94,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.36,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.10592414314269169,0.07788539936962624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.36,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.84,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06565193307519313,0.07815706318475373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.84,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,Structure,roof_materials,A_structural,binary,binary,-0.04,,-0.33,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",pct_premium,-4.624283996290812,11.645932595785736,100*(exp(b - se^2/2) - 1),|b/t|,-0.025838964808374536,0.07829989335871071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,Structure,roof_materials,A_structural,binary,binary,-0.01,,-0.21,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",pct_premium,-1.1072035630721344,4.714523017853182,100*(exp(b - se^2/2) - 1),|b/t|,-0.016446244816782134,0.07831545150848634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,Structure,roof_materials,A_structural,binary,binary,0.02,,0.29,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",pct_premium,1.7778060074763014,7.035871310529352,100*(exp(b - se^2/2) - 1),|b/t|,0.02270869551892764,0.07830584661699187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.29,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,Structure,roof_materials,A_structural,binary,binary,0.04,,0.6,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",pct_premium,3.850042936142084,6.938738494615921,100*(exp(b - se^2/2) - 1),|b/t|,0.04694381579433043,0.07823969299055072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,Structure,roof_materials,A_structural,binary,binary,0.09,,1.19,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",pct_premium,9.104945138591813,8.275267691888144,100*(exp(b - se^2/2) - 1),|b/t|,0.09280572978466087,0.0779880082224041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.19,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,Structure,roof_materials,A_structural,binary,binary,0.06,,0.81,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",pct_premium,5.892740714413414,7.865455900335997,100*(exp(b - se^2/2) - 1),|b/t|,0.06331679451827393,0.07816888212132585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.81,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,Structure,roof_materials,A_structural,binary,binary,0.06,,0.58,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",pct_premium,5.617007386293293,10.984515998745099,100*(exp(b - se^2/2) - 1),|b/t|,0.045382300157365094,0.07824534509890534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.58,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,Structure,roof_materials,A_structural,binary,binary,-0.1,,-0.86,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",pct_premium,-10.125902438083422,10.521365325999529,100*(exp(b - se^2/2) - 1),|b/t|,-0.06720809547985139,0.07814894823238534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,Structure,roof_materials,A_structural,binary,binary,-0.07,,-0.55,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",pct_premium,-7.512730091716035,11.866830435166616,100*(exp(b - se^2/2) - 1),|b/t|,-0.04303940638866546,0.07825346616120993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.55,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c1,14,1,20,Duration,time_on_market,G_market,level,days,-0.0,,-1.46,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5844",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.11361554637548585,0.07781886738046975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.46,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c2,14,2,20,Duration,time_on_market,G_market,level,days,-0.0,,-0.94,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.6082",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.07342773060273564,0.07811460702418685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.94,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c3,14,3,20,Duration,time_on_market,G_market,level,days,-0.0,,-0.82,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5934",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.06409529150234007,0.0781649896370001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.82,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c4,14,4,20,Duration,time_on_market,G_market,level,days,-0.0,,-0.7,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.571",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.054746006305799545,0.07820858043685651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c5,14,5,20,Duration,time_on_market,G_market,level,days,-0.0,,-0.09,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5546",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.007049168904005755,0.07832409893339728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c6,14,6,20,Duration,time_on_market,G_market,level,days,-0.0,,-0.59,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5453",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.046163100276330755,0.07824254284123856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c7,14,7,20,Duration,time_on_market,G_market,level,days,-0.0,,-0.87,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5363",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.06798599350774875,0.07814482012384914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.87,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c8,14,8,20,Duration,time_on_market,G_market,level,days,-0.0,,-1.39,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5186",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.1082336254184039,0.07786591756719705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.39,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T14-c9,14,9,20,Duration,time_on_market,G_market,level,days,-0.0,,-1.73,,†,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with useful surface + square, Duration control; pseudo-R2 = 0.5181",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.13427691256247734,0.07761671246385972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.73,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,S_constructed,living_area,A_structural,level,m2,0.01,,6.74,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,1.0,0.1483679525222552,100*b/1 (X per m2->m2),|b/t|,0.4668552883253223,0.06926636325301518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.74,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,S_constructed,living_area,A_structural,level,m2,0.01,,6.59,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,1.0,0.15174506828528073,100*b/1 (X per m2->m2),|b/t|,0.45867070927872133,0.06960101809995772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.59,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,S_constructed,living_area,A_structural,level,m2,0.01,,8.25,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,1.0,0.12121212121212122,100*b/1 (X per m2->m2),|b/t|,0.5427367311306341,0.06578627044007686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.25,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,S_constructed,living_area,A_structural,level,m2,0.01,,9.41,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,1.0,0.10626992561105207,100*b/1 (X per m2->m2),|b/t|,0.5933064951550262,0.06305063710467866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.41,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,S_constructed,living_area,A_structural,level,m2,0.01,,9.9,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,1.0,0.10101010101010101,100*b/1 (X per m2->m2),|b/t|,0.6127828769611389,0.06189726029910493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.9,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,S_constructed,living_area,A_structural,level,m2,0.01,,8.45,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,1.0,0.11834319526627221,100*b/1 (X per m2->m2),|b/t|,0.5519193103906693,0.06531589472078926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.45,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,S_constructed,living_area,A_structural,level,m2,0.01,,7.48,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,1.0,0.1336898395721925,100*b/1 (X per m2->m2),|b/t|,0.5055088071173947,0.0675813913258549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.48,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,S_constructed,living_area,A_structural,level,m2,0.01,,6.12,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,1.0,0.16339869281045752,100*b/1 (X per m2->m2),|b/t|,0.43225858293354363,0.07063048740744177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.12,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,S_constructed,living_area,A_structural,level,m2,0.01,,3.84,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,1.0,0.2604166666666667,100*b/1 (X per m2->m2),|b/t|,0.28802606753872173,0.07500678842154213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.84,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,Comfort,condition,A_structural,binary,binary,0.14,,1.42,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",pct_premium,14.469686268086491,11.340727594367031,100*(exp(b - se^2/2) - 1),|b/t|,0.11054135663698533,0.0778460258006939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.42,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,Comfort,condition,A_structural,binary,binary,0.16,,1.81,,†,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",pct_premium,16.89348100389678,10.373576760148598,100*(exp(b - se^2/2) - 1),|b/t|,0.14036656109869525,0.07755058624237307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.81,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,Comfort,condition,A_structural,binary,binary,0.2,,2.8,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",pct_premium,21.82909056846871,8.724305415429786,100*(exp(b - se^2/2) - 1),|b/t|,0.2142215956964861,0.07650771274874506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,Comfort,condition,A_structural,binary,binary,0.2,,3.21,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",pct_premium,21.903434525923938,7.609986032150592,100*(exp(b - se^2/2) - 1),|b/t|,0.24383756556406858,0.075961858431174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.21,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,Comfort,condition,A_structural,binary,binary,0.21,,3.25,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",pct_premium,23.110535205154115,7.971458233566648,100*(exp(b - se^2/2) - 1),|b/t|,0.24669219681861024,0.07590529132880315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,Comfort,condition,A_structural,binary,binary,0.25,,3.65,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",pct_premium,28.101706462213706,8.794694634847543,100*(exp(b - se^2/2) - 1),|b/t|,0.2748773764109295,0.07530887024956973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.65,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,Comfort,condition,A_structural,binary,binary,0.25,,3.38,,***,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",pct_premium,28.051792703746937,9.497229413370869,100*(exp(b - se^2/2) - 1),|b/t|,0.25592521771509946,0.0757175200340531,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.38,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,Comfort,condition,A_structural,binary,binary,0.3,,3.23,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",pct_premium,34.404902962690564,12.537388305659473,100*(exp(b - se^2/2) - 1),|b/t|,0.24526567865625545,0.07593364664280354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.23,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,Comfort,condition,A_structural,binary,binary,0.31,,2.08,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",pct_premium,34.836637686966185,20.32027814331611,100*(exp(b - se^2/2) - 1),|b/t|,0.160798169784715,0.0773068123964976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.08,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,Floor,floor_level,A_structural,level,count,0.0,,0.06,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,0.0,0.0,100*b/1 (per unit count),|b/t|,0.004699510803775961,0.0783251800629327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.06,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,Floor,floor_level,A_structural,level,count,-0.0,,-0.42,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.032879152659595456,0.07828369680856062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,Floor,floor_level,A_structural,level,count,-0.01,,-1.41,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,-1.0,0.7092198581560284,100*b/1 (per unit count),|b/t|,-0.10977230965416428,0.07785270188238601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.41,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,Floor,floor_level,A_structural,level,count,-0.0,,-0.28,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.021926020227473576,0.0783072150981199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.28,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,Floor,floor_level,A_structural,level,count,-0.0,,-0.18,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.01409728708933399,0.07831826160741105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,Floor,floor_level,A_structural,level,count,-0.0,,-0.62,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.04850498735522779,0.07823385057294804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.62,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,Floor,floor_level,A_structural,level,count,-0.01,,-1.08,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,-1.0,0.9259259259259258,100*b/1 (per unit count),|b/t|,-0.08429107995739295,0.07804729625684532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.08,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,Floor,floor_level,A_structural,level,count,-0.01,,-1.4,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,-1.0,0.7142857142857143,100*b/1 (per unit count),|b/t|,-0.10900306547625242,0.07785933248303746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,Floor,floor_level,A_structural,level,count,-0.02,,-1.24,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,-2.0,1.6129032258064515,100*b/1 (per unit count),|b/t|,-0.09666941847029474,0.07795920844378608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.24,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,No_bathrooms,bathrooms,A_structural,level,count,0.02,,0.23,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,2.0,8.695652173913043,100*b/1 (per unit count),|b/t|,0.018012067769695272,0.07831333812910987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,No_bathrooms,bathrooms,A_structural,level,count,0.05,,0.79,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,5.0,6.329113924050633,100*b/1 (per unit count),|b/t|,0.06175945514506461,0.07817652550008178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.79,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,No_bathrooms,bathrooms,A_structural,level,count,0.1,,1.78,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,10.0,5.617977528089888,100*b/1 (per unit count),|b/t|,0.13808477074149605,0.07757571389971687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.78,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,No_bathrooms,bathrooms,A_structural,level,count,0.06,,1.33,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,6.0,4.511278195488721,100*b/1 (per unit count),|b/t|,0.10361294545812871,0.0779044702692697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.33,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,No_bathrooms,bathrooms,A_structural,level,count,0.06,,1.12,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,6.0,5.357142857142856,100*b/1 (per unit count),|b/t|,0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,No_bathrooms,bathrooms,A_structural,level,count,0.12,,2.25,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,12.0,5.333333333333333,100*b/1 (per unit count),|b/t|,0.17355898767151737,0.07713732785400773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,No_bathrooms,bathrooms,A_structural,level,count,0.17,,2.84,,**,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,17.0,5.985915492957748,100*b/1 (per unit count),|b/t|,0.21713858208422665,0.07645724721275586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.84,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,No_bathrooms,bathrooms,A_structural,level,count,0.11,,1.44,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,11.0,7.638888888888889,100*b/1 (per unit count),|b/t|,0.11207885376546127,0.07783253733712589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.44,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,No_bathrooms,bathrooms,A_structural,level,count,0.11,,0.92,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,11.0,11.956521739130434,100*b/1 (per unit count),|b/t|,0.07187359623253212,0.07812347416579578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.92,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.12,,-1.67,,†,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,-12.0,7.18562874251497,100*b/1 (per unit count),|b/t|,-0.129699632680199,0.077664450706706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.67,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.37,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,-2.0,5.405405405405405,100*b/1 (per unit count),|b/t|,-0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-1.08,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,-6.0,5.555555555555555,100*b/1 (per unit count),|b/t|,-0.08429107995739295,0.07804729625684532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.08,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.07,,-1.44,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,-7.000000000000001,4.861111111111112,100*b/1 (per unit count),|b/t|,-0.11207885376546127,0.07783253733712589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.44,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.1,,-2.1,,*,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,-10.0,4.761904761904762,100*b/1 (per unit count),|b/t|,-0.16230376580572478,0.0772875075265356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.08,,-1.67,,†,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,-8.0,4.790419161676647,100*b/1 (per unit count),|b/t|,-0.129699632680199,0.077664450706706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.67,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.07,,-1.29,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,-7.000000000000001,5.426356589147288,100*b/1 (per unit count),|b/t|,-0.1005287420017387,0.07792925736568891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.29,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.39,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,-3.0,7.692307692307692,100*b/1 (per unit count),|b/t|,-0.030532915299387793,0.07828952640868665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.01,,-0.08,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,-1.0,12.5,100*b/1 (per unit count),|b/t|,-0.006265960588387915,0.07832450735484893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,No_parking,parking,A_structural,level,count,-0.26,,-2.14,,*,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,-26.0,12.149532710280374,100*b/1 (per unit count),|b/t|,-0.16531155025463876,0.07724838796945735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.14,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,True
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,No_parking,parking,A_structural,level,count,-0.01,,-0.12,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,-1.0,8.333333333333334,100*b/1 (per unit count),|b/t|,-0.009398710251270781,0.07832258542725651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,No_parking,parking,A_structural,level,count,-0.09,,-1.11,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,-9.0,8.108108108108107,100*b/1 (per unit count),|b/t|,-0.08661516872350343,0.07803168353468777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.11,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,No_parking,parking,A_structural,level,count,0.03,,0.38,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,3.0,7.894736842105263,100*b/1 (per unit count),|b/t|,0.029750722089589628,0.07829137391997269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.38,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,No_parking,parking,A_structural,level,count,0.13,,1.76,,†,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,13.0,7.386363636363637,100*b/1 (per unit count),|b/t|,0.1365623523826234,0.07759224567194511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.76,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,No_parking,parking,A_structural,level,count,0.07,,0.9,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,7.000000000000001,7.777777777777778,100*b/1 (per unit count),|b/t|,0.07031893818579951,0.07813215353977723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,No_parking,parking,A_structural,level,count,0.04,,0.43,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,4.0,9.30232558139535,100*b/1 (per unit count),|b/t|,0.03366111292083537,0.0782816579554311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,No_parking,parking,A_structural,level,count,0.05,,0.42,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,5.0,11.904761904761905,100*b/1 (per unit count),|b/t|,0.032879152659595456,0.07828369680856062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,No_parking,parking,A_structural,level,count,0.06,,0.34,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,6.0,17.64705882352941,100*b/1 (per unit count),|b/t|,0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,Seniority_construction,age,A_structural,level,years,0.0,,0.12,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.009398710251270781,0.07832258542725651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.15,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.011748095942956003,0.0783206396197067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.34,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,Seniority_construction,age,A_structural,level,years,-0.0,,-1.0,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.07808688094430304,0.07808688094430304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,Seniority_construction,age,A_structural,level,years,-0.0,,-1.36,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.10592414314269169,0.07788539936962624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.36,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.59,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.046163100276330755,0.07824254284123856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.71,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.05552569778801017,0.07820520815212702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.71,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.31,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.024273919409963114,0.07830296583859069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.01,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.0007832602097241262,0.07832602097241262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.01,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,Seniority_renovation,renovation,A_structural,level,years,-0.01,,-1.42,,,,,high,Years since renovation,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,-1.0,0.7042253521126761,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.11054135663698533,0.0778460258006939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.42,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.31,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.10207121166292979,0.07791695546788532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.31,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.2,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09357880867084666,0.07798234055903888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.21,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09435171832946507,0.07797662671856617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.21,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.89,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.06954141625164179,0.07813642275465368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.89,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.04,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.08119016016629894,0.07806746169836437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.04,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.04,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.08119016016629894,0.07806746169836437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.04,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.3,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.10130006814979362,0.0779231293459951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,Seniority_renovation,renovation,A_structural,level,years,0.0,,0.53,,,,,medium,"Years since renovation ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,0.04147708024010317,0.07825864196245881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.53,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,Structure,roof_materials,A_structural,binary,binary,0.84,,6.52,,***,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",pct_premium,129.72226605451928,29.84276473153554,100*(exp(b - se^2/2) - 1),|b/t|,0.4548107209358334,0.06975624554230575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.52,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,Structure,roof_materials,A_structural,binary,binary,0.46,,4.17,,***,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",pct_premium,57.44651996533576,17.474197436389968,100*(exp(b - se^2/2) - 1),|b/t|,0.31047822711185186,0.07445521033857358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.17,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,Structure,roof_materials,A_structural,binary,binary,0.25,,2.79,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",pct_premium,27.888090960737966,11.505604092184063,100*(exp(b - se^2/2) - 1),|b/t|,0.21349144934754488,0.07652023274105552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.79,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,Structure,roof_materials,A_structural,binary,binary,0.25,,3.17,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",pct_premium,28.00385656456601,10.126383412363891,100*(exp(b - se^2/2) - 1),|b/t|,0.2409765823537718,0.07601784932295641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.17,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,Structure,roof_materials,A_structural,binary,binary,0.27,,3.3,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",pct_premium,30.558718864649027,10.717890960544754,100*(exp(b - se^2/2) - 1),|b/t|,0.2502514693627675,0.07583377859477804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,Structure,roof_materials,A_structural,binary,binary,0.13,,1.52,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",pct_premium,13.467086790340987,9.739979594223739,100*(exp(b - se^2/2) - 1),|b/t|,0.11822069266068613,0.0777767714872935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,Structure,roof_materials,A_structural,binary,binary,0.09,,0.99,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",pct_premium,8.966223121049467,9.94703894277464,100*(exp(b - se^2/2) - 1),|b/t|,0.07731070277406268,0.07809161896369968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.99,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,Structure,roof_materials,A_structural,binary,binary,0.05,,0.43,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",pct_premium,4.418804081737182,12.224082516000282,100*(exp(b - se^2/2) - 1),|b/t|,0.03366111292083537,0.0782816579554311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,Structure,roof_materials,A_structural,binary,binary,0.07,,0.37,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",pct_premium,5.34850099445483,20.29069532102572,100*(exp(b - se^2/2) - 1),|b/t|,0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c1,15,1,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.37,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4729",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c2,15,2,21,Duration,time_on_market,G_market,level,days,0.0,,0.75,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4941",semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.05864343398897361,0.07819124531863149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c3,15,3,21,Duration,time_on_market,G_market,level,days,0.0,,0.31,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.496",semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.024273919409963114,0.07830296583859069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c4,15,4,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.12,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4844",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.009398710251270781,0.07832258542725651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.12,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c5,15,5,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.44,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4753",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.03444301136946857,0.07827957129424674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.44,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c6,15,6,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.07,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4788",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.005482740741241676,0.07832486773202393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c7,15,7,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.06,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4792",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.004699510803775961,0.0783251800629327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c8,15,8,21,Duration,time_on_market,G_market,level,days,0.0,,0.29,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4667",semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.02270869551892764,0.07830584661699187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.29,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T15-c9,15,9,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.1,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface (linear), Duration control; pseudo-R2 = 0.4491",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.007832364246992385,0.07832364246992383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,S_constructed,living_area,A_structural,level,m2,0.03,,7.03,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,3.0,0.42674253200568985,100*b/1 (X per m2->m2),|b/t|,0.4823438024700204,0.06861220518776963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.03,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,S_constructed,living_area,A_structural,level,m2,0.02,,8.59,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,2.0,0.23282887077997672,100*b/1 (X per m2->m2),|b/t|,0.5582300708718622,0.06498603851826103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.59,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,S_constructed,living_area,A_structural,level,m2,0.02,,7.78,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,2.0,0.2570694087403599,100*b/1 (X per m2->m2),|b/t|,0.5203711877421267,0.06688575677919364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.78,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,S_constructed,living_area,A_structural,level,m2,0.02,,8.25,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,2.0,0.24242424242424243,100*b/1 (X per m2->m2),|b/t|,0.5427367311306341,0.06578627044007686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.25,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,S_constructed,living_area,A_structural,level,m2,0.03,,7.54,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,3.0,0.3978779840848806,100*b/1 (X per m2->m2),|b/t|,0.5085182345615921,0.06744273667925624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.54,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,S_constructed,living_area,A_structural,level,m2,0.03,,8.96,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,3.0,0.3348214285714285,100*b/1 (X per m2->m2),|b/t|,0.5744515134501954,0.06411289212613787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.96,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,S_constructed,living_area,A_structural,level,m2,0.02,,6.11,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,2.0,0.3273322422258592,100*b/1 (X per m2->m2),|b/t|,0.4316839863350638,0.07065204359002679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.11,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,S_constructed,living_area,A_structural,level,m2,0.03,,7.48,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,3.0,0.4010695187165775,100*b/1 (X per m2->m2),|b/t|,0.5055088071173947,0.0675813913258549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.48,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,S_constructed,living_area,A_structural,level,m2,0.03,,6.27,,***,,,high,Constructed (gross built) surface in square metres,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,3.0,0.4784688995215311,100*b/1 (X per m2->m2),|b/t|,0.4408143797372346,0.07030532372204698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.27,163.0,m2,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-4.7,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",other,-0.0,0.0,not harmonised,|b/t|,-0.3454668501247132,0.0735035851329177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.7,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-5.31,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",other,-0.0,0.0,not harmonised,|b/t|,-0.3840210284880996,0.07232034434804135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.31,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-4.66,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",other,-0.0,0.0,not harmonised,|b/t|,-0.3428736646410651,0.07357803962254615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.66,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-5.28,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",other,-0.0,0.0,not harmonised,|b/t|,-0.3821690647807465,0.0723805046933232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.28,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-5.13,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",other,-0.0,0.0,not harmonised,|b/t|,-0.37284014903019824,0.07267839162382032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.13,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-6.43,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",other,-0.0,0.0,not harmonised,|b/t|,-0.44980996383152166,0.06995489328639529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.43,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-4.49,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",other,-0.0,0.0,not harmonised,|b/t|,-0.33176525774708154,0.07388981241583106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.49,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-5.3,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",other,-0.0,0.0,not harmonised,|b/t|,-0.38340422060622875,0.07234041898230732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,S_constructed_sq,living_area,A_structural,squared,m2,-0.0,,-4.63,,***,,,medium,"Square of constructed surface (m2^2); printed coefficient rounded to -0.00 ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",other,-0.0,0.0,not harmonised,|b/t|,-0.3409236143318069,0.07363361000686974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.63,163.0,m2,nonstandard_x_transform,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,Comfort,condition,A_structural,binary,binary,0.04,,0.47,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",pct_premium,3.7048244996988,8.8579640356799,100*(exp(b - se^2/2) - 1),|b/t|,0.03678832155190899,0.07827302457852976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,Comfort,condition,A_structural,binary,binary,0.09,,1.59,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",pct_premium,9.24228268001006,6.193439341727605,100*(exp(b - se^2/2) - 1),|b/t|,0.12358371871647124,0.07772560925564229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,Comfort,condition,A_structural,binary,binary,0.14,,2.31,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",pct_premium,14.816320532357551,6.971356356710469,100*(exp(b - se^2/2) - 1),|b/t|,0.1780423628397666,0.07707461594795091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.31,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,Comfort,condition,A_structural,binary,binary,0.14,,2.45,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",pct_premium,14.839733304392254,6.572993136327013,100*(exp(b - se^2/2) - 1),|b/t|,0.18846014453071327,0.07692250797171969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,Comfort,condition,A_structural,binary,binary,0.15,,2.04,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",pct_premium,15.869771247573228,8.542898843590317,100*(exp(b - se^2/2) - 1),|b/t|,0.1577836170801829,0.07734491033342297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.04,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,Comfort,condition,A_structural,binary,binary,0.17,,2.78,,**,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",pct_premium,18.309072510347214,7.248267076419502,100*(exp(b - se^2/2) - 1),|b/t|,0.21276094507182663,0.0765327140546139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.78,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,Comfort,condition,A_structural,binary,binary,0.21,,2.42,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",pct_premium,22.904185767942643,10.705470768219673,100*(exp(b - se^2/2) - 1),|b/t|,0.18623298443757855,0.07695577869321428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.42,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,Comfort,condition,A_structural,binary,binary,0.19,,2.19,,*,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",pct_premium,20.47071706603416,10.491206555017252,100*(exp(b - se^2/2) - 1),|b/t|,0.16906479552348647,0.07719853676871528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.19,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,Comfort,condition,A_structural,binary,binary,0.13,,1.08,,,,,high,"Romanian apartment comfort class: 1 = Comfort 1 (higher standard), 0 = Comfort 2",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",pct_premium,13.060793877251564,13.708119428907482,100*(exp(b - se^2/2) - 1),|b/t|,0.08429107995739295,0.07804729625684532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.08,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,Floor,floor_level,A_structural,level,count,-0.0,,-0.34,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.02662141698558542,0.0782982852517218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,Floor,floor_level,A_structural,level,count,-0.01,,-1.72,,†,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,-1.0,0.5813953488372093,100*b/1 (per unit count),|b/t|,-0.1335146210126104,0.07762477965849443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.72,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,Floor,floor_level,A_structural,level,count,-0.01,,-2.09,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,-1.0,0.47846889952153115,100*b/1 (per unit count),|b/t|,-0.1615511088030177,0.07729718124546302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.09,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,Floor,floor_level,A_structural,level,count,-0.01,,-1.12,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,-1.0,0.8928571428571428,100*b/1 (per unit count),|b/t|,-0.0873895527330593,0.07802638636880294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.12,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,Floor,floor_level,A_structural,level,count,0.0,,0.1,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,0.0,0.0,100*b/1 (per unit count),|b/t|,0.007832364246992385,0.07832364246992383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,Floor,floor_level,A_structural,level,count,-0.0,,-0.58,,,,,medium,"Floor level of the apartment (0 = ground, up to 10) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.045382300157365094,0.07824534509890534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.58,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,Floor,floor_level,A_structural,level,count,-0.01,,-1.45,,,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,-1.0,0.6896551724137931,100*b/1 (per unit count),|b/t|,-0.11284730128956229,0.07782572502728434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.45,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,Floor,floor_level,A_structural,level,count,-0.02,,-2.18,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,-2.0,0.9174311926605503,100*b/1 (per unit count),|b/t|,-0.1683147291176314,0.07720859133836303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.18,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,Floor,floor_level,A_structural,level,count,-0.02,,-2.32,,*,,,high,"Floor level of the apartment (0 = ground, up to 10)",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,-2.0,0.8620689655172415,100*b/1 (per unit count),|b/t|,-0.17878852324638245,0.07706401864068209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.32,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,No_bathrooms,bathrooms,A_structural,level,count,0.07,,1.13,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,7.000000000000001,6.194690265486727,100*b/1 (per unit count),|b/t|,0.08816377835032914,0.07802104278790191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.13,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,No_bathrooms,bathrooms,A_structural,level,count,0.1,,2.45,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,10.0,4.081632653061225,100*b/1 (per unit count),|b/t|,0.18846014453071327,0.07692250797171969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,No_bathrooms,bathrooms,A_structural,level,count,0.09,,1.97,,†,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,9.0,4.568527918781726,100*b/1 (per unit count),|b/t|,0.15249756513080967,0.07740993153848208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.97,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,No_bathrooms,bathrooms,A_structural,level,count,0.1,,2.21,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,10.0,4.524886877828055,100*b/1 (per unit count),|b/t|,0.17056404630215613,0.0771783014941883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.21,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,No_bathrooms,bathrooms,A_structural,level,count,0.13,,2.37,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,13.0,5.485232067510548,100*b/1 (per unit count),|b/t|,0.18251467764361257,0.0770104125078534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.37,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,No_bathrooms,bathrooms,A_structural,level,count,0.15,,3.27,,**,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,15.0,4.587155963302751,100*b/1 (per unit count),|b/t|,0.24811711316950952,0.07587679301819863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.27,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,No_bathrooms,bathrooms,A_structural,level,count,0.14,,2.09,,*,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,14.000000000000002,6.6985645933014375,100*b/1 (per unit count),|b/t|,0.1615511088030177,0.07729718124546302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.09,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,No_bathrooms,bathrooms,A_structural,level,count,0.07,,1.08,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,7.000000000000001,6.481481481481481,100*b/1 (per unit count),|b/t|,0.08429107995739295,0.07804729625684532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.08,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,No_bathrooms,bathrooms,A_structural,level,count,0.11,,1.2,,,,,high,Number of bathrooms,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,11.0,9.166666666666668,100*b/1 (per unit count),|b/t|,0.09357880867084666,0.07798234055903888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.12,,-2.09,,*,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,-12.0,5.741626794258373,100*b/1 (per unit count),|b/t|,-0.1615511088030177,0.07729718124546302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.09,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.86,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,-3.0,3.488372093023256,100*b/1 (per unit count),|b/t|,-0.06720809547985139,0.07814894823238534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.05,,-1.22,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,-5.0,4.0983606557377055,100*b/1 (per unit count),|b/t|,-0.09512445741279114,0.07797086673179603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.22,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.03,,-0.71,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,-3.0,4.225352112676056,100*b/1 (per unit count),|b/t|,-0.05552569778801017,0.07820520815212702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.71,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.08,,-1.6,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,-8.0,5.0,100*b/1 (per unit count),|b/t|,-0.12434899333339904,0.0777181208333744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.02,,-0.55,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,-2.0,3.6363636363636362,100*b/1 (per unit count),|b/t|,-0.04303940638866546,0.07825346616120993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.55,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.01,,-0.09,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,-1.0,11.111111111111112,100*b/1 (per unit count),|b/t|,-0.007049168904005755,0.07832409893339728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.04,,-0.69,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,-4.0,5.797101449275363,100*b/1 (per unit count),|b/t|,-0.053966214667216864,0.07821190531480704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.69,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,No_balconies,terrace_balcony,C_exterior,level,count,-0.06,,-0.68,,,,,high,Number of balconies,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,-6.0,8.823529411764705,100*b/1 (per unit count),|b/t|,-0.05318632428208009,0.07821518276776482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.68,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,No_parking,parking,A_structural,level,count,0.15,,1.58,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,15.0,9.493670886075948,100*b/1 (per unit count),|b/t|,0.12281822353975222,0.0777330528732609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.58,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,No_parking,parking,A_structural,level,count,0.1,,1.49,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,10.0,6.7114093959731544,100*b/1 (per unit count),|b/t|,0.11591905394981178,0.07779802278510858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.49,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,No_parking,parking,A_structural,level,count,0.06,,0.84,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,6.0,7.142857142857142,100*b/1 (per unit count),|b/t|,0.06565193307519313,0.07815706318475373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.84,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,No_parking,parking,A_structural,level,count,0.23,,3.48,,***,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,23.0,6.609195402298851,100*b/1 (per unit count),|b/t|,0.2629803347816215,0.07556906171885676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.48,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,No_parking,parking,A_structural,level,count,0.22,,2.73,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,22.0,8.058608058608058,100*b/1 (per unit count),|b/t|,0.20910308960733248,0.0765945383177042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.73,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,No_parking,parking,A_structural,level,count,0.21,,2.99,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,21.0,7.023411371237458,100*b/1 (per unit count),|b/t|,0.22802507163322808,0.07626256576362142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.99,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,No_parking,parking,A_structural,level,count,0.26,,2.68,,**,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,26.0,9.701492537313433,100*b/1 (per unit count),|b/t|,0.20543643595198066,0.07665538654924652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.68,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,No_parking,parking,A_structural,level,count,0.17,,1.73,,†,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,17.0,9.826589595375722,100*b/1 (per unit count),|b/t|,0.13427691256247734,0.07761671246385972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.73,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,No_parking,parking,A_structural,level,count,0.06,,0.42,,,,,high,Number of parking spaces related to the apartment,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,6.0,14.285714285714285,100*b/1 (per unit count),|b/t|,0.032879152659595456,0.07828369680856062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42,163.0,count,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,Seniority_construction,age,A_structural,level,years,-0.0,,-2.06,,*,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.1592914504015278,0.07732594679685816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.06,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,Seniority_construction,age,A_structural,level,years,-0.0,,-1.15,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.08971174897143021,0.07801021649689584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.15,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,Seniority_construction,age,A_structural,level,years,-0.0,,-1.13,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.08816377835032914,0.07802104278790191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.13,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,Seniority_construction,age,A_structural,level,years,-0.0,,-1.14,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.08893784421580979,0.07801565282088578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.14,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,Seniority_construction,age,A_structural,level,years,-0.0,,-1.59,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.12358371871647124,0.07772560925564229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.59,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,Seniority_construction,age,A_structural,level,years,-0.0,,-0.6,,,,,medium,"Years since construction (age) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,-0.0,0.0,100*b/1 (per year),|b/t|,-0.04694381579433043,0.07823969299055072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,Seniority_construction,age,A_structural,level,years,0.0,,0.03,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.0023497748628643093,0.07832582876214365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,Seniority_construction,age,A_structural,level,years,0.0,,1.58,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.12281822353975222,0.0777330528732609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.58,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,Seniority_construction,age,A_structural,level,years,0.0,,0.6,,,,,medium,"Years since construction (age) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,0.04694381579433043,0.07823969299055072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,163.0,years,year_built_not_age,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,False,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.75,,†,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.1358007794987115,0.07760044542783513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.75,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.96,,†,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.15174133420410427,0.0774190480633185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.96,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.72,,†,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.1335146210126104,0.07762477965849443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.72,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-2.35,,*,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.18102514986083396,0.07703197866418467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.35,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.19,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.09280572978466087,0.0779880082224041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.19,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-1.58,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.12281822353975222,0.0777330528732609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.58,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.99,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07731070277406268,0.07809161896369968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.99,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,Seniority_renovation,renovation,A_structural,level,years,-0.01,,-1.78,,†,,,high,Years since renovation,ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,-1.0,0.5617977528089888,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.13808477074149605,0.07757571389971687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.78,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,Seniority_renovation,renovation,A_structural,level,years,-0.0,,-0.91,,,,,medium,"Years since renovation ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,-0.0,0.0,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07109633197544415,0.07812783733565291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.91,163.0,years,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,Structure,roof_materials,A_structural,binary,binary,0.11,,1.1,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",pct_premium,11.071061035570517,11.16278070458871,100*(exp(b - se^2/2) - 1),|b/t|,0.08584062768258548,0.07803693425689588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,Structure,roof_materials,A_structural,binary,binary,0.21,,2.92,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",pct_premium,23.04917846944616,8.872342212017672,100*(exp(b - se^2/2) - 1),|b/t|,0.2229550763794247,0.07635447821213175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.92,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,Structure,roof_materials,A_structural,binary,binary,0.23,,3.17,,**,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",pct_premium,25.529156851494417,9.131798179299052,100*(exp(b - se^2/2) - 1),|b/t|,0.2409765823537718,0.07601784932295641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.17,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,Structure,roof_materials,A_structural,binary,binary,0.17,,2.44,,*,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",pct_premium,18.243148283466624,8.258271505100907,100*(exp(b - se^2/2) - 1),|b/t|,0.18771807904379892,0.0769336389523766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.44,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,Structure,roof_materials,A_structural,binary,binary,0.05,,0.56,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",pct_premium,4.708910215505191,9.38634907478593,100*(exp(b - se^2/2) - 1),|b/t|,0.04382045179337803,0.07825080677388933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,Structure,roof_materials,A_structural,binary,binary,0.06,,0.83,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",pct_premium,5.906573660360759,7.675926842496575,100*(exp(b - se^2/2) - 1),|b/t|,0.06487367148697346,0.07816104998430536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.83,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,Structure,roof_materials,A_structural,binary,binary,0.04,,0.37,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",pct_premium,3.474634406287591,11.252008369647442,100*(exp(b - se^2/2) - 1),|b/t|,0.028968474220789676,0.07829317356970182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,Structure,roof_materials,A_structural,binary,binary,0.03,,0.31,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",pct_premium,2.5640584356238394,9.972140651163068,100*(exp(b - se^2/2) - 1),|b/t|,0.024273919409963114,0.07830296583859069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,Structure,roof_materials,A_structural,binary,binary,0.04,,0.24,,,,,high,"1 = concrete structure, 0 = brick",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",pct_premium,2.6454992692278356,17.346846236539808,100*(exp(b - se^2/2) - 1),|b/t|,0.018794930270996775,0.07831220946248657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24,163.0,binary,,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,True,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c1,16,1,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.68,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.1; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5574",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.05318632428208009,0.07821518276776482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.68,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c2,16,2,21,Duration,time_on_market,G_market,level,days,0.0,,0.27,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.2; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5769",semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.021143304620203605,0.07830853563038372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c3,16,3,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.01,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.3; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5539",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.0007832602097241262,0.07832602097241262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.01,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c4,16,4,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.86,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.4; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5284",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.06720809547985139,0.07814894823238534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c5,16,5,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.73,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.5; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5185",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.057084774648618365,0.07819832143646352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.73,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c6,16,6,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.52,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.6; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5133",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.040695802340931446,0.07826115834794509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.52,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c7,16,7,21,Duration,time_on_market,G_market,level,days,0.0,,0.02,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as 0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.7; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5152",semi_elasticity,0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,0.0015665189778679186,0.07832594889339593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.02,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c8,16,8,21,Duration,time_on_market,G_market,level,days,-0.0,,-1.29,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.8; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5028",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.1005287420017387,0.07792925736568891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.29,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819,2799534456858542797f23b6cf17bd1f12439819:T16-c9,16,9,21,Duration,time_on_market,G_market,level,days,-0.0,,-0.96,,,,,medium,"Duration until completion of the transaction, in days (control variable) ; coefficient printed as -0.00 (rounded to two decimals), magnitude not recoverable - use t-statistic",ln(Price): log sale price of the apartment,log,quantile,173.0,,t,"Quantile regression at tau = 0.9; all structural attributes with constructed surface + square, Duration control; pseudo-R2 = 0.5006",semi_elasticity,-0.0,0.0,100*b (unit as reported: days; no harmonised unit defined for this characteristic),|b/t|,-0.07498133021571891,0.07810555230804055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.96,163.0,days,unit_not_harmonisable,2019.0,Romania,Europe,High income,2014.0,2018.0,condo_apartment,transaction,journal,False,False,unknown,,2016.0,quantile,journal,False,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnLANDSIZE,lot_size,A_structural,log,m2,0.553,,7.84,,***,,,medium,"Plot area; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,0.553,0.0705357142857143,b (log-log),|b/t|,0.4515409907243725,0.05759451412300669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.84,240.0,m2,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c2,4,Fit Model,9,LnLANDSIZE,lot_size,A_structural,log,m2,0.528,,7.658,,***,,,medium,"Plot area; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.763,t,Insignificant variables dropped,elasticity,0.528,0.06894750587620789,b (log-log),|b/t|,0.4431367458515154,0.057865858690456434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.658,240.0,m2,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnBUILDSIZE,living_area,A_structural,log,m2,0.113,,1.38,,,,,medium,"Building area; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,0.113,0.0818840579710145,b (log-log),|b/t|,0.08872728702514433,0.06429513552546691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.38,240.0,m2,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c2,4,Fit Model,9,LnBUILDSIZE,living_area,A_structural,log,m2,0.164,,2.093,,**,,,medium,"Building area; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.763,t,Insignificant variables dropped,elasticity,0.164,0.07835642618251315,b (log-log),|b/t|,0.13388620184149586,0.06396856275274526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.093,240.0,m2,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnNUMROOM,bedrooms,A_structural,log,count,0.094,,1.179,,,,,medium,"Number of rooms (all rooms); Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,0.094,0.07972858354537743,b (log-log),|b/t|,0.07588468415273403,0.06436359979027483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.179,240.0,count,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,WSTRUCTDUM,roof_materials,A_structural,binary,binary,-0.067,,-1.088,,,,,medium,"Wall structure dummy; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,pct_premium,-6.657634460870154,5.759014567055517,100*(exp(b - se^2/2) - 1),|b/t|,-0.07005753929300708,0.06439112067371973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.088,240.0,binary,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnDISSCHOOL,school_distance,D_location,log,m,-0.071,,-1.809,,*,,,medium,"Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,-0.071,0.0392482034273079,b (log-log),|b/t|,-0.11598239557868535,0.0641140937416724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.809,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c2,4,Fit Model,9,LnDISSCHOOL,school_distance,D_location,log,m,-0.068,,-1.91,,**,,,medium,"Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.763,t,Insignificant variables dropped,elasticity,-0.068,0.0356020942408377,b (log-log),|b/t|,-0.1223634892875675,0.06406465407726047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.91,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnDISHPITAL,hospital_distance,D_location,log,m,-0.085,,-1.98,,**,,,medium,"Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,-0.085,0.042929292929292935,b (log-log),|b/t|,-0.12677719205089263,0.0640288848741882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.98,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c2,4,Fit Model,9,LnDISHPITAL,hospital_distance,D_location,log,m,-0.075,,-1.885,,*,,,medium,"Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.763,t,Insignificant variables dropped,elasticity,-0.075,0.03978779840848806,b (log-log),|b/t|,-0.12078539175196361,0.06407713090289847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.885,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnDISSPMARKET,commercial_distance,D_location,log,m,0.023,,0.56,,,,,medium,"Distance from supermarket; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,0.023,0.041071428571428564,b (log-log),|b/t|,0.036124251091907554,0.0645075912355492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnDISREST,neighbourhood_amenities,F_neighbourhood,log,m,-0.023,,-0.652,,,,,medium,"Distance from restaurant; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,-0.023,0.0352760736196319,b (log-log),|b/t|,-0.04204919533573716,0.06449263088303245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.652,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnDISCITY,cbd_distance,D_location,log,m,0.054,,1.274,,,,,medium,"Distance from city; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,0.054,0.0423861852433281,b (log-log),|b/t|,0.08195967424153908,0.0643325543497167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.274,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c2,4,Fit Model,9,LnDISCITY,cbd_distance,D_location,log,m,0.064,,1.538,,,,,medium,"Distance from city; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.763,t,Insignificant variables dropped,elasticity,0.064,0.04161248374512354,b (log-log),|b/t|,0.09879181991336715,0.06423395312962754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.538,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnDISMSTREET,highway_distance,D_location,log,m,0.024,,1.03,,,,,medium,"Distance from main street; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,0.024,0.02330097087378641,b (log-log),|b/t|,0.06633975111753143,0.06440752535682664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.03,240.0,m,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,GARDENDUM,garden,C_exterior,binary,binary,0.129,,1.673,,*,,,medium,"Dummy for close to garden; Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,pct_premium,13.431308445239454,8.77238649237175,100*(exp(b - se^2/2) - 1),|b/t|,0.10736742996573884,0.06417658694903695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.673,240.0,binary,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c1,4,Full Model,9,LnCO,air_pollution,E_environment,log,ug/m3,-1.305,,-13.115,,***,,,medium,"Carbon monoxide concentration (2010 ambient monitoring); Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.766,t,All variables,elasticity,-1.305,0.0995043842927945,b (log-log),|b/t|,-0.6461271453261794,0.049266271088538274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.115,240.0,ug/m3,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1,281b355bd08bd1b8b228829d9c97b819d0d2a4d1:T4-c2,4,Fit Model,9,LnCO,air_pollution,E_environment,log,ug/m3,-1.375,,-15.066,,***,,,medium,"Carbon monoxide concentration (2010 ambient monitoring); Parentheses contain t-statistics (not stated explicitly; inferred from magnitudes); star legend not printed, conventional ***1% **5% *10% assumed","ln PPRICE (house sales price, Rupiah)",log,OLS,250.0,0.763,t,Insignificant variables dropped,elasticity,-1.375,0.0912651002256737,b (log-log),|b/t|,-0.6971830133824944,0.046275256430538585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.066,240.0,ug/m3,,2013.0,Indonesia,Asia,Upper middle income,,,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Temperature index,urban_heat,E_environment,level,index,-0.001,,,,,,,medium,"city temperature discomfort index (Zheng et al. 2009); smaller = more comfortable; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,-0.1,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,421650.0,index,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Temperature index,urban_heat,E_environment,level,index,-0.005,,,,,,,medium,"city temperature discomfort index (Zheng et al. 2009); smaller = more comfortable; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,-0.5,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,227584.0,index,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Mean monthly precipitation,other,other,level,mm_per_month,0.001,,,,,,,medium,"climate variable; no taxonomy leaf; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,0.1,,100*b (unit as reported: mm_per_month; no harmonised unit defined for this characteristic),,,,,,421650.0,mm_per_month,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Mean monthly precipitation,other,other,level,mm_per_month,0.002,,,,***,,,medium,"climate variable; no taxonomy leaf; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,0.2,,100*b (unit as reported: mm_per_month; no harmonised unit defined for this characteristic),,,,,,227584.0,mm_per_month,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Mean daily sunshine duration,sunlight,E_environment,level,hours,0.056,0.02,,,***,,,high,city average daily sunshine hours; city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%,"ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,5.6000000000000005,2.0,100*b (unit as reported: hours; no harmonised unit defined for this characteristic),reported,0.0043119919509328735,0.0015399971253331692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,421650.0,hours,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,True,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Mean daily sunshine duration,sunlight,E_environment,level,hours,0.078,0.02,,,***,,,high,city average daily sunshine hours; city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%,ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,7.8,2.0,100*b (unit as reported: hours; no harmonised unit defined for this characteristic),reported,0.008174839446384566,0.0020961126785601454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9,227584.0,hours,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,True,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Annual mean concentration of PM2.5,air_pollution,E_environment,level,ug_m3,-0.0,,,,,,,medium,"city annual mean PM2.5 (Dalhousie gridded data); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,-0.0,,100*b (unit as reported: ug_m3; pollution unit not ug/m3),,,,,,421650.0,ug_m3,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Annual mean concentration of PM2.5,air_pollution,E_environment,level,ug_m3,-0.002,,,,,,,medium,"city annual mean PM2.5 (Dalhousie gridded data); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,-0.2,,100*b (unit as reported: ug_m3; pollution unit not ug/m3),,,,,,227584.0,ug_m3,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Green coverage ratio,green_space,E_environment,level,percent,0.002,,,,,,,medium,"greened land area / built land area of city, %; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,0.2,,"100*b (unit as reported: percent; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,421650.0,percent,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Green coverage ratio,green_space,E_environment,level,percent,0.002,,,,,,,medium,"greened land area / built land area of city, %; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,0.2,,"100*b (unit as reported: percent; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,227584.0,percent,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Coastal city,waterfront,C_exterior,binary,binary,0.178,0.04,,,***,,,high,city-level coastal location dummy (not property frontage); city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%,"ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,pct_premium,19.38698432199777,4.779301284939203,100*(exp(b - se^2/2) - 1),reported,0.00685288999963231,0.0015399752808162497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.449999999999999,421650.0,binary,,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,True,True,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Coastal city,waterfront,C_exterior,binary,binary,0.019,0.05,,,,,,high,city-level coastal location dummy (not property frontage); city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%,ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,pct_premium,1.7908467460638011,5.095908243087041,100*(exp(b - se^2/2) - 1),reported,0.0007965491814987221,0.0020961820565755847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37999999999999995,227584.0,binary,,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,True,True,True
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Population density,density,F_neighbourhood,level,100_persons_per_km2,-0.002,,,,,,,medium,"city population density; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,-0.2,,100*b (unit as reported: 100_persons_per_km2; no harmonised unit defined for this characteristic),,,,,,421650.0,100_persons_per_km2,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Population density,density,F_neighbourhood,level,100_persons_per_km2,-0.003,,,,,,,medium,"city population density; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,-0.3,,100*b (unit as reported: 100_persons_per_km2; no harmonised unit defined for this characteristic),,,,,,227584.0,100_persons_per_km2,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Population size,other,other,level,10000_persons,-0.001,,,,***,,,medium,"city population size (control); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,-0.1,,100*b (unit as reported: 10000_persons; no harmonised unit defined for this characteristic),,,,,,421650.0,10000_persons,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Population size,other,other,level,10000_persons,-0.0,,,,,,,medium,"city population size (control); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,-0.0,,100*b (unit as reported: 10000_persons; no harmonised unit defined for this characteristic),,,,,,227584.0,10000_persons,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Number of theatres,neighbourhood_amenities,F_neighbourhood,level,per_100000_pop,0.055,0.02,,,***,,,high,"theatres per 100,000 population; city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,5.5,2.0,100*b (unit as reported: per_100000_pop; no harmonised unit defined for this characteristic),reported,0.004234993488226081,0.0015399976320822114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,421650.0,per_100000_pop,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,True,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Number of theatres,neighbourhood_amenities,F_neighbourhood,level,per_100000_pop,0.108,0.03,,,***,,,high,"theatres per 100,000 population; city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,10.8,3.0,100*b (unit as reported: per_100000_pop; no harmonised unit defined for this characteristic),reported,0.007546042942239886,0.0020961230395110794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6,227584.0,per_100000_pop,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,True,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Number of hospital bed,public_services,F_neighbourhood,level,per_10000_pop,0.001,,,,,,,medium,"hospital beds per 10,000 population; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,0.1,,100*b (unit as reported: per_10000_pop; no harmonised unit defined for this characteristic),,,,,,421650.0,per_10000_pop,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Number of hospital bed,public_services,F_neighbourhood,level,per_10000_pop,0.003,,,,***,,,medium,"hospital beds per 10,000 population; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,0.3,,100*b (unit as reported: per_10000_pop; no harmonised unit defined for this characteristic),,,,,,227584.0,per_10000_pop,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Teacher-student ratio in high schools,school_quality,F_neighbourhood,level,per_mille,0.001,,,,,,,medium,"teacher-student ratio in high schools (per mille); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,0.1,,100*b (unit as reported: per_mille; no harmonised unit defined for this characteristic),,,,,,421650.0,per_mille,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Teacher-student ratio in high schools,school_quality,F_neighbourhood,level,per_mille,-0.003,,,,,,,medium,"teacher-student ratio in high schools (per mille); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,-0.3,,100*b (unit as reported: per_mille; no harmonised unit defined for this characteristic),,,,,,227584.0,per_mille,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Number of higher education institutions,university_distance,D_location,level,count,0.001,,,,,,,medium,"number of colleges/universities in city (count, not distance); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,421650.0,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Number of higher education institutions,university_distance,D_location,level,count,0.001,,,,,,,medium,"number of colleges/universities in city (count, not distance); SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,227584.0,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Road area ratio,other,other,level,percent,-0.003,,,,,,,medium,"total road area / urban area, %; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,-0.3,,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),,,,,,421650.0,percent,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Road area ratio,other,other,level,percent,0.006,,,,,,,medium,"total road area / urban area, %; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,0.6,,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),,,,,,227584.0,percent,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Number of airline routes,other,other,level,count,0.003,,,,***,,,medium,"inter-city air accessibility; no taxonomy leaf; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,semi_elasticity,0.3,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,421650.0,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Number of airline routes,other,other,level,count,0.002,,,,*,,,medium,"inter-city air accessibility; no taxonomy leaf; SE printed as (0.00) (rounded to 2 decimals, unusable) -> se set to null; city-level amenity; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,semi_elasticity,0.2,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,227584.0,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c1,2,1,27,Capital city,other,other,binary,binary,0.163,0.06,,,***,,,high,"1 = provincial capital, sub-provincial or centrally administered city; city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%","ln(housing price), self-reported purchase cost of owner-occupied unit (CNY)",log,OLS,421660.0,0.734,se,owner-occupied units; housing attributes and 432 attribute-group FE included (not reported); SE clustered by city,pct_premium,17.491992930290003,7.062220138130802,100*(exp(b - se^2/2) - 1),reported,0.004183661137822438,0.0015399979648426153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.716666666666667,421650.0,binary,,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0,28afe169f4e2851e590b8152147dbd5bb4ee90f0:T2-c2,2,2,27,Capital city,other,other,binary,binary,0.107,0.06,,,*,,,high,"1 = provincial capital, sub-provincial or centrally administered city; city-level amenity; SE clustered by city; stars * 10% ** 5% *** 1%",ln(monthly housing rent) (CNY),log,OLS,227594.0,0.542,se,rental units; housing attributes and group FE included (not reported); SE clustered by city,pct_premium,11.093277469346962,6.677605526875954,100*(exp(b - se^2/2) - 1),reported,0.0037381664015175235,0.0020961680756173026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7833333333333334,227584.0,binary,,2021.0,China,Asia,Upper middle income,2005.0,2005.0,mixed,transaction,working_paper,False,False,clustered,,2005.0,ols,other,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,minDist,other,other,level,m,2.197e-05,2.881e-06,,,***,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy)","ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.002197,0.0002881,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.04551603337541895,0.005968670557787073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.625824366539397,28012.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Count500,other,other,level,count,0.003,0.003,,,,,,high,Number of dispensaries within 500 m,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.3,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005974756210647855,0.005974756210647855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Count1000,other,other,level,count,-0.004,0.002,,,**,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-0.4,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.011948872617308781,0.005974436308654391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Count2000,other,other,level,count,0.004,0.001,,,***,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.4,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.02389262885734545,0.005973157214336363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Age,age,A_structural,level,years,-0.012,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-1.2,0.04,100*b/1 (per year),reported,-0.17643396214858012,0.005881132071619336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Age2,age,A_structural,squared,years,8.527e-05,4.01e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,8.527e-05,4.01e-06,not harmonised,reported,0.12603832180421126,0.005927215555704083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.264339152119703,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,SQFT,living_area,A_structural,level,sqft,0.001,1e-05,,,***,,,high,Living area in square feet; SQFT printed as 0.001 (rounded),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,1.0763910416709723,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.5129081969303817,0.0051290819693038175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),100.0,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,SQFT2,living_area,A_structural,squared,sqft,-3.323e-08,1.372e-09,,,***,,,high,Square of living area (sqft),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,-3.323e-08,1.372e-09,not harmonised,reported,-0.1432200221363706,0.005913267239575699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.220116618075803,28012.0,sqft,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Baths,bathrooms,A_structural,level,count,0.069,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,6.9,0.5,100*b/1 (per unit count),reported,0.08217424913693558,0.005954655734560548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.8,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Density,density,F_neighbourhood,level,count,0.0003,0.0001,,,**,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.03,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017921709758504354,0.005973903252834787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Dist to CBD (km),cbd_distance,D_location,level,km,-0.0007,0.0004,,,*,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-0.06999999999999999,0.04,100*b/1 (X per km->km),reported,-0.010455438476888016,0.0059745362725074375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.75,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c1,4,1 (Benchmark),16,Neighborhood,income,F_neighbourhood,level,currency,3.823e-07,1.721e-08,,,***,,,high,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth)","ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,3.823e-05,1.721e-06,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.13157077867029454,0.005922922053140908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.213829169087738,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,minDist,other,other,level,m,1.467e-05,2.598e-06,,,***,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.001467,0.00025979999999999997,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.03371878218435849,0.00597146531117678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.646651270207852,28012.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Age,age,A_structural,level,years,-0.012,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-1.2,0.04,100*b/1 (per year),reported,-0.17643396214858012,0.005881132071619336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Age2,age,A_structural,squared,years,8.834e-05,3.975e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,8.834e-05,3.975e-06,not harmonised,reported,0.13162939049444894,0.005922875562773767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.22389937106918,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,SQFT,living_area,A_structural,level,sqft,0.001,1e-05,,,***,,,high,Living area in square feet; SQFT printed as 0.001 (rounded),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,1.0763910416709723,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.5129081969303817,0.0051290819693038175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),100.0,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,SQFT2,living_area,A_structural,squared,sqft,-3.328e-08,1.373e-09,,,***,,,high,Square of living area (sqft),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,-3.328e-08,1.373e-09,not harmonised,reported,-0.14332877149071754,0.005913173174782307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.23889293517844,28012.0,sqft,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Baths,bathrooms,A_structural,level,count,0.069,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,6.9,0.5,100*b/1 (per unit count),reported,0.08217424913693558,0.005954655734560548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.8,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Density,density,F_neighbourhood,level,count,0.0003,0.0001,,,*,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.03,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017921709758504354,0.005973903252834787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Dist to CBD (km),cbd_distance,D_location,level,km,-0.001,0.0004,,,***,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-0.1,0.04,100*b/1 (X per km->km),reported,-0.014935491039694328,0.005974196415877732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c2,4,2 (No counts),16,Neighborhood,income,F_neighbourhood,level,currency,3.736e-07,1.713e-08,,,***,,,high,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,3.736e-05,1.713e-06,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.12921742908335918,0.005924771306739675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.8096906012843,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,minDist,other,other,level,m,1.135e-06,7.023e-06,,,,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.0001135,0.0007023,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.0009656081703140719,0.005974860070586544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16161184678912144,28012.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,minDist2,other,other,squared,m,3.041e-09,1.466e-09,,,**,,,high,Square of minDist (metres),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,3.041e-09,1.466e-09,not harmonised,reported,0.012393016772849519,0.0059744040082201225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0743519781718964,28012.0,m,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Age,age,A_structural,level,years,-0.012,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-1.2,0.04,100*b/1 (per year),reported,-0.17643396214858012,0.005881132071619336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Age2,age,A_structural,squared,years,8.786e-05,3.981e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,8.786e-05,3.981e-06,not harmonised,reported,0.13073251628722526,0.005923584649891234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.069831700577748,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,SQFT,living_area,A_structural,level,sqft,0.001,1e-05,,,***,,,high,Living area in square feet; SQFT printed as 0.001 (rounded),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,1.0763910416709723,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.5129081969303817,0.0051290819693038175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),100.0,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,SQFT2,living_area,A_structural,squared,sqft,-3.318e-08,1.374e-09,,,***,,,high,Square of living area (sqft),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,-3.318e-08,1.374e-09,not harmonised,reported,-0.14280501837885748,0.005913625535037677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.148471615720528,28012.0,sqft,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Baths,bathrooms,A_structural,level,count,0.069,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,6.9,0.5,100*b/1 (per unit count),reported,0.08217424913693558,0.005954655734560548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.8,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Density,density,F_neighbourhood,level,count,0.0003,0.0001,,,**,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.03,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017921709758504354,0.005973903252834787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Dist to CBD (km),cbd_distance,D_location,level,km,-0.001,0.0004,,,***,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-0.1,0.04,100*b/1 (X per km->km),reported,-0.014935491039694328,0.005974196415877732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c3,4,3 (No counts & minDist2),16,Neighborhood,income,F_neighbourhood,level,currency,3.3739e-07,1.713e-08,,,***,,,high,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,3.3739e-05,1.713e-06,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.1168735494136554,0.005933915947289241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.6958552247519,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,minDist,other,other,level,m,-1.613e-05,7.761e-06,,,**,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy)","ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-0.001613,0.0007761,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,-0.012416841665671833,0.005974402242236771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0783404200489626,28012.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,minDist2,other,other,squared,m,2.437e-09,1.47e-09,,,*,,,high,Square of minDist (metres),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,2.437e-09,1.47e-09,not harmonised,reported,0.009904779948577852,0.005974569767915242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6578231292517007,28012.0,m,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,minDist × Neighborhood,other,other,other,m,8.908e-11,1.707e-11,,,***,,,high,Interaction minDist x historical neighbourhood value index (USD),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,8.908e-11,1.707e-11,not harmonised,reported,0.03116474828655398,0.005971960633716618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.21851200937317,28012.0,m,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Age,age,A_structural,level,years,-0.012,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-1.2,0.04,100*b/1 (per year),reported,-0.17643396214858012,0.005881132071619336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Age2,age,A_structural,squared,years,8.726e-05,3.981e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,8.726e-05,3.981e-06,not harmonised,reported,0.12985484304986308,0.005924273781589559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.91911580005024,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,SQFT,living_area,A_structural,level,sqft,0.001,1e-05,,,***,,,high,Living area in square feet; SQFT printed as 0.001 (rounded),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,1.0763910416709723,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.5129081969303817,0.0051290819693038175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),100.0,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,SQFT2,living_area,A_structural,squared,sqft,-3.355e-08,1.375e-09,,,***,,,high,Square of living area (sqft),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",other,-3.355e-08,1.375e-09,not harmonised,reported,-0.14426166347548727,0.0059123632571921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.400000000000002,28012.0,sqft,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Baths,bathrooms,A_structural,level,count,0.068,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,6.800000000000001,0.5,100*b/1 (per unit count),reported,0.08099118704793636,0.005955234341760025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.600000000000001,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Density,density,F_neighbourhood,level,count,0.0003,0.0001,,,**,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,0.03,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017921709758504354,0.005973903252834787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Dist to CBD (km),cbd_distance,D_location,level,km,-0.001,0.0004,,,***,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,-0.1,0.04,100*b/1 (X per km->km),reported,-0.014935491039694328,0.005974196415877732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T4-c4,4,4 (No counts & Interaction),16,Neighborhood,income,F_neighbourhood,level,currency,2.815e-07,2.463e-08,,,***,,,high,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth)","ln(Price/100), sale price USD",log,OLS,28022.0,0.64,se,"Pooled 4-county benchmark sample (Comanche, Oklahoma, Wagoner, Washington), sales after July 2020-2023; year and county dummies not extracted",semi_elasticity,2.8150000000000003e-05,2.463e-06,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.06812894779227253,0.005960980405412691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.42915144133171,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Count500,other,other,level,count,0.0001,0.003,,,,,,high,Number of dispensaries within 500 m,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.01,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0001991620912522535,0.005974862737567606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03333333333333333,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Count1000,other,other,level,count,-0.005,0.002,,,***,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-0.5,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.014935491039694328,0.005974196415877732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Count2000,other,other,level,count,0.003,0.001,,,***,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.3,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017921709758504357,0.005973903252834787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Age,age,A_structural,level,years,-0.013,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-1.3,0.04,100*b/1 (per year),reported,-0.1906223931913883,0.00586530440588887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.5,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Age2,age,A_structural,squared,years,9e-05,4e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,other,9e-05,4e-06,not harmonised,reported,0.13323584547750486,0.005921593132333549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.500000000000004,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,SQFT,living_area,A_structural,level,sqft,0.0006,1e-05,,,***,,,high,Living area in square feet,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.6458346250025833,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.337462301239245,0.00562437168732075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.99999999999999,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Baths,bathrooms,A_structural,level,count,0.07,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,7.000000000000001,0.5,100*b/1 (per unit count),reported,0.08335696394016968,0.005954068852869262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.000000000000002,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Density,density,F_neighbourhood,level,count,6e-05,0.0001,,,,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.006,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.003584894677834441,0.0059748244630574016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),19,Dist to CBD (km),cbd_distance,D_location,level,km,-0.0002,0.0004,,,,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-0.02,0.04,100*b/1 (X per km->km),reported,-0.002987418097087927,0.005974836194175853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c1,5,1 (All),20,Neighborhood,income,F_neighbourhood,level,currency,4e-07,,,,***,,,medium,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth); printed as 0.0000004 (0.0000000): SE rounded to zero in print, set se=null","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,3.9999999999999996e-05,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Count500,other,other,level,count,0.001,0.003,,,,,,high,Number of dispensaries within 500 m,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.1,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.001991617002097602,0.0059748510062928055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3333333333333333,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Age,age,A_structural,level,years,-0.012,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-1.2,0.04,100*b/1 (per year),reported,-0.17643396214858012,0.005881132071619336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Age2,age,A_structural,squared,years,9e-05,4e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,other,9e-05,4e-06,not harmonised,reported,0.13323584547750486,0.005921593132333549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.500000000000004,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,SQFT,living_area,A_structural,level,sqft,0.0006,1e-05,,,***,,,high,Living area in square feet,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.6458346250025833,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.337462301239245,0.00562437168732075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.99999999999999,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Baths,bathrooms,A_structural,level,count,0.07,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,7.000000000000001,0.5,100*b/1 (per unit count),reported,0.08335696394016968,0.005954068852869262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.000000000000002,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Density,density,F_neighbourhood,level,count,8e-05,0.0001,,,,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.008,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0047798356818723325,0.005974794602340415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),19,Dist to CBD (km),cbd_distance,D_location,level,km,-0.001,0.0004,,,*,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-0.1,0.04,100*b/1 (X per km->km),reported,-0.014935491039694328,0.005974196415877732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c2,5,2 (Count500),20,Neighborhood,income,F_neighbourhood,level,currency,4e-07,,,,***,,,medium,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth); printed as 0.0000004 (0.0000000): SE rounded to zero in print, set se=null","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,3.9999999999999996e-05,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Count1000,other,other,level,count,0.0004,0.001,,,,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.04,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0023899383169660297,0.005974845792415075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Age,age,A_structural,level,years,-0.012,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-1.2,0.04,100*b/1 (per year),reported,-0.17643396214858012,0.005881132071619336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Age2,age,A_structural,squared,years,9e-05,4e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,other,9e-05,4e-06,not harmonised,reported,0.13323584547750486,0.005921593132333549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.500000000000004,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,SQFT,living_area,A_structural,level,sqft,0.0006,1e-05,,,***,,,high,Living area in square feet,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.6458346250025833,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.337462301239245,0.00562437168732075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.99999999999999,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Baths,bathrooms,A_structural,level,count,0.07,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,7.000000000000001,0.5,100*b/1 (per unit count),reported,0.08335696394016968,0.005954068852869262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.000000000000002,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Density,density,F_neighbourhood,level,count,8e-05,0.0001,,,,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.008,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0047798356818723325,0.005974794602340415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),19,Dist to CBD (km),cbd_distance,D_location,level,km,-0.001,0.0004,,,*,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-0.1,0.04,100*b/1 (X per km->km),reported,-0.014935491039694328,0.005974196415877732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c3,5,3 (Count1000),20,Neighborhood,income,F_neighbourhood,level,currency,4e-07,,,,***,,,medium,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth); printed as 0.0000004 (0.0000000): SE rounded to zero in print, set se=null","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,3.9999999999999996e-05,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Count2000,other,other,level,count,0.002,0.0004,,,***,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.2,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.029860992162758483,0.0059721984325516965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Age,age,A_structural,level,years,-0.013,0.0004,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-1.3,0.04,100*b/1 (per year),reported,-0.1906223931913883,0.00586530440588887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.5,28012.0,years,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Age2,age,A_structural,squared,years,9e-05,4e-06,,,***,,,high,Age squared,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,other,9e-05,4e-06,not harmonised,reported,0.13323584547750486,0.005921593132333549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.500000000000004,28012.0,years,nonstandard_x_transform,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,SQFT,living_area,A_structural,level,sqft,0.0006,1e-05,,,***,,,high,Living area in square feet,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.6458346250025833,0.010763910416709722,100*b/0.092903 (X per sqft->m2),reported,0.337462301239245,0.00562437168732075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.99999999999999,28012.0,sqft,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Beds,bedrooms,A_structural,level,count,-0.043,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-4.3,0.5,100*b/1 (per unit count),reported,-0.05131612032022764,0.005966990734910192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.6,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Baths,bathrooms,A_structural,level,count,0.07,0.005,,,***,,,high,,"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,7.000000000000001,0.5,100*b/1 (per unit count),reported,0.08335696394016968,0.005954068852869262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.000000000000002,28012.0,count,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Density,density,F_neighbourhood,level,count,6e-05,0.0001,,,,,,high,"Number of home sales within 500 m radius, Jan 2016-Jul 2018 (local sales density, not population density)","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,0.006,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.003584894677834441,0.0059748244630574016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,28012.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),19,Dist to CBD (km),cbd_distance,D_location,level,km,-0.0002,0.0004,,,,,,high,Distance to nearest CBD (downtown Oklahoma City or Tulsa),"ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,-0.02,0.04,100*b/1 (X per km->km),reported,-0.002987418097087927,0.005974836194175853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,28012.0,km,,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,True,True,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T5-c4,5,4 (Count2000),20,Neighborhood,income,F_neighbourhood,level,currency,4e-07,,,,***,,,medium,"Historical neighbourhood value index: mean sale price (USD) of homes within 500 m sold Jan 2016-Jul 2018 (neighbourhood quality proxy, closest leaf = neighbourhood income/wealth); printed as 0.0000004 (0.0000000): SE rounded to zero in print, set se=null","ln(Price/100), sale price USD",log,OLS,28022.0,0.639,se,Pooled 4-county sample without minDist; count variables jointly (col 1) or one at a time (cols 2-4); SQFT2 printed as -0.0000000*** (0.00000) and not extracted,semi_elasticity,3.9999999999999996e-05,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,28012.0,currency,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c1,6,1 (Comanche),22,minDist,other,other,level,m,-0.00013,3e-05,,,***,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy); county-specific regression; structural controls not printed","ln(Price/100), sale price USD",log,OLS,4068.0,0.246,se,"Comanche County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.013,0.003,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,-0.06786776979773748,0.015661793030247114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.333333333333333,4058.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c1,6,1 (Comanche),22,Count500,other,other,level,count,-0.005,0.011,,,,,,high,Number of dispensaries within 500 m; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,4068.0,0.246,se,"Comanche County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.5,1.0999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.007135267216668838,0.015697587876671443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4545454545454546,4058.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c1,6,1 (Comanche),22,Count1000,other,other,level,count,0.004,0.006,,,,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,4068.0,0.246,se,"Comanche County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.4,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.010464751943385881,0.015697127915078823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,4058.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c1,6,1 (Comanche),22,Count2000,other,other,level,count,-0.003,0.003,,,,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,4068.0,0.246,se,"Comanche County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.3,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.015696053644933705,0.015696053644933705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,4058.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c2,6,2 (Wagoner),22,minDist,other,other,level,m,1e-05,1e-05,,,,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy); county-specific regression; structural controls not printed","ln(Price/100), sale price USD",log,OLS,396.0,0.536,se,"Wagoner County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.001,0.001,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.05083285677753489,0.05083285677753489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,386.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c2,6,2 (Wagoner),22,Count500,other,other,level,count,0.054,0.055,,,,,,high,Number of dispensaries within 500 m; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,396.0,0.536,se,"Wagoner County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,5.4,5.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.049910946643127375,0.050835223432814915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9818181818181818,386.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c2,6,2 (Wagoner),22,Count1000,other,other,level,count,-0.008,0.027,,,,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,396.0,0.536,se,"Wagoner County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.8,2.7,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.015079369680064753,0.050892872670218546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2962962962962963,386.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c2,6,2 (Wagoner),22,Count2000,other,other,level,count,-0.003,0.011,,,,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,396.0,0.536,se,"Wagoner County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.3,1.0999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.013880115439857931,0.05089375661281241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.27272727272727276,386.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c3,6,3 (Washington),22,minDist,other,other,level,m,-1e-05,2e-05,,,,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy); county-specific regression; structural controls not printed","ln(Price/100), sale price USD",log,OLS,286.0,0.772,se,"Washington County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.001,0.002,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,-0.030082841879809342,0.060165683759618685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,276.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c3,6,3 (Washington),22,Count500,other,other,level,count,-0.008,0.035,,,,,,high,Number of dispensaries within 500 m; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,286.0,0.772,se,"Washington County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.8,3.5000000000000004,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.013757081211080631,0.06018723029847776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22857142857142856,276.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c3,6,3 (Washington),22,Count1000,other,other,level,count,0.001,0.013,,,,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,286.0,0.772,se,"Washington County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.1,1.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.004630175485818866,0.06019228131564526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.07692307692307693,276.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c3,6,3 (Washington),22,Count2000,other,other,level,count,-0.007,0.008,,,,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,286.0,0.772,se,"Washington County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.7000000000000001,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.05259591062298201,0.060109612140550876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.875,276.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c4,6,4 (Oklahoma),22,minDist,other,other,level,m,2e-05,3e-06,,,***,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy); county-specific regression; structural controls not printed","ln(Price/100), sale price USD",log,OLS,21463.0,0.732,se,"Oklahoma County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.002,0.00030000000000000003,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.045468991669831586,0.0068203487504747366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.666666666666667,21453.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c4,6,4 (Oklahoma),22,Count500,other,other,level,count,0.001,0.003,,,,,,high,Number of dispensaries within 500 m; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,21463.0,0.732,se,"Oklahoma County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.1,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.00227579743831842,0.006827392314955261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3333333333333333,21453.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c4,6,4 (Oklahoma),22,Count1000,other,other,level,count,-0.008,0.002,,,***,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,21463.0,0.732,se,"Oklahoma County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.8,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.027299461685516283,0.006824865421379071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,21453.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c4,6,4 (Oklahoma),22,Count2000,other,other,level,count,0.006,0.001,,,***,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,21463.0,0.732,se,"Oklahoma County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.6,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04093013220675759,0.006821688701126266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,21453.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c5,6,5 (Payne),22,minDist,other,other,level,m,-5e-05,2e-05,,,**,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy); county-specific regression; structural controls not printed","ln(Price/100), sale price USD",log,OLS,622.0,0.307,se,"Payne County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.005,0.002,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,-0.10054441374166079,0.040217765496664316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,612.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c5,6,5 (Payne),22,Count500,other,other,level,count,0.023,0.11,,,,,,high,Number of dispensaries within 500 m; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,622.0,0.307,se,"Payne County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,2.3,11.0,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.008451697180750532,0.04042116042967646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.20909090909090908,612.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c5,6,5 (Payne),22,Count1000,other,other,level,count,0.012,0.077,,,,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,622.0,0.307,se,"Payne County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,1.2,7.7,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0062995016267683515,0.040421802105096924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15584415584415584,612.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c5,6,5 (Payne),22,Count2000,other,other,level,count,-0.044,0.015,,,***,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,622.0,0.307,se,"Payne County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-4.3999999999999995,1.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.11774811862239296,0.04014140407581578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.933333333333333,612.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c6,6,6 (Tulsa),22,minDist,other,other,level,m,0.00011,1e-05,,,***,,,high,"Printed name: minDist = distance in metres to nearest medical marijuana dispensary (disamenity/amenity, not in taxonomy); county-specific regression; structural controls not printed","ln(Price/100), sale price USD",log,OLS,24233.0,0.197,se,"Tulsa County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,0.011000000000000001,0.001,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.07050123349104927,0.006409203044640844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,24223.0,m,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c6,6,6 (Tulsa),22,Count500,other,other,level,count,0.019,0.006,,,***,,,high,Number of dispensaries within 500 m; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,24233.0,0.197,se,"Tulsa County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,1.9,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.020342227664414226,0.006423861367709756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1666666666666665,24223.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c6,6,6 (Tulsa),22,Count1000,other,other,level,count,0.011,0.003,,,***,,,high,Number of dispensaries within 500-1000 m band (Table 4/5 'All'); within 1000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,24233.0,0.197,se,"Tulsa County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,1.0999999999999999,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.023552498048130602,0.006423408558581072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6666666666666665,24223.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c,28fd8140cfc928298ce0dcc59bc4a1264ace9c6c:T6-c6,6,6 (Tulsa),22,Count2000,other,other,level,count,-0.002,0.001,,,,,,high,Number of dispensaries within 1000-2000 m band (Table 4/5 'All'); within 2000 m when entered alone; county-specific regression; structural controls not printed,"ln(Price/100), sale price USD",log,OLS,24233.0,0.197,se,"Tulsa County only, sales within 2x cross-validated minDist threshold; county-specific control variables included but withheld from the table",semi_elasticity,-0.2,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.012849320919287728,0.006424660459643864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,24223.0,count,unit_not_harmonisable,2026.0,United States,North America,High income,2020.0,2023.0,single_family,transaction,journal,True,True,unknown,cross-validation selection of minDist threshold (Fitzpatrick and Parmeter 2021) for county models,2021.5,ols,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,SIZE,living_area,A_structural,log,m2,0.787,,68.211,,***,,,high,"Living area m2, logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.787,0.011537728518860596,b (log-log),|b/t|,0.4505961415028659,0.006605916076627902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.211,18263.0,m2,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,SIZE,living_area,A_structural,log,m2,0.787,,67.918,,***,,,high,"Living area m2, logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.787,0.011587502576636531,b (log-log),|b/t|,0.44905157473693247,0.00661167252770889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),67.918,18263.0,m2,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,SIZE,living_area,A_structural,log,m2,0.789,,68.015,,***,,,high,"Living area m2, logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.789,0.011600382268617216,b (log-log),|b/t|,0.44956336286735926,0.006609767887485986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.015,18263.0,m2,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,SIZE,living_area,A_structural,log,m2,0.789,,68.157,,***,,,high,"Living area m2, logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.789,0.011576213741802018,b (log-log),|b/t|,0.45031178069910316,0.006606977723478193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.157,18263.0,m2,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,SIZE,living_area,A_structural,log,m2,0.789,,68.033,,***,,,high,"Living area m2, logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.789,0.011597313068657857,b (log-log),|b/t|,0.4496582852007423,0.006609414331291319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.033,18263.0,m2,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,SIZE,living_area,A_structural,log,m2,0.789,,68.16,,***,,,high,"Living area m2, logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.789,0.011575704225352115,b (log-log),|b/t|,0.45032758211801543,0.006606918751731447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.16,18263.0,m2,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,AGE,age,A_structural,log,years,-0.021,,-5.367,,***,,,high,"Age of building (years), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.021,0.003912800447177194,b (log-log),|b/t|,-0.03968289222583491,0.0073938684974538685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.367,18263.0,years,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,AGE,age,A_structural,log,years,-0.021,,-5.353,,***,,,high,"Age of building (years), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.021,0.003923033812815244,b (log-log),|b/t|,-0.039579540437662125,0.0073938988301255615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.353,18263.0,years,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,AGE,age,A_structural,log,years,-0.021,,-5.484,,***,,,high,"Age of building (years), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.021,0.003829321663019694,b (log-log),|b/t|,-0.04054656777025603,0.0073936119201779785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.484,18263.0,years,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,AGE,age,A_structural,log,years,-0.021,,-5.467,,***,,,high,"Age of building (years), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.021,0.0038412291933418697,b (log-log),|b/t|,-0.04042108204902615,0.007393649542532678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.467,18263.0,years,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,AGE,age,A_structural,log,years,-0.021,,-5.497,,***,,,high,"Age of building (years), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.021,0.0038202655994178645,b (log-log),|b/t|,-0.04064252614522423,0.00739358307171625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.497,18263.0,years,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,AGE,age,A_structural,log,years,-0.021,,-5.488,,***,,,high,"Age of building (years), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.021,0.003826530612244898,b (log-log),|b/t|,-0.040576093543708995,0.007393603050967382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.488,18263.0,years,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,DEED,other,other,binary,binary,0.367,,18.589,,***,,,high,"1 if plot owned by property owner (freehold) vs leasehold/cooperative; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,44.31166423662869,2.8496801135901357,100*(exp(b - se^2/2) - 1),|b/t|,0.1362698411975826,0.007330670891257334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.589,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,DEED,other,other,binary,binary,0.367,,18.559,,***,,,high,"1 if plot owned by property owner (freehold) vs leasehold/cooperative; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,44.31157323714583,2.854286525757154,100*(exp(b - se^2/2) - 1),|b/t|,0.13605399517326236,0.007330890412913538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.559,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,DEED,other,other,binary,binary,0.366,,18.538,,***,,,high,"1 if plot owned by property owner (freehold) vs leasehold/cooperative; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,44.16742364694131,2.8468854181225205,100*(exp(b - se^2/2) - 1),|b/t|,0.135902891425563,0.007331043878819883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.538,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,DEED,other,other,binary,binary,0.366,,18.536,,***,,,high,"1 if plot owned by property owner (freehold) vs leasehold/cooperative; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,44.16741758319604,2.847192591775749,100*(exp(b - se^2/2) - 1),|b/t|,0.13588850009762812,0.0073310584860610765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.536,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,DEED,other,other,binary,binary,0.366,,18.563,,***,,,high,"1 if plot owned by property owner (freehold) vs leasehold/cooperative; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,44.16749927848515,2.8430513322822435,100*(exp(b - se^2/2) - 1),|b/t|,0.13608277576331992,0.007330861162706455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.563,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,DEED,other,other,binary,binary,0.366,,18.559,,***,,,high,"1 if plot owned by property owner (freehold) vs leasehold/cooperative; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,44.167487197971326,2.843664091877541,100*(exp(b - se^2/2) - 1),|b/t|,0.13605399517326236,0.007330890412913538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.559,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,BEACH,waterfront,C_exterior,binary,binary,0.413,,13.019,,***,,,high,"Beach-side property: within 150 m of waterfront. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,51.058475354699496,4.794419661346796,100*(exp(b - se^2/2) - 1),|b/t|,0.09589270555183466,0.00736559686241913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.019,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,BEACH,waterfront,C_exterior,binary,binary,0.413,,13.015,,***,,,high,"Beach-side property: within 150 m of waterfront. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,51.05842862721195,4.7958931671973835,100*(exp(b - se^2/2) - 1),|b/t|,0.09586351395944923,0.007365617668801324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.015,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,BEACH,waterfront,C_exterior,binary,binary,0.415,,12.998,,***,,,high,"Beach-side property: within 150 m of waterfront. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,51.359906527091994,4.8350812231666405,100*(exp(b - se^2/2) - 1),|b/t|,0.09573944693324346,0.007365706026561277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.998,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,BEACH,waterfront,C_exterior,binary,binary,0.415,,12.986,,***,,,high,"Beach-side property: within 150 m of waterfront. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,51.35976388088266,4.839549186718004,100*(exp(b - se^2/2) - 1),|b/t|,0.0956518675220082,0.007365768329124302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.986,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,BEACH,waterfront,C_exterior,binary,binary,0.415,,13.0,,***,,,high,"Beach-side property: within 150 m of waterfront. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,51.35993026307639,4.834337364516923,100*(exp(b - se^2/2) - 1),|b/t|,0.09575404328567833,0.00736569563735987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,BEACH,waterfront,C_exterior,binary,binary,0.415,,12.988,,***,,,high,"Beach-side property: within 150 m of waterfront. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",pct_premium,51.359787682701665,4.838803952781029,100*(exp(b - se^2/2) - 1),|b/t|,0.09566646424486233,0.007365757949250257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.988,18263.0,binary,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,INC,income,F_neighbourhood,log,currency,0.532,,15.052,,***,,,high,"Average income in local small area (SEK 1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.532,0.035344140313579596,b (log-log),|b/t|,0.11069573538134113,0.007354221059084582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.052,18263.0,currency,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,INC,income,F_neighbourhood,log,currency,0.512,,14.107,,***,,,high,"Average income in local small area (SEK 1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.512,0.03629403842064224,b (log-log),|b/t|,0.10382339005468508,0.007359707241418096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.107,18263.0,currency,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,INC,income,F_neighbourhood,log,currency,0.525,,14.443,,***,,,high,"Average income in local small area (SEK 1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.525,0.03634978882503635,b (log-log),|b/t|,0.10626864684873813,0.007357795946045706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.443,18263.0,currency,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,INC,income,F_neighbourhood,log,currency,0.523,,14.466,,***,,,high,"Average income in local small area (SEK 1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.523,0.036153739803677594,b (log-log),|b/t|,0.1064359605288757,0.00735766352335654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.466,18263.0,currency,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,INC,income,F_neighbourhood,log,currency,0.528,,14.482,,***,,,high,"Average income in local small area (SEK 1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.528,0.03645905261704185,b (log-log),|b/t|,0.1065523473229278,0.007357571283174134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.482,18263.0,currency,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,INC,income,F_neighbourhood,log,currency,0.526,,4.534,,***,,,high,"Average income in local small area (SEK 1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.526,0.11601235112483459,b (log-log),|b/t|,0.03353136004331495,0.007395535960148864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.534,18263.0,currency,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,POP,density,F_neighbourhood,log,count,0.043,,5.114,,***,,,high,"Population of local small area (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.043,0.008408290965975753,b (log-log),|b/t|,0.03781498454219757,0.007394404486155175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.114,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,POP,density,F_neighbourhood,log,count,0.041,,4.783,,***,,,high,"Population of local small area (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.041,0.008572025925151577,b (log-log),|b/t|,0.03537060454592268,0.007395066808681303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.783,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,POP,density,F_neighbourhood,log,count,0.042,,4.932,,***,,,high,"Population of local small area (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.042,0.00851581508515815,b (log-log),|b/t|,0.036471025980874185,0.007394774124264839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.932,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,POP,density,F_neighbourhood,log,count,0.042,,4.938,,***,,,high,"Population of local small area (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.042,0.008505467800729041,b (log-log),|b/t|,0.03651533550170574,0.007394762151013719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.938,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,POP,density,F_neighbourhood,log,count,0.043,,4.955,,***,,,high,"Population of local small area (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.043,0.008678102926337033,b (log-log),|b/t|,0.03664087797366679,0.00739472814806595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.955,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,POP,density,F_neighbourhood,log,count,0.042,,4.973,,***,,,high,"Population of local small area (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.042,0.008445606273878947,b (log-log),|b/t|,0.036773803405936493,0.007394692018084958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.973,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,LOC_LABOR,employment_access,D_location,log,count,0.062,,5.739,,***,,,high,"Number of jobs in home municipality (1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.062,0.010803275832026485,b (log-log),|b/t|,0.042428620061168085,0.007393033640210504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.739,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,LOC_LABOR,employment_access,D_location,log,count,0.055,,5.187,,***,,,high,"Number of jobs in home municipality (1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.055,0.010603431656063235,b (log-log),|b/t|,0.03835398760051412,0.00739425247744633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.187,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,LOC_LABOR,employment_access,D_location,log,count,0.06,,5.687,,***,,,high,"Number of jobs in home municipality (1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.06,0.010550378055213644,b (log-log),|b/t|,0.04204486501111937,0.007393153685795563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.687,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,LOC_LABOR,employment_access,D_location,log,count,0.061,,5.686,,***,,,high,"Number of jobs in home municipality (1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.061,0.010728104115371086,b (log-log),|b/t|,0.04203748492339296,0.0073931559837131485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.686,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,LOC_LABOR,employment_access,D_location,log,count,0.061,,5.758,,***,,,high,"Number of jobs in home municipality (1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.061,0.010593956234803751,b (log-log),|b/t|,0.0425688335773428,0.007392989506311706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.758,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,LOC_LABOR,employment_access,D_location,log,count,0.062,,5.732,,***,,,high,"Number of jobs in home municipality (1,000), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.062,0.010816468946266573,b (log-log),|b/t|,0.04237696181755689,0.007393049863495618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.732,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,STATION_DIST,transit_distance,D_location,log,km,-0.016,,-2.431,,**,,,high,"Road-network travel distance to closest train station (km), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.016,0.00658165364047717,b (log-log),|b/t|,-0.01798575377139862,0.007398500111640731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.431,18263.0,km,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,STATION_DIST,transit_distance,D_location,log,km,-0.013,,-2.118,,**,,,high,"Road-network travel distance to closest train station (km), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.013,0.006137865911237016,b (log-log),|b/t|,-0.01567063391501869,0.007398788439574451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.118,18263.0,km,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,STATION_DIST,transit_distance,D_location,log,km,-0.013,,-2.124,,**,,,high,"Road-network travel distance to closest train station (km), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.013,0.006120527306967985,b (log-log),|b/t|,-0.015715015697832176,0.00739878328523172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.124,18263.0,km,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,STATION_DIST,transit_distance,D_location,log,km,-0.013,,-2.076,,**,,,high,"Road-network travel distance to closest train station (km), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.013,0.006262042389210019,b (log-log),|b/t|,-0.015359958856520431,0.00739882411200406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.076,18263.0,km,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,STATION_DIST,transit_distance,D_location,log,km,-0.013,,-2.121,,**,,,high,"Road-network travel distance to closest train station (km), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.013,0.006129184347006129,b (log-log),|b/t|,-0.015692824818019847,0.00739878586422435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.121,18263.0,km,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,STATION_DIST,transit_distance,D_location,log,km,-0.013,,-2.082,,**,,,high,"Road-network travel distance to closest train station (km), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.013,0.006243996157540827,b (log-log),|b/t|,-0.015404341282200334,0.00739881905965434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.082,18263.0,km,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ci,4,i,12,PASSENGERS,other,other,log,count,0.034,,6.644,,***,,,high,"Passengers per day at closest train station (10,000), logged; station ridership, no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.449,t,"Base: station distance + passengers; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.034,0.005117399157134257,b (log-log),|b/t|,0.04910427914701505,0.0073907704917241185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.644,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,PASSENGERS,other,other,log,count,0.029,,5.886,,***,,,high,"Passengers per day at closest train station (10,000), logged; station ridership, no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.029,0.0049269452939177715,b (log-log),|b/t|,0.04351336393852709,0.007392688402739906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.886,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,PASSENGERS,other,other,log,count,0.019,,3.516,,***,,,high,"Passengers per day at closest train station (10,000), logged; station ridership, no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.019,0.005403868031854379,b (log-log),|b/t|,0.02600853375069939,0.0073971938995163215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.516,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,PASSENGERS,other,other,log,count,0.026,,3.703,,***,,,high,"Passengers per day at closest train station (10,000), logged; station ridership, no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.026,0.0070213340534701595,b (log-log),|b/t|,0.02739079738546684,0.0073969207090107585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.703,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,PASSENGERS,other,other,log,count,0.02,,3.566,,***,,,high,"Passengers per day at closest train station (10,000), logged; station ridership, no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.02,0.005608524957936063,b (log-log),|b/t|,0.026378137897980596,0.0073971222372351654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.566,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,PASSENGERS,other,other,log,count,0.026,,3.563,,***,,,high,"Passengers per day at closest train station (10,000), logged; station ridership, no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.026,0.007297221442604546,b (log-log),|b/t|,0.02635596195269239,0.007397126565448327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.563,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cii,4,ii,12,CLOSENESS (Gravity),employment_access,D_location,log,count,0.187,,2.991,,***,,,high,"Gravity-form closeness centrality of closest station: rail-network accessibility to employment in other nodes (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.479,t,"+ CLOSENESS (gravity); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.187,0.06252089602139753,b (log-log),|b/t|,0.022127075133898527,0.007397885367401713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.991,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,CLOSENESS (Gravity),employment_access,D_location,log,count,0.172,,2.733,,***,,,high,"Gravity-form closeness centrality of closest station: rail-network accessibility to employment in other nodes (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.172,0.06293450420783021,b (log-log),|b/t|,0.020219237814519488,0.00739818434486626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.733,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,CLOSENESS (Gravity),employment_access,D_location,log,count,0.175,,2.855,,***,,,high,"Gravity-form closeness centrality of closest station: rail-network accessibility to employment in other nodes (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.175,0.06129597197898423,b (log-log),|b/t|,0.021121422250686493,0.007398046322482134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.855,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,CLOSENESS (Gravity),employment_access,D_location,log,count,0.184,,2.937,,***,,,high,"Gravity-form closeness centrality of closest station: rail-network accessibility to employment in other nodes (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.184,0.06264896152536602,b (log-log),|b/t|,0.02172777965281289,0.0073979501711994855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.937,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,CLOSENESS (Gravity),employment_access,D_location,log,count,0.181,,2.864,,***,,,high,"Gravity-form closeness centrality of closest station: rail-network accessibility to employment in other nodes (1,000 inh), logged. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.181,0.06319832402234637,b (log-log),|b/t|,0.02118797482367033,0.00739803590211953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.864,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,True,True,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-ciii,4,iii,12,DEGREE,other,other,log,count,0.089,,3.843,,***,,,high,"Degree centrality: number of stations reachable without changing trains, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.509,t,"+ DEGREE; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.089,0.023158990372105125,b (log-log),|b/t|,0.028425544762557743,0.0073967069379541365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.843,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,DEGREE,other,other,log,count,0.087,,3.789,,***,,,high,"Degree centrality: number of stations reachable without changing trains, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.087,0.022961203483768802,b (log-log),|b/t|,0.028026438560426633,0.007396790330014947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.789,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,DEGREE,other,other,log,count,0.092,,3.977,,***,,,high,"Degree centrality: number of stations reachable without changing trains, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.092,0.023133014835302994,b (log-log),|b/t|,0.02941586028620227,0.007396494917325188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.977,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,DEGREE,other,other,log,count,0.09,,3.933,,***,,,high,"Degree centrality: number of stations reachable without changing trains, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,0.09,0.02288329519450801,b (log-log),|b/t|,0.029090691463559495,0.0073965653352554015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.933,18263.0,count,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-civ,4,iv,12,BETWEENNESS (Oresund train),other,other,log,index,-0.303,,-1.173,,,,,high,"Betweenness centrality of closest station in the Oresund train network, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.51,t,"+ BETWEENNESS (Oresund trains); spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.303,0.25831202046035806,b (log-log),|b/t|,-0.00867951770444759,0.007399418332862395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.173,18263.0,index,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,BETWEENNESS (Oresund train),other,other,log,index,-0.291,,-0.988,,,,,high,"Betweenness centrality of closest station in the Oresund train network, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.291,0.29453441295546556,b (log-log),|b/t|,-0.007310705324534839,0.007399499316330808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.988,18263.0,index,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cv,4,v,12,BETWEENNESS (Pagatag train),other,other,log,index,-0.742,,-0.797,,,,,high,"Betweenness centrality of closest station in the Pagatag train network, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"+ BETWEENNESS (Pagatag trains), without Oresund; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.742,0.9309912170639899,b (log-log),|b/t|,-0.005897455999497247,0.007399568380799556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.797,18263.0,index,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65,290a4bf33ce230c96c2113bb929c287ce4514c65:T4-cvi,4,vi,12,BETWEENNESS (Pagatag train),other,other,log,index,-0.26,,-0.239,,,,,high,"Betweenness centrality of closest station in the Pagatag train network, logged; no taxonomy leaf. Z values in parentheses (recorded as t); *** p<0.01, ** p<0.05, * p<0.1; all continuous variables in natural logs; N=18,273.","ln PRICE (single-family house sale price, SEK)",log,spatial_error,18273.0,0.512,t,"All network measures; spatial error model, GMM (Kelejian-Prucha), pseudo R2 reported",elasticity,-0.26,1.087866108786611,b (log-log),|b/t|,-0.0017685248322657447,0.007399685490651651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.239,18263.0,index,,2021.0,Sweden,Europe,High income,2016.0,2016.0,single_family,transaction,journal,False,False,robust,,2016.0,spatial,journal,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Urban distance),cbd_distance,D_location,log,"km (log Euclidean distance to centroid of nearest major urban centre; unit not printed, km implied by descriptives)",-0.0053,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0053,0.002,b (log-log),reported,-0.021743218753978184,0.00820498820904837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.65,14847.0,km,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Urban distance),cbd_distance,D_location,log,"km (log Euclidean distance to centroid of nearest major urban centre; unit not printed, km implied by descriptives)",-0.0055,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0055,0.002,b (log-log),reported,-0.022563307447751178,0.00820483907190952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.75,14847.0,km,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Urban distance),cbd_distance,D_location,log,"km (log Euclidean distance to centroid of nearest major urban centre; unit not printed, km implied by descriptives)",-0.0058,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0058,0.002,b (log-log),reported,-0.023793354574970293,0.008204605025851826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,14847.0,km,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Urban distance),cbd_distance,D_location,log,"km (log Euclidean distance to centroid of nearest major urban centre; unit not printed, km implied by descriptives)",-0.0058,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0058,0.002,b (log-log),reported,-0.023793354574970293,0.008204605025851826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,14847.0,km,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Urban distance),cbd_distance,D_location,log,"km (log Euclidean distance to centroid of nearest major urban centre; unit not printed, km implied by descriptives)",-0.0119,0.003,,,***,,,medium,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0119,0.003,b (log-log),reported,-0.03253691311209422,0.008202583137502745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.966666666666667,14847.0,km,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Urban distance),cbd_distance,D_location,log,"km (log Euclidean distance to centroid of nearest major urban centre; unit not printed, km implied by descriptives)",-0.0116,0.003,,,***,,,medium,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0116,0.003,b (log-log),reported,-0.031717490636968826,0.008202799302664352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8666666666666663,14847.0,km,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Density),density,F_neighbourhood,log,per km2 (municipal population per square kilometre),0.1008,0.005,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.1008,0.005,b (log-log),reported,0.1632325757669029,0.008096853956691612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.16,14847.0,per km2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Density),density,F_neighbourhood,log,per km2 (municipal population per square kilometre),0.1006,0.005,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.1006,0.005,b (log-log),reported,0.16291730621007686,0.008097281620779169,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.119999999999997,14847.0,per km2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Density),density,F_neighbourhood,log,per km2 (municipal population per square kilometre),0.1003,0.005,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.1003,0.005,b (log-log),reported,0.16244430831693044,0.008097921650893842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.06,14847.0,per km2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Density),density,F_neighbourhood,log,per km2 (municipal population per square kilometre),0.1003,0.005,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.1003,0.005,b (log-log),reported,0.16244430831693044,0.008097921650893842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.06,14847.0,per km2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Density),density,F_neighbourhood,log,per km2 (municipal population per square kilometre),0.0859,0.007,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0859,0.007,b (log-log),reported,0.10020385156660398,0.008165622362819881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.271428571428572,14847.0,per km2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Density),density,F_neighbourhood,log,per km2 (municipal population per square kilometre),0.0859,0.007,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0859,0.007,b (log-log),reported,0.10020385156660398,0.008165622362819881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.271428571428572,14847.0,per km2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Foreign),demographics,F_neighbourhood,log,"share (log of 1 + percentage of population with a non-western background, municipality)",-0.1536,0.009,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.1536,0.009,b (log-log),reported,-0.13871089275093337,0.008127591372125003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.066666666666666,14847.0,share,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Foreign),demographics,F_neighbourhood,log,"share (log of 1 + percentage of population with a non-western background, municipality)",-0.1535,0.009,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.1535,0.009,b (log-log),reported,-0.13862232207896777,0.008127693151209836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.055555555555557,14847.0,share,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Foreign),demographics,F_neighbourhood,log,"share (log of 1 + percentage of population with a non-western background, municipality)",-0.1531,0.009,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.1531,0.009,b (log-log),reported,-0.13826800614648044,0.008128099642836863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.011111111111113,14847.0,share,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Foreign),demographics,F_neighbourhood,log,"share (log of 1 + percentage of population with a non-western background, municipality)",-0.1531,0.009,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.1531,0.009,b (log-log),reported,-0.13826800614648044,0.008128099642836863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.011111111111113,14847.0,share,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Foreign),demographics,F_neighbourhood,log,"share (log of 1 + percentage of population with a non-western background, municipality)",-0.1164,0.013,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.1164,0.013,b (log-log),reported,-0.07328597477678585,0.00818485972592969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.953846153846154,14847.0,share,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Foreign),demographics,F_neighbourhood,log,"share (log of 1 + percentage of population with a non-western background, municipality)",-0.1159,0.013,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.1159,0.013,b (log-log),reported,-0.07297285241014245,0.008185048156443933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.915384615384616,14847.0,share,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Burglary,crime,F_neighbourhood,log,"rate (log of 1 + burglaries per 10,000 inhabitants, municipality, 2012)",-0.0185,0.005,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0185,0.005,b (log-log),reported,-0.030351645191858387,0.008203147349150916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6999999999999997,14847.0,rate,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Burglary,crime,F_neighbourhood,log,"rate (log of 1 + burglaries per 10,000 inhabitants, municipality, 2012)",-0.0182,0.005,,,***,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0182,0.005,b (log-log),reported,-0.029859898806705937,0.00820326890294119,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.64,14847.0,rate,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Burglary,crime,F_neighbourhood,log,"rate (log of 1 + burglaries per 10,000 inhabitants, municipality, 2012)",-0.0171,0.005,,,**,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0171,0.005,b (log-log),reported,-0.02805664593302658,0.008203697641235843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.42,14847.0,rate,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Burglary,crime,F_neighbourhood,log,"rate (log of 1 + burglaries per 10,000 inhabitants, municipality, 2012)",-0.0172,0.005,,,**,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0172,0.005,b (log-log),reported,-0.02822058959663041,0.008203659766462328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.44,14847.0,rate,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Burglary,crime,F_neighbourhood,log,"rate (log of 1 + burglaries per 10,000 inhabitants, municipality, 2012)",-0.0118,0.008,,,,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0118,0.008,b (log-log),reported,-0.012104332594370807,0.008206327182624276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4749999999999999,14847.0,rate,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Burglary,crime,F_neighbourhood,log,"rate (log of 1 + burglaries per 10,000 inhabitants, municipality, 2012)",-0.012,0.008,,,,,,high,Municipal/neighbourhood variable; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.012,0.008,b (log-log),reported,-0.012309459946806333,0.008206306631204223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,14847.0,rate,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Water distance),water_proximity,E_environment,log,distance to water (log; unit not printed),-0.0087,0.002,,,***,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0087,0.002,b (log-log),reported,-0.03567741045221419,0.008201703552233148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.35,14847.0,distance to water,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Water distance),water_proximity,E_environment,log,distance to water (log; unit not printed),-0.0087,0.002,,,***,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0087,0.002,b (log-log),reported,-0.03567741045221419,0.008201703552233148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.35,14847.0,distance to water,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Water distance),water_proximity,E_environment,log,distance to water (log; unit not printed),-0.0088,0.002,,,***,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0088,0.002,b (log-log),reported,-0.03608696461902369,0.00820158286795993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.4,14847.0,distance to water,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Water distance),water_proximity,E_environment,log,distance to water (log; unit not printed),-0.0088,0.002,,,***,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0088,0.002,b (log-log),reported,-0.03608696461902369,0.00820158286795993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.4,14847.0,distance to water,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Water distance),water_proximity,E_environment,log,distance to water (log; unit not printed),-0.0116,0.002,,,***,,,medium,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0116,0.002,b (log-log),reported,-0.04754635060316749,0.008197646655718533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8,14847.0,distance to water,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Water distance),water_proximity,E_environment,log,distance to water (log; unit not printed),-0.0115,0.002,,,***,,,medium,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0115,0.002,b (log-log),reported,-0.0471373829522357,0.0081978057308236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.75,14847.0,distance to water,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Forest distance),green_space,E_environment,log,distance to forest (log; unit not printed),-0.0045,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0045,0.002,b (log-log),reported,-0.018462441577416236,0.008205529589962771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,14847.0,distance to forest,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Forest distance),green_space,E_environment,log,distance to forest (log; unit not printed),-0.0043,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0043,0.002,b (log-log),reported,-0.01764214994924424,0.008205651139183368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.15,14847.0,distance to forest,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Forest distance),green_space,E_environment,log,distance to forest (log; unit not printed),-0.0038,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0038,0.002,b (log-log),reported,-0.015591268635961223,0.008205930861032223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9,14847.0,distance to forest,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Forest distance),green_space,E_environment,log,distance to forest (log; unit not printed),-0.0039,0.002,,,*,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0039,0.002,b (log-log),reported,-0.016001461470432812,0.008205877677145031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.95,14847.0,distance to forest,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Forest distance),green_space,E_environment,log,distance to forest (log; unit not printed),-0.0039,0.002,,,,,,medium,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0039,0.002,b (log-log),reported,-0.016001461470432812,0.008205877677145031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.95,14847.0,distance to forest,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Forest distance),green_space,E_environment,log,distance to forest (log; unit not printed),-0.004,0.002,,,†,,,medium,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.004,0.002,b (log-log),reported,-0.01641164622586575,0.008205823112932876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,14847.0,distance to forest,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Nature distance),green_space,E_environment,log,distance to nature area (log; unit not printed),-0.0553,0.003,,,***,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0553,0.003,b (log-log),reported,-0.14957909956586185,0.00811459852979359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.433333333333334,14847.0,distance to nature area,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Nature distance),green_space,E_environment,log,distance to nature area (log; unit not printed),-0.0549,0.003,,,***,,,medium,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0549,0.003,b (log-log),reported,-0.14852110424384518,0.008115907335729247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.299999999999997,14847.0,distance to nature area,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Nature distance),green_space,E_environment,log,distance to nature area (log; unit not printed),-0.0546,0.003,,,***,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0546,0.003,b (log-log),reported,-0.1477272727272727,0.008116883116883116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.2,14847.0,distance to nature area,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Nature distance),green_space,E_environment,log,distance to nature area (log; unit not printed),-0.0547,0.003,,,***,,,medium,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0547,0.003,b (log-log),reported,-0.1479919150365674,0.008116558411511925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.23333333333333,14847.0,distance to nature area,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Nature distance),green_space,E_environment,log,distance to nature area (log; unit not printed),-0.0357,0.004,,,***,,,medium,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0357,0.004,b (log-log),reported,-0.0730511350285056,0.008185001123642083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.925,14847.0,distance to nature area,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Nature distance),green_space,E_environment,log,distance to nature area (log; unit not printed),-0.0359,0.004,,,***,,,medium,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,-0.0359,0.004,b (log-log),reported,-0.07345818284696569,0.008184755748965537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.975,14847.0,distance to nature area,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.6616,0.009,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.6616,0.009,b (log-log),reported,0.5165721638386598,0.007027130402883823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),73.51111111111112,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.6508,0.009,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.6508,0.009,b (log-log),reported,0.5103493402401468,0.0070576890936713595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),72.31111111111112,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.6485,0.009,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.6485,0.009,b (log-log),reported,0.5090136337295211,0.007064183043277855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),72.05555555555556,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.6471,0.009,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.6471,0.009,b (log-log),reported,0.5081987899224374,0.007068133378615264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.9,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.6471,0.009,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.6471,0.009,b (log-log),reported,0.5081987899224374,0.007068133378615264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.9,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.5932,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.5932,0.008,b (log-log),reported,0.5198522565594356,0.007010819373694346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),74.14999999999999,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Floor Area),living_area,A_structural,log,"m2 (net floorspace, NVM definition)",0.5931,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.5931,0.008,b (log-log),reported,0.5197883000529032,0.0070111387631482495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),74.13749999999999,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0461,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0461,0.001,b (log-log),reported,0.35386020683712865,0.0076759263955993185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),46.1,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0514,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0514,0.001,b (log-log),reported,0.38867009524788926,0.007561675004822748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.4,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0514,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0514,0.001,b (log-log),reported,0.38867009524788926,0.007561675004822748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.4,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0514,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0514,0.001,b (log-log),reported,0.38867009524788926,0.007561675004822748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.4,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0514,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0514,0.001,b (log-log),reported,0.38867009524788926,0.007561675004822748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.4,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0553,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0553,0.001,b (log-log),reported,0.41327285184219953,0.007473288460075941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.300000000000004,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Ln(Lotsize),lot_size,A_structural,log,m2 (log of 1 + lot size; flats = 0),0.0552,0.001,,,***,,,high,Log(1+lot size); apartments recorded with zero land; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0552,0.001,b (log-log),reported,0.4126528755536585,0.00747559557162425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.199999999999996,14847.0,m2,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.0309,0.009,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0309,0.009,b (log-log),reported,0.02816594196114244,0.00820367241586673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4333333333333336,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.0327,0.009,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0327,0.009,b (log-log),reported,0.029805258974566824,0.008203282286578024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6333333333333337,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.0326,0.009,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0326,0.009,b (log-log),reported,0.02971419199415272,0.00820330453826302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6222222222222222,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.0323,0.009,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0323,0.009,b (log-log),reported,0.02944098662233312,0.008203370885479816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5888888888888895,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.0323,0.009,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0323,0.009,b (log-log),reported,0.02944098662233312,0.008203370885479816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5888888888888895,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.0498,0.008,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0498,0.008,b (log-log),reported,0.051021589725736706,0.00819623931337136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.225,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Rooms,bedrooms,A_structural,log,count (natural log of number of rooms per Table 1),0.05,0.008,,,***,,,high,Printed as 'Rooms' in Table 7 but defined as Ln(rooms) in Tables 1 and 3; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.05,0.008,b (log-log),reported,0.05122595908758647,0.008196153454013835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,14847.0,count,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.1438,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,15.461620648957354,0.9237225238394808,100*(exp(b - se^2/2) - 1),reported,0.14594010967580615,0.008119060343577532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.975,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.1395,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.96620159449189,0.91975904457445,100*(exp(b - se^2/2) - 1),reported,0.14166501755606656,0.008124158712892704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.4375,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.1386,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.862778560403257,0.9189316338250204,100*(exp(b - se^2/2) - 1),reported,0.14076921849291268,0.008125207416618336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.325,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.1374,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.725025894260767,0.9178295772306339,100*(exp(b - se^2/2) - 1),reported,0.1395742820754585,0.008126595754029606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.175,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.1375,0.008,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.736498970494448,0.9179213647776578,100*(exp(b - se^2/2) - 1),reported,0.13967388347796864,0.00812648049326363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.1875,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.1164,0.007,,,***,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,12.341763626450074,0.7864116122336304,100*(exp(b - se^2/2) - 1),reported,0.13521617616475085,0.008131556985852714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.62857142857143,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Two Bathrooms,bathrooms,A_structural,dummy,binary (two bathrooms vs one),0.116,0.007,,,***,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,12.29683590714239,0.7860971104932782,100*(exp(b - se^2/2) - 1),reported,0.13475996839791785,0.00813206705849504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.571428571428573,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.2432,0.063,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,27.279529314155827,8.034539079082041,100*(exp(b - se^2/2) - 1),reported,0.03166546163641855,0.008202812841670924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8603174603174604,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.2489,0.061,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,28.022968085193757,7.823943968208005,100*(exp(b - se^2/2) - 1),reported,0.03346819879622231,0.008202330761629411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.080327868852459,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.2487,0.061,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,27.997366051865402,7.82237933588281,100*(exp(b - se^2/2) - 1),reported,0.03344133599549222,0.00820233814123452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.077049180327869,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.2542,0.061,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,28.703291079458083,7.8655209529242605,100*(exp(b - se^2/2) - 1),reported,0.03418003640649839,0.00820213304797955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.167213114754098,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.254,0.061,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,28.677552995136434,7.863948006033605,100*(exp(b - se^2/2) - 1),reported,0.034153175547499776,0.008202140584242073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.163934426229508,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.1801,0.053,,,***,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,19.565661129332824,6.345886581475366,100*(exp(b - se^2/2) - 1),reported,0.027877233291980413,0.008203738836618334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.39811320754717,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Three Bathrooms,bathrooms,A_structural,dummy,binary (three bathrooms vs one),0.1806,0.053,,,***,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,19.625458908096395,6.349060318134148,100*(exp(b - se^2/2) - 1),reported,0.02795456665828642,0.00820372111234319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.407547169811321,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.1196,0.006,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,12.702565638207242,0.67622756581588,100*(exp(b - se^2/2) - 1),reported,0.16144538972416456,0.008099267043018289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.933333333333334,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.1158,0.006,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,12.275108571581871,0.673662777250349,100*(exp(b - se^2/2) - 1),reported,0.15644340477362445,0.008105875895006448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.3,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.1153,0.006,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,12.218985049345864,0.673326030055538,100*(exp(b - se^2/2) - 1),reported,0.1557843430917401,0.008106730776673381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.216666666666665,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.1153,0.006,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,12.218985049345864,0.673326030055538,100*(exp(b - se^2/2) - 1),reported,0.1557843430917401,0.008106730776673381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.216666666666665,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.1153,0.006,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,12.218985049345864,0.673326030055538,100*(exp(b - se^2/2) - 1),reported,0.1557843430917401,0.008106730776673381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.216666666666665,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.087,0.005,,,***,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,9.088304359500722,0.5454483398591389,100*(exp(b - se^2/2) - 1),reported,0.14136645642295834,0.008124508989825193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.4,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Balcony,terrace_balcony,C_exterior,binary,binary,0.0871,0.005,,,***,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,9.099213735396372,0.5455028874204574,100*(exp(b - se^2/2) - 1),reported,0.14152569383305474,0.0081243222636656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.419999999999998,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Parking,parking,A_structural,binary,binary (private parking),0.1244,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,13.245970820701602,0.45298750716836816,100*(exp(b - se^2/2) - 1),reported,0.24730713042511457,0.00795199776286542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.099999999999998,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Parking,parking,A_structural,binary,binary (private parking),0.1342,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.36123721423841,0.45744860843118273,100*(exp(b - se^2/2) - 1),reported,0.26546341843908533,0.007912471488497327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.550000000000004,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Parking,parking,A_structural,binary,binary (private parking),0.1338,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.315501867031944,0.45726566557882004,100*(exp(b - se^2/2) - 1),reported,0.26472769954952086,0.007914131526144121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.45,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Parking,parking,A_structural,binary,binary (private parking),0.1327,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.189823950504831,0.45676294989100213,100*(exp(b - se^2/2) - 1),reported,0.26270209021542923,0.007918676419455288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.175000000000004,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Parking,parking,A_structural,binary,binary (private parking),0.1327,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.189823950504831,0.45676294989100213,100*(exp(b - se^2/2) - 1),reported,0.26270209021542923,0.007918676419455288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.175000000000004,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Parking,parking,A_structural,binary,binary (private parking),0.1203,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,12.78261287321616,0.4511340605509128,100*(exp(b - se^2/2) - 1),reported,0.23963188261182908,0.007967809895655164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.075,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Parking,parking,A_structural,binary,binary (private parking),0.1205,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,12.805171651593428,0.45122429638630573,100*(exp(b - se^2/2) - 1),reported,0.24000734235927335,0.007967048709021523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.125,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Basement,basement,A_structural,binary,binary,-0.0192,0.008,,,*,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-1.9048244965777283,0.7847865167941092,100*(exp(b - se^2/2) - 1),reported,-0.019692808602938178,0.008205336917890908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Basement,basement,A_structural,binary,binary,-0.0228,0.008,,,**,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-2.2573322337548496,0.7819663646532675,100*(exp(b - se^2/2) - 1),reported,-0.023383350596322596,0.008204684419762314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.85,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Basement,basement,A_structural,binary,binary,-0.0227,0.008,,,**,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-2.2475574782485963,0.7820445651996951,100*(exp(b - se^2/2) - 1),reported,-0.02328084774996487,0.008204704052851057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8375,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Basement,basement,A_structural,binary,binary,-0.0223,0.008,,,**,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-2.2084486800016956,0.7823574455976827,100*(exp(b - se^2/2) - 1),reported,-0.022870829054520515,0.008204781723594803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7875,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Basement,basement,A_structural,binary,binary,-0.0223,0.008,,,**,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-2.2084486800016956,0.7823574455976827,100*(exp(b - se^2/2) - 1),reported,-0.022870829054520515,0.008204781723594803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7875,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Basement,basement,A_structural,binary,binary,-0.032,0.007,,,***,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,-3.1517146041392663,0.6779546074554383,100*(exp(b - se^2/2) - 1),reported,-0.037491011037961444,0.008201158664554066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.571428571428571,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,True
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Basement,basement,A_structural,binary,binary,-0.0319,0.007,,,***,,,high,Negative because floorspace measure already includes basement area; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,-3.142029291342119,0.6780224063060698,100*(exp(b - se^2/2) - 1),reported,-0.03737401553487276,0.00820119463147678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.557142857142857,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.0392,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-3.8449314197986806,0.3846233512953078,100*(exp(b - se^2/2) - 1),reported,-0.08016902396149082,0.008180512649131716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.799999999999999,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,True
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.0371,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-3.642793605360739,0.3854319090214955,100*(exp(b - se^2/2) - 1),reported,-0.07589969192609944,0.008183255194188619,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.275,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.0373,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-3.662063119624004,0.3853548303478154,100*(exp(b - se^2/2) - 1),reported,-0.07630647861273315,0.008183000387424467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.325,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.0389,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-3.816080571813796,0.38473875561047804,100*(exp(b - se^2/2) - 1),reported,-0.07955938390387053,0.008180913511966122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.725,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.0389,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-3.816080571813796,0.38473875561047804,100*(exp(b - se^2/2) - 1),reported,-0.07955938390387053,0.008180913511966122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.725,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.0316,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,-3.1113688918605553,0.3875576248811551,100*(exp(b - se^2/2) - 1),reported,-0.06469889485833985,0.008189733526372133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.9,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Attic,storage,B_interior,binary,binary (attic/loft),-0.032,0.004,,,***,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,-3.150116594246688,0.3874026328316791,100*(exp(b - se^2/2) - 1),reported,-0.06551437494467446,0.008189296868084308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0064,0.006,,,,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-0.6397448627740276,0.5961722618274894,100*(exp(b - se^2/2) - 1),reported,-0.008753721576912033,0.00820661397835503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0666666666666667,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0051,0.006,,,,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-0.5104925352858003,0.5969477897517957,100*(exp(b - se^2/2) - 1),reported,-0.0069757194319330995,0.008206728743450704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8500000000000001,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0054,0.005,,,,,,high,SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-0.5397878812827273,0.49730727689569565,100*(exp(b - se^2/2) - 1),reported,-0.008863134554054382,0.008206606068568871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.08,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0073,0.006,,,,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-0.729128863563755,0.5956359481691921,100*(exp(b - se^2/2) - 1),reported,-0.009984598517632823,0.008206519329561226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2166666666666666,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0073,0.006,,,,,,high,SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,-0.729128863563755,0.5956359481691921,100*(exp(b - se^2/2) - 1),reported,-0.009984598517632823,0.008206519329561226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2166666666666666,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0106,0.005,,,*,,,high,SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,-1.0556386100222137,0.4947279910111261,100*(exp(b - se^2/2) - 1),reported,-0.017396055438504966,0.008205686527596682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.12,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Under roof,storage,B_interior,binary,binary (under-roof storage area),-0.0112,0.005,,,*,,,high,SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,-1.1149874204326116,0.49443124324975024,100*(exp(b - se^2/2) - 1),reported,-0.018380414064904104,0.008205541993260762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2399999999999998,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Listed,other,other,binary,binary (listed/heritage building),0.2465,0.029,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,27.900125009942435,3.7106636313310486,100*(exp(b - se^2/2) - 1),reported,0.06958977462777566,0.008187032309150077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.5,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Listed,other,other,binary,binary (listed/heritage building),0.2313,0.028,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,25.974333773076296,3.5286643109771254,100*(exp(b - se^2/2) - 1),reported,0.06763982684920249,0.008188132951913833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.260714285714286,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Listed,other,other,binary,binary (listed/heritage building),0.2287,0.028,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,25.647225929733363,3.519501700324037,100*(exp(b - se^2/2) - 1),reported,0.0668829206520548,0.008188551719534476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.167857142857143,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Listed,other,other,binary,binary (listed/heritage building),0.2257,0.028,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,25.270849099472148,3.5089590171548295,100*(exp(b - se^2/2) - 1),reported,0.06600942392464361,0.008189029108950027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.060714285714287,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Listed,other,other,binary,binary (listed/heritage building),0.2258,0.028,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,25.283376810757208,3.5093099306019253,100*(exp(b - se^2/2) - 1),reported,0.06603854293855996,0.008189013296189896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.064285714285715,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Listed,other,other,binary,binary (listed/heritage building),0.1899,0.025,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,20.875088506476168,3.0228216968588306,100*(exp(b - se^2/2) - 1),reported,0.06221904602198263,0.008191027649023517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.596,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Listed,other,other,binary,binary (listed/heritage building),0.1895,0.025,,,***,,,high,Listed (protected) building dummy; SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,20.82674813979144,3.0216128099735826,100*(exp(b - se^2/2) - 1),reported,0.062088495330880664,0.008191094370828584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.58,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c1,7,1,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.1416,0.024,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7035,se,Structure characteristics only; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,15.178394852288468,2.7650777041714303,100*(exp(b - se^2/2) - 1),reported,0.04836421397221881,0.008197324402070983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c2,7,2,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.1425,0.023,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7223,se,Structure + neighbourhood characteristics; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,15.284811230133899,2.65225208620195,100*(exp(b - se^2/2) - 1),reported,0.05078166968904669,0.008196339669109291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.195652173913043,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.14,0.023,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,14.996959167060343,2.645629737371623,100*(exp(b - se^2/2) - 1),reported,0.04989300069816131,0.008196707257555072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.086956521739131,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.1418,0.023,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,15.204140100462272,2.650396159391776,100*(exp(b - se^2/2) - 1),reported,0.0505328544389004,0.008196443244673548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.165217391304348,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.1418,0.023,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),pct_premium,15.204140100462272,2.650396159391776,100*(exp(b - se^2/2) - 1),reported,0.0505328544389004,0.008196443244673548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.165217391304348,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.1648,0.02,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,17.892145580277653,2.3583145273478765,100*(exp(b - se^2/2) - 1),reported,0.06747098861730998,0.008188226773945387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.24,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,25,Monument,architectural_style,H_ai_derived,binary,binary (monumental appearance assessed by realtor),0.1648,0.02,,,***,,,high,Monumental appearance (not listed status); SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",pct_premium,17.892145580277653,2.3583145273478765,100*(exp(b - se^2/2) - 1),reported,0.06747098861730998,0.008188226773945387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.24,14847.0,binary,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,True,True,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Well-being,other,other,log,"index (log of 1 + RAND-36 emotional well-being score, occupant level)",0.0385,0.009,,,***,,,high,"Occupant subjective well-being (individual-level, not a physical characteristic); instrumented in IV columns; SE printed on sigma row",log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.0385,0.009,b (log-log),reported,0.03508580052836309,0.008201875448188773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.277777777777778,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Well-being,other,other,log,"index (log of 1 + RAND-36 emotional well-being score, occupant level)",0.181,0.029,,,***,,,high,"Occupant subjective well-being (individual-level, not a physical characteristic); instrumented in IV columns; SE printed on sigma row",log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.181,0.029,b (log-log),reported,0.05115548761949781,0.008196183099256555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.241379310344827,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Well-being,other,other,log,"index (log of 1 + RAND-36 emotional well-being score, occupant level)",0.1744,0.026,,,***,,,high,"Occupant subjective well-being (individual-level, not a physical characteristic); instrumented in IV columns; SE printed on sigma row",log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.1744,0.026,b (log-log),reported,0.054966327220953126,0.00819452126000448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.707692307692308,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Well-being,other,other,log,"index (log of 1 + RAND-36 emotional well-being score, occupant level)",0.0893,0.018,,,***,,,high,"Occupant subjective well-being (individual-level, not a physical characteristic); instrumented in IV columns; SE printed on sigma row",log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0893,0.018,b (log-log),reported,0.040681777630518036,0.008200134348816623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.961111111111112,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Well-being,other,other,log,"index (log of 1 + RAND-36 emotional well-being score, occupant level)",0.1149,0.023,,,***,,,high,"Occupant subjective well-being (individual-level, not a physical characteristic); instrumented in IV columns; SE printed on sigma row",log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.1149,0.023,b (log-log),reported,0.04096454532087961,0.008200039533335344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.995652173913044,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c3,7,3,24,Positive affect,other,other,log,"index (log of 1 + PANAS positive affect scale, occupant level)",0.1018,0.02,,,***,,,high,Occupant positive affect (individual-level); SE printed on sigma row,log transaction price,log,OLS,14857.0,0.7235,se,Structure + neighbourhood + subjective well-being and positive affect; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,0.1018,0.02,b (log-log),reported,0.04173686601131641,0.008199777212439373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.09,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,ols,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c4,7,4,24,Positive affect,other,other,log,"index (log of 1 + PANAS positive affect scale, occupant level)",-0.0077,0.029,,,,,,high,Occupant positive affect (individual-level); SE printed on sigma row,log transaction price,log,IV,14857.0,0.7191,se,Well-being instrumented by Ln(Health+1); Wu-Hausman 28.05***; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0077,0.029,b (log-log),reported,-0.0021790758215666354,0.008206908938367848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2655172413793103,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c5,7,5,24,Positive affect,other,other,log,"index (log of 1 + PANAS positive affect scale, occupant level)",-0.0027,0.028,,,,,,high,Occupant positive affect (individual-level); SE printed on sigma row,log transaction price,log,IV,14857.0,0.7195,se,Well-being instrumented by Ln(Health+1) and Ln(Pain+1); Sargan 0.332; N inferred from Table 2 / Table 6 (not printed in Table 7),elasticity,-0.0027,0.028,b (log-log),reported,-0.0007913821358464007,0.008206925853221933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09642857142857143,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,iv,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Positive affect,other,other,log,"index (log of 1 + PANAS positive affect scale, occupant level)",0.0321,0.021,,,,,,high,Occupant positive affect (individual-level); SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0321,0.021,b (log-log),reported,0.012543889302283562,0.00820628272112009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5285714285714282,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c7,7,7,24,Positive affect,other,other,log,"index (log of 1 + PANAS positive affect scale, occupant level)",0.0094,0.024,,,,,,high,Occupant positive affect (individual-level); SE printed on sigma row,log transaction price,log,SAC,14857.0,0.7568,se,"Spatial lag and spatial error model (rho 0.4238***, lambda 0.2445***); N inferred from Table 2 / Table 6 (not printed in Table 7)",elasticity,0.0094,0.024,b (log-log),reported,0.0032143636933254587,0.00820688602551181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39166666666666666,14847.0,index,,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,spatial,other,False,False,False
291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5,291ca9688b933cfaf7d01e6f6552e428bb2432b5:T7-c6,7,6,24,Lag well-being (θ),other,other,other,"index (spatial lag of log(1+well-being), inverse-distance weights)",0.1007,0.035,,,**,,,high,Spatially lagged well-being (GNS only); SE printed on sigma row,log transaction price,log,other,14857.0,0.7574,se,"General nesting spatial model (rho 0.4282***, lambda 0.2409***, spatial lag of well-being); N inferred from Table 2 / Table 6 (not printed in Table 7)",other,0.1007,0.035,not harmonised,reported,0.023605925661973958,0.008204641491252123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.877142857142857,14847.0,index inverse-distance weights),nonstandard_x_transform,2020.0,Netherlands,Europe,High income,2000.0,2012.0,mixed,transaction,working_paper,True,False,unknown,,2006.0,other,other,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c1,3,Local Market,36,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.038,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.8,p,spatial FE level: Local Market (50 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.038,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c2,3,Micro market,36,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.046,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.841,p,spatial FE level: Micro market (261 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.046,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c3,3,Electoral District,36,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.04,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.854,p,spatial FE level: Electoral District (832 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.04,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c4,3,Small Area,36,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.061,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.882,p,spatial FE level: Small Area (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.061,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c5,3,ED x Year,36,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.038,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.868,p,spatial FE level: ED x Year (4509 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.038,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c6,3,Small Area + Micro-Market x Year,36,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.061,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,spatial FE level: Small Area + Micro-Market x Year (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.061,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c1,3,Local Market,36,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.012,,,0.34,,,,high,distance to transitional (estuarine) waters; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.8,p,spatial FE level: Local Market (50 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.012,0.01257643784494571,b (log-log),|b/z(p)| (two-sided normal),-0.003017020044668816,0.0031619470890610576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9541652531461942,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c2,3,Micro market,36,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.012,,,0.1,,,,high,distance to transitional (estuarine) waters; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.841,p,spatial FE level: Micro market (261 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.012,0.007295481982941227,b (log-log),|b/z(p)| (two-sided normal),-0.005200893466704698,0.003161918715128405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6448536269514729,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c3,3,Electoral District,36,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.018,,,0.0,,,,high,distance to transitional (estuarine) waters; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.854,p,spatial FE level: Electoral District (832 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.018,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c4,3,Small Area,36,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.014,,,0.01,,,,high,distance to transitional (estuarine) waters; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.882,p,spatial FE level: Small Area (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.014,0.0054351427638125,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c5,3,ED x Year,36,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.018,,,0.01,,,,high,distance to transitional (estuarine) waters; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.868,p,spatial FE level: ED x Year (4509 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.018,0.006988040696330356,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c6,3,Small Area + Micro-Market x Year,36,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.014,,,0.01,,,,high,distance to transitional (estuarine) waters; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,spatial FE level: Small Area + Micro-Market x Year (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,-0.014,0.0054351427638125,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c1,3,Local Market,36,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.01,,,0.08,,,,high,GIS 3D viewshed breadth restricted to NLP-flagged listings; AI/NLP-assisted; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.8,p,spatial FE level: Local Market (50 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,0.01,0.005712046359543804,b (log-log),|b/z(p)| (two-sided normal),0.005535517109104458,0.003161913035125256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7506860712521697,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c2,3,Micro market,36,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.01,,,,high,GIS 3D viewshed breadth restricted to NLP-flagged listings; AI/NLP-assisted; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.841,p,spatial FE level: Micro market (261 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,0.011,0.004270469314424107,b (log-log),|b/z(p)| (two-sided normal),0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c3,3,Electoral District,36,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,GIS 3D viewshed breadth restricted to NLP-flagged listings; AI/NLP-assisted; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.854,p,spatial FE level: Electoral District (832 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,0.011,,b (log-log),,,,,,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c4,3,Small Area,36,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,GIS 3D viewshed breadth restricted to NLP-flagged listings; AI/NLP-assisted; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.882,p,spatial FE level: Small Area (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,0.011,,b (log-log),,,,,,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c5,3,ED x Year,36,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,GIS 3D viewshed breadth restricted to NLP-flagged listings; AI/NLP-assisted; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.868,p,spatial FE level: ED x Year (4509 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,0.011,,b (log-log),,,,,,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c6,3,Small Area + Micro-Market x Year,36,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,GIS 3D viewshed breadth restricted to NLP-flagged listings; AI/NLP-assisted; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,spatial FE level: Small Area + Micro-Market x Year (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,elasticity,0.011,,b (log-log),,,,,,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c1,3,Local Market,36,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.023,,,0.04,,,,high,"view depth (horizon angle), 0 if no NLP-indicated view; robust clustered p-value printed below coefficient; no SE",natural log of listed sale price (EUR),log,OLS,100030.0,0.8,p,spatial FE level: Local Market (50 absorbed units); robust p-values clustered at SFE level; controls included but not reported,semi_elasticity,2.3,1.1199032111926572,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.00649373802429801,0.0031618948113283493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.053748910631823,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c2,3,Micro market,36,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.02,,,0.0,,,,high,"view depth (horizon angle), 0 if no NLP-indicated view; robust clustered p-value printed below coefficient; no SE",natural log of listed sale price (EUR),log,OLS,100030.0,0.841,p,spatial FE level: Micro market (261 absorbed units); robust p-values clustered at SFE level; controls included but not reported,semi_elasticity,2.0,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c3,3,Electoral District,36,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.018,,,0.0,,,,high,"view depth (horizon angle), 0 if no NLP-indicated view; robust clustered p-value printed below coefficient; no SE",natural log of listed sale price (EUR),log,OLS,100030.0,0.854,p,spatial FE level: Electoral District (832 absorbed units); robust p-values clustered at SFE level; controls included but not reported,semi_elasticity,1.7999999999999998,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c4,3,Small Area,36,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.013,,,0.0,,,,high,"view depth (horizon angle), 0 if no NLP-indicated view; robust clustered p-value printed below coefficient; no SE",natural log of listed sale price (EUR),log,OLS,100030.0,0.882,p,spatial FE level: Small Area (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,semi_elasticity,1.3,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c5,3,ED x Year,36,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.017,,,0.0,,,,high,"view depth (horizon angle), 0 if no NLP-indicated view; robust clustered p-value printed below coefficient; no SE",natural log of listed sale price (EUR),log,OLS,100030.0,0.868,p,spatial FE level: ED x Year (4509 absorbed units); robust p-values clustered at SFE level; controls included but not reported,semi_elasticity,1.7000000000000002,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T3-c6,3,Small Area + Micro-Market x Year,36,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.013,,,0.0,,,,high,"view depth (horizon angle), 0 if no NLP-indicated view; robust clustered p-value printed below coefficient; no SE",natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,spatial FE level: Small Area + Micro-Market x Year (5571 absorbed units); robust p-values clustered at SFE level; controls included but not reported,semi_elasticity,1.3,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c1,4,Baseline,37,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.061,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.061,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c2,4,Spec 1: GIS-only breadth and depth,37,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.062,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 1: GIS-only breadth and depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.062,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c3,4,Spec 2: GIS-only breadth,37,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.062,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 2: GIS-only breadth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.062,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c4,4,Spec 3: GIS-only depth,37,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.062,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 3: GIS-only depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.062,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c5,4,Spec 4: NLP sea-view dummy only,37,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.061,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 4: NLP sea-view dummy only; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.061,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c6,4,Spec 5: NLP sea-view dummy set to 0 when GIS measure is 0,37,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.06,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 5: NLP sea-view dummy set to 0 when GIS measure is 0; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.06,,b (log-log),,,,,,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c1,4,Baseline,37,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.014,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.014,0.0054351427638125,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c2,4,Spec 1: GIS-only breadth and depth,37,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.015,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 1: GIS-only breadth and depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.015,0.005823367246941964,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c3,4,Spec 2: GIS-only breadth,37,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.014,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 2: GIS-only breadth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.014,0.0054351427638125,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c4,4,Spec 3: GIS-only depth,37,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.015,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 3: GIS-only depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.015,0.005823367246941964,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c5,4,Spec 4: NLP sea-view dummy only,37,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.012,,,0.02,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 4: NLP sea-view dummy only; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.012,0.0051582998974079195,b (log-log),|b/z(p)| (two-sided normal),-0.007355623369604289,0.0031618759394000918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c6,4,Spec 5: NLP sea-view dummy set to 0 when GIS measure is 0,37,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.013,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 5: NLP sea-view dummy set to 0 when GIS measure is 0; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,-0.013,0.005046918280683036,b (log-log),|b/z(p)| (two-sided normal),-0.008144402908585707,0.003161856609583792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,100020.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c1,4,Baseline,37,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,baseline NLP-restricted GIS measure; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,0.011,,b (log-log),,,,,,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c1,4,Baseline,37,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.013,,,0.0,,,,high,baseline; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",semi_elasticity,1.3,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c2,4,Spec 1: GIS-only breadth and depth,37,Seaview breadth (GIS only),view,E_environment,log,"index (ln of visible near-shore sea area score, all listings)",0.003,,,0.14,,,,high,GIS measure without NLP restriction; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 1: GIS-only breadth and depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,0.003,0.002032808129821332,b (log-log),|b/z(p)| (two-sided normal),0.004666343578288186,0.0031619270541612637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4757910281791702,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c2,4,Spec 1: GIS-only breadth and depth,37,Seaview depth (GIS only),view,E_environment,level,"degrees (vertical horizon angle, all listings)",0.013,,,0.0,,,,high,GIS measure without NLP restriction; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 1: GIS-only breadth and depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",semi_elasticity,1.3,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c3,4,Spec 2: GIS-only breadth,37,Seaview breadth (GIS only),view,E_environment,log,"index (ln of visible near-shore sea area score, all listings)",0.008,,,0.0,,,,high,GIS measure without NLP restriction; depth excluded; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 2: GIS-only breadth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",elasticity,0.008,,b (log-log),,,,,,100020.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c4,4,Spec 3: GIS-only depth,37,Seaview depth (GIS only),view,E_environment,level,"degrees (vertical horizon angle, all listings)",0.015,,,0.0,,,,high,GIS measure without NLP restriction; breadth excluded; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 3: GIS-only depth; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",semi_elasticity,1.5,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,100020.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c5,4,Spec 4: NLP sea-view dummy only,37,Sea Views NLP,view,E_environment,binary,"binary (sea view mentioned in listing text, NLP)",0.082,,,0.0,,,,high,AI/NLP-derived sea-view dummy; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 4: NLP sea-view dummy only; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",pct_premium,8.545580982954903,,100*(exp(b) - 1),,,,,,100020.0,binary,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T4-c6,4,Spec 5: NLP sea-view dummy set to 0 when GIS measure is 0,37,Sea Views NLP (GIS=0),view,E_environment,binary,"binary (NLP sea view, set to 0 if GIS viewshed score is 0)",0.079,,,0.0,,,,high,AI/NLP-derived sea-view dummy validated by GIS; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,100030.0,0.89,p,"Spec 5: NLP sea-view dummy set to 0 when GIS measure is 0; spatial FE Small Area + Micro-Market x Year (5,571 units); controls not reported",pct_premium,8.22043220703148,,100*(exp(b) - 1),,,,,,100020.0,binary,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c1,5 (printed 'Table 1' p.38),Baseline,38,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.06,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.06,,b (log-log),,,,,,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c2,5 (printed 'Table 1' p.38),Adding land viewshed,38,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.061,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.061,,b (log-log),,,,,,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c3,5 (printed 'Table 1' p.38),Adding slope,38,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.06,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.06,,b (log-log),,,,,,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c4,5 (printed 'Table 1' p.38),Adding land viewshed and slope,38,Distance To Coastline (log),water_proximity,E_environment,log,"m (Euclidean distance to coastline), log",-0.061,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed and slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.061,,b (log-log),,,,,,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c1,5 (printed 'Table 1' p.38),Baseline,38,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.015,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.015,0.005823367246941964,b (log-log),|b/z(p)| (two-sided normal),-0.008168241779845766,0.0031711114430571182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c2,5 (printed 'Table 1' p.38),Adding land viewshed,38,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.016,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.016,,b (log-log),,,,,,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c3,5 (printed 'Table 1' p.38),Adding slope,38,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.015,,,0.01,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.015,0.005823367246941964,b (log-log),|b/z(p)| (two-sided normal),-0.008168241779845766,0.0031711114430571182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,True,True,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c4,5 (printed 'Table 1' p.38),Adding land viewshed and slope,38,Distance To Transitional (log),water_proximity,E_environment,log,"m (Euclidean distance to transitional water body/estuary), log",-0.016,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed and slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,-0.016,,b (log-log),,,,,,99437.0,m log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c1,5 (printed 'Table 1' p.38),Baseline,38,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.011,,b (log-log),,,,,,99437.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c2,5 (printed 'Table 1' p.38),Adding land viewshed,38,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.011,,b (log-log),,,,,,99437.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c3,5 (printed 'Table 1' p.38),Adding slope,38,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.011,,b (log-log),,,,,,99437.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c4,5 (printed 'Table 1' p.38),Adding land viewshed and slope,38,Seaview breadth,view,E_environment,log,"index (ln of visible near-shore sea area score, x NLP sea-view indicator)",0.011,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed and slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.011,,b (log-log),,,,,,99437.0,index,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c1,5 (printed 'Table 1' p.38),Baseline,38,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.013,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Baseline; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",semi_elasticity,1.3,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,99437.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c2,5 (printed 'Table 1' p.38),Adding land viewshed,38,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.012,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",semi_elasticity,1.2,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,99437.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c3,5 (printed 'Table 1' p.38),Adding slope,38,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.013,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",semi_elasticity,1.3,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,99437.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c4,5 (printed 'Table 1' p.38),Adding land viewshed and slope,38,Seaview depth,view,E_environment,level,degrees (vertical horizon angle),0.012,,,0.0,,,,high,robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed and slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",semi_elasticity,1.2,,100*b (unit as reported: degrees; no harmonised unit defined for this characteristic),,,,,,99437.0,degrees,unit_not_harmonisable,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c2,5 (printed 'Table 1' p.38),Adding land viewshed,38,Log of Land Area Viewshed,view,E_environment,log,"area (total land viewshed), log",0.005,,,0.0,,,,high,control for total land viewshed; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.005,,b (log-log),,,,,,99437.0,area log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c4,5 (printed 'Table 1' p.38),Adding land viewshed and slope,38,Log of Land Area Viewshed,view,E_environment,log,"area (total land viewshed), log",0.005,,,0.0,,,,high,control for total land viewshed; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed and slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.005,,b (log-log),,,,,,99437.0,area log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c3,5 (printed 'Table 1' p.38),Adding slope,38,Log of % Rise of Slope,other,other,log,"share (% rise of slope around property), log",0.0,,,0.97,,,,high,topography control; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.0,,b (log-log),,,,,,99437.0,share log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8,2b0979784a826ff5181dbb43492b0239ca3062e8:T5-c4,5 (printed 'Table 1' p.38),Adding land viewshed and slope,38,Log of % Rise of Slope,other,other,log,"share (% rise of slope around property), log",0.001,,,0.83,,,,high,topography control; robust clustered p-value printed below coefficient; no SE,natural log of listed sale price (EUR),log,OLS,99447.0,0.89,p,"Adding land viewshed and slope; spatial FE Small Area + Micro-Market x Year (5,566 units); controls not reported",elasticity,0.001,0.004657627838059732,b (log-log),|b/z(p)| (two-sided normal),0.0006808651554206343,0.0031712165018520123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21470156800174456,99437.0,share log,,2023.0,Ireland,Europe,High income,2014.0,2019.0,mixed,listing,journal,True,True,clustered,NLP keyword classification of listing text to identify sea-view properties (combined with GIS viewshed),2016.5,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,HIGH,energy_efficiency,A_structural,dummy,binary,0.001,0.007,,,,,,high,"=1 if building energy performance <= 75 kWh/m2; reference: >75 kWh/m2 (c1,c2,c4) or medium efficiency 75-140 (c3,c5)",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,0.09759759548770841,0.7007003501166958,100*(exp(b - se^2/2) - 1),reported,0.0009700900025774411,0.0067906300180420875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.14285714285714285,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,HIGH,energy_efficiency,A_structural,dummy,binary,0.011,0.005,,,**,,,high,"=1 if building energy performance <= 75 kWh/m2; reference: >75 kWh/m2 (c1,c2,c4) or medium efficiency 75-140 (c3,c5)",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,1.1048084264677849,0.5055303612223597,100*(exp(b - se^2/2) - 1),reported,0.014937726220546576,0.006789875554793898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1999999999999997,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,HIGH,energy_efficiency,A_structural,dummy,binary,0.011,0.005,,,**,,,high,"=1 if building energy performance <= 75 kWh/m2; reference: >75 kWh/m2 (c1,c2,c4) or medium efficiency 75-140 (c3,c5)",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,1.1048084264677849,0.5055303612223597,100*(exp(b - se^2/2) - 1),reported,0.014937726220546576,0.006789875554793898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1999999999999997,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,HIGH,energy_efficiency,A_structural,dummy,binary,0.007,0.004,,,*,,,high,"=1 if building energy performance <= 75 kWh/m2; reference: >75 kWh/m2 (c1,c2,c4) or medium efficiency 75-140 (c3,c5)",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,0.7016501102615091,0.40280982290673945,100*(exp(b - se^2/2) - 1),reported,0.011883043053773672,0.006790310316442099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,HIGH,energy_efficiency,A_structural,dummy,binary,0.007,0.004,,,*,,,high,"=1 if building energy performance <= 75 kWh/m2; reference: >75 kWh/m2 (c1,c2,c4) or medium efficiency 75-140 (c3,c5)",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,0.7016501102615091,0.40280982290673945,100*(exp(b - se^2/2) - 1),reported,0.01188276910970561,0.006790153776974635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,LOW,energy_efficiency,A_structural,dummy,binary,-0.001,0.002,,,,,,high,=1 if building energy performance > 140 kWh/m2; reference medium efficiency,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-0.10014981656266686,0.199800099966675,100*(exp(b - se^2/2) - 1),reported,-0.0033952970359119893,0.0067905940718239795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,LOW,energy_efficiency,A_structural,dummy,binary,-0.001,0.002,,,,,,high,=1 if building energy performance > 140 kWh/m2; reference medium efficiency,log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-0.10014981656266686,0.199800099966675,100*(exp(b - se^2/2) - 1),reported,-0.0033952970359119893,0.0067905940718239795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Area,living_area,A_structural,log,m2,0.862,0.019,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,elasticity,0.862,0.019,b (log-log),reported,0.2944245620682365,0.006489636518905444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.36842105263158,21686.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Rent,other,other,log,currency,-0.277,0.023,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,elasticity,-0.277,0.023,b (log-log),reported,-0.08151070828751229,0.0067680371502266525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.043478260869566,21686.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Room,bedrooms,A_structural,level,count,0.049,0.004,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,semi_elasticity,4.9,0.4,100*b/1 (per unit count),reported,0.08289892987235534,0.006767259581416762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.25,21686.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Area,living_area,A_structural,log,m2,0.746,0.012,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,elasticity,0.746,0.012,b (log-log),reported,0.38891637330459794,0.006256027452620878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),62.166666666666664,21686.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Rent,other,other,log,currency,-0.144,0.014,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,elasticity,-0.144,0.014,b (log-log),reported,-0.06967675956762741,0.006774129402408222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.285714285714285,21686.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Room,bedrooms,A_structural,level,count,0.06,0.002,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,semi_elasticity,6.0,0.2,100*b/1 (per unit count),reported,0.19961887000309445,0.006653962333436481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.0,21686.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Area,living_area,A_structural,log,m2,0.746,0.012,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,elasticity,0.746,0.012,b (log-log),reported,0.38891637330459794,0.006256027452620878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),62.166666666666664,21686.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Rent,other,other,log,currency,-0.143,0.014,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,elasticity,-0.143,0.014,b (log-log),reported,-0.06919521798488465,0.006774357005513184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.214285714285714,21686.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Room,bedrooms,A_structural,level,count,0.06,0.002,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,semi_elasticity,6.0,0.2,100*b/1 (per unit count),reported,0.19961887000309445,0.006653962333436481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.0,21686.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Area,living_area,A_structural,log,m2,0.751,0.006,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),elasticity,0.751,0.006,b (log-log),reported,0.6476398037701645,0.005174219470866827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),125.16666666666666,21685.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Rent,other,other,log,currency,-0.156,0.004,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),elasticity,-0.156,0.004,b (log-log),reported,-0.2560144409009784,0.006564472843614831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.0,21685.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Room,bedrooms,A_structural,level,count,0.058,0.002,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),semi_elasticity,5.800000000000001,0.2,100*b/1 (per unit count),reported,0.19322172634637602,0.006662818149875035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.0,21685.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Area,living_area,A_structural,log,m2,0.751,0.006,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,elasticity,0.751,0.006,b (log-log),reported,0.647631134460653,0.005174150208740237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),125.16666666666666,21686.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Rent,other,other,log,currency,-0.156,0.004,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,elasticity,-0.156,0.004,b (log-log),reported,-0.25600892495322763,0.0065643314090571185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.0,21686.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Room,bedrooms,A_structural,level,count,0.058,0.002,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,semi_elasticity,5.800000000000001,0.2,100*b/1 (per unit count),reported,0.19321743762904284,0.006662670263070442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.0,21686.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 1901–1921,age,A_structural,dummy,binary,-0.041,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-4.017087005220111,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 1901–1921,age,A_structural,dummy,binary,-0.002,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 1901–1921,age,A_structural,dummy,binary,-0.002,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 1901–1921,age,A_structural,dummy,binary,-0.006,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 1901–1921,age,A_structural,dummy,binary,-0.006,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 1922–1941,age,A_structural,dummy,binary,-0.143,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-13.324593110451099,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 1922–1941,age,A_structural,dummy,binary,-0.066,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-6.386913570838115,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 1922–1941,age,A_structural,dummy,binary,-0.066,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-6.386913570838115,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 1922–1941,age,A_structural,dummy,binary,-0.07,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 1922–1941,age,A_structural,dummy,binary,-0.07,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 1942–1961,age,A_structural,dummy,binary,-0.28,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-24.421625854427454,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 1942–1961,age,A_structural,dummy,binary,-0.116,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-10.952477670252737,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 1942–1961,age,A_structural,dummy,binary,-0.116,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-10.952477670252737,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 1942–1961,age,A_structural,dummy,binary,-0.12,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-11.307956328284252,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 1942–1961,age,A_structural,dummy,binary,-0.12,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-11.307956328284252,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 1962–1976,age,A_structural,dummy,binary,-0.467,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-37.311990958762294,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 1962–1976,age,A_structural,dummy,binary,-0.165,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-15.210629591208413,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 1962–1976,age,A_structural,dummy,binary,-0.165,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-15.210629591208413,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 1962–1976,age,A_structural,dummy,binary,-0.163,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-15.040881158540975,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 1962–1976,age,A_structural,dummy,binary,-0.163,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-15.040881158540975,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 1977–1991,age,A_structural,dummy,binary,-0.304,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-26.21391335494089,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 1977–1991,age,A_structural,dummy,binary,-0.143,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-13.324593110451099,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 1977–1991,age,A_structural,dummy,binary,-0.144,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-13.4112251940795,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 1977–1991,age,A_structural,dummy,binary,-0.139,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-12.977197154174846,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 1977–1991,age,A_structural,dummy,binary,-0.139,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-12.977197154174846,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 1992–2005,age,A_structural,dummy,binary,-0.218,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-19.587455833344038,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 1992–2005,age,A_structural,dummy,binary,-0.074,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-7.132830615871278,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 1992–2005,age,A_structural,dummy,binary,-0.074,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-7.132830615871278,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 1992–2005,age,A_structural,dummy,binary,-0.063,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-6.105652631086677,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 1992–2005,age,A_structural,dummy,binary,-0.063,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-6.105652631086677,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c1,2,1,8,Building year 2006–2019,age,A_structural,dummy,binary,-0.123,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.812,se,Full sample; 2-digit postcode FE; robust SE,pct_premium,-11.57363374391791,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c2,2,2,8,Building year 2006–2019,age,A_structural,dummy,binary,-0.001,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; robust SE,pct_premium,-0.09995001666249781,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c3,2,3,8,Building year 2006–2019,age,A_structural,dummy,binary,-0.002,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.933,se,Full sample; 4-digit postcode FE; HIGH and LOW dummies; robust SE,pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c4,2,4,8,Building year 2006–2019,age,A_structural,dummy,binary,-0.004,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21695.0,0.943,se,Full sample; 4-digit postcode FE; robust regression (outlier-robust),pct_premium,-0.39920106560085156,,100*(exp(b) - 1),,,,,,21685.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T2-c5,2,5,8,Building year 2006–2019,age,A_structural,dummy,binary,-0.004,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.943,se,Full sample; 4-digit postcode FE; HIGH and LOW; robust regression,pct_premium,-0.39920106560085156,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T3-c1,3,1,9,HIGH,energy_efficiency,A_structural,dummy,binary,0.004,0.006,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,OLS,11468.0,0.913,se,Regional subsample: Stockholm; 4-digit postcode FE; robust SE; dwelling controls included but not reported,pct_premium,0.3989938695798001,0.6024048064064051,100*(exp(b - se^2/2) - 1),reported,0.006227961357189108,0.009341942035783663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,11458.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T3-c2,3,2,9,HIGH,energy_efficiency,A_structural,dummy,binary,0.015,0.017,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,OLS,4142.0,0.885,se,Regional subsample: South; 4-digit postcode FE; robust SE; dwelling controls included but not reported,pct_premium,1.4966391375278887,1.7256922098467222,100*(exp(b - se^2/2) - 1),reported,0.013725281817756866,0.015555319393457783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8823529411764705,4132.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T3-c3,3,3,9,HIGH,energy_efficiency,A_structural,dummy,binary,0.002,0.008,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,OLS,5285.0,0.912,se,Regional subsample: Mid; 4-digit postcode FE; robust SE; dwelling controls included but not reported,pct_premium,0.19699377829771603,0.8016016010672002,100*(exp(b - se^2/2) - 1),reported,0.0034421215624525553,0.013768486249810221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,5275.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,AB,energy_efficiency,A_structural,dummy,binary,0.015,0.009,,,*,,,high,EPC grade A or B; reference C-G,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,1.507195336911038,0.9136017581541469,100*(exp(b - se^2/2) - 1),reported,0.02059689636394069,0.012358137818364414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,AB,energy_efficiency,A_structural,dummy,binary,0.01,0.008,,,,,,high,EPC grade A or B; reference C-G,log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,1.001784599596145,0.8080401336673343,100*(exp(b - se^2/2) - 1),reported,0.015449106028584252,0.012359284822867403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,ABC,energy_efficiency,A_structural,dummy,binary,0.001,0.005,,,,,,high,EPC grade A-C; reference D-G,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,0.09879877386591573,0.5005002500833542,100*(exp(b - se^2/2) - 1),reported,0.0024721444481789587,0.012360722240894791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,ABC,energy_efficiency,A_structural,dummy,binary,0.002,0.006,,,,,,high,EPC grade A-C; reference D,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,0.19839654602995527,0.6012012008004002,100*(exp(b - se^2/2) - 1),reported,0.004120218364175057,0.012360655092525171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3333333333333333,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,ABC,energy_efficiency,A_structural,dummy,binary,-0.001,0.006,,,,,,high,EPC grade A-C; reference D,log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-0.10174820137849316,0.599400299900025,100*(exp(b - se^2/2) - 1),reported,-0.0020604371306068726,0.012362622783641235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16666666666666666,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,EFG,energy_efficiency,A_structural,dummy,binary,0.002,0.005,,,,,,high,EPC grade E-G; reference D,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,0.19894763872647658,0.5010010006670002,100*(exp(b - se^2/2) - 1),reported,0.00494424357146234,0.012360608928655849,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,EFG,energy_efficiency,A_structural,dummy,binary,-0.002,0.005,,,,,,high,EPC grade E-G; reference D,log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-0.201047627968165,0.49900099933366654,100*(exp(b - se^2/2) - 1),reported,-0.004944999149166611,0.012362497872916526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Area,living_area,A_structural,log,m2,0.749,0.023,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,elasticity,0.749,0.023,b (log-log),reported,0.37341373243039067,0.011466643318957257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.56521739130435,6545.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Rent,other,other,log,currency,-0.164,0.025,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,elasticity,-0.164,0.025,b (log-log),reported,-0.08082131952290386,0.012320323098003638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.56,6545.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Room,bedrooms,A_structural,level,count,0.065,0.004,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,semi_elasticity,6.5,0.4,100*b/1 (per unit count),reported,0.1969290133594376,0.012118708514426929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.25,6545.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Area,living_area,A_structural,log,m2,0.75,0.023,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,elasticity,0.75,0.023,b (log-log),reported,0.3738426456347446,0.011464507799465501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.608695652173914,6545.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Rent,other,other,log,currency,-0.165,0.025,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,elasticity,-0.165,0.025,b (log-log),reported,-0.0813108840424445,0.012319830915521895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.6,6545.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Room,bedrooms,A_structural,level,count,0.065,0.004,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,semi_elasticity,6.5,0.4,100*b/1 (per unit count),reported,0.1969290133594376,0.012118708514426929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.25,6545.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Area,living_area,A_structural,log,m2,0.75,0.023,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,elasticity,0.75,0.023,b (log-log),reported,0.3738426456347446,0.011464507799465501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.608695652173914,6545.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Rent,other,other,log,currency,-0.165,0.025,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,elasticity,-0.165,0.025,b (log-log),reported,-0.0813108840424445,0.012319830915521895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.6,6545.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Room,bedrooms,A_structural,level,count,0.065,0.004,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,semi_elasticity,6.5,0.4,100*b/1 (per unit count),reported,0.1969290133594376,0.012118708514426929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.25,6545.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Area,living_area,A_structural,log,m2,0.75,0.012,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,elasticity,0.75,0.012,b (log-log),reported,0.6113584216501162,0.009781734746401857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),62.5,6545.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Rent,other,other,log,currency,-0.17,0.009,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,elasticity,-0.17,0.009,b (log-log),reported,-0.22736600847386046,0.012037023978027904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.888888888888893,6545.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Room,bedrooms,A_structural,level,count,0.059,0.003,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,semi_elasticity,5.8999999999999995,0.3,100*b/1 (per unit count),reported,0.23621552688563624,0.012010958994184896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.666666666666664,6545.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Area,living_area,A_structural,log,m2,0.75,0.012,,,***,,,high,,log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,elasticity,0.75,0.012,b (log-log),reported,0.6114169262481257,0.00978267081997001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),62.5,6543.0,m2,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Rent,other,other,log,currency,-0.17,0.009,,,***,,,high,"Monthly fee paid to tenant association (SEK, log); not market rent",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,elasticity,-0.17,0.009,b (log-log),reported,-0.22739895868401097,0.012038768400918226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.888888888888893,6543.0,currency,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Room,bedrooms,A_structural,level,count,0.059,0.003,,,***,,,high,"Total number of rooms, not bedrooms; mapped to bedrooms as closest leaf",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,semi_elasticity,5.8999999999999995,0.3,100*b/1 (per unit count),reported,0.2362496114542047,0.012012692107840919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.666666666666664,6543.0,count,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 1901–1921,age,A_structural,dummy,binary,0.0,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,0.0,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 1901–1921,age,A_structural,dummy,binary,0.0,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,0.0,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 1901–1921,age,A_structural,dummy,binary,0.0,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,0.0,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 1901–1921,age,A_structural,dummy,binary,0.0,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,0.0,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 1901–1921,age,A_structural,dummy,binary,-0.001,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-0.09995001666249781,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 1922–1941,age,A_structural,dummy,binary,-0.059,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,-5.729323084290028,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 1922–1941,age,A_structural,dummy,binary,-0.059,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,-5.729323084290028,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 1922–1941,age,A_structural,dummy,binary,-0.06,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,-5.823546641575128,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 1922–1941,age,A_structural,dummy,binary,-0.068,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,-6.5739526422786465,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 1922–1941,age,A_structural,dummy,binary,-0.068,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-6.5739526422786465,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 1942–1961,age,A_structural,dummy,binary,-0.129,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,-12.102603445441684,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 1942–1961,age,A_structural,dummy,binary,-0.129,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,-12.102603445441684,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 1942–1961,age,A_structural,dummy,binary,-0.129,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,-12.102603445441684,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 1942–1961,age,A_structural,dummy,binary,-0.136,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,-12.71573675112807,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 1942–1961,age,A_structural,dummy,binary,-0.136,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-12.71573675112807,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 1962–1976,age,A_structural,dummy,binary,-0.172,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,-15.80208268315001,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 1962–1976,age,A_structural,dummy,binary,-0.172,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,-15.80208268315001,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 1962–1976,age,A_structural,dummy,binary,-0.172,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,-15.80208268315001,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 1962–1976,age,A_structural,dummy,binary,-0.165,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,-15.210629591208413,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 1962–1976,age,A_structural,dummy,binary,-0.165,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-15.210629591208413,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 1977–1991,age,A_structural,dummy,binary,-0.166,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,-15.295376581060038,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 1977–1991,age,A_structural,dummy,binary,-0.166,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,-15.295376581060038,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 1977–1991,age,A_structural,dummy,binary,-0.166,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,-15.295376581060038,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 1977–1991,age,A_structural,dummy,binary,-0.158,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,-14.615021803151828,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 1977–1991,age,A_structural,dummy,binary,-0.158,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-14.615021803151828,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 1992–2005,age,A_structural,dummy,binary,-0.091,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,-8.698228910073425,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 1992–2005,age,A_structural,dummy,binary,-0.091,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,-8.698228910073425,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 1992–2005,age,A_structural,dummy,binary,-0.091,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,-8.698228910073425,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 1992–2005,age,A_structural,dummy,binary,-0.079,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,-7.596007555491324,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 1992–2005,age,A_structural,dummy,binary,-0.079,,,,***,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,-7.596007555491324,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c1,4,1,10,Building year 2006–2019,age,A_structural,dummy,binary,0.016,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample (buildings with EPC grade); AB vs C-G; robust SE,pct_premium,1.6128685406094911,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c2,4,2,10,Building year 2006–2019,age,A_structural,dummy,binary,0.019,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC vs D-G; robust SE,pct_premium,1.918164861740812,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c3,4,3,10,Building year 2006–2019,age,A_structural,dummy,binary,0.019,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,6555.0,0.936,se,Graded sample; ABC and EFG vs D; robust SE,pct_premium,1.918164861740812,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c4,4,4,10,Building year 2006–2019,age,A_structural,dummy,binary,0.013,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6555.0,0.945,se,Graded sample; AB vs C-G; robust regression,pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,6545.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T4-c5,4,5,10,Building year 2006–2019,age,A_structural,dummy,binary,0.014,,,,,,,high,"Construction-period dummy (not age); omitted category pre-1901; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,6553.0,0.945,se,Graded sample; ABC and EFG vs D; robust regression,pct_premium,1.4098458938492264,,100*(exp(b) - 1),,,,,,6543.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c1,5,1,11,HIGH,energy_efficiency,A_structural,dummy,binary,0.008,0.005,,,*,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.935,se,Full sample; income-category dummies; 4-digit postcode FE,pct_premium,0.8019485181956831,0.5040160427521367,100*(exp(b - se^2/2) - 1),reported,0.01086437189872603,0.006790232436703768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c2,5,2,11,HIGH,energy_efficiency,A_structural,dummy,binary,0.006,0.004,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,other,21691.0,0.944,se,Full sample; income-category dummies; robust regression; 4-digit postcode FE,pct_premium,0.6009987941968831,0.40240721442162597,100*(exp(b - se^2/2) - 1),reported,0.010186595721122791,0.006791063814081862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,21681.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c3,5,3,11,HIGH,energy_efficiency,A_structural,dummy,binary,-0.003,0.005,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.924,se,Full sample; neighbourhood controls (unreported); 3-digit postcode FE,pct_premium,-0.30079669749301097,0.4985022477516865,100*(exp(b - se^2/2) - 1),reported,-0.004074346109876248,0.006790576849793746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c4,5,4,11,HIGH,energy_efficiency,A_structural,dummy,binary,0.003,0.005,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.938,se,Full sample; neighbourhood controls (unreported); 4-digit postcode FE,pct_premium,0.2991967025430142,0.5015022522516885,100*(exp(b - se^2/2) - 1),reported,0.004074346109876248,0.006790576849793746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c5,5,5,11,HIGH,energy_efficiency,A_structural,dummy,binary,0.0,0.004,,,,,,high,<= 75 kWh/m2 dummy,log sales price (SEK) of tenant-owned apartment,log,other,21696.0,0.946,se,Full sample; neighbourhood controls (unreported); robust regression; 4-digit postcode FE,pct_premium,-0.0007999968000049762,0.4,100*(exp(b - se^2/2) - 1),reported,0.0,0.006790633213289102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c1,5,1,11,IC1,income,F_neighbourhood,dummy,binary,-0.113,,,,***,,,high,"Dummy for postcode (DeSO) income quartile category 1 (IC1 lowest); reference = top income quartile; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.935,se,Full sample; income-category dummies; 4-digit postcode FE,pct_premium,-10.684933988398448,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c1,5,1,11,IC2,income,F_neighbourhood,dummy,binary,-0.053,,,,***,,,high,"Dummy for postcode (DeSO) income quartile category 2 (IC1 lowest); reference = top income quartile; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.935,se,Full sample; income-category dummies; 4-digit postcode FE,pct_premium,-5.161998751770181,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c1,5,1,11,IC3,income,F_neighbourhood,dummy,binary,-0.022,,,,***,,,high,"Dummy for postcode (DeSO) income quartile category 3 (IC1 lowest); reference = top income quartile; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.935,se,Full sample; income-category dummies; 4-digit postcode FE,pct_premium,-2.175976494878995,,100*(exp(b) - 1),,,,,,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c2,5,2,11,IC1,income,F_neighbourhood,dummy,binary,-0.092,,,,***,,,high,"Dummy for postcode (DeSO) income quartile category 1 (IC1 lowest); reference = top income quartile; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21691.0,0.944,se,Full sample; income-category dummies; robust regression; 4-digit postcode FE,pct_premium,-8.789485045490963,,100*(exp(b) - 1),,,,,,21681.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c2,5,2,11,IC2,income,F_neighbourhood,dummy,binary,-0.038,,,,***,,,high,"Dummy for postcode (DeSO) income quartile category 2 (IC1 lowest); reference = top income quartile; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21691.0,0.944,se,Full sample; income-category dummies; robust regression; 4-digit postcode FE,pct_premium,-3.728705910880048,,100*(exp(b) - 1),,,,,,21681.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T5-c2,5,2,11,IC3,income,F_neighbourhood,dummy,binary,-0.015,,,,***,,,high,"Dummy for postcode (DeSO) income quartile category 3 (IC1 lowest); reference = top income quartile; SE not printed, stars only",log sales price (SEK) of tenant-owned apartment,log,other,21691.0,0.944,se,Full sample; income-category dummies; robust regression; 4-digit postcode FE,pct_premium,-1.4888060396937353,,100*(exp(b) - 1),,,,,,21681.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,other,journal,False,False,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T6-c1,6,1,11,HIGH (≤ 50 kWh/m2),energy_efficiency,A_structural,dummy,binary,0.01,0.012,,,,,,high,Narrow boundary,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.938,se,Full sample; HIGH defined as <= 50 kWh/m2; neighbourhood + dwelling controls; 4-digit postcode FE,pct_premium,0.9977446090125186,1.2120602005010015,100*(exp(b - se^2/2) - 1),reported,0.005658770407223558,0.006790524488668269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T6-c2,6,2,11,HIGH (≤ 100 kWh/m2),energy_efficiency,A_structural,dummy,binary,0.005,0.003,,,,,,high,Wide boundary,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.938,se,Full sample; HIGH defined as <= 100 kWh/m2; neighbourhood + dwelling controls,pct_premium,0.5007998313232953,0.3015037562578203,100*(exp(b - se^2/2) - 1),reported,0.011316997243562096,0.006790198346137258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,21686.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T6-c3,6,3,11,HIGH (≤ 100 kWh/m2),energy_efficiency,A_structural,other,binary,0.468,0.098,,,***,,,high,Main effect in a specification with HIGH x log net income interaction; not interpretable alone,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.938,se,Full sample; HIGH (<= 100 kWh/m2) interacted with log net income; neighbourhood + dwelling controls,other,0.468,0.098,not harmonised,reported,0.03241170023472777,0.00678706543376778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.775510204081633,21686.0,binary,nonstandard_x_transform,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T6-c3,6,3,11,HIGH (≤ 100 kWh/m2) × net income,energy_efficiency,A_structural,other,index,-0.082,0.017,,,***,,,high,Interaction of HIGH dummy with neighbourhood (DeSO) log net income,log sales price (SEK) of tenant-owned apartment,log,OLS,21696.0,0.938,se,Full sample; HIGH (<= 100 kWh/m2) interacted with log net income; neighbourhood + dwelling controls,other,-0.082,0.017,not harmonised,reported,-0.03273726218966318,0.0067869933807838305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.823529411764706,21686.0,index,nonstandard_x_transform,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,False,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T8-c1,8,1,12,HIGH,energy_efficiency,A_structural,dummy,binary,0.009,0.005,,,*,,,high,<= 75 kWh/m2,log sales price (SEK) of tenant-owned apartment,log,OLS,16511.0,0.927,se,Full sample; propensity score (probit) included as covariate,pct_premium,0.9028008844926605,0.5045203108869339,100*(exp(b - se^2/2) - 1),reported,0.014011180886090095,0.007783989381161165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7999999999999998,16501.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T8-c2,8,2,12,HIGH,energy_efficiency,A_structural,dummy,binary,0.012,0.005,,,**,,,high,<= 75 kWh/m2,log sales price (SEK) of tenant-owned apartment,log,WLS,2754.0,0.911,se,"Full sample; nearest-neighbour PS-matched sample, weighted",pct_premium,1.205963804153476,0.5060361444330389,100*(exp(b - se^2/2) - 1),reported,0.04576820148524767,0.019070083952186533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,2744.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T8-c3,8,3,12,AB,energy_efficiency,A_structural,dummy,binary,0.011,0.009,,,,,,high,EPC grade A or B,log sales price (SEK) of tenant-owned apartment,log,OLS,3638.0,0.929,se,Graded sample; propensity score as covariate,pct_premium,1.1019775314645486,0.9099546502002475,100*(exp(b - se^2/2) - 1),reported,0.0202874351533894,0.016598810580045874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2222222222222223,3628.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T8-c4,8,4,12,AB,energy_efficiency,A_structural,dummy,binary,0.014,0.009,,,,,,high,EPC grade A or B,log sales price (SEK) of tenant-owned apartment,log,WLS,788.0,0.939,se,"Graded sample; PS-matched sample, weighted",pct_premium,1.405738878258167,0.9126886130446429,100*(exp(b - se^2/2) - 1),reported,0.05568284283033449,0.035796113248072164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5555555555555558,778.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T9-c1,9,1,13,HIGH,energy_efficiency,A_structural,dummy,binary,0.005,0.005,,,,,,high,<= 75 kWh/m2,log sales price (SEK) of tenant-owned apartment,log,WLS,17702.0,0.931,se,"Full sample after coarsened exact matching (CEM), weighted hedonic regression",pct_premium,0.4999958281406647,0.5025062604297005,100*(exp(b - se^2/2) - 1),reported,0.0075179470267321075,0.0075179470267321075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,17692.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f,2bbd25e60ca8ea2625da98d69a5096e00200229f:T9-c2,9,2,13,AB,energy_efficiency,A_structural,dummy,binary,0.007,0.009,,,,,,high,EPC grade A or B,log sales price (SEK) of tenant-owned apartment,log,WLS,4373.0,0.933,se,"Graded sample after CEM, weighted hedonic regression",pct_premium,0.6983773598154164,0.9063221015401637,100*(exp(b - se^2/2) - 1),reported,0.011774238218514897,0.015138306280947722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7777777777777779,4363.0,binary,,2022.0,Sweden,Europe,High income,2019.0,2019.0,condo_apartment,transaction,journal,True,True,robust,,2019.0,ols,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Age,age,A_structural,log,years,0.0616,,15.2607,,,,,medium,Age of house (data section lists year built); positive coefficient as printed; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.0616,0.004036512086601532,b (log-log),|b/t|,0.13617003682328652,0.008922922069320969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.2607,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Bathrooms,bathrooms,A_structural,log,count,0.0286,,1.9343,,,,,medium,Number of bathrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.0286,0.014785710592979373,b (log-log),|b/t|,0.017419241283239004,0.009005449663050717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9343,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Bedrooms,bedrooms,A_structural,log,count,0.086,,5.1746,,,,,medium,Number of bedrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.086,0.016619642097939938,b (log-log),|b/t|,0.04655613452786352,0.008997049922286462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.1746,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Dock,waterfront,C_exterior,dummy,binary,0.2154,,5.5903,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,23.94375400547686,4.779226525869481,100*(exp(b - se^2/2) - 1),|b/t|,0.05028710113380218,0.008995420842137664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5903,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Square feet,living_area,A_structural,log,sqft,0.7462,,41.7834,,,,,medium,House square footage; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.7462,0.017858766878712597,b (log-log),|b/t|,0.35221894214068855,0.008429638137171425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.7834,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Central air,air_conditioning,A_structural,dummy,binary,0.1035,,6.461,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,10.890350386959913,1.776601758447369,100*(exp(b - se^2/2) - 1),|b/t|,0.05809475592793831,0.00899160438445106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.461,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Lot size,lot_size,A_structural,log,acres,0.0046,,1.5806,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.0046,0.0029102872326964443,b (log-log),|b/t|,0.014234731348383177,0.009005903674796392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5806,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Adjacent golf,green_space,E_environment,dummy,binary,0.0163,,0.6336,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,1.6097273494849018,2.6148780282190507,100*(exp(b - se^2/2) - 1),|b/t|,0.005706625844883544,0.009006669578414683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6336,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Distance fixed,green_space,E_environment,log,miles,-0.0316,,-16.7966,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,-0.0316,0.0018813331269423575,b (log-log),|b/t|,-0.14958184702665084,0.008905483670900707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.7966,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Adjacent fixed,green_space,E_environment,dummy,binary,-0.1379,,-2.0083,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,-13.086554673834483,5.982000828728144,100*(exp(b - se^2/2) - 1),|b/t|,-0.018085430603751985,0.009005343127895226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0083,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Distance historic,neighbourhood_amenities,F_neighbourhood,log,miles,-0.0489,,-6.4415,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,-0.0489,0.0075913995187456345,b (log-log),|b/t|,-0.057920008735210006,0.008991695837182335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.4415,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.5175,,-5.3227,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,-40.680159605596025,5.794700185740714,100*(exp(b - se^2/2) - 1),|b/t|,-0.04788558465651213,0.008996483862797477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3227,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,True
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Adjacent adjusted,other,other,dummy,binary,-0.3929,,-25.4133,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,-32.49844235245264,1.0437263878843857,100*(exp(b - se^2/2) - 1),|b/t|,-0.22312262074375427,0.008779757872600342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.4133,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Distance railroad,other,other,log,miles,0.0568,,7.337,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.0568,0.007741583753577757,b (log-log),|b/t|,0.0659391905190219,0.008987214190952966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.337,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Distance water,water_proximity,E_environment,log,miles,0.024,,4.2566,,,,,medium,Distance to nearest open water; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",elasticity,0.024,0.005638302870835879,b (log-log),|b/t|,0.03831026945897289,0.009000204261375953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2566,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c1,1,1 Strict Contiguity,13,Adjacent water,waterfront,C_exterior,dummy,binary,0.274,,4.5128,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.56,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Strict Contiguity",pct_premium,31.279279490248648,7.985482534422322,100*(exp(b - se^2/2) - 1),|b/t|,0.04061242637826273,0.00899938538784407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5128,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Age,age,A_structural,log,years,0.0657,,16.5206,,,,,medium,Age of house (data section lists year built); positive coefficient as printed; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.0657,0.003976853140927084,b (log-log),|b/t|,0.14717760995485718,0.008908732730945435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.5206,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Bathrooms,bathrooms,A_structural,log,count,0.0169,,1.1437,,,,,medium,Number of bathrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.0169,0.01477660225583632,b (log-log),|b/t|,0.010300549234374136,0.00900633840550331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1437,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Bedrooms,bedrooms,A_structural,log,count,0.1049,,5.4593,,,,,medium,Number of bedrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.1049,0.019214917663436703,b (log-log),|b/t|,0.04911157725876088,0.008995947696364163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.4593,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Dock,waterfront,C_exterior,dummy,binary,0.1814,,4.8653,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,19.80616212728674,4.470011878464146,100*(exp(b - se^2/2) - 1),|b/t|,0.043778849628308705,0.008998180919636755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8653,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Square feet,living_area,A_structural,log,sqft,0.681,,39.3776,,,,,medium,House square footage; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.681,0.017294096135874203,b (log-log),|b/t|,0.3342659447337591,0.008488733308626201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.3776,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Central air,air_conditioning,A_structural,dummy,binary,0.1171,,7.1324,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,12.408033804567054,1.8457678945863583,100*(exp(b - se^2/2) - 1),|b/t|,0.06410807154600548,0.008988288871348421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.1324,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Lot size,lot_size,A_structural,log,acres,0.006,,1.7832,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.006,0.0033647375504710637,b (log-log),|b/t|,0.016058883616282577,0.009005654787058422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7832,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Adjacent golf,green_space,E_environment,dummy,binary,0.0703,,2.4266,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,7.237986639388416,3.108050265100677,100*(exp(b - se^2/2) - 1),|b/t|,0.021850722050961613,0.009004665808522877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4266,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Distance fixed,green_space,E_environment,log,miles,-0.0283,,-15.1693,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,-0.0283,0.0018656101468096748,b (log-log),|b/t|,-0.13536947090039608,0.008923910193640847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.1693,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Adjacent fixed,green_space,E_environment,dummy,binary,-0.0995,,-1.3958,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,-9.700728747190501,6.453385156320142,100*(exp(b - se^2/2) - 1),|b/t|,-0.01257072075242873,0.0090061045654311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3958,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Distance historic,neighbourhood_amenities,F_neighbourhood,log,miles,-0.0444,,-5.1727,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,-0.0444,0.008583525044947514,b (log-log),|b/t|,-0.04653907716430166,0.008997057081273156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.1727,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.4934,,-4.8451,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,-39.261059375309124,6.217495830801905,100*(exp(b - se^2/2) - 1),|b/t|,-0.04359743257653115,0.008998252373848041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.8451,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,True
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Adjacent adjusted,other,other,dummy,binary,-0.3664,,-23.9456,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,-30.685569257772194,1.0607285134553037,100*(exp(b - se^2/2) - 1),|b/t|,-0.21082605096728912,0.008804375374485881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.9456,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Distance railroad,other,other,log,miles,0.0589,,6.7463,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.0589,0.008730711649348532,b (log-log),|b/t|,0.06065082295943365,0.008990235085814988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.7463,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Distance water,water_proximity,E_environment,log,miles,0.0199,,3.229,,,,,medium,Distance to nearest open water; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",elasticity,0.0199,0.006162898730257046,b (log-log),|b/t|,0.029070717905305188,0.009003009571169151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.229,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c2,1,2 200-meter Contiguity,13,Adjacent water,waterfront,C_exterior,dummy,binary,0.2564,,3.9774,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,28.958721911545737,8.33051511297538,100*(exp(b - se^2/2) - 1),|b/t|,0.03580074602783284,0.009001042396498427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9774,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Age,age,A_structural,log,years,0.053,,13.5754,,,,,medium,Age of house (data section lists year built); positive coefficient as printed; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.053,0.003904120688893145,b (log-log),|b/t|,0.1213672645939632,0.008940234880295475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.5754,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Bathrooms,bathrooms,A_structural,log,count,0.0101,,0.6857,,,,,medium,Number of bathrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.0101,0.014729473530698556,b (log-log),|b/t|,0.006175856112172244,0.00900664446867762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6857,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Bedrooms,bedrooms,A_structural,log,count,0.12,,6.3163,,,,,medium,Number of bedrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.12,0.01899846429080316,b (log-log),|b/t|,0.056797915997789536,0.008992276490633683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.3163,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Dock,waterfront,C_exterior,dummy,binary,0.2231,,5.9027,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,24.905307128548436,4.724327085769552,100*(exp(b - se^2/2) - 1),|b/t|,0.05308955917369604,0.008994114417757304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9027,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Square feet,living_area,A_structural,log,sqft,0.7053,,38.7594,,,,,medium,House square footage; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.7053,0.018196876112633324,b (log-log),|b/t|,0.32959232231746793,0.00850354552231118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.7594,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Central air,air_conditioning,A_structural,dummy,binary,0.0919,,5.6053,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,9.610786291418648,1.797332026123044,100*(exp(b - se^2/2) - 1),|b/t|,0.05042168986207548,0.008995359724203072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6053,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Lot size,lot_size,A_structural,log,acres,0.012,,3.4345,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.012,0.003493958363662833,b (log-log),|b/t|,0.03091912054027778,0.009002509984066905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4345,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Adjacent golf,green_space,E_environment,dummy,binary,0.0395,,1.3148,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,3.98211438872893,3.1253023049859934,100*(exp(b - se^2/2) - 1),|b/t|,0.01184133172056785,0.009006184758570011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3148,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Distance fixed,green_space,E_environment,log,miles,-0.0289,,-16.0611,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,-0.0289,0.0017993786228838622,b (log-log),|b/t|,-0.14316912963334263,0.0089140301494507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0611,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Adjacent fixed,green_space,E_environment,dummy,binary,-0.1498,,-2.3457,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,-14.087353795166791,5.497712591478039,100*(exp(b - se^2/2) - 1),|b/t|,-0.021122575209034572,0.009004806756633232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3457,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Distance historic,neighbourhood_amenities,F_neighbourhood,log,miles,-0.0265,,-2.5745,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,-0.0265,0.010293260827345115,b (log-log),|b/t|,-0.02318181697188166,0.009004395794088817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5745,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.2972,,-2.6345,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,-26.181671831308872,8.38066116356929,100*(exp(b - se^2/2) - 1),|b/t|,-0.023721780160836016,0.009004281708421338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6345,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Adjacent adjusted,other,other,dummy,binary,-0.3657,,-24.6169,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,-30.63656601391026,1.030552466023336,100*(exp(b - se^2/2) - 1),|b/t|,-0.21646309360196944,0.008793271841782248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.6169,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Distance railroad,other,other,log,miles,0.0483,,4.5809,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.0483,0.01054377960662752,b (log-log),|b/t|,0.04122425073689202,0.008999159714661314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5809,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Distance water,water_proximity,E_environment,log,miles,0.0185,,2.648,,,,,medium,Distance to nearest open water; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",elasticity,0.0185,0.006986404833836857,b (log-log),|b/t|,0.02384326903416647,0.009004255677555313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.648,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c3,1,3 400-meter Contiguity,13,Adjacent water,waterfront,C_exterior,dummy,binary,0.265,,3.814,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.57,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,30.02885547739107,9.056350513405027,100*(exp(b - se^2/2) - 1),|b/t|,0.034331746340227176,0.00900150664400293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.814,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Age,age,A_structural,log,years,0.0478,,12.0331,,,,,medium,Age of house (data section lists year built); positive coefficient as printed; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.0478,0.0039723761956603045,b (log-log),|b/t|,0.10774894719476605,0.008954379768701835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0331,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Bathrooms,bathrooms,A_structural,log,count,0.0134,,0.9073,,,,,medium,Number of bathrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.0134,0.014769095117381241,b (log-log),|b/t|,0.00817161152634583,0.009006515514544063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9073,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Bedrooms,bedrooms,A_structural,log,count,0.1305,,7.4568,,,,,medium,Number of bedrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.1305,0.01750080463469585,b (log-log),|b/t|,0.06701106268479361,0.008986571006972643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.4568,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Dock,waterfront,C_exterior,dummy,binary,0.2198,,5.6961,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,24.490035507086418,4.807375104499882,100*(exp(b - se^2/2) - 1),|b/t|,0.05123634139389025,0.008994986287791691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6961,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Square feet,living_area,A_structural,log,sqft,0.7546,,42.7783,,,,,medium,House square footage; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.7546,0.017639784657174317,b (log-log),|b/t|,0.35953251049322327,0.008404553488409386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.7783,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Central air,air_conditioning,A_structural,dummy,binary,0.0926,,5.6566,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,9.687585518822072,1.795854662377801,100*(exp(b - se^2/2) - 1),|b/t|,0.05088196250643488,0.008995149472551513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6566,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Lot size,lot_size,A_structural,log,acres,0.0121,,3.6175,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.0121,0.003344851416724257,b (log-log),|b/t|,0.03256487692987019,0.009002039234241931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6175,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Adjacent golf,green_space,E_environment,dummy,binary,-0.0061,,-0.2161,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,-0.6477331800559871,2.805600768203364,100*(exp(b - se^2/2) - 1),|b/t|,-0.0019463693017567308,0.00900679917518154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2161,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Distance fixed,green_space,E_environment,log,miles,-0.0291,,-16.4908,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,-0.0291,0.0017646202731219833,b (log-log),|b/t|,-0.14691786513856364,0.008909080526024429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.4908,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Adjacent fixed,green_space,E_environment,dummy,binary,-0.1727,,-2.6857,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,-16.03477594506556,5.410434968948365,100*(exp(b - se^2/2) - 1),|b/t|,-0.0241825323509895,0.009004182280593326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6857,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Distance historic,neighbourhood_amenities,F_neighbourhood,log,miles,-0.0095,,-0.913,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,-0.0095,0.010405257393209199,b (log-log),|b/t|,-0.00822294520435934,0.009006511724380438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.913,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1366,,-1.1847,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,-13.346037685267543,10.058140193548011,100*(exp(b - se^2/2) - 1),|b/t|,-0.010669767798342826,0.009006303535361547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1847,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Adjacent adjusted,other,other,dummy,binary,-0.3649,,-24.8259,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,-30.58089171706073,1.0204572257512075,100*(exp(b - se^2/2) - 1),|b/t|,-0.21821372772426603,0.008789761004606721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.8259,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Distance railroad,other,other,log,miles,0.0394,,3.6151,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.0394,0.010898730325578821,b (log-log),|b/t|,0.032543294924304904,0.009002045565628863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6151,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Distance water,water_proximity,E_environment,log,miles,0.0274,,3.9995,,,,,medium,Distance to nearest open water; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",elasticity,0.0274,0.006850856357044631,b (log-log),|b/t|,0.03599941198029344,0.009000978117338026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9995,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T1-c4,1,4 600-meter Contiguity,13,Adjacent water,waterfront,C_exterior,dummy,binary,0.3246,,4.7136,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.55,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: 600-meter Contiguity",pct_premium,38.02005794602186,9.527254775161108,100*(exp(b - se^2/2) - 1),|b/t|,0.04241632090389059,0.008998710307172987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7136,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Age,age,A_structural,level,years,0.0017,,4.5662,,,,,medium,Age of house (data section lists year built); positive coefficient as printed,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,0.16999999999999998,0.03723008190618019,100*b/1 (per year),|b/t|,0.041092186824701254,0.008999208712868741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5662,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Bathrooms,bathrooms,A_structural,level,count,0.0052,,0.7222,,,,,high,Number of bathrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,0.52,0.7200221545278316,100*b/1 (per unit count),|b/t|,0.006504585077771182,0.009006625696166134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7222,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Bedrooms,bedrooms,A_structural,level,count,0.0336,,13.0655,,,,,high,Number of bedrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,3.36,0.25716581837664076,100*b/1 (per unit count),|b/t|,0.11687210328403942,0.00894509228763074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0655,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Dock,waterfront,C_exterior,dummy,binary,0.27,,6.7807,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,30.892635873037698,5.216134052501611,100*(exp(b - se^2/2) - 1),|b/t|,0.060958940727726195,0.008990066029720559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.7807,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Square feet,living_area,A_structural,level,sqft,0.0002,,35.8411,,,,,high,House square footage,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,0.21527820833419445,0.006006462087776169,100*b/0.092903 (X per sqft->m2),|b/t|,0.3072040869746576,0.00857127953591429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.8411,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Central air,air_conditioning,A_structural,dummy,binary,0.1409,,7.9052,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,15.112664910762753,2.0520607971830183,100*(exp(b - se^2/2) - 1),|b/t|,0.07102088975941993,0.008984072478801288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.9052,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Lot size,lot_size,A_structural,level,acres,0.0164,,1.4767,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table",log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,0.0004052528256061512,0.0002744313845778772,100*b/4046.86 (X per acre->m2),|b/t|,0.013299189275823043,0.009006019689729155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4767,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Adjacent golf,green_space,E_environment,dummy,binary,0.1628,,6.1477,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,17.638875219813286,3.1163402996054153,100*(exp(b - se^2/2) - 1),|b/t|,0.055286515629554,0.00899304058909088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1477,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Distance fixed,green_space,E_environment,level,miles,-1.3682,,-11.7357,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated",log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,-85.01600652191205,7.24422118168597,100*b/1.60934 (X per miles->km),|b/t|,-0.10511570793816721,0.008956918457200442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.7357,12327.0,miles,implausible_magnitude,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Adjacent fixed,green_space,E_environment,dummy,binary,-0.0374,,-0.5251,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,-3.9149516824738106,6.860992906113953,100*(exp(b - se^2/2) - 1),|b/t|,-0.004729426311820209,0.009006715505275584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5251,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Distance historic,neighbourhood_amenities,F_neighbourhood,level,miles,-0.0364,,-5.8955,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,-3.64,0.61742006615215,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,-0.053024983736458794,0.008994145320406885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8955,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.017,,0.2389,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,1.4573334617687506,7.2379533191833785,100*(exp(b - se^2/2) - 1),|b/t|,0.0021517234175444664,0.009006795385284498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2389,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Adjacent adjusted,other,other,dummy,binary,-0.2399,,-25.4139,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,-21.332852245001998,0.7426286245552423,100*(exp(b - se^2/2) - 1),|b/t|,-0.2231276263363329,0.008779747552966405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.4139,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Distance railroad,other,other,level,miles,0.0479,,7.5602,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,4.79,0.6335811221925346,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,0.06793601472916963,0.008986007609477212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5602,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Distance water,water_proximity,E_environment,level,miles,-0.0005,,-0.0355,,,,,medium,Distance to nearest open water; unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",semi_elasticity,-0.0310685596118667,0.8751706932920197,100*b/1.60934 (X per miles->km),|b/t|,-0.00031974196002293107,0.009006815775293832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0355,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c1,2,1 Strict Contiguity,14,Adjacent water,waterfront,C_exterior,dummy,binary,0.1557,,4.6742,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.5236,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Strict Contiguity",pct_premium,16.782752302722482,3.892252195834757,100*(exp(b - se^2/2) - 1),|b/t|,0.04206240164143918,0.008998845073261558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6742,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Age,age,A_structural,level,years,0.0015,,3.7826,,,,,medium,Age of house (data section lists year built); positive coefficient as printed,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,0.15,0.03965526357531857,100*b/1 (per year),|b/t|,0.03404942808111245,0.00900159363430245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7826,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Bathrooms,bathrooms,A_structural,level,count,0.0048,,0.6573,,,,,high,Number of bathrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,0.48,0.7302601551802829,100*b/1 (per unit count),|b/t|,0.005920076567629075,0.009006658401991594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6573,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Bedrooms,bedrooms,A_structural,level,count,0.0296,,11.4472,,,,,high,Number of bedrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,2.96,0.25857851701726187,100*b/1 (per unit count),|b/t|,0.1025591560193025,0.00895932245608555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.4472,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Dock,waterfront,C_exterior,dummy,binary,0.2326,,6.0702,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,26.09505639028098,4.8353020263903534,100*(exp(b - se^2/2) - 1),|b/t|,0.054591645318832795,0.008993384949232776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0702,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Square feet,living_area,A_structural,level,sqft,0.0002,,29.6874,,,,,high,House square footage,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,0.21527820833419445,0.007251500917365429,100*b/0.092903 (X per sqft->m2),|b/t|,0.2583140424901331,0.00870113389822393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.6874,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Central air,air_conditioning,A_structural,dummy,binary,0.1412,,8.0795,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,15.147909894940415,2.0126700882399273,100*(exp(b - se^2/2) - 1),|b/t|,0.07257865335369054,0.008983062485759087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0795,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Lot size,lot_size,A_structural,level,acres,0.0247,,2.2378,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table",log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,0.0006103502922238984,0.0002727456842541328,100*b/4046.86 (X per acre->m2),|b/t|,0.02015136062031847,0.009004987318043825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2378,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Adjacent golf,green_space,E_environment,dummy,binary,0.2005,,6.5199,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,22.14359283453977,3.757936920108112,100*(exp(b - se^2/2) - 1),|b/t|,0.05862254957520138,0.008991326488934092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5199,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Distance fixed,green_space,E_environment,level,miles,-1.0616,,-9.1511,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated",log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,-65.96476576791538,7.208397435053204,100*b/1.60934 (X per miles->km),|b/t|,-0.08214372942246377,0.008976377640115808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.1511,12327.0,miles,implausible_magnitude,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Adjacent fixed,green_space,E_environment,dummy,binary,0.0028,,0.0381,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,0.009955510993697736,7.369687627956539,100*(exp(b - se^2/2) - 1),|b/t|,0.00034315967837515043,0.009006815705384525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0381,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Distance historic,neighbourhood_amenities,F_neighbourhood,level,miles,-0.039,,-5.3752,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,-3.9,0.7255543979758892,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,-0.04835680098747182,0.008996279391924359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3752,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0202,,-0.2998,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,-2.2219346163425135,6.603086589102788,100*(exp(b - se^2/2) - 1),|b/t|,-0.002700233663353524,0.00900678340011182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2998,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Adjacent adjusted,other,other,dummy,binary,-0.2266,,-24.5871,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,-20.279424313177362,0.7347531698132711,100*(exp(b - se^2/2) - 1),|b/t|,-0.21621331105109298,0.008793770353197124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.5871,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Distance railroad,other,other,level,miles,0.052,,7.0782,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,5.2,0.7346500522731768,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,0.06362288579083336,0.008988568533078094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0782,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Distance water,water_proximity,E_environment,level,miles,0.0019,,0.1194,,,,,medium,Distance to nearest open water; unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",semi_elasticity,0.11806052652509345,0.9887816291883874,100*b/1.60934 (X per miles->km),|b/t|,0.0010754132366769384,0.009006811027445044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1194,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c2,2,2 200-meter Contiguity,14,Adjacent water,waterfront,C_exterior,dummy,binary,0.167,,5.1277,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.5418,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 200-meter Contiguity",pct_premium,18.112769508725847,3.8487618680206794,100*(exp(b - se^2/2) - 1),|b/t|,0.04613507510719028,0.00899722587265056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.1277,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Age,age,A_structural,level,years,0.0005,,1.4055,,,,,medium,Age of house (data section lists year built); positive coefficient as printed,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,0.05,0.03557452863749556,100*b/1 (per year),|b/t|,0.012658066017733557,0.009006094640863434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4055,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Bathrooms,bathrooms,A_structural,level,count,-0.0034,,-0.4631,,,,,high,Number of bathrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,-0.33999999999999997,0.7341826819261498,100*b/1 (per unit count),|b/t|,-0.004171020315802711,0.009006737887719092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4631,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Bedrooms,bedrooms,A_structural,level,count,0.0354,,13.0245,,,,,high,Number of bedrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,3.54,0.2717954623977888,100*b/1 (per unit count),|b/t|,0.11651034071933383,0.008945475121450637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0245,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Dock,waterfront,C_exterior,dummy,binary,0.2801,,7.1615,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,32.22503939979819,5.175531985685468,100*(exp(b - se^2/2) - 1),|b/t|,0.06436854922396044,0.008988137851561887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.1615,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Square feet,living_area,A_structural,level,sqft,0.0002,,29.8074,,,,,high,House square footage,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,0.21527820833419445,0.007222307491904508,100*b/0.092903 (X per sqft->m2),|b/t|,0.2592881127885074,0.008698783281618236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.8074,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Central air,air_conditioning,A_structural,dummy,binary,0.1097,,6.1635,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,11.57664967283203,1.9861924738865104,100*(exp(b - se^2/2) - 1),|b/t|,0.05542816968800274,0.008992969852843796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1635,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Lot size,lot_size,A_structural,level,acres,0.0164,,1.4318,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table",log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,0.0004052528256061512,0.0002830373135955798,100*b/4046.86 (X per acre->m2),|b/t|,0.012894887283764636,0.009006067386342112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4318,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Adjacent golf,green_space,E_environment,dummy,binary,0.1573,,5.0233,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,16.977300165825504,3.6648324894447053,100*(exp(b - se^2/2) - 1),|b/t|,0.045197703478094785,0.00899761182451671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0233,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Distance fixed,green_space,E_environment,level,miles,-1.0559,,-9.8384,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated",log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,-65.6105841883401,6.668826657621168,100*b/1.60934 (X per miles->km),|b/t|,-0.08826679405798632,0.008971661454909977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.8384,12327.0,miles,implausible_magnitude,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Adjacent fixed,green_space,E_environment,dummy,binary,-0.0427,,-0.6496,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,-4.386904947810921,6.298505086450091,100*(exp(b - se^2/2) - 1),|b/t|,-0.005850727685967398,0.009006662078151784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6496,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Distance historic,neighbourhood_amenities,F_neighbourhood,level,miles,-0.032,,-3.2372,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,-3.2,0.9885085876683554,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,-0.029144479956406774,0.00900299022501136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2372,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0374,,0.574,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,3.590692189470679,6.763980134517483,100*(exp(b - se^2/2) - 1),|b/t|,0.005169843429977262,0.009006695871040526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.574,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Adjacent adjusted,other,other,dummy,binary,-0.2217,,-24.6576,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,-19.887670732829598,0.7203304990956423,100*(exp(b - se^2/2) - 1),|b/t|,-0.21680417089910245,0.008792590150667643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.6576,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Distance railroad,other,other,level,miles,0.0486,,4.8212,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,4.859999999999999,1.008047788932216,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,0.04338278009983769,0.008998336534439079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8212,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Distance water,water_proximity,E_environment,level,miles,0.003,,0.1373,,,,,medium,Distance to nearest open water; unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",semi_elasticity,0.18641135767120018,1.3576937922155874,100*b/1.60934 (X per miles->km),|b/t|,0.0012366349235886148,0.009006809348788163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1373,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c3,2,3 400-meter Contiguity,14,Adjacent water,waterfront,C_exterior,dummy,binary,0.193,,5.9676,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.5372,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 400-meter Contiguity",pct_premium,21.22486457221524,3.9226218102619157,100*(exp(b - se^2/2) - 1),|b/t|,0.053671604834570986,0.008993834176984213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9676,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Age,age,A_structural,level,years,0.0005,,1.4055,,,,,low,Age of house (data section lists year built); positive coefficient as printed; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,0.05,0.03557452863749556,100*b/1 (per year),|b/t|,0.012658066017733557,0.009006094640863434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4055,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Bathrooms,bathrooms,A_structural,level,count,-0.0034,,-0.4631,,,,,low,Number of bathrooms; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,-0.33999999999999997,0.7341826819261498,100*b/1 (per unit count),|b/t|,-0.004171020315802711,0.009006737887719092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4631,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Bedrooms,bedrooms,A_structural,level,count,0.0354,,13.0245,,,,,low,Number of bedrooms; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,3.54,0.2717954623977888,100*b/1 (per unit count),|b/t|,0.11651034071933383,0.008945475121450637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0245,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Dock,waterfront,C_exterior,dummy,binary,0.2801,,7.1615,,,,,low,Presence of a dock (water access); printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,32.22503939979819,5.175531985685468,100*(exp(b - se^2/2) - 1),|b/t|,0.06436854922396044,0.008988137851561887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.1615,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Square feet,living_area,A_structural,level,sqft,0.0002,,29.8074,,,,,low,House square footage; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,0.21527820833419445,0.007222307491904508,100*b/0.092903 (X per sqft->m2),|b/t|,0.2592881127885074,0.008698783281618236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.8074,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Central air,air_conditioning,A_structural,dummy,binary,0.1097,,6.1635,,,,,low,Presence of central air conditioning; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,11.57664967283203,1.9861924738865104,100*(exp(b - se^2/2) - 1),|b/t|,0.05542816968800274,0.008992969852843796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1635,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Lot size,lot_size,A_structural,level,acres,0.0164,,1.4318,,,,,low,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; printed column identical to 400-m column (probable typesetting error)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,0.0004052528256061512,0.0002830373135955798,100*b/4046.86 (X per acre->m2),|b/t|,0.012894887283764636,0.009006067386342112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4318,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Adjacent golf,green_space,E_environment,dummy,binary,0.1573,,5.0233,,,,,low,Parcel shares a border with a golf course (fixed open space type); printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,16.977300165825504,3.6648324894447053,100*(exp(b - se^2/2) - 1),|b/t|,0.045197703478094785,0.00899761182451671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0233,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Distance fixed,green_space,E_environment,level,miles,-1.0559,,-9.8384,,,,,low,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; printed column identical to 400-m column (probable typesetting error)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,-65.6105841883401,6.668826657621168,100*b/1.60934 (X per miles->km),|b/t|,-0.08826679405798632,0.008971661454909977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.8384,12327.0,miles,implausible_magnitude,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Adjacent fixed,green_space,E_environment,dummy,binary,-0.0427,,-0.6496,,,,,low,"Parcel adjacent to fixed open space (golf course, resource protection area or park); printed column identical to 400-m column (probable typesetting error)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,-4.386904947810921,6.298505086450091,100*(exp(b - se^2/2) - 1),|b/t|,-0.005850727685967398,0.009006662078151784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6496,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Distance historic,neighbourhood_amenities,F_neighbourhood,level,miles,-0.032,,-3.2372,,,,,low,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); printed column identical to 400-m column (probable typesetting error)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,-3.2,0.9885085876683554,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,-0.029144479956406774,0.00900299022501136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2372,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0374,,0.574,,,,,low,Parcel adjacent to a historic area; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,3.590692189470679,6.763980134517483,100*(exp(b - se^2/2) - 1),|b/t|,0.005169843429977262,0.009006695871040526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.574,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Adjacent adjusted,other,other,dummy,binary,-0.2217,,-24.6576,,,,,low,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year; printed column identical to 400-m column (probable typesetting error)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,-19.887670732829598,0.7203304990956423,100*(exp(b - se^2/2) - 1),|b/t|,-0.21680417089910245,0.008792590150667643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.6576,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Distance railroad,other,other,level,miles,0.0486,,4.8212,,,,,low,Distance to nearest railroad line (disamenity); unit inferred (miles); printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,4.859999999999999,1.008047788932216,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,0.04338278009983769,0.008998336534439079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8212,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Distance water,water_proximity,E_environment,level,miles,0.003,,0.1373,,,,,low,Distance to nearest open water; unit inferred (miles); printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",semi_elasticity,0.18641135767120018,1.3576937922155874,100*b/1.60934 (X per miles->km),|b/t|,0.0012366349235886148,0.009006809348788163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1373,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T2-c4,2,4 600-meter Contiguity,14,Adjacent water,waterfront,C_exterior,dummy,binary,0.193,,5.9676,,,,,low,Parcel adjacent to open water; printed column identical to 400-m column (probable typesetting error),log sale price (2004 dollars),log,spatial_error,12337.0,0.5145,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: 600-meter Contiguity; printed values identical to column 3 (400 m) -- probable typesetting error",pct_premium,21.22486457221524,3.9226218102619157,100*(exp(b - se^2/2) - 1),|b/t|,0.053671604834570986,0.008993834176984213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9676,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Age,age,A_structural,log,years,0.0603,,14.9488,,,,,medium,Age of house (data section lists year built); positive coefficient as printed; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.0603,0.004033768596810447,b (log-log),|b/t|,0.13343703859871045,0.008926270911291239,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.9488,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Bathrooms,bathrooms,A_structural,log,count,0.0142,,0.9694,,,,,medium,Number of bathrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.0142,0.014648236022281824,b (log-log),|b/t|,0.008730874870527854,0.009006472942570513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9694,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Bedrooms,bedrooms,A_structural,log,count,0.1059,,5.3971,,,,,medium,Number of bedrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.1059,0.01962164866317096,b (log-log),|b/t|,0.048553355989217326,0.008996193509332295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3971,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Dock,waterfront,C_exterior,dummy,binary,0.2008,,5.3418,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,22.15169447704919,4.594967211071576,100*(exp(b - se^2/2) - 1),|b/t|,0.04805702136208362,0.008996409704984016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3418,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Square feet,living_area,A_structural,log,sqft,0.6713,,38.6585,,,,,medium,House square footage; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.6713,0.01736487447779919,b (log-log),|b/t|,0.3288271966066714,0.008505948151290698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.6585,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Central air,air_conditioning,A_structural,dummy,binary,0.1066,,6.4215,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,11.233589268741762,1.8467857276449906,100*(exp(b - se^2/2) - 1),|b/t|,0.05774077531479171,0.008991789350586579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4215,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Lot size,lot_size,A_structural,log,acres,0.016,,3.8439,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.016,0.004162439189364968,b (log-log),|b/t|,0.034600570420288364,0.009001423143236912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8439,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Adjacent golf,green_space,E_environment,dummy,binary,0.033,,1.0085,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,3.299736550794452,3.3819700342055614,100*(exp(b - se^2/2) - 1),|b/t|,0.009082999472795686,0.009006444692905987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0085,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Distance fixed,green_space,E_environment,log,miles,-0.0294,,-15.9305,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,-0.0294,0.0018455164621323874,b (log-log),|b/t|,-0.14202853540630175,0.008915510210370155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.9305,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Adjacent fixed,green_space,E_environment,dummy,binary,-0.18,,-2.7616,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,-16.65021785407169,5.4442583304616505,100*(exp(b - se^2/2) - 1),|b/t|,-0.02486553303593655,0.009004031371645623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7616,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Distance historic,neighbourhood_amenities,F_neighbourhood,log,miles,-0.0076,,-0.6301,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,-0.0076,0.012061577527376606,b (log-log),|b/t|,-0.005675103519444755,0.009006671194167204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6301,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1041,,-0.83,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,-10.592471840501528,11.302189418707636,100*(exp(b - se^2/2) - 1),|b/t|,-0.007475448594126978,0.009006564571237323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.83,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Adjacent adjusted,other,other,dummy,binary,-0.3739,,-24.8995,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,-31.203185680796096,1.0331946106379823,100*(exp(b - se^2/2) - 1),|b/t|,-0.21882971872657273,0.008788518593810026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.8995,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Distance railroad,other,other,log,miles,0.0524,,4.043,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.0524,0.012960672767746723,b (log-log),|b/t|,0.036390438808416956,0.009000850558599297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.043,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Distance water,water_proximity,E_environment,log,miles,0.0285,,3.6211,,,,,medium,Distance to nearest open water; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",elasticity,0.0285,0.007870536577283146,b (log-log),|b/t|,0.03259724985279739,0.009002029729308053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6211,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c1,3,"1 Log linear, 600 m 1/distance",15,Adjacent water,waterfront,C_exterior,dummy,binary,0.3598,,4.7185,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.5682,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance",pct_premium,42.88825946115173,10.927387756441446,100*(exp(b - se^2/2) - 1),|b/t|,0.042460335129980806,0.008998693468259152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7185,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Age,age,A_structural,log,years,0.0679,,16.6913,,,,,medium,Age of house (data section lists year built); positive coefficient as printed; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.0679,0.004067987514453638,b (log-log),|b/t|,0.14866488890683718,0.008906729188669379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.6913,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,True
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Bathrooms,bathrooms,A_structural,log,count,0.0213,,1.4479,,,,,medium,Number of bathrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.0213,0.01471096070170592,b (log-log),|b/t|,0.01303986045062796,0.009006050452813013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4479,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Bedrooms,bedrooms,A_structural,log,count,0.0883,,4.619,,,,,medium,Number of bedrooms; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.0883,0.019116691924659018,b (log-log),|b/t|,0.04156652876186445,0.008999031990011789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.619,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Dock,waterfront,C_exterior,dummy,binary,0.1933,,5.1092,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,21.237870914160318,4.590162640173766,100*(exp(b - se^2/2) - 1),|b/t|,0.04596897879451747,0.008997294839606488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.1092,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Square feet,living_area,A_structural,log,sqft,0.6849,,39.1968,,,,,medium,House square footage; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.6849,0.017473365172667153,b (log-log),|b/t|,0.3329016181611144,0.008493081531173832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.1968,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Central air,air_conditioning,A_structural,dummy,binary,0.1141,,6.9647,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,12.071380137542986,1.8362686960017978,100*(exp(b - se^2/2) - 1),|b/t|,0.06260671450212615,0.008989147343335125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9647,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Lot size,lot_size,A_structural,log,acres,0.0082,,2.2801,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.0082,0.0035963334941449938,b (log-log),|b/t|,0.020532112493041144,0.00900491754442399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2801,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Adjacent golf,green_space,E_environment,dummy,binary,0.0278,,0.9556,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,2.775502629350046,2.991176508859367,100*(exp(b - se^2/2) - 1),|b/t|,0.008606594816934338,0.009006482646436102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9556,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Distance fixed,green_space,E_environment,log,miles,-0.0312,,-16.2618,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated; entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,-0.0312,0.0019186067962956128,b (log-log),|b/t|,-0.14492082891219785,0.008911733566530019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.2618,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Adjacent fixed,green_space,E_environment,dummy,binary,-0.165,,-2.4259,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,-15.406528190382108,5.767033314419643,100*(exp(b - se^2/2) - 1),|b/t|,-0.021844421793114098,0.00900466704856511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4259,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Distance historic,neighbourhood_amenities,F_neighbourhood,log,miles,-0.0426,,-4.7608,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity)",log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,-0.0426,0.00894807595362124,b (log-log),|b/t|,-0.04284028436810568,0.00899854738029442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.7608,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.441,,-4.1709,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,-36.01936258428234,6.802756805453829,100*(exp(b - se^2/2) - 1),|b/t|,-0.03754005008823063,0.009000467546148465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.1709,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,True
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Adjacent adjusted,other,other,dummy,binary,-0.39,,-25.165,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,-32.302442809993906,1.049283453424535,100*(exp(b - se^2/2) - 1),|b/t|,-0.2210496283468507,0.008784010663494962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.165,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Distance railroad,other,other,log,miles,0.0641,,6.9113,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.0641,0.009274666126488505,b (log-log),|b/t|,0.06212855402265752,0.00898941646617243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9113,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Distance water,water_proximity,E_environment,log,miles,0.0291,,4.4791,,,,,medium,Distance to nearest open water; unit inferred (miles); entered in logarithm (inferred from 'log linear' model label; square-feet coefficient is an elasticity),log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",elasticity,0.0291,0.006496840883213146,b (log-log),|b/t|,0.0403096417300587,0.008999495820602065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.4791,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c2,3,"2 Log linear, 600 m 1/distance^2",15,Adjacent water,waterfront,C_exterior,dummy,binary,0.3419,,5.0645,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.584,t,"Log-linear functional form (continuous regressors in logs); spatial error model, weight matrix: Log linear, 600 m 1/distance^2",pct_premium,40.4415579408822,9.50271728308788,100*(exp(b - se^2/2) - 1),|b/t|,0.04556763848130472,0.008997460456373724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0645,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Age,age,A_structural,level,years,0.0008,,1.9952,,,,,medium,Age of house (data section lists year built); positive coefficient as printed,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,0.08,0.040096230954290296,100*b/1 (per year),|b/t|,0.017967498818135388,0.00900536227853618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9952,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Bathrooms,bathrooms,A_structural,level,count,0.0002,,0.0288,,,,,high,Number of bathrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,0.02,0.6944444444444445,100*b/1 (per unit count),|b/t|,0.00025939629886121606,0.009006815932681113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0288,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Bedrooms,bedrooms,A_structural,level,count,0.0299,,10.697,,,,,high,Number of bedrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,2.9899999999999998,0.2795176217631112,100*b/1 (per unit count),|b/t|,0.09590183536311268,0.008965301987764112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.697,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Dock,waterfront,C_exterior,dummy,binary,0.2597,,6.6802,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,29.556167260727296,5.040443622033872,100*(exp(b - se^2/2) - 1),|b/t|,0.0600587224924566,0.008990557542058112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6802,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Square feet,living_area,A_structural,level,sqft,0.0002,,26.4267,,,,,high,House square footage,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,0.21527820833419445,0.008146238778742501,100*b/0.092903 (X per sqft->m2),|b/t|,0.23155165652476223,0.008762034477432378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.4267,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Central air,air_conditioning,A_structural,dummy,binary,0.1196,,6.7155,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,12.686721904123566,2.0072175532122016,100*(exp(b - se^2/2) - 1),|b/t|,0.06037493535943291,0.008990385728454012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.7155,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Lot size,lot_size,A_structural,level,acres,0.0186,,1.57,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table",log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,0.0004596160095289275,0.0002927490506553678,100*b/4046.86 (X per acre->m2),|b/t|,0.014139287918671362,0.0090059158717652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.57,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Adjacent golf,green_space,E_environment,dummy,binary,0.1443,,4.2511,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,15.456528574625473,3.9213328148759854,100*(exp(b - se^2/2) - 1),|b/t|,0.038260840846638874,0.009000221318397327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2511,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Distance fixed,green_space,E_environment,level,miles,-1.0972,,-9.5646,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated",log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,-68.17684721228028,7.128039563837513,100*b/1.60934 (X per miles->km),|b/t|,-0.08582870572987505,0.00897358025739446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.5646,12327.0,miles,implausible_magnitude,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Adjacent fixed,green_space,E_environment,dummy,binary,-0.0883,,-1.3155,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,-8.65738350029469,6.144996620863342,100*(exp(b - se^2/2) - 1),|b/t|,-0.011847635165217871,0.009006184086064517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3155,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Distance historic,neighbourhood_amenities,F_neighbourhood,level,miles,-0.026,,-2.1335,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,-2.6,1.2186547925943285,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,-0.019212495598863195,0.009005153784327721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1335,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0642,,0.9276,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,6.37548156669423,7.379993746856687,100*(exp(b - se^2/2) - 1),|b/t|,0.008354431169859908,0.009006501907999037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9276,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Adjacent adjusted,other,other,dummy,binary,-0.2304,,-25.0725,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,-20.581768012463197,0.7298328146090433,100*(exp(b - se^2/2) - 1),|b/t|,-0.2202766015101036,0.008785585861406065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.0725,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Distance railroad,other,other,level,miles,0.0543,,4.2643,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,5.43,1.2733625682996037,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,0.03837946904372916,0.009000180344658949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2643,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Distance water,water_proximity,E_environment,level,miles,0.0185,,0.6811,,,,,medium,Distance to nearest open water; unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",semi_elasticity,1.1495367056390677,1.6877649473485064,100*b/1.60934 (X per miles->km),|b/t|,0.0061344271119626705,0.009006646765471546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6811,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c3,3,"3 Semi-log, 600 m 1/distance",15,Adjacent water,waterfront,C_exterior,dummy,binary,0.1963,,5.8833,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.5571,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance",pct_premium,21.621474496463144,4.060236322027545,100*(exp(b - se^2/2) - 1),|b/t|,0.05291556272745345,0.008994197597853831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8833,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Age,age,A_structural,level,years,0.0015,,3.7181,,,,,medium,Age of house (data section lists year built); positive coefficient as printed,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,0.15,0.04034318603587854,100*b/1 (per year),|b/t|,0.03346948132148818,0.009001770076514396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7181,12327.0,years,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Bathrooms,bathrooms,A_structural,level,count,0.0047,,0.6598,,,,,high,Number of bathrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,0.47000000000000003,0.7123370718399514,100*b/1 (per unit count),|b/t|,0.005942592419978497,0.009006657199118667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6598,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Bedrooms,bedrooms,A_structural,level,count,0.0262,,10.1092,,,,,high,Number of bedrooms,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,2.62,0.2591698650733985,100*b/1 (per unit count),|b/t|,0.09067660923033831,0.008969711671580175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.1092,12327.0,count,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Dock,waterfront,C_exterior,dummy,binary,0.2494,,6.3897,,,,,high,Presence of a dock (water access),log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,28.227810866917835,5.008746185102772,100*(exp(b - se^2/2) - 1),|b/t|,0.057455782686566856,0.00899193744410017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.3897,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Square feet,living_area,A_structural,level,sqft,0.0002,,30.1076,,,,,high,House square footage,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,0.21527820833419445,0.007150294554670396,100*b/0.092903 (X per sqft->m2),|b/t|,0.2617214445344718,0.008692869725068483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.1076,12327.0,sqft,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Central air,air_conditioning,A_structural,dummy,binary,0.134,,7.7014,,,,,high,Presence of central air conditioning,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,14.321975720119307,1.9894387751887024,100*(exp(b - se^2/2) - 1),|b/t|,0.06919881870379768,0.008985225894486415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.7014,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Lot size,lot_size,A_structural,level,acres,0.0157,,1.3638,,,,,medium,"Lot size; unit (acres) inferred from the 0.10-acre price-change exercise, not stated in table",log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,0.0003879554489034495,0.0002844665265460109,100*b/4046.86 (X per acre->m2),|b/t|,0.01228256939391731,0.009006136819121068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3638,12327.0,acre,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Adjacent golf,green_space,E_environment,dummy,binary,0.1643,,5.3385,,,,,high,Parcel shares a border with a golf course (fixed open space type),log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,17.800980133632493,3.627211667422008,100*(exp(b - se^2/2) - 1),|b/t|,0.048027401710794844,0.008996422536441857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3385,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Distance fixed,green_space,E_environment,level,miles,-1.1832,,-9.7271,,,,,medium,"Distance to nearest fixed open space (parks, large water bodies, golf courses); unit inferred (miles), not stated",log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,-73.52063946552136,7.558330793918162,100*b/1.60934 (X per miles->km),|b/t|,-0.08727589732417866,0.008972447833802332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.7271,12327.0,miles,implausible_magnitude,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Adjacent fixed,green_space,E_environment,dummy,binary,-0.0725,,-1.031,,,,,high,"Parcel adjacent to fixed open space (golf course, resource protection area or park)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,-7.223096016196684,6.540229547320096,100*(exp(b - se^2/2) - 1),|b/t|,-0.0092856271963923,0.009006427930545393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.031,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Distance historic,neighbourhood_amenities,F_neighbourhood,level,miles,-0.0357,,-4.6262,,,,,medium,"Distance to nearest historic area (Jamestown, Yorktown, Colonial Williamsburg etc.); cultural amenity; unit inferred (miles)",log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,-3.5700000000000003,0.7716916691885349,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,-0.041631209582894166,0.008999007734835106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.6262,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Adjacent historic,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0506,,0.7207,,,,,high,Parcel adjacent to a historic area,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,4.931263005165065,7.3853536345227155,100*(exp(b - se^2/2) - 1),|b/t|,0.006491075709047892,0.009006626486815448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7207,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Adjacent adjusted,other,other,dummy,binary,-0.2365,,-25.0276,,,,,high,"Parcel adjacent to adjustable (developable: vacant, agricultural) open space, lagged one year",log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,-21.06493607527287,0.7459355318305217,100*(exp(b - se^2/2) - 1),|b/t|,-0.21990122032948947,0.008786348684232188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.0276,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Distance railroad,other,other,level,miles,0.0516,,6.6416,,,,,medium,Distance to nearest railroad line (disamenity); unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,5.16,0.7769212238014936,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),|b/t|,0.05971292795580705,0.008990744392286053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6416,12327.0,miles,unit_not_harmonisable,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,False,False,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Distance water,water_proximity,E_environment,level,miles,0.0026,,0.1546,,,,,medium,Distance to nearest open water; unit inferred (miles),log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",semi_elasticity,0.16155650998170681,1.0449968304120754,100*b/1.60934 (X per miles->km),|b/t|,0.0013924524401075801,0.009006807503930014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1546,12327.0,miles,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec,2bbdabdc28e5b845377e3ce418d4b3325d2d76ec:T3-c4,3,"4 Semi-log, 600 m 1/distance^2",15,Adjacent water,waterfront,C_exterior,dummy,binary,0.181,,5.3169,,,,,high,Parcel adjacent to open water,log sale price (2004 dollars),log,spatial_error,12337.0,0.5563,t,"Semi-log functional form (regressors in levels); spatial error model, weight matrix: Semi-log, 600 m 1/distance^2",pct_premium,19.772096803463725,4.079692065904363,100*(exp(b - se^2/2) - 1),|b/t|,0.047833524504420535,0.008996506329707261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3169,12327.0,binary,,2016.0,United States,North America,High income,1998.0,2004.0,single_family,transaction,journal,True,True,unknown,,2001.0,spatial,journal,True,True,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c2,slide 16 'Regression results',Sales particular features,16,Foreclosure sale,foreclosure,G_market,dummy,binary,-0.1156,,,,***,,,medium,"Resale was a foreclosure sale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.908,none,Adds sale-condition dummies; repeat-sales DID on single-family sale-resale pairs,pct_premium,-10.916851536569116,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c2,slide 16 'Regression results',Sales particular features,16,Risk sale,distressed_sale,G_market,dummy,binary,-0.0754,,,,***,,,medium,"'Risk sale' (sale under risk conditions; not further defined on slides); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.908,none,Adds sale-condition dummies; repeat-sales DID on single-family sale-resale pairs,pct_premium,-7.262753685639456,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c2,slide 16 'Regression results',Sales particular features,16,Succession sale,distressed_sale,G_market,dummy,binary,-0.0821,,,,***,,,medium,"Estate/succession sale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.908,none,Adds sale-condition dummies; repeat-sales DID on single-family sale-resale pairs,pct_premium,-7.88201638933147,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c2,slide 16 'Regression results',Sales particular features,16,Transfer sale,distressed_sale,G_market,dummy,binary,-0.0185,,,,***,,,medium,"Transfer sale (e.g. between related parties/relocation; not defined on slides); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.908,none,Adds sale-condition dummies; repeat-sales DID on single-family sale-resale pairs,pct_premium,-1.8329925408208547,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,Foreclosure sale,foreclosure,G_market,dummy,binary,-0.1156,,,,***,,,medium,"Resale was a foreclosure sale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-10.916851536569116,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,Risk sale,distressed_sale,G_market,dummy,binary,-0.0755,,,,***,,,medium,"'Risk sale' (sale under risk conditions; not further defined on slides); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-7.272026946600119,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,Succession sale,distressed_sale,G_market,dummy,binary,-0.0813,,,,***,,,medium,"Estate/succession sale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-7.808292516825876,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,Transfer sale,distressed_sale,G_market,dummy,binary,-0.0185,,,,***,,,medium,"Transfer sale (e.g. between related parties/relocation; not defined on slides); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-1.8329925408208547,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,Within 0 to 500 m. walking distance,transit_distance,D_location,dummy,binary,0.0556,,,,,,,medium,"House within 0-500 m walking distance of a commuter rail station opened between sale and resale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,5.717472925965139,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,"Within 500 to 1,000 m. walking distance",transit_distance,D_location,dummy,binary,0.0305,,,,*,,,medium,"House within 500-1,000 m walking distance of a station; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,3.0969890048780924,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c3,slide 16 'Regression results',Spatio-temporal on-foot accessibility indices,16,"Within 1,000 to 1,500 m. walking distance",transit_distance,D_location,dummy,binary,0.043,,,,***,,,medium,"House within 1,000-1,500 m walking distance of a station; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9081,none,Adds walking-distance buffer dummies to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,4.393789485061261,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,Foreclosure sale,foreclosure,G_market,dummy,binary,-0.1154,,,,***,,,medium,"Resale was a foreclosure sale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-10.899033125094682,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,Risk sale,distressed_sale,G_market,dummy,binary,-0.0756,,,,***,,,medium,"'Risk sale' (sale under risk conditions; not further defined on slides); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-7.281299280281051,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,Succession sale,distressed_sale,G_market,dummy,binary,-0.0813,,,,***,,,medium,"Estate/succession sale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-7.808292516825876,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,Transfer sale,distressed_sale,G_market,dummy,binary,-0.0184,,,,***,,,medium,"Transfer sale (e.g. between related parties/relocation; not defined on slides); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,-1.823175349223538,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,Within 0 to 500 m. walking distance,transit_distance,D_location,dummy,binary,0.0475,,,,,,,medium,"House within 0-500 m walking distance of a commuter rail station opened between sale and resale; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,4.864620112128515,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,"Within 500 to 1,000 m. walking distance",transit_distance,D_location,dummy,binary,0.022,,,,,,,medium,"House within 500-1,000 m walking distance of a station; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,2.2243784470438177,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,"Within 1,000 to 1,500 m. walking distance",transit_distance,D_location,dummy,binary,0.035,,,,***,,,medium,"House within 1,000-1,500 m walking distance of a station; stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,pct_premium,3.5619708799623284,,100*(exp(b) - 1),,,,,,26356.0,binary,,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba,2d5062b633e25ceaf8877d3b26abf6c4fd403dba:T16-c4,slide 16 'Regression results',Spatio-temporal on-foot and car accessibility indices,16,Driving time accessibility improvment (in min.),transit_distance,D_location,level,minutes,-0.0007,,,,***,,,medium,"Improvement in car travel time to nearest station between sale and resale, in minutes (negative sign = price rises as travel time falls); stars only (*** p<0.001, ** p<0.01, * p<0.05), no SE printed; differenced repeat-sales specification",log resale price - log sale price (repeat-sales difference),log,other,26366.0,0.9082,none,Adds car travel-time improvement to nearest station; repeat-sales DID on single-family sale-resale pairs,semi_elasticity,-0.06999999999999999,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,26356.0,minutes,unit_not_harmonisable,2012.0,Canada,North America,High income,1992.0,2009.0,single_family,transaction,conference,True,False,unknown,,2000.5,other,other,False,False,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x1 (Carpet area),living_area,A_structural,level,sqft,0.085,,3.645,0.0,**,,,high,Usable (carpet) area in square feet,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",monetary,0.085,0.023319615912208505,"b (currency per unit, as reported)",|b/t|,0.12127879885561163,0.033272647148315945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.645,890.0,sqft,,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x2 (Floor),floor_level,A_structural,level,count,17.03,,3.483,0.001,**,,,high,Floor on which the home is located,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",monetary,17.03,4.88946310651737,"b (currency per unit, as reported)",|b/t|,0.11596277553229274,0.033293934979125105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.483,890.0,count,,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x3 (Total floors),storeys,A_structural,level,count,29.62,,8.702,0.0,**,,,high,Total floors of the building,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",monetary,29.62,3.403815214893128,"b (currency per unit, as reported)",|b/t|,0.2800221500778866,0.03217905654767716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.702,890.0,count,,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x4 (Transaction),age,A_structural,dummy,"binary (1 = new property, 0 = resale)",-314.184,,-6.454,0.0,**,,,high,"New-property vs resale dummy, not an age in years; mapped to age as a dummy (x_transform dummy); negative = new property cheaper","Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",monetary,-314.184,48.68050821196158,"b (currency per unit, as reported)",|b/t|,-0.21144703884903532,0.032762169019063425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.454,890.0,binary,,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x5 (Furnishing),other,other,other,"ordinal 0-2 (0 unfurnished, 1 semi-furnished, 2 furnished)",353.179,,12.373,0.0,**,,,high,Furnishing status ordinal; taxonomy other,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",other,353.179,28.54433039683181,not harmonised,|b/t|,0.3831015304543898,0.030962703503951333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.373,890.0,ordinal 0-2,nonstandard_x_transform,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,False,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x6 (Facing),sunlight,E_environment,other,"nominal code 0-7 (E, W, N, S, SW, NE, NW, SE)",28.751,,2.18,0.029,*,,,medium,Orientation entered as an arbitrary nominal code 0-7; coefficient not interpretable,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",other,28.751,13.188532110091742,not harmonised,|b/t|,0.07287944515598896,0.03343093814494907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.18,890.0,nominal code 0-7,nonstandard_x_transform,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x7 (Bathrooms),bathrooms,A_structural,level,count,431.747,,11.769,0.0,**,,,high,Number of bathrooms,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",monetary,431.747,36.6851049366981,"b (currency per unit, as reported)",|b/t|,0.36697412062122076,0.031181419034856044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.769,890.0,count,,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x8 (Balconies),terrace_balcony,C_exterior,level,count,134.352,,4.934,0.0,**,,,high,Number of balconies,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",monetary,134.352,27.2298338062424,"b (currency per unit, as reported)",|b/t|,0.1631714793354628,0.03307083083410271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.934,890.0,count,,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x9 (Car parking),parking,A_structural,other,"ordinal code 0-3 (1 open, 1 covered, 2 open, 2 covered)",403.357,,12.619,0.0,**,,,high,Car parking entered as a coded variable 0-3 mixing count and covered/open,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",other,403.357,31.964260242491484,not harmonised,|b/t|,0.38957196380413983,0.030871857025448916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.619,890.0,ordinal code 0-3,nonstandard_x_transform,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,True,False
309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b,309364b6d8361dba9a9e751b55ade4c452cd784b:T3-c1,3,1,6,x10 (Ownership),other,other,other,"nominal code 0-3 (freehold, co-operative society, power of attorney, leasehold)",73.178,,3.032,0.002,**,,,medium,Ownership/tenure type as nominal code; taxonomy other,"Amount: total price of the home (rupees; Table 2 values in thousands, mean 1,493.58)",level,OLS,900.0,0.74,t,"Full sample after outlier processing; N inferred from F(10,889) => 900 (text says 910 cases); adjusted R2 0.737; t and p in separate columns",other,73.178,24.13522427440633,not harmonised,|b/t|,0.10111200736766056,0.033348287390389364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.032,890.0,nominal code 0-3,nonstandard_x_transform,2023.0,India,Asia,Lower middle income,,,unknown,listing,conference,False,False,OLS,,2020.0,ols,other,False,False,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A,4,A,15,Tested,water_quality,E_environment,binary,binary,-0.0239,0.0189,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,-2.3791028164274475,1.8453645186678003,100*(exp(b - se^2/2) - 1),reported,-0.030476850515107658,0.024100940365503542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2645502645502646,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B,4,B,15,Tested,water_quality,E_environment,binary,binary,0.0021,0.0188,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7701,se,"Base model, census block-group fixed effects (65 FE); wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,0.19251306892535958,1.883952148303304,100*(exp(b - se^2/2) - 1),reported,0.002693367695042742,0.024112053650858837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11170212765957446,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C,4,C,15,Tested,water_quality,E_environment,binary,binary,-0.022,0.0172,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Base model, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,-2.1904455542778534,1.6825732042880812,100*(exp(b - se^2/2) - 1),reported,-0.030826453542490966,0.02410068186049294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2790697674418603,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D,4,D,15,Tested,water_quality,E_environment,binary,binary,0.0022,0.017,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Base model, spatial error model + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,0.20576139883112798,1.7037441170185934,100*(exp(b - se^2/2) - 1),reported,0.0031203795403733957,0.024112023721067147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12941176470588237,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A1,4,A1,15,Tested,water_quality,E_environment,binary,binary,-0.0239,0.0189,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model plus Tested x Above MCL interaction, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,-2.3791028164274475,1.8453645186678003,100*(exp(b - se^2/2) - 1),reported,-0.030476850515107658,0.024100940365503542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2645502645502646,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B1,4,B1,15,Tested,water_quality,E_environment,binary,binary,0.002,0.0189,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7702,se,"Plus Tested x Above MCL, block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,0.1823054747405184,1.8937837825212607,100*(exp(b - se^2/2) - 1),reported,0.002551541017885131,0.024112062619014482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.10582010582010583,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C1,4,C1,15,Tested,water_quality,E_environment,binary,binary,-0.022,0.0172,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Plus Tested x Above MCL, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,-2.1904455542778534,1.6825732042880812,100*(exp(b - se^2/2) - 1),reported,-0.030826453542490966,0.02410068186049294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2790697674418603,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D1,4,D1,15,Tested,water_quality,E_environment,binary,binary,0.002,0.017,,,,,,high,Private well tested for contamination within 3 years prior to sale (any result),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Plus Tested x Above MCL, SEM + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",pct_premium,0.18572225053297142,1.7034034022678006,100*(exp(b - se^2/2) - 1),reported,0.0028367110698792122,0.024112044093973304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11764705882352941,1720.0,binary,,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,True,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A,4,A,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.0146,0.0181,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0146,0.0181,not harmonised,reported,-0.019445894680097102,0.024107581760942298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8066298342541436,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B,4,B,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.0276,0.0173,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7701,se,"Base model, census block-group fixed effects (65 FE); wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0276,0.0173,not harmonised,reported,-0.038439494036253075,0.024094320537216603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5953757225433527,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C,4,C,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.015,0.0182,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Base model, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.015,0.0182,not harmonised,reported,-0.0198687208608351,0.024107381311146592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8241758241758241,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D,4,D,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.0277,0.0179,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Base model, spatial error model + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0277,0.0179,not harmonised,reported,-0.037287253576498244,0.024095373249794893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.547486033519553,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A1,4,A1,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.0145,0.0181,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model plus Tested x Above MCL interaction, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0145,0.0181,not harmonised,reported,-0.019312753469810463,0.024107643986453063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8011049723756906,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B1,4,B1,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.0288,0.0177,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7702,se,"Plus Tested x Above MCL, block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0288,0.0177,not harmonised,reported,-0.039203154162206266,0.02409360516218927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6271186440677965,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C1,4,C1,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.015,0.0185,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Plus Tested x Above MCL, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.015,0.0185,not harmonised,reported,-0.01954664950330665,0.024107534387411533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8108108108108109,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D1,4,D1,15,Tested x Above DL,water_quality,E_environment,other,binary,-0.0289,0.0181,,,,,,high,Interaction: tested within 3 years x contaminant concentration above detectable limit (incremental effect of contamination),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Plus Tested x Above MCL, SEM + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0289,0.0181,not harmonised,reported,-0.03847099551103884,0.02409429130622156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.596685082872928,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A1,4,A1,15,Tested x Above MCL,water_quality,E_environment,other,binary,-0.0007,0.04,,,,,,high,Interaction: tested x concentration above maximum contaminant level / health advisory level (additional increment),ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model plus Tested x Above MCL interaction, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0007,0.04,not harmonised,reported,-0.00042196243183341604,0.024112138961909495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.017499999999999998,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B1,4,B1,15,Tested x Above MCL,water_quality,E_environment,other,binary,0.0153,0.0379,,,,,,high,Interaction: tested x concentration above maximum contaminant level / health advisory level (additional increment),ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7702,se,"Plus Tested x Above MCL, block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,0.0153,0.0379,not harmonised,reported,0.009733463930023535,0.02411099888548314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.40369393139841686,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C1,4,C1,15,Tested x Above MCL,water_quality,E_environment,other,binary,-0.0004,0.0367,,,,,,high,Interaction: tested x concentration above maximum contaminant level / health advisory level (additional increment),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Plus Tested x Above MCL, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-0.0004,0.0367,not harmonised,reported,-0.0002628026188105228,0.02411214027586547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.010899182561307902,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D1,4,D1,15,Tested x Above MCL,water_quality,E_environment,other,binary,0.0155,0.0354,,,,,,high,Interaction: tested x concentration above maximum contaminant level / health advisory level (additional increment),ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Plus Tested x Above MCL, SEM + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,0.0155,0.0354,not harmonised,reported,0.010556987571858245,0.02411079742217948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43785310734463273,1720.0,binary,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A,4,A,15,%DeltapDL,water_quality,E_environment,other,percent,-3.77,1.32,,,***,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-3.77,1.32,not harmonised,reported,-0.06870301736874737,0.024055167885078655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.856060606060606,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B,4,B,15,%DeltapDL,water_quality,E_environment,other,percent,-2.52,1.23,,,**,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7701,se,"Base model, census block-group fixed effects (65 FE); wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-2.52,1.23,not harmonised,reported,-0.04934031566230266,0.02408277312088582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.048780487804878,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C,4,C,15,%DeltapDL,water_quality,E_environment,other,percent,-3.63,1.31,,,***,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Base model, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-3.63,1.31,not harmonised,reported,-0.06666592014268859,0.024058500106590098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7709923664122136,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D,4,D,15,%DeltapDL,water_quality,E_environment,other,percent,-2.52,1.33,,,*,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Base model, spatial error model + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-2.52,1.33,not harmonised,reported,-0.045638557948700115,0.024087016695147286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.894736842105263,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-A1,4,A1,15,%DeltapDL,water_quality,E_environment,other,percent,-3.77,1.35,,,***,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,OLS,1730.0,0.7981,se,"Base model plus Tested x Above MCL interaction, OLS; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-3.77,1.35,not harmonised,reported,-0.0671832526445175,0.024057663413819264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7925925925925923,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,ols,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-B1,4,B1,15,%DeltapDL,water_quality,E_environment,other,percent,-2.65,1.33,,,**,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,FE_panel,1730.0,0.7702,se,"Plus Tested x Above MCL, block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-2.65,1.33,not harmonised,reported,-0.047987639019523284,0.024084362224892818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9924812030075185,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,panel,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-C1,4,C1,15,%DeltapDL,water_quality,E_environment,other,percent,-3.63,1.35,,,***,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8001,se,"Plus Tested x Above MCL, spatial error model; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-3.63,1.35,not harmonised,reported,-0.0646990277903126,0.02406162190548816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6888888888888887,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f,3244bfbe46126d82d9734bb6ef3476cad91ccc3f:T4-D1,4,D1,15,%DeltapDL,water_quality,E_environment,other,percent,-2.65,1.36,,,**,,,high,Derived joint percentage price change for a home tested within 3 years with contamination above detectable limit vs untested home (eq. 5: 100[exp(theta_test+theta_DL)-1]); delta-method SE,ln real transaction price (2013 USD),log,spatial_error,1730.0,0.8224,se,"Plus Tested x Above MCL, SEM + block-group FE; wells tested 0-3 years prior to sale; year and quarter dummies; full Table 3 controls included but not printed",other,-2.65,1.36,not harmonised,reported,-0.04693144591737214,0.024085572244387215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9485294117647056,1720.0,percent,nonstandard_x_transform,2016.0,United States,North America,High income,1990.0,2013.0,single_family,transaction,journal,True,True,robust,,2001.5,spatial,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Freehold (r),other,other,binary,binary,0.12,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,pct_premium,12.74511888815626,1.014747166421438,100*(exp(b - se^2/2) - 1),reported,0.13453075425028052,0.010089806568771039,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.333333333333334,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,New-built (n),age,A_structural,dummy,binary,0.228,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,pct_premium,25.597919070660048,1.632910923062047,100*(exp(b - se^2/2) - 1),reported,0.17580178136501223,0.01002378577958403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.53846153846154,9645.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Detached (d),dwelling_type,A_structural,binary,binary,0.651,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,pct_premium,91.7211909101826,3.067931724692258,100*(exp(b - se^2/2) - 1),reported,0.3827477986223604,0.009407011947707782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.6875,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.468,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,pct_premium,59.66409242091599,2.235516363761874,100*(exp(b - se^2/2) - 1),reported,0.3222269442468668,0.009639267562940458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.42857142857143,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Terraced (t),dwelling_type,A_structural,binary,binary,0.36,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,pct_premium,43.31889551603363,2.006661180384476,100*(exp(b - se^2/2) - 1),reported,0.25329386217599803,0.009850316862399925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.71428571428571,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9645.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Distance to CBD (z),cbd_distance,D_location,level,m,0.035,0.004,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,semi_elasticity,3500.0000000000005,400.0,100*b/0.001 (X per m->km),reported,0.08874420776176675,0.010142195172773342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.75,9645.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c1,3,1,7,Number of bus stops (δ),transit_distance,D_location,level,count,0.003,0.001,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 300 m,"log(p), natural log of property sale price (GBP)",log,OLS,9655.0,0.85,se,Model 1: bus stops counted within 300 m; year and ward dummies included,semi_elasticity,0.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03053286851352797,0.010177622837842657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,9645.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Freehold (r),other,other,binary,binary,0.121,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,pct_premium,12.857920398399415,1.0157624211306095,100*(exp(b - se^2/2) - 1),reported,0.1356658264505665,0.01009084659549668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.444444444444445,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,New-built (n),age,A_structural,dummy,binary,0.23,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,pct_premium,25.84936627218717,1.6361800129083213,100*(exp(b - se^2/2) - 1),reported,0.17734015935858247,0.01002357422461553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.692307692307693,9640.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Detached (d),dwelling_type,A_structural,binary,binary,0.654,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,pct_premium,92.29721809166504,3.0771493393751492,100*(exp(b - se^2/2) - 1),reported,0.3843366178331483,0.009402730711514332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.875,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.471,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,pct_premium,60.14380390562224,2.242232982744169,100*(exp(b - se^2/2) - 1),reported,0.3241513893932211,0.009635073145446062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.64285714285714,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Terraced (t),dwelling_type,A_structural,binary,binary,0.361,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,pct_premium,43.46228609488989,2.008668845229978,100*(exp(b - se^2/2) - 1),reported,0.2540137411426779,0.009850948409965348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.785714285714285,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9640.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Distance to CBD (z),cbd_distance,D_location,level,m,0.038,0.004,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,semi_elasticity,3799.9999999999995,400.0,100*b/0.001 (X per m->km),reported,0.09630783186724858,0.010137666512341957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.5,9640.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c2,3,2,7,Number of bus stops (δ),transit_distance,D_location,level,count,0.003,0.001,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 400 m,"log(p), natural log of property sale price (GBP)",log,OLS,9650.0,0.85,se,Model 2: bus stops counted within 400 m; year and ward dummies included,semi_elasticity,0.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.030540778378304986,0.010180259459434994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,9640.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Freehold (r),other,other,binary,binary,0.123,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,pct_premium,13.083862105589494,1.0177959788527402,100*(exp(b - se^2/2) - 1),reported,0.13782388570292364,0.010084674563628559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.666666666666668,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,New-built (n),age,A_structural,dummy,binary,0.232,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,pct_premium,26.10131687134718,1.639455647476828,100*(exp(b - se^2/2) - 1),reported,0.17877928629756182,0.010017804835639238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.846153846153847,9646.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Detached (d),dwelling_type,A_structural,binary,binary,0.654,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,pct_premium,92.29721809166504,3.0771493393751492,100*(exp(b - se^2/2) - 1),reported,0.3842347192200732,0.00940023777908436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.875,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.469,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,pct_premium,59.82383637200046,2.2377529982564965,100*(exp(b - se^2/2) - 1),reported,0.32282877797961507,0.009636679939690002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.5,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Terraced (t),dwelling_type,A_structural,binary,binary,0.359,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,pct_premium,43.17564825608484,2.004655522200322,100*(exp(b - se^2/2) - 1),reported,0.25262297332722194,0.009851592274599185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.642857142857142,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9646.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Distance to CBD (z),cbd_distance,D_location,level,m,0.042,0.004,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,semi_elasticity,4200.0,400.0,100*b/0.001 (X per m->km),reported,0.1063035673460363,0.010124149271051076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.5,9646.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c3,3,3,7,Number of bus stops (δ),transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 500 m,"log(p), natural log of property sale price (GBP)",log,OLS,9656.0,0.85,se,Model 3: bus stops counted within 500 m; year and ward dummies included,semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9646.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Freehold (r),other,other,binary,binary,0.126,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,pct_premium,13.42362307854501,1.0208539514547226,100*(exp(b - se^2/2) - 1),reported,0.1411407937159825,0.01008148526542732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.000000000000002,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,New-built (n),age,A_structural,dummy,binary,0.23,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,pct_premium,25.84936627218717,1.6361800129083213,100*(exp(b - se^2/2) - 1),reported,0.1773134388074945,0.010022063932597514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.692307692307693,9643.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Detached (d),dwelling_type,A_structural,binary,binary,0.647,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,pct_premium,90.9558378730865,3.0556845085553794,100*(exp(b - se^2/2) - 1),reported,0.38077161637449913,0.00941629963213599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.4375,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.465,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,pct_premium,59.185817914119276,2.2288198644419417,100*(exp(b - se^2/2) - 1),reported,0.3204038194048625,0.009646566605737797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.214285714285715,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Terraced (t),dwelling_type,A_structural,binary,binary,0.356,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,pct_premium,42.74676495792627,1.9986505675693826,100*(exp(b - se^2/2) - 1),reported,0.2506815998847677,0.009858265164007719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.428571428571427,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9643.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Distance to CBD (z),cbd_distance,D_location,level,m,0.046,0.005,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,semi_elasticity,4600.0,500.0,100*b/0.001 (X per m->km),reported,0.09327904313529377,0.010139026427749323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.2,9643.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c4,3,4,7,Number of bus stops (δ),transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 750 m,"log(p), natural log of property sale price (GBP)",log,OLS,9653.0,0.851,se,Model 4: bus stops counted within 750 m; year and ward dummies included,semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9643.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Freehold (r),other,other,binary,binary,0.127,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,pct_premium,13.537103432343756,1.0218753160033376,100*(exp(b - se^2/2) - 1),reported,0.14219504707933214,0.01007681435995267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.111111111111112,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,New-built (n),age,A_structural,dummy,binary,0.237,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,pct_premium,26.73340235256274,1.6476734531078685,100*(exp(b - se^2/2) - 1),reported,0.1824778323276177,0.010009332574932616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.23076923076923,9649.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Detached (d),dwelling_type,A_structural,binary,binary,0.644,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,pct_premium,90.38382880208049,3.0465311918697284,100*(exp(b - se^2/2) - 1),reported,0.3791595428087891,0.009420112864814636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.25,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.464,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,pct_premium,59.02671166258977,2.226592158616055,100*(exp(b - se^2/2) - 1),reported,0.3196960341275535,0.009646001029710664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.142857142857146,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Terraced (t),dwelling_type,A_structural,binary,binary,0.354,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,pct_premium,42.46155673130643,1.9946572610718438,100*(exp(b - se^2/2) - 1),reported,0.24928835693369972,0.00985886157364914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.285714285714285,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9649.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Distance to CBD (z),cbd_distance,D_location,level,m,0.055,0.005,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,semi_elasticity,5500.0,500.0,100*b/0.001 (X per m->km),reported,0.111287248085705,0.010117022553245909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,9649.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c5,3,5,7,Number of bus stops (δ),transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 1000 m,"log(p), natural log of property sale price (GBP)",log,OLS,9659.0,0.85,se,Model 5: bus stops counted within 1000 m; year and ward dummies included,semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9649.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Freehold (r),other,other,binary,binary,0.125,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,pct_premium,13.310256148378796,1.0198336077601435,100*(exp(b - se^2/2) - 1),reported,0.13992891893878356,0.010074882163592418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.88888888888889,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,New-built (n),age,A_structural,dummy,binary,0.217,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,pct_premium,24.223912849633212,1.6150473327790928,100*(exp(b - se^2/2) - 1),reported,0.16744604784368103,0.010031330055151399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.692307692307693,9659.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Detached (d),dwelling_type,A_structural,binary,binary,0.645,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,pct_premium,90.57430785453555,3.0495792468350764,100*(exp(b - se^2/2) - 1),reported,0.37949522343820885,0.009413835000017584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.3125,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.465,0.015,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,pct_premium,59.183509736493846,2.3880212833306516,100*(exp(b - se^2/2) - 1),reported,0.3008149633654507,0.0097037084956597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.000000000000004,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Terraced (t),dwelling_type,A_structural,binary,binary,0.357,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,pct_premium,42.88958312006375,2.0006502177954277,100*(exp(b - se^2/2) - 1),reported,0.2511462192720896,0.009848871344003514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.5,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9659.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Distance to CBD (z),cbd_distance,D_location,level,m,0.066,0.007,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,semi_elasticity,6600.0,700.0,100*b/0.001 (X per m->km),reported,0.09549714946852594,0.010128485549692146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.428571428571429,9659.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T3-c6,3,6,7,Number of bus stops (δ),transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 1500 m,"log(p), natural log of property sale price (GBP)",log,OLS,9669.0,0.849,se,Model 6: bus stops counted within 1500 m; year and ward dummies included,semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,ols,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Freehold (r),other,other,binary,binary,0.116,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",pct_premium,12.295039172141786,1.010696284919929,100*(exp(b - se^2/2) - 1),reported,0.1301236118366878,0.010095797470087845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.888888888888891,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,New-built (n),age,A_structural,dummy,binary,0.221,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",pct_premium,24.721803618715743,1.6215204597332842,100*(exp(b - se^2/2) - 1),reported,0.17056379231195498,0.010033164253644412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,9645.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Detached (d),dwelling_type,A_structural,binary,binary,0.659,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",pct_premium,93.26111190855431,3.092573614626259,100*(exp(b - se^2/2) - 1),reported,0.38675140808771674,0.009390019012751848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.1875,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,True
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.476,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",pct_premium,60.946528063203,2.253472222341811,100*(exp(b - se^2/2) - 1),reported,0.32715000705770964,0.009622059031109107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.0,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Terraced (t),dwelling_type,A_structural,binary,binary,0.369,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",pct_premium,44.61458744344257,2.0248026451446357,100*(exp(b - se^2/2) - 1),reported,0.2592056016894121,0.00983435887168501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.357142857142858,9645.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9645.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Distance to CBD (z),cbd_distance,D_location,level,m,0.035,0.004,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",semi_elasticity,3500.0000000000005,400.0,100*b/0.001 (X per m->km),reported,0.08874420776176675,0.010142195172773342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.75,9645.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c1,5,1,9,Number of bus stops (δ),transit_distance,D_location,level,count,0.003,0.001,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 300 m,"log(p), natural log of property sale price (GBP)",log,other,9655.0,,se,"Model 7: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 300 m",semi_elasticity,0.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03053286851352797,0.010177622837842657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,9645.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Freehold (r),other,other,binary,binary,0.117,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",pct_premium,12.407390377554028,1.011707486721483,100*(exp(b - se^2/2) - 1),reported,0.1312595723708348,0.010096890182371905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.000000000000002,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,New-built (n),age,A_structural,dummy,binary,0.223,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",pct_premium,24.971496835939334,1.6247667458567792,100*(exp(b - se^2/2) - 1),reported,0.17210515200725365,0.010033035767239002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.153846153846153,9640.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Detached (d),dwelling_type,A_structural,binary,binary,0.662,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",pct_premium,93.84176578961123,3.1018652659784287,100*(exp(b - se^2/2) - 1),reported,0.388332651181383,0.009385683412238863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.375,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,True
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.48,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",pct_premium,61.591603466161416,2.262504163070051,100*(exp(b - se^2/2) - 1),reported,0.3296777943830783,0.009615602336173118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.285714285714285,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Terraced (t),dwelling_type,A_structural,binary,binary,0.371,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",pct_premium,44.90410604042025,2.028856302741303,100*(exp(b - se^2/2) - 1),reported,0.2605783670103244,0.0098331459249179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.5,9640.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9640.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Distance to CBD (z),cbd_distance,D_location,level,m,0.038,0.004,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",semi_elasticity,3799.9999999999995,400.0,100*b/0.001 (X per m->km),reported,0.09630783186724858,0.010137666512341957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.5,9640.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c2,5,2,9,Number of bus stops (δ),transit_distance,D_location,level,count,0.003,0.001,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 400 m,"log(p), natural log of property sale price (GBP)",log,other,9650.0,,se,"Model 8: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 400 m",semi_elasticity,0.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.030540778378304986,0.010180259459434994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,9640.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Freehold (r),other,other,binary,binary,0.119,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",pct_premium,12.632430123041383,1.0137329264595176,100*(exp(b - se^2/2) - 1),reported,0.1334229129187089,0.01009080854007042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.222222222222223,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,New-built (n),age,A_structural,dummy,binary,0.224,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",pct_premium,25.096530839357477,1.6263923252568708,100*(exp(b - se^2/2) - 1),reported,0.17280175271238063,0.010028673148486374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.230769230769234,9646.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Detached (d),dwelling_type,A_structural,binary,binary,0.663,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",pct_premium,94.03570450859877,3.104968682694147,100*(exp(b - se^2/2) - 1),reported,0.3887279547858488,0.009381066782162264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.4375,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,True
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.478,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",pct_premium,61.26874322708824,2.2579836767370725,100*(exp(b - se^2/2) - 1),reported,0.3283614546016392,0.009617281097119141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.14285714285714,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Terraced (t),dwelling_type,A_structural,binary,binary,0.37,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",pct_premium,44.759274362288195,2.0268284605286544,100*(exp(b - se^2/2) - 1),reported,0.2598481199789754,0.0098320910262315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.428571428571427,9646.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9646.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Distance to CBD (z),cbd_distance,D_location,level,m,0.042,0.004,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",semi_elasticity,4200.0,400.0,100*b/0.001 (X per m->km),reported,0.1063035673460363,0.010124149271051076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.5,9646.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c3,5,3,9,Number of bus stops (δ),transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 500 m,"log(p), natural log of property sale price (GBP)",log,other,9656.0,,se,"Model 9: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 500 m",semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9646.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Freehold (r),other,other,binary,binary,0.122,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",pct_premium,12.97083476657237,1.0167786916022867,100*(exp(b - se^2/2) - 1),reported,0.13674526567017953,0.010087765500259143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.555555555555557,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,New-built (n),age,A_structural,dummy,binary,0.221,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",pct_premium,24.721803618715743,1.6215204597332842,100*(exp(b - se^2/2) - 1),reported,0.17058096460420208,0.010034174388482475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,9643.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Detached (d),dwelling_type,A_structural,binary,binary,0.655,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",pct_premium,92.48961149042331,3.0802280278021805,100*(exp(b - se^2/2) - 1),reported,0.3847863084573217,0.009399360206591064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.9375,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.473,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",pct_premium,60.464412014673165,2.2467219361667627,100*(exp(b - se^2/2) - 1),reported,0.3253371151306416,0.009629428354818146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.785714285714285,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Terraced (t),dwelling_type,A_structural,binary,binary,0.366,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",pct_premium,44.18139379647788,2.0187373397163224,100*(exp(b - se^2/2) - 1),reported,0.25726312923267924,0.00984066614551232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.142857142857142,9643.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9643.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Distance to CBD (z),cbd_distance,D_location,level,m,0.045,0.005,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",semi_elasticity,4500.0,500.0,100*b/0.001 (X per m->km),reported,0.09126831536041913,0.01014092392893546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,9643.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c4,5,4,9,Number of bus stops (δ),transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 750 m,"log(p), natural log of property sale price (GBP)",log,other,9653.0,,se,"Model 10: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 750 m",semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9643.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Freehold (r),other,other,binary,binary,0.123,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",pct_premium,13.083862105589494,1.0177959788527402,100*(exp(b - se^2/2) - 1),reported,0.13780286534238903,0.01008313648846749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.666666666666668,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,New-built (n),age,A_structural,dummy,binary,0.226,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",pct_premium,25.346974260976673,1.6296483648616429,100*(exp(b - se^2/2) - 1),reported,0.17427167970545113,0.010024477151198516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.384615384615387,9649.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Detached (d),dwelling_type,A_structural,binary,binary,0.653,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",pct_premium,92.10501699014091,3.074073728097714,100*(exp(b - se^2/2) - 1),reported,0.3836828978580497,0.009401112351805201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.8125,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.472,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",pct_premium,60.30402780812712,2.2444763372172036,100*(exp(b - se^2/2) - 1),reported,0.32463156077262467,0.009628902226306663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.714285714285715,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Terraced (t),dwelling_type,A_structural,binary,binary,0.364,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",pct_premium,43.89331917952675,2.014703899821265,100*(exp(b - se^2/2) - 1),reported,0.2558752722786639,0.009841356626102457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,9649.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9649.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Distance to CBD (z),cbd_distance,D_location,level,m,0.054,0.005,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",semi_elasticity,5400.0,500.0,100*b/0.001 (X per m->km),reported,0.10928823200208923,0.010119280740934189,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.799999999999999,9649.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c5,5,5,9,Number of bus stops (δ),transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 1000 m,"log(p), natural log of property sale price (GBP)",log,other,9659.0,,se,"Model 11: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1000 m",semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9649.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Freehold (r),other,other,binary,binary,0.121,0.009,,,***,,,high,Freehold tenure (vs leasehold); no taxonomy leaf -> other,"log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",pct_premium,12.857920398399415,1.0157624211306095,100*(exp(b - se^2/2) - 1),reported,0.13553478168721148,0.010081099464338043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.444444444444445,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,New-built (n),age,A_structural,dummy,binary,0.208,0.013,,,***,,,high,"Dummy = 1 if newly built when sold; mapped to age as a new-build dummy, not years","log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",pct_premium,23.110913643150187,1.600577120413528,100*(exp(b - se^2/2) - 1),reported,0.16068436593638855,0.010042772871024284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,9659.0,binary,year_built_not_age,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Detached (d),dwelling_type,A_structural,binary,binary,0.654,0.016,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",pct_premium,92.29721809166504,3.0771493393751492,100*(exp(b - se^2/2) - 1),reported,0.38401421664334906,0.009394843220632394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.875,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Semi-detached (s),dwelling_type,A_structural,binary,binary,0.474,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",pct_premium,60.624956685644605,2.248969781838445,100*(exp(b - se^2/2) - 1),reported,0.32571050683866537,0.009620141552196869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.857142857142854,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Terraced (t),dwelling_type,A_structural,binary,binary,0.367,0.014,,,***,,,high,Reference category = flat,"log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",pct_premium,44.3256473050075,2.020757086761249,100*(exp(b - se^2/2) - 1),reported,0.2577198259969989,0.009831274016234291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.21428571428571,9659.0,binary,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,True,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Floor area (f),living_area,A_structural,level,m2,0.004,0.0,,,***,,,high,Floor area in square metres (Table 1); s.e. printed as 0.000 (rounded),"log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,9659.0,m2,,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Distance to CBD (z),cbd_distance,D_location,level,m,0.065,0.007,,,***,,,medium,"Table 1 defines z as distance to CBD in metres (mean 3225 m), but coefficient magnitude (0.035-0.066) implies the regression used km; unit ambiguous. Positive sign (polycentric city).","log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",semi_elasticity,6500.0,700.0,100*b/0.001 (X per m->km),reported,0.09406312277455178,0.010129874760336346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.285714285714286,9659.0,m,implausible_magnitude,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T5-c6,5,6,9,Number of bus stops (δ),transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,Count of bus stops within the stated radius of the property (not a distance); radius differs by model; radius 1500 m,"log(p), natural log of property sale price (GBP)",log,other,9669.0,,se,"Model 12: multilevel model (random intercepts for ward and year, MLwiN); bus stops counted within 1500 m",semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,other,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q10-c1,6,"1 (δ300m), row 10% quantile",10,δ300m,transit_distance,D_location,level,count,-0.004,0.003,,,,,,high,"Count of bus stops within 300 m; bivariate quantile regression (10% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 10% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,-0.4,0.3,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.013565402692380628,0.010174052019285472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3333333333333333,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q10-c2,6,"2 (δ400m), row 10% quantile",10,δ400m,transit_distance,D_location,level,count,0.001,0.002,,,,,,high,"Count of bus stops within 400 m; bivariate quantile regression (10% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 10% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,0.1,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.005087428294395889,0.01017485658879178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q10-c3,6,"3 (δ500m), row 10% quantile",10,δ500m,transit_distance,D_location,level,count,0.007,0.002,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (10% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 10% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.7000000000000001,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.035589897679736,0.010168542194210284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q10-c4,6,"4 (δ750m), row 10% quantile",10,δ750m,transit_distance,D_location,level,count,0.004,0.001,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (10% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 10% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.4,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.040666285422027916,0.010166571355506977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q10-c5,6,"5 (δ1000m), row 10% quantile",10,δ1000m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (10% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 10% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q10-c6,6,"6 (δ1500m), row 10% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (10% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 10% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q20-c1,6,"1 (δ300m), row 20% quantile",10,δ300m,transit_distance,D_location,level,count,0.004,0.003,,,,,,high,"Count of bus stops within 300 m; bivariate quantile regression (20% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 20% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,0.4,0.3,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.013565402692380628,0.010174052019285472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q20-c2,6,"2 (δ400m), row 20% quantile",10,δ400m,transit_distance,D_location,level,count,0.003,0.002,,,*,,,high,"Count of bus stops within 400 m; bivariate quantile regression (20% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 20% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,0.3,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.015260705059165302,0.010173803372776869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q20-c3,6,"3 (δ500m), row 20% quantile",10,δ500m,transit_distance,D_location,level,count,0.005,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (20% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 20% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.5,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0508092300708077,0.01016184601416154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q20-c4,6,"4 (δ750m), row 20% quantile",10,δ750m,transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (20% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 20% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q20-c5,6,"5 (δ1000m), row 20% quantile",10,δ1000m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (20% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 20% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q20-c6,6,"6 (δ1500m), row 20% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (20% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 20% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q30-c1,6,"1 (δ300m), row 30% quantile",10,δ300m,transit_distance,D_location,level,count,0.011,0.002,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (30% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 30% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,1.0999999999999999,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.055875009332945584,0.010159092605990107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q30-c2,6,"2 (δ400m), row 30% quantile",10,δ400m,transit_distance,D_location,level,count,0.007,0.002,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (30% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 30% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,0.7000000000000001,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.035589897679736,0.010168542194210284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q30-c3,6,"3 (δ500m), row 30% quantile",10,δ500m,transit_distance,D_location,level,count,0.007,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (30% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 30% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.7000000000000001,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.07104494071468909,0.010149277244955585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q30-c4,6,"4 (δ750m), row 30% quantile",10,δ750m,transit_distance,D_location,level,count,0.003,0.001,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (30% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 30% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03051075354559004,0.010170251181863347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q30-c5,6,"5 (δ1000m), row 30% quantile",10,δ1000m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (30% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 30% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q30-c6,6,"6 (δ1500m), row 30% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (30% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 30% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q40-c1,6,"1 (δ300m), row 40% quantile",10,δ300m,transit_distance,D_location,level,count,0.013,0.002,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (40% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 40% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,1.3,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.06599324872835056,0.010152807496669317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q40-c2,6,"2 (δ400m), row 40% quantile",10,δ400m,transit_distance,D_location,level,count,0.009,0.001,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (40% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 40% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,0.8999999999999999,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.09119332084047471,0.01013259120449719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q40-c3,6,"3 (δ500m), row 40% quantile",10,δ500m,transit_distance,D_location,level,count,0.009,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (40% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 40% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.8999999999999999,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.09119332084047471,0.01013259120449719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q40-c4,6,"4 (δ750m), row 40% quantile",10,δ750m,transit_distance,D_location,level,count,0.004,0.001,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (40% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 40% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.4,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.040666285422027916,0.010166571355506977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q40-c5,6,"5 (δ1000m), row 40% quantile",10,δ1000m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (40% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 40% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q40-c6,6,"6 (δ1500m), row 40% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (40% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 40% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q50-c1,6,"1 (δ300m), row 50% quantile",10,δ300m,transit_distance,D_location,level,count,0.013,0.001,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (50% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 50% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,1.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.13113263207824052,0.01008712554448004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q50-c2,6,"2 (δ400m), row 50% quantile",10,δ400m,transit_distance,D_location,level,count,0.013,0.001,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (50% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 50% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,1.3,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.13113263207824052,0.01008712554448004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q50-c3,6,"3 (δ500m), row 50% quantile",10,δ500m,transit_distance,D_location,level,count,0.009,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (50% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 50% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.8999999999999999,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.09119332084047471,0.01013259120449719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q50-c4,6,"4 (δ750m), row 50% quantile",10,δ750m,transit_distance,D_location,level,count,0.004,0.0,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (50% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 50% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.4,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q50-c5,6,"5 (δ1000m), row 50% quantile",10,δ1000m,transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (50% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 50% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q50-c6,6,"6 (δ1500m), row 50% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (50% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 50% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q60-c1,6,"1 (δ300m), row 60% quantile",10,δ300m,transit_distance,D_location,level,count,0.01,0.002,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (60% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 60% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,1.0,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0508092300708077,0.01016184601416154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q60-c2,6,"2 (δ400m), row 60% quantile",10,δ400m,transit_distance,D_location,level,count,0.01,0.002,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (60% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 60% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,1.0,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0508092300708077,0.01016184601416154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q60-c3,6,"3 (δ500m), row 60% quantile",10,δ500m,transit_distance,D_location,level,count,0.009,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (60% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 60% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.8999999999999999,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.09119332084047471,0.01013259120449719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q60-c4,6,"4 (δ750m), row 60% quantile",10,δ750m,transit_distance,D_location,level,count,0.004,0.0,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (60% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 60% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.4,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q60-c5,6,"5 (δ1000m), row 60% quantile",10,δ1000m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (60% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 60% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q60-c6,6,"6 (δ1500m), row 60% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (60% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 60% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q70-c1,6,"1 (δ300m), row 70% quantile",10,δ300m,transit_distance,D_location,level,count,0.013,0.002,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (70% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 70% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,1.3,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.06599324872835056,0.010152807496669317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q70-c2,6,"2 (δ400m), row 70% quantile",10,δ400m,transit_distance,D_location,level,count,0.013,0.002,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (70% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 70% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,1.3,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.06599324872835056,0.010152807496669317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q70-c3,6,"3 (δ500m), row 70% quantile",10,δ500m,transit_distance,D_location,level,count,0.008,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (70% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 70% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.8,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.08113156325631,0.010141445407038749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q70-c4,6,"4 (δ750m), row 70% quantile",10,δ750m,transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (70% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 70% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q70-c5,6,"5 (δ1000m), row 70% quantile",10,δ1000m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (70% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 70% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q70-c6,6,"6 (δ1500m), row 70% quantile",10,δ1500m,transit_distance,D_location,level,count,0.001,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (70% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 70% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q80-c1,6,"1 (δ300m), row 80% quantile",10,δ300m,transit_distance,D_location,level,count,0.02,0.002,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (80% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 80% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,2.0,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10122722722627175,0.010122722722627176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q80-c2,6,"2 (δ400m), row 80% quantile",10,δ400m,transit_distance,D_location,level,count,0.02,0.002,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (80% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 80% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,2.0,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10122722722627175,0.010122722722627176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q80-c3,6,"3 (δ500m), row 80% quantile",10,δ500m,transit_distance,D_location,level,count,0.01,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (80% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 80% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,1.0,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10122722722627175,0.010122722722627176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q80-c4,6,"4 (δ750m), row 80% quantile",10,δ750m,transit_distance,D_location,level,count,0.005,0.001,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (80% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 80% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.5,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0508092300708077,0.01016184601416154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q80-c5,6,"5 (δ1000m), row 80% quantile",10,δ1000m,transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (80% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 80% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q80-c6,6,"6 (δ1500m), row 80% quantile",10,δ1500m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (80% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 80% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q90-c1,6,"1 (δ300m), row 90% quantile",10,δ300m,transit_distance,D_location,level,count,0.011,0.002,,,***,,,high,"Count of bus stops within 300 m; bivariate quantile regression (90% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 90% quantile; single regressor = number of bus stops within 300 m; no other controls (post hoc calibration),semi_elasticity,1.0999999999999999,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.055875009332945584,0.010159092605990107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q90-c2,6,"2 (δ400m), row 90% quantile",10,δ400m,transit_distance,D_location,level,count,0.011,0.002,,,***,,,high,"Count of bus stops within 400 m; bivariate quantile regression (90% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 90% quantile; single regressor = number of bus stops within 400 m; no other controls (post hoc calibration),semi_elasticity,1.0999999999999999,0.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.055875009332945584,0.010159092605990107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q90-c3,6,"3 (δ500m), row 90% quantile",10,δ500m,transit_distance,D_location,level,count,0.008,0.001,,,***,,,high,"Count of bus stops within 500 m; bivariate quantile regression (90% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 90% quantile; single regressor = number of bus stops within 500 m; no other controls (post hoc calibration),semi_elasticity,0.8,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.08113156325631,0.010141445407038749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q90-c4,6,"4 (δ750m), row 90% quantile",10,δ750m,transit_distance,D_location,level,count,0.005,0.001,,,***,,,high,"Count of bus stops within 750 m; bivariate quantile regression (90% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 90% quantile; single regressor = number of bus stops within 750 m; no other controls (post hoc calibration),semi_elasticity,0.5,0.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0508092300708077,0.01016184601416154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,True,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q90-c5,6,"5 (δ1000m), row 90% quantile",10,δ1000m,transit_distance,D_location,level,count,0.003,0.0,,,***,,,high,"Count of bus stops within 1000 m; bivariate quantile regression (90% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 90% quantile; single regressor = number of bus stops within 1000 m; no other controls (post hoc calibration),semi_elasticity,0.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee,327115a5186cc436d1749ca2acaa98314f882fee:T6-q90-c6,6,"6 (δ1500m), row 90% quantile",10,δ1500m,transit_distance,D_location,level,count,0.002,0.0,,,***,,,high,"Count of bus stops within 1500 m; bivariate quantile regression (90% quantile), no controls. Column header for the 6th column is misprinted 'δ750 m' in the PDF but corresponds to 1500 m per the text/order.","log(p), natural log of property sale price (GBP)",log,quantile,9669.0,,se,Bivariate quantile regression at the 90% quantile; single regressor = number of bus stops within 1500 m; no other controls (post hoc calibration),semi_elasticity,0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9659.0,count,unit_not_harmonisable,2015.0,United Kingdom,Europe,High income,2000.0,2008.0,mixed,transaction,journal,True,True,OLS,,2004.0,quantile,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Dwelling area (overall mean effect),living_area,A_structural,log,m2,0.614,0.019,,,,,,high,Log dwelling area (Table 2); random slope at municipality level in ML model,log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,elasticity,0.614,0.019,b (log-log),reported,0.28400435045962613,0.008788408238978659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.31578947368421,11903.0,m2,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Dwelling area (overall mean effect),living_area,A_structural,log,m2,0.63,0.011,,,,,,high,Log dwelling area (Table 2); random slope at municipality level in ML model,log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,elasticity,0.63,0.011,b (log-log),reported,0.46480062603292505,0.008115566486289168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),57.27272727272727,11903.0,m2,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Dwelling area (overall mean effect),living_area,A_structural,log,m2,0.603,0.017,,,,,,high,Log dwelling area (Table 2); random slope at municipality level in ML model,log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",elasticity,0.603,0.017,b (log-log),reported,0.3091870267312343,0.008716715513152545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.470588235294116,11903.0,m2,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Macro-location (in log),other,other,log,CHF per m2 (log),0.766,0.025,,,,,,high,Municipality-level virtual asking rent per m2 from separate spatial model (location attractiveness index),log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,elasticity,0.766,0.025,b (log-log),reported,0.27038067486168266,0.008824434558148912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.64,11903.0,chf per m2,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Macro-location (in log),other,other,log,CHF per m2 (log),0.761,0.022,,,,,,high,Municipality-level virtual asking rent per m2 from separate spatial model (location attractiveness index),log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,elasticity,0.761,0.022,b (log-log),reported,0.3022275862114648,0.008737196973261793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.59090909090909,11903.0,chf per m2,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Macro-location (in log),other,other,log,CHF per m2 (log),0.789,0.024,,,,,,high,Municipality-level virtual asking rent per m2 from separate spatial model (location attractiveness index),log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",elasticity,0.789,0.024,b (log-log),reported,0.288513027381098,0.008776061669387013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.875,11903.0,chf per m2,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Age in years,age,A_structural,level,years,-0.007,0.0,,,,,,medium,"Age of building, mean-centred; SE printed as 0.000 (rounded)",log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,-0.7000000000000001,,100*b/1 (per year),,,,,,11903.0,years,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Age in years,age,A_structural,level,years,-0.007,0.0,,,,,,medium,"Age of building, mean-centred; SE printed as 0.000 (rounded)",log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,-0.7000000000000001,,100*b/1 (per year),,,,,,11903.0,years,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Age in years,age,A_structural,level,years,-0.007,0.0,,,,,,medium,"Age of building, mean-centred; SE printed as 0.000 (rounded)",log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,-0.7000000000000001,,100*b/1 (per year),,,,,,11903.0,years,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Age squared,age,A_structural,squared,years,0.005,0.0,,,,,,medium,Squared age term; scaling not stated (coefficient implausibly large for raw years^2); SE printed as 0.000 (rounded),log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,other,0.005,,not harmonised,,,,,,11903.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Age squared,age,A_structural,squared,years,0.005,0.0,,,,,,medium,Squared age term; scaling not stated (coefficient implausibly large for raw years^2); SE printed as 0.000 (rounded),log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,other,0.005,,not harmonised,,,,,,11903.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Age squared,age,A_structural,squared,years,0.006,0.0,,,,,,medium,Squared age term; scaling not stated (coefficient implausibly large for raw years^2); SE printed as 0.000 (rounded),log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",other,0.006,,not harmonised,,,,,,11903.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Number of rooms,bedrooms,A_structural,level,count,0.053,0.005,,,,,,high,Number of rooms in the apartment (not bedrooms specifically),log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,5.3,0.5,100*b/1 (per unit count),reported,0.09670244871739685,0.009122872520509138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.6,11903.0,count,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Number of rooms,bedrooms,A_structural,level,count,0.054,0.005,,,,,,high,Number of rooms in the apartment (not bedrooms specifically),log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,5.4,0.5,100*b/1 (per unit count),reported,0.09850947971745741,0.009121248121986796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.799999999999999,11903.0,count,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Number of rooms,bedrooms,A_structural,level,count,0.052,0.005,,,,,,high,Number of rooms in the apartment (not bedrooms specifically),log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,5.2,0.5,100*b/1 (per unit count),reported,0.09489446096438996,0.009124467400422113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.399999999999999,11903.0,count,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Number of apartments,other,other,level,count,-0.0001,0.0,,,,,,medium,Number of apartments in the building; SE printed as 0.000 (rounded),log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,-0.01,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,11903.0,count,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Number of apartments,other,other,level,count,-0.0001,0.0,,,,,,medium,Number of apartments in the building; SE printed as 0.000 (rounded),log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,-0.01,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,11903.0,count,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Number of apartments,other,other,level,count,-0.0002,0.0,,,,,,medium,Number of apartments in the building; SE printed as 0.000 (rounded),log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,-0.02,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,11903.0,count,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Floor level,floor_level,A_structural,level,count,0.006,0.001,,,,,,high,Position of the apartment in the building,log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,0.6,0.1,100*b/1 (per unit count),reported,0.05491200157443693,0.009152000262406156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,11903.0,count,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Floor level,floor_level,A_structural,level,count,0.007,0.001,,,,,,high,Position of the apartment in the building,log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,0.7000000000000001,0.1,100*b/1 (per unit count),reported,0.0640291516606527,0.009147021665807529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,11903.0,count,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Floor level,floor_level,A_structural,level,count,0.009,0.001,,,,,,high,Position of the apartment in the building,log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,0.8999999999999999,0.1,100*b/1 (per unit count),reported,0.08221321071471033,0.00913480119052337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,11903.0,count,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Condition,condition,A_structural,level,1-5 scale,0.063,0.003,,,,,,high,"Condition of the building, scale 1 (worst) to 5 (best)",log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,6.3,0.3,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.18901285331105258,0.009000612062431076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Condition,condition,A_structural,level,1-5 scale,0.063,0.003,,,,,,high,"Condition of the building, scale 1 (worst) to 5 (best)",log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,6.3,0.3,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.18901285331105258,0.009000612062431076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Condition,condition,A_structural,level,1-5 scale,0.065,0.003,,,,,,high,"Condition of the building, scale 1 (worst) to 5 (best)",log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,6.5,0.3,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.19478896308409732,0.008990259834650646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.666666666666668,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Standard,luxury_finishes,B_interior,level,1-5 scale,0.018,0.004,,,,,,high,"Standard (fit-out quality) of the apartment, scale 1-5",log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,1.7999999999999998,0.4,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.041211193177652906,0.009158042928367313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Standard,luxury_finishes,B_interior,level,1-5 scale,0.019,0.004,,,,,,high,"Standard (fit-out quality) of the apartment, scale 1-5",log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,1.9,0.4,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.043496486068661494,0.009157154961823474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Standard,luxury_finishes,B_interior,level,1-5 scale,0.024,0.004,,,,,,high,"Standard (fit-out quality) of the apartment, scale 1-5",log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,2.4,0.4,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.05491200157443693,0.009152000262406156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Micro-location,other,other,level,1-5 scale,0.007,0.003,,,,,,high,"Composite site-quality rating within municipality (prestige, noise, view, public transport access), 1-5",log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,semi_elasticity,0.7000000000000001,0.3,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.021382046408991395,0.009163734175282027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3333333333333335,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Micro-location,other,other,level,1-5 scale,0.007,0.003,,,,,,high,"Composite site-quality rating within municipality (prestige, noise, view, public transport access), 1-5",log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,semi_elasticity,0.7000000000000001,0.3,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.021382046408991395,0.009163734175282027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3333333333333335,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Micro-location,other,other,level,1-5 scale,0.009,0.003,,,,,,high,"Composite site-quality rating within municipality (prestige, noise, view, public transport access), 1-5",log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",semi_elasticity,0.8999999999999999,0.3,100*b (unit as reported: 1-5 scale; no harmonised unit defined for this characteristic),reported,0.027487099361267125,0.00916236645375571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,11903.0,1-5 scale,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,False,False,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c1,5,1,17,Mixed building,dwelling_type,A_structural,binary,binary,0.033,0.005,,,,,,high,Dummy for mixed commercial and residential building,log yearly rent (CHF),log,OLS,11913.0,0.858,se,OLS 1: with cantonal dummies,pct_premium,3.353761994031079,0.5167752696206528,100*(exp(b - se^2/2) - 1),reported,0.06038408658610734,0.009149104028198081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6000000000000005,11903.0,binary,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c2,5,2,17,Mixed building,dwelling_type,A_structural,binary,binary,0.032,0.005,,,,,,high,Dummy for mixed commercial and residential building,log yearly rent (CHF),log,OLS,11913.0,0.859,se,OLS 2: cantonal dummies + cantonal interaction terms,pct_premium,3.250459891696722,0.5162587526525592,100*(exp(b - se^2/2) - 1),reported,0.05856063857503699,0.009150099777349529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4,11903.0,binary,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,ols,journal,True,True,False
3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796,3372ad7c12532642d33524d3fe7c38467c784796:T5-c3,5,3,17,Mixed building,dwelling_type,A_structural,binary,binary,0.038,0.005,,,,,,high,Dummy for mixed commercial and residential building,log yearly rent (CHF),log,other,11913.0,0.877,se,"ML: two-level multilevel model (random intercept + random slope on dwelling area at municipality level, MLwiN); same estimates as Table 4 p. 14",pct_premium,3.8718248819237244,0.5193656164392488,100*(exp(b - se^2/2) - 1),reported,0.06949190291450835,0.00914367143611952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,11903.0,binary,,2008.0,Switzerland,Europe,High income,2006.0,2007.0,rental,rent,journal,True,True,OLS,,2006.5,other,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Dwelling,dwelling_type,A_structural,binary,binary,0.4,0.18,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,46.78518419546045,26.852844557542866,100*(exp(b - se^2/2) - 1),reported,0.1443676470241678,0.0649654411608755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2222222222222223,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Roof,roof_materials,A_structural,binary,binary,-0.38,0.33,,,,,,medium,"1 if roof type is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,-35.237923943834105,22.56742650400774,100*(exp(b - se^2/2) - 1),reported,-0.07538554879765468,0.06546639764006854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1515151515151514,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Floor,flooring,B_interior,binary,binary,0.64,0.24,,,,,,medium,"1 if floor is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,84.26412421009857,45.51554110331883,100*(exp(b - se^2/2) - 1),reported,0.17245224542369075,0.06466959203388403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.666666666666667,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,True
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,External wall,roof_materials,A_structural,binary,binary,0.32,0.22,,,,,,medium,"1 if external wall is in mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,34.42012896882969,30.296810815391055,100*(exp(b - se^2/2) - 1),reported,0.09506311166323297,0.06535588926847266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4545454545454546,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Number of rooms,bedrooms,A_structural,level,count,0.36,0.18,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),semi_elasticity,36.0,18.0,100*b/1 (per unit count),reported,0.13018891098082389,0.06509445549041193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,232.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,True
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Electricity,other,other,binary,binary,0.6,0.16,,,,,,medium,"1 if the house has electricity; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,79.89443128737565,29.153900806248146,100*(exp(b - se^2/2) - 1),reported,0.23906090081865877,0.06374957355164233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.75,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Private tap water,other,other,binary,binary,2.36,0.85,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,637.9825549102522,900.2308734568713,100*(exp(b - se^2/2) - 1),reported,0.17932923605426096,0.06458891976530585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.776470588235294,232.0,binary,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Public tap water,other,other,binary,binary,-0.15,0.41,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,-20.867775120602516,35.28902703342737,100*(exp(b - se^2/2) - 1),reported,-0.024012543587000026,0.06563428580446674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36585365853658536,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Bore hole water,other,other,binary,binary,-0.13,0.3,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,-16.054297923079265,26.34286292761684,100*(exp(b - se^2/2) - 1),reported,-0.02843822068474392,0.06562666311863981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43333333333333335,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Protected well water,other,other,binary,binary,0.02,0.39,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,-5.450813988449399,39.787852261043476,100*(exp(b - se^2/2) - 1),reported,0.0033668125294973616,0.06565284432519855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05128205128205128,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Unprotected water,other,other,binary,binary,-0.04,0.33,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,-9.012677284993508,31.706051492026667,100*(exp(b - se^2/2) - 1),reported,-0.007957713653964091,0.06565113764520375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.12121212121212122,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Covered private toilet,other,other,binary,binary,0.42,0.79,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,11.39920443917346,120.23496289387208,100*(exp(b - se^2/2) - 1),reported,0.03488299902929984,0.06561326007892114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5316455696202531,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Cover shared toilet,other,other,binary,binary,0.53,0.75,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,28.242138764389214,127.41992314639131,100*(exp(b - se^2/2) - 1),reported,0.046345087743706395,0.06558267133543358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7066666666666667,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,VIP latrine toilet,other,other,binary,binary,0.22,1.01,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,-25.177384466449983,125.85374978932545,100*(exp(b - se^2/2) - 1),reported,0.01429923851432884,0.06564650408850967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21782178217821782,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c1,2,1,11,Flush toilet,other,other,binary,binary,0.62,0.75,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,242.0,0.75,se,Uganda LSMS 2010 renter households; OLS with district fixed effects (not reported),pct_premium,40.319250323349756,139.41960313847565,100*(exp(b - se^2/2) - 1),reported,0.054193568151018855,0.06555673566655507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8266666666666667,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Dwelling,dwelling_type,A_structural,binary,binary,0.16,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,17.35108709918103,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Roof,roof_materials,A_structural,binary,binary,0.01,,,,,,,medium,"1 if roof type is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Floor,flooring,B_interior,binary,binary,0.28,,,,,,,medium,"1 if floor is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,32.312981233743685,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,External wall,roof_materials,A_structural,binary,binary,0.28,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,32.312981233743685,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Number of rooms,bedrooms,A_structural,level,count,0.22,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",semi_elasticity,22.0,,100*b/1 (per unit count),,,,,,232.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Electricity,other,other,binary,binary,0.38,,,,,,,medium,"1 if the house has electricity; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,46.228458943422446,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Private tap water,other,other,binary,binary,0.94,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,155.99814183292713,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Public tap water,other,other,binary,binary,0.22,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,24.60767305873808,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Bore hole water,other,other,binary,binary,0.04,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,4.081077419238821,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Protected well water,other,other,binary,binary,-0.05,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-4.877057549928598,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Unprotected water,other,other,binary,binary,-0.01,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Covered private toilet,other,other,binary,binary,-0.13,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-12.190456907943869,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Cover shared toilet,other,other,binary,binary,-0.02,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-1.9801326693244747,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,VIP latrine toilet,other,other,binary,binary,0.09,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c2,2,2,11,Flush toilet,other,other,binary,binary,0.06,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,6.183654654535964,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c3,2,3,11,Dwelling,dwelling_type,A_structural,binary,binary,0.11,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,11.627807045887128,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c3,2,3,11,Floor,flooring,B_interior,binary,binary,0.29,,,,,,,medium,"1 if floor is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,33.6427488025472,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c3,2,3,11,External wall,roof_materials,A_structural,binary,binary,0.29,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,33.6427488025472,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c3,2,3,11,Number of rooms,bedrooms,A_structural,level,count,0.12,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",semi_elasticity,12.0,,100*b/1 (per unit count),,,,,,232.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c3,2,3,11,Electricity,other,other,binary,binary,0.49,,,,,,,medium,"1 if the house has electricity; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,63.23162199553789,,100*(exp(b) - 1),,,,,,232.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c3,2,3,11,Private tap water,other,other,binary,binary,1.15,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,242.0,,none,"Uganda LSMS 2010 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,215.81929096897673,,100*(exp(b) - 1),,,,,,232.0,binary,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Dwelling,dwelling_type,A_structural,binary,binary,0.22,0.14,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,23.392482024394077,17.445074228223334,100*(exp(b - se^2/2) - 1),reported,0.09831622879049641,0.06256487286667954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5714285714285714,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Roof,roof_materials,A_structural,binary,binary,0.23,0.73,,,,,,medium,"1 if roof type is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,-3.5793696993763446,91.87780072485188,100*(exp(b - se^2/2) - 1),reported,0.0198043015826003,0.06285713110999226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31506849315068497,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Floor,flooring,B_interior,binary,binary,0.01,0.09,,,,,,medium,"1 if floor is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,0.5967736409763669,9.09045150375751,100*(exp(b - se^2/2) - 1),reported,0.006985325275228183,0.06286792747705364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11111111111111112,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,External wall,roof_materials,A_structural,binary,binary,0.4,0.18,,,,,,medium,"1 if external wall is in mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,46.78518419546045,26.852844557542866,100*(exp(b - se^2/2) - 1),reported,0.13836606606450116,0.06226472972902551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2222222222222223,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Number of rooms,bedrooms,A_structural,level,count,6.43,2.34,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),semi_elasticity,643.0,234.0,100*b/1 (per unit count),reported,0.17023503708416787,0.061951786434984876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.747863247863248,253.0,count,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Electricity,other,other,binary,binary,0.5,0.11,,,,,,medium,"1 if the house has electricity; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,63.87766199142577,18.13593397770141,100*(exp(b - se^2/2) - 1),reported,0.2747709022294505,0.06044959849047911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.545454545454546,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Private tap water,other,other,binary,binary,0.29,0.3,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,27.76213132048866,40.09282464076416,100*(exp(b - se^2/2) - 1),reported,0.06066188986515684,0.0627536791708519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9666666666666667,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Public tap water,other,other,binary,binary,0.05,0.24,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,2.1426316473659,25.23050631302458,100*(exp(b - se^2/2) - 1),reported,0.01309668111122833,0.06286406933389597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.20833333333333334,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Bore hole water,other,other,binary,binary,-0.58,0.33,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,-46.977296699641755,18.476646096658268,100*(exp(b - se^2/2) - 1),reported,-0.109829379513737,0.06248912972333313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7575757575757573,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Protected well water,other,other,binary,binary,0.25,0.26,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,24.13506243001744,33.38466083388128,100*(exp(b - se^2/2) - 1),reported,0.060341250984283695,0.06275490102365505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9615384615384615,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Unprotected water,other,other,binary,binary,-0.87,0.36,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,-60.73356094431346,15.082255772915003,100*(exp(b - se^2/2) - 1),reported,-0.15021068383173852,0.06215614503382283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.416666666666667,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Covered private toilet,other,other,binary,binary,0.63,0.32,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,78.38964700666186,60.08353853645898,100*(exp(b - se^2/2) - 1),reported,0.12283689384906424,0.062393342907461194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.96875,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Cover shared toilet,other,other,binary,binary,0.4,0.28,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,43.447653687973656,41.77109153395558,100*(exp(b - se^2/2) - 1),reported,0.08945345415863279,0.06261741791104296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4285714285714286,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,VIP latrine toilet,other,other,binary,binary,-0.14,0.45,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,-21.43548094627471,39.12112059294626,100*(exp(b - se^2/2) - 1),reported,-0.01955564763335937,0.06285743882151226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3111111111111111,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c4,2,4,11,Flush toilet,other,other,binary,binary,1.29,0.49,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,263.0,0.41,se,Uganda LSMS 2012 renter households; OLS with district fixed effects (not reported),pct_premium,222.1831542923997,178.00654123188767,100*(exp(b - se^2/2) - 1),reported,0.16329192417393035,0.06202561460870222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.63265306122449,253.0,binary,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Dwelling,dwelling_type,A_structural,binary,binary,0.08,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,8.328706767495863,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Roof,roof_materials,A_structural,binary,binary,-0.27,,,,,,,medium,"1 if roof type is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-23.662050566314687,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Floor,flooring,B_interior,binary,binary,-0.08,,,,,,,medium,"1 if floor is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-7.688365361336425,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,External wall,roof_materials,A_structural,binary,binary,0.29,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,33.6427488025472,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Number of rooms,bedrooms,A_structural,level,count,0.39,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",semi_elasticity,39.0,,100*b/1 (per unit count),,,,,,253.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Electricity,other,other,binary,binary,0.42,,,,,,,medium,"1 if the house has electricity; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,52.196155561863364,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Private tap water,other,other,binary,binary,0.31,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,36.34251141321778,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Public tap water,other,other,binary,binary,0.07,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,7.250818125421654,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Bore hole water,other,other,binary,binary,-0.42,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-34.29531801849433,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Protected well water,other,other,binary,binary,0.2,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,22.140275816016985,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Unprotected water,other,other,binary,binary,-0.54,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-41.725174762601036,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Covered private toilet,other,other,binary,binary,0.17,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,18.530485132036546,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Cover shared toilet,other,other,binary,binary,0.02,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,VIP latrine toilet,other,other,binary,binary,-0.34,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-28.822967723739033,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c5,2,5,11,Flush toilet,other,other,binary,binary,0.86,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,136.31606937057947,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Dwelling,dwelling_type,A_structural,binary,binary,0.13,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,13.882838332462178,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Roof,roof_materials,A_structural,binary,binary,-0.09,,,,,,,medium,"1 if roof type is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-8.60688147287718,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,External wall,roof_materials,A_structural,binary,binary,0.37,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,44.773461466332456,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Number of rooms,bedrooms,A_structural,level,count,0.53,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",semi_elasticity,53.0,,100*b/1 (per unit count),,,,,,253.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Electricity,other,other,binary,binary,0.49,,,,,,,medium,"1 if the house has electricity; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,63.23162199553789,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Private tap water,other,other,binary,binary,0.2,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,22.140275816016985,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Bore hole water,other,other,binary,binary,-0.56,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-42.879093615118514,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Protected well water,other,other,binary,binary,0.13,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,13.882838332462178,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Unprotected water,other,other,binary,binary,-0.74,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-52.28860844789656,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Covered private toilet,other,other,binary,binary,0.21,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,23.367805995674317,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Cover shared toilet,other,other,binary,binary,0.04,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,4.081077419238821,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,VIP latrine toilet,other,other,binary,binary,-0.38,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-31.613859078764417,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T2-c6,2,6,11,Flush toilet,other,other,binary,binary,0.87,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,263.0,,none,"Uganda LSMS 2012 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,138.69108535242765,,100*(exp(b) - 1),,,,,,253.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Roof,roof_materials,A_structural,binary,binary,-0.63,0.11,,,,,,medium,"1 if roof type is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-47.062065192044464,5.858509811075869,100*(exp(b - se^2/2) - 1),reported,-0.19135593409256046,0.03341135357171691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.7272727272727275,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Floor,flooring,B_interior,binary,binary,-0.16,0.07,,,,,,medium,"1 if floor is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-14.994140790755273,5.965006522763479,100*(exp(b - se^2/2) - 1),reported,-0.07757216754198588,0.03393782329961883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2857142857142856,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,External wall,roof_materials,A_structural,binary,binary,0.42,0.05,,,,,,medium,"1 if external wall is in mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,52.00602922113005,7.609807778093169,100*(exp(b - se^2/2) - 1),reported,0.2749211604401788,0.03272870957621177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.399999999999999,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Number of rooms,bedrooms,A_structural,level,count,0.66,0.05,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),semi_elasticity,66.0,5.0,100*b/1 (per unit count),reported,0.4098588365968505,0.031049911863397773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.2,863.0,count,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Electricity,other,other,binary,binary,0.64,0.05,,,,,,medium,"1 if the house has electricity; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,89.41117592143559,9.482404396524757,100*(exp(b - se^2/2) - 1),reported,0.3994464636331454,0.031206754971339484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.799999999999999,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Private tap water,other,other,binary,binary,0.15,0.09,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,15.713832968786479,10.456508184554547,100*(exp(b - se^2/2) - 1),reported,0.05664290680368047,0.03398574408220828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Public tap water,other,other,binary,binary,-0.15,0.11,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-14.44835865190689,9.467787740675636,100*(exp(b - se^2/2) - 1),reported,-0.04636879316675828,0.03400378165562274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3636363636363635,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Protected well water,other,other,binary,binary,-0.27,0.18,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-24.888762155205267,13.740830898063356,100*(exp(b - se^2/2) - 1),reported,-0.050994161502742774,0.03399610766849518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5000000000000002,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Unprotected water,other,other,binary,binary,-0.15,0.12,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-14.54668649011368,10.328495717100694,100*(exp(b - se^2/2) - 1),reported,-0.04251202729107061,0.03400962183285649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,VIP latrine toilet,other,other,binary,binary,-0.18,0.11,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-16.976791765999167,9.187972325523992,100*(exp(b - se^2/2) - 1),reported,-0.055616250737365515,0.033987708783945594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6363636363636362,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Flush toilet,other,other,binary,binary,0.03,0.11,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,2.423910465506851,11.334999873488687,100*(exp(b - se^2/2) - 1),reported,0.009283344283101676,0.034038929038039484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2727272727272727,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Pit latrine,other,other,binary,binary,0.14,0.1,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,14.453678435131456,11.502737988572274,100*(exp(b - se^2/2) - 1),reported,0.04760252871348968,0.0340018062239212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4000000000000001,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Private toilet,other,other,binary,binary,-0.16,0.13,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-15.5026488961711,11.077869256560748,100*(exp(b - se^2/2) - 1),reported,-0.04185915102500362,0.03401056020781544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2307692307692308,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c1,3,1,11,Shared toilet,other,other,binary,binary,-0.31,0.07,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,873.0,0.75,se,Tanzania LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-26.834778935737614,5.134128693570026,100*(exp(b - se^2/2) - 1),reported,-0.14906602807185892,0.03366007085493589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.428571428571428,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Roof,roof_materials,A_structural,binary,binary,-0.53,,,,,,,medium,"1 if roof type is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-41.139503032164484,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Floor,flooring,B_interior,binary,binary,-0.21,,,,,,,medium,"1 if floor is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-18.941575402981293,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,External wall,roof_materials,A_structural,binary,binary,0.4,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,49.182469764127035,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Number of rooms,bedrooms,A_structural,level,count,0.6,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",semi_elasticity,60.0,,100*b/1 (per unit count),,,,,,863.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Electricity,other,other,binary,binary,0.62,,,,,,,medium,"1 if the house has electricity; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,85.8928041846342,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Private tap water,other,other,binary,binary,0.14,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,15.027379885722736,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Public tap water,other,other,binary,binary,-0.19,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-17.30408660566377,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Protected well water,other,other,binary,binary,-0.29,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-25.173643242143473,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Unprotected water,other,other,binary,binary,-0.16,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-14.785621103378865,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,VIP latrine toilet,other,other,binary,binary,-0.17,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-15.63351834036163,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Flush toilet,other,other,binary,binary,0.05,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Pit latrine,other,other,binary,binary,0.15,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,16.183424272828304,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Private toilet,other,other,binary,binary,-0.19,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-17.30408660566377,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c2,3,2,11,Shared toilet,other,other,binary,binary,-0.3,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-25.91817793182821,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Roof,roof_materials,A_structural,binary,binary,-0.52,,,,,,,medium,"1 if roof type is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-40.547945202980564,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Floor,flooring,B_interior,binary,binary,-0.21,,,,,,,medium,"1 if floor is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-18.941575402981293,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,External wall,roof_materials,A_structural,binary,binary,0.44,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,55.2707218511336,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Number of rooms,bedrooms,A_structural,level,count,0.61,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",semi_elasticity,61.0,,100*b/1 (per unit count),,,,,,863.0,count,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Electricity,other,other,binary,binary,0.7,,,,,,,medium,"1 if the house has electricity; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,101.37527074704767,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Private tap water,other,other,binary,binary,0.1,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,10.517091807564771,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Protected well water,other,other,binary,binary,-0.13,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-12.190456907943869,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Unprotected water,other,other,binary,binary,-0.05,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-4.877057549928598,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,VIP latrine toilet,other,other,binary,binary,-0.04,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-3.9210560847676823,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Flush toilet,other,other,binary,binary,0.01,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Pit latrine,other,other,binary,binary,0.1,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,10.517091807564771,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Private toilet,other,other,binary,binary,-0.07,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c3,3,3,11,Shared toilet,other,other,binary,binary,-0.22,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,873.0,,none,"Tanzania LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-19.74812020375215,,100*(exp(b) - 1),,,,,,863.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Roof,roof_materials,A_structural,binary,binary,-0.73,0.14,,,,,,medium,"1 if roof type is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-52.279065215294686,6.746725861262835,100*(exp(b - se^2/2) - 1),reported,-0.14035425356565667,0.026917254108482107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.2142857142857135,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,True
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Floor,flooring,B_interior,binary,binary,-0.28,0.08,,,,,,medium,"1 if floor is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-24.663090102846365,6.046269931645805,100*(exp(b - se^2/2) - 1),reported,-0.09472441934290148,0.02706411981225756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5000000000000004,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Number of rooms,bedrooms,A_structural,level,count,0.8,0.05,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),semi_elasticity,80.0,5.0,100*b/1 (per unit count),reported,0.3988797239553825,0.02492998274721141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,1353.0,count,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Electricity,other,other,binary,binary,0.54,0.05,,,,,,medium,"1 if the house has electricity; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,71.38631936790671,8.580034310924294,100*(exp(b - se^2/2) - 1),reported,0.2817203852029224,0.026085220852122448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.8,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Private tap water,other,other,binary,binary,0.1,0.05,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,10.379031748318912,5.525854590378239,100*(exp(b - se^2/2) - 1),reported,0.05429252897979031,0.027146264489895156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Public tap water,other,other,binary,binary,0.31,0.09,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,35.79144091300146,12.2708260271896,100*(exp(b - se^2/2) - 1),reported,0.09323403144389433,0.027067944612743512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4444444444444446,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Protected well water,other,other,binary,binary,-0.04,0.12,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-4.610340080867104,11.529473269827877,100*(exp(b - se^2/2) - 1),reported,-0.00906174872014079,0.027185246160422366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33333333333333337,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,VIP latrine toilet,other,other,binary,binary,0.89,0.24,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,136.59981886947355,58.443111630956984,100*(exp(b - se^2/2) - 1),reported,0.10030762503437937,0.027049247200282078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7083333333333335,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Flush toilet,other,other,binary,binary,0.72,0.22,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,100.5312682558249,45.197530634165524,100*(exp(b - se^2/2) - 1),reported,0.08862345653471776,0.027079389496719317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2727272727272725,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c4,3,4,11,Pit latrine,other,other,binary,binary,0.49,0.22,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1363.0,0.58,se,Tanzania LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,59.328830981863035,35.91095683901834,100*(exp(b - se^2/2) - 1),reported,0.06044074258011912,0.027136659933931037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.227272727272727,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Roof,roof_materials,A_structural,binary,binary,-0.66,,,,,,,medium,"1 if roof type is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-48.31486655083008,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Floor,flooring,B_interior,binary,binary,-0.32,,,,,,,medium,"1 if floor is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-27.385096292630905,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,External wall,roof_materials,A_structural,binary,binary,0.11,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,11.627807045887128,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Number of rooms,bedrooms,A_structural,level,count,0.72,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",semi_elasticity,72.0,,100*b/1 (per unit count),,,,,,1353.0,count,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Electricity,other,other,binary,binary,0.5,,,,,,,medium,"1 if the house has electricity; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,64.87212707001282,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Private tap water,other,other,binary,binary,0.05,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Public tap water,other,other,binary,binary,-0.15,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-13.92920235749422,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Protected well water,other,other,binary,binary,0.12,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,12.749685157937574,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Unprotected water,other,other,binary,binary,0.12,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,12.749685157937574,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,VIP latrine toilet,other,other,binary,binary,0.32,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,37.71277643359572,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Flush toilet,other,other,binary,binary,0.19,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,20.92495976572515,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c5,3,5,11,Pit latrine,other,other,binary,binary,-0.01,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Roof,roof_materials,A_structural,binary,binary,-0.72,,,,,,,medium,"1 if roof type is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-51.324774404002824,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Floor,flooring,B_interior,binary,binary,-0.28,,,,,,,medium,"1 if floor is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-24.421625854427454,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,External wall,roof_materials,A_structural,binary,binary,-0.0,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,0.0,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Number of rooms,bedrooms,A_structural,level,count,0.8,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",semi_elasticity,80.0,,100*b/1 (per unit count),,,,,,1353.0,count,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Electricity,other,other,binary,binary,0.5,,,,,,,medium,"1 if the house has electricity; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,64.87212707001282,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Public tap water,other,other,binary,binary,-0.2,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-18.12692469220182,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Protected well water,other,other,binary,binary,0.05,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Unprotected water,other,other,binary,binary,0.09,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,VIP latrine toilet,other,other,binary,binary,0.8,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,122.55409284924679,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Flush toilet,other,other,binary,binary,0.63,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,87.76105792643432,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T3-c6,3,6,11,Pit latrine,other,other,binary,binary,0.4,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1363.0,,none,"Tanzania LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,49.182469764127035,,100*(exp(b) - 1),,,,,,1353.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Roof,roof_materials,A_structural,binary,binary,1.11,,,,,,,medium,"1 if roof type is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed); no value in parentheses printed for this cell","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,203.43583944356757,,100*(exp(b) - 1),,,,,,652.0,binary,implausible_magnitude,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,External wall,roof_materials,A_structural,binary,binary,0.14,0.13,,,,,,medium,"1 if external wall is in mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,14.059493604345352,14.953559385143956,100*(exp(b - se^2/2) - 1),reported,0.042138102176325735,0.0391282377351596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.076923076923077,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Number of rooms,bedrooms,A_structural,level,count,0.5,0.07,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),semi_elasticity,50.0,7.000000000000001,100*b/1 (per unit count),reported,0.2693940491899717,0.037715166886596034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.142857142857142,652.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,True
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Electricity,other,other,binary,binary,0.82,0.08,,,,,,medium,"1 if the house has electricity; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,126.32458506214275,18.163998700259246,100*(exp(b - se^2/2) - 1),reported,0.3725272085271632,0.036344117905089096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.25,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Private tap water,other,other,binary,binary,0.58,0.18,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,75.73377116783834,32.148691753501325,100*(exp(b - se^2/2) - 1),reported,0.12519903822498027,0.038854873931890424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2222222222222223,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Public tap water,other,other,binary,binary,0.24,0.09,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,26.6111003034063,11.441242352892642,100*(exp(b - se^2/2) - 1),reported,0.10386982880091547,0.038951185800343305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6666666666666665,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Bore hole water,other,other,binary,binary,-0.12,0.19,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,-12.894486209993373,16.85148829762599,100*(exp(b - se^2/2) - 1),reported,-0.02472697773096566,0.03915104807402896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.631578947368421,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Protected well water,other,other,binary,binary,0.06,0.14,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,5.148137162212296,14.865711651635035,100*(exp(b - se^2/2) - 1),reported,0.016781788885987386,0.03915750740063724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4285714285714285,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Unprotected water,other,other,binary,binary,0.55,0.41,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,59.3527320990493,71.06337373256319,100*(exp(b - se^2/2) - 1),reported,0.052463412103137104,0.03910908902233857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3414634146341464,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,VIP latrine toilet,other,other,binary,binary,0.13,0.13,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,12.92458268049641,14.804768983220082,100*(exp(b - se^2/2) - 1),reported,0.03913302400974161,0.03913302400974162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c1,4,1,12,Flush toilet,other,other,binary,binary,0.26,0.17,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,662.0,0.75,se,Malawi LSMS 2014 renter households; OLS with district fixed effects (not reported),pct_premium,27.832419820280528,22.047811473318124,100*(exp(b - se^2/2) - 1),reported,0.059789234123991154,0.039092960773378836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5294117647058822,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Dwelling,dwelling_type,A_structural,binary,binary,0.3,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,34.98588075760032,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Roof,roof_materials,A_structural,binary,binary,0.18,,,,,,,medium,"1 if roof type is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,19.721736312181015,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Floor,flooring,B_interior,binary,binary,-0.15,,,,,,,medium,"1 if floor is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-13.92920235749422,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,External wall,roof_materials,A_structural,binary,binary,0.11,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,11.627807045887128,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Number of rooms,bedrooms,A_structural,level,count,0.42,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",semi_elasticity,42.0,,100*b/1 (per unit count),,,,,,652.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Electricity,other,other,binary,binary,0.58,,,,,,,medium,"1 if the house has electricity; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,78.60384307500735,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Private tap water,other,other,binary,binary,0.49,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,63.23162199553789,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Public tap water,other,other,binary,binary,0.17,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,18.530485132036546,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Bore hole water,other,other,binary,binary,0.03,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Protected well water,other,other,binary,binary,0.05,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Unprotected water,other,other,binary,binary,0.41,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,50.681778511285344,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,VIP latrine toilet,other,other,binary,binary,0.04,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,4.081077419238821,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Flush toilet,other,other,binary,binary,0.26,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,29.69300866657718,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c2,4,2,12,Pit latrine,other,other,binary,binary,-0.05,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-4.877057549928598,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Dwelling,dwelling_type,A_structural,binary,binary,0.3,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,34.98588075760032,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Roof,roof_materials,A_structural,binary,binary,0.12,,,,,,,medium,"1 if roof type is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,12.749685157937574,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Floor,flooring,B_interior,binary,binary,-0.11,,,,,,,medium,"1 if floor is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-10.416586470347177,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Number of rooms,bedrooms,A_structural,level,count,0.42,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",semi_elasticity,42.0,,100*b/1 (per unit count),,,,,,652.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Electricity,other,other,binary,binary,0.69,,,,,,,medium,"1 if the house has electricity; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,99.37155332430822,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Private tap water,other,other,binary,binary,0.47,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,59.99941932173603,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Public tap water,other,other,binary,binary,0.09,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Unprotected water,other,other,binary,binary,0.09,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c3,4,3,12,Flush toilet,other,other,binary,binary,0.2,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,662.0,,none,"Malawi LSMS 2014 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,22.140275816016985,,100*(exp(b) - 1),,,,,,652.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Dwelling,dwelling_type,A_structural,binary,binary,-0.01,0.12,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-1.7052924440454476,11.880598004990016,100*(exp(b - se^2/2) - 1),reported,-0.002174236737031047,0.02609084084437256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08333333333333334,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Roof,roof_materials,A_structural,binary,binary,0.34,0.1,,,,,,medium,"1 if roof type is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,39.7940385222467,14.049475905635939,100*(exp(b - se^2/2) - 1),reported,0.08836207606623302,0.02598884590183324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Floor,flooring,B_interior,binary,binary,-0.15,0.06,,,,,,medium,"1 if floor is mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-14.083990442181726,5.164247858550347,100*(exp(b - se^2/2) - 1),reported,-0.06508893971228141,0.02603557588491257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,External wall,roof_materials,A_structural,binary,binary,0.17,0.12,,,,,,medium,"1 if external wall is in mud, 0 otherwise; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,17.680130588969377,14.223658215844385,100*(exp(b - se^2/2) - 1),reported,0.03693688896050249,0.026073098089766465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4166666666666667,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Number of rooms,bedrooms,A_structural,level,count,0.41,0.05,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),semi_elasticity,41.0,5.0,100*b/1 (per unit count),reported,0.2092109048991957,0.02551352498770679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.2,1469.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,True,True,True
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Electricity,other,other,binary,binary,0.71,0.05,,,,,,medium,"1 if the house has electricity; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,103.14503579672132,10.169956293233753,100*(exp(b - se^2/2) - 1),reported,0.347413672897144,0.024465751612474926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.2,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Private tap water,other,other,binary,binary,0.14,0.2,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,12.749685157937574,23.00547597714455,100*(exp(b - se^2/2) - 1),reported,0.018260586511060727,0.026086552158658176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7000000000000001,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Public tap water,other,other,binary,binary,0.01,0.2,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-0.9950166250832004,20.20100334168336,100*(exp(b - se^2/2) - 1),reported,0.001304544015653066,0.026090880313061324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.049999999999999996,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Bore hole water,other,other,binary,binary,-0.22,0.2,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-21.337213893344654,16.05037595924957,100*(exp(b - se^2/2) - 1),reported,-0.028688180120107595,0.026080163745552363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0999999999999999,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Protected well water,other,other,binary,binary,-0.07,0.22,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-8.989927610723958,20.51266403793086,100*(exp(b - se^2/2) - 1),reported,-0.008301364750663278,0.026090003502084583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31818181818181823,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Unprotected water,other,other,binary,binary,-0.21,0.28,,,,,,medium,"water source dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,-22.05759270194164,22.696358887165243,100*(exp(b - se^2/2) - 1),reported,-0.01956443150128641,0.026085908668381882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7499999999999999,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,VIP latrine toilet,other,other,binary,binary,0.24,0.15,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,25.70277427887513,19.06873725482107,100*(exp(b - se^2/2) - 1),reported,0.04170911697686629,0.026068198110541427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Flush toilet,other,other,binary,binary,0.86,0.16,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,133.31050036022583,37.810571099292716,100*(exp(b - se^2/2) - 1),reported,0.1388795873030572,0.02583806275405715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.375,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c4,4,4,12,Pit latrine,other,other,binary,binary,0.14,0.14,,,,,,medium,"toilet type dummy; parentheses not labelled in table, assumed standard errors (no stars printed)","natural log of monthly house rent (USD), renter households",log,OLS,1479.0,0.65,se,Malawi LSMS 2016 renter households; OLS with district fixed effects (not reported),pct_premium,13.905617177937302,16.103833184001186,100*(exp(b - se^2/2) - 1),reported,0.026082026547865053,0.026082026547865053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,ols,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Dwelling,dwelling_type,A_structural,binary,binary,0.06,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,6.183654654535964,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Roof,roof_materials,A_structural,binary,binary,0.29,,,,,,,medium,"1 if roof type is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,33.6427488025472,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Floor,flooring,B_interior,binary,binary,-0.15,,,,,,,medium,"1 if floor is mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-13.92920235749422,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,External wall,roof_materials,A_structural,binary,binary,0.15,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,16.183424272828304,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Number of rooms,bedrooms,A_structural,level,count,0.4,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",semi_elasticity,40.0,,100*b/1 (per unit count),,,,,,1469.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Electricity,other,other,binary,binary,0.67,,,,,,,medium,"1 if the house has electricity; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,95.42373206359396,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Private tap water,other,other,binary,binary,0.17,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,18.530485132036546,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Public tap water,other,other,binary,binary,0.02,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Bore hole water,other,other,binary,binary,-0.18,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-16.4729788588728,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Protected well water,other,other,binary,binary,-0.08,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-7.688365361336425,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Unprotected water,other,other,binary,binary,-0.17,,,,,,,medium,"water source dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-15.63351834036163,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,VIP latrine toilet,other,other,binary,binary,-0.07,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Flush toilet,other,other,binary,binary,0.52,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,68.20276496988865,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c5,4,5,12,Pit latrine,other,other,binary,binary,-0.18,,,,,,,medium,"toilet type dummy; Ridge shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; Ridge with district fixed effects (not reported); penalised regression (Ridge), no standard errors reported",pct_premium,-16.4729788588728,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Dwelling,dwelling_type,A_structural,binary,binary,0.01,,,,,,,medium,"1 if the rented room is located within a house, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Roof,roof_materials,A_structural,binary,binary,0.3,,,,,,,medium,"1 if roof type is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,34.98588075760032,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Floor,flooring,B_interior,binary,binary,-0.14,,,,,,,medium,"1 if floor is mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-13.064176460119414,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,External wall,roof_materials,A_structural,binary,binary,0.09,,,,,,,medium,"1 if external wall is in mud, 0 otherwise; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Number of rooms,bedrooms,A_structural,level,count,0.4,,,,,,,medium,"Number of rooms rented (total rooms, not bedrooms); LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",semi_elasticity,40.0,,100*b/1 (per unit count),,,,,,1469.0,count,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Electricity,other,other,binary,binary,0.72,,,,,,,medium,"1 if the house has electricity; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,105.44332106438875,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Private tap water,other,other,binary,binary,0.15,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,16.183424272828304,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Bore hole water,other,other,binary,binary,-0.18,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-16.4729788588728,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Protected well water,other,other,binary,binary,-0.04,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-3.9210560847676823,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Unprotected water,other,other,binary,binary,-0.1,,,,,,,medium,"water source dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-9.51625819640405,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Flush toilet,other,other,binary,binary,0.61,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,84.04313987816374,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a,33b79cf36579ae3f85aa1db50b2d2c31cb4ba23a:T4-c6,4,6,12,Pit latrine,other,other,binary,binary,-0.07,,,,,,,medium,"toilet type dummy; LASSO shrinkage coefficient, no SE (LASSO zero coefficients printed as '-' omitted)","natural log of monthly house rent (USD), renter households",log,other,1479.0,,none,"Malawi LSMS 2016 renter households; LASSO with district fixed effects (not reported); penalised regression (LASSO), no standard errors reported",pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,1469.0,binary,,2021.0,Uganda,,,2010.0,2016.0,rental,rent,journal,False,True,unknown,Ridge;LASSO;regression tree;bagging;random forest;boosting,2013.0,other,journal,False,False,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c1,2,Model 1,4,Milfoil presence,water_quality,E_environment,dummy,binary,-94385.4,46712.9,,,**,,,high,"presence of invasive Eurasian milfoil in the lake adjacent to the property (endogenous, instrumented by boat launch, fish stocking, water clarity, lake area, temperature); lake-level attribute; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.931, relative likelihood 1.0); n_obs from text (1,258 transactions), not printed in table",monetary,-94385.4,46712.9,"b (currency per unit, as reported)",reported,-0.05710202399824106,0.028260738809470904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0205425053893036,1248.0,binary,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c1,2,Model 1,4,House size,living_area,A_structural,level,m2,2608.1,344.2,,,***,,,high,"total living area in m2; coefficient in 2006 USD per m2; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.931, relative likelihood 1.0); n_obs from text (1,258 transactions), not printed in table",monetary,2608.1,344.2,"b (currency per unit, as reported)",reported,0.2097196110125949,0.027677424220902254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.577280650784427,1248.0,m2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c1,2,Model 1,4,Parcel density,density,F_neighbourhood,level,count,102.8,70.3,,,,,,high,"number of lakefront parcels per km2 (lake-level); SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.931, relative likelihood 1.0); n_obs from text (1,258 transactions), not printed in table",monetary,102.8,70.3,"b (currency per unit, as reported)",reported,0.041357926146829316,0.0282827063046897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4623044096728308,1248.0,count,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c1,2,Model 1,4,Lake area,other,other,level,km2,209407.8,81848.0,,,**,,,high,"surface area of the adjacent lake in km2 (lake-level attribute); SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.931, relative likelihood 1.0); n_obs from text (1,258 transactions), not printed in table",monetary,209407.8,81848.0,"b (currency per unit, as reported)",reported,0.07223397329855151,0.02823298008259408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5584962369269864,1248.0,km2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,False,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c1,2,Model 1,4,Water clarity,water_quality,E_environment,level,m,-7430.3,11160.2,,,,,,high,"mean Secchi depth of the lake (m) during milfoil growing season; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.931, relative likelihood 1.0); n_obs from text (1,258 transactions), not printed in table",monetary,-7430.3,11160.2,"b (currency per unit, as reported)",reported,-0.018842996390098603,0.028301900099966144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6657855593985771,1248.0,m,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c2,2,Model 2,4,Milfoil presence,water_quality,E_environment,dummy,binary,-94670.0,49174.7,,,*,,,high,"presence of invasive Eurasian milfoil in the lake adjacent to the property (endogenous, instrumented by boat launch, fish stocking, water clarity, lake area, temperature); lake-level attribute; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.986, relative likelihood 0.969); n_obs from text (1,258 transactions), not printed in table",monetary,-94670.0,49174.7,"b (currency per unit, as reported)",reported,-0.05441510078193353,0.02826498633591789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9251769710847246,1248.0,binary,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c2,2,Model 2,4,House size,living_area,A_structural,level,m2,2474.0,703.9,,,***,,,high,"total living area in m2; coefficient in 2006 USD per m2; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.986, relative likelihood 0.969); n_obs from text (1,258 transactions), not printed in table",monetary,2474.0,703.9,"b (currency per unit, as reported)",reported,0.09900168943300396,0.02816786143568775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5147037931524365,1248.0,m2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c2,2,Model 2,4,Lot size,lot_size,A_structural,level,m2,-1.2,13.2,,,,,,high,"parcel size in m2; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.986, relative likelihood 0.969); n_obs from text (1,258 transactions), not printed in table",monetary,-1.2,13.2,"b (currency per unit, as reported)",reported,-0.002573348375227589,0.02830683212750348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09090909090909091,1248.0,m2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c2,2,Model 2,4,Lake area,other,other,level,km2,154409.5,74073.6,,,*,,,high,"surface area of the adjacent lake in km2 (lake-level attribute); SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.986, relative likelihood 0.969); n_obs from text (1,258 transactions), not printed in table",monetary,154409.5,74073.6,"b (currency per unit, as reported)",reported,0.05890450586782124,0.02825777433286581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.084541591066183,1248.0,km2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,False,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c2,2,Model 2,4,Water clarity,water_quality,E_environment,level,m,-6962.7,12820.2,,,,,,high,"mean Secchi depth of the lake (m) during milfoil growing season; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.986, relative likelihood 0.969); n_obs from text (1,258 transactions), not printed in table",monetary,-6962.7,12820.2,"b (currency per unit, as reported)",reported,-0.015371784027104323,0.02830358130959008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5431038517339822,1248.0,m,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c3,2,Model 3,4,Milfoil presence,water_quality,E_environment,dummy,binary,-92558.0,48255.6,,,*,,,high,"presence of invasive Eurasian milfoil in the lake adjacent to the property (endogenous, instrumented by boat launch, fish stocking, water clarity, lake area, temperature); lake-level attribute; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.998, relative likelihood 0.963); n_obs from text (1,258 transactions), not printed in table",monetary,-92558.0,48255.6,"b (currency per unit, as reported)",reported,-0.05421503655446308,0.028265294387924855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.918077901839372,1248.0,binary,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c3,2,Model 3,4,House size,living_area,A_structural,level,m2,2681.6,726.0,,,***,,,high,"total living area in m2; coefficient in 2006 USD per m2; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.998, relative likelihood 0.963); n_obs from text (1,258 transactions), not printed in table",monetary,2681.6,726.0,"b (currency per unit, as reported)",reported,0.10398940868490526,0.028153457154400815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.69366391184573,1248.0,m2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c3,2,Model 3,4,Lot size,lot_size,A_structural,level,m2,3.1,12.9,,,,,,high,"parcel size in m2; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.998, relative likelihood 0.963); n_obs from text (1,258 transactions), not printed in table",monetary,3.1,12.9,"b (currency per unit, as reported)",reported,0.006802282166411724,0.02830627095055201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24031007751937986,1248.0,m2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c3,2,Model 3,4,Parcel density,density,F_neighbourhood,level,count,115.4,78.6,,,,,,high,"number of lakefront parcels per km2 (lake-level); SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.998, relative likelihood 0.963); n_obs from text (1,258 transactions), not printed in table",monetary,115.4,78.6,"b (currency per unit, as reported)",reported,0.04152419567357024,0.028282511091357196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4681933842239188,1248.0,count,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c3,2,Model 3,4,Lake area,other,other,level,km2,215595.0,84521.5,,,**,,,high,"surface area of the adjacent lake in km2 (lake-level attribute); SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.998, relative likelihood 0.963); n_obs from text (1,258 transactions), not printed in table",monetary,215595.0,84521.5,"b (currency per unit, as reported)",reported,0.07201700270551561,0.028233424217510787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5507711055766875,1248.0,km2,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,False,False
35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da,35b0e34c43595f2139ae225aceb59ae4af4bb8da:T2-c3,2,Model 3,4,Water clarity,water_quality,E_environment,level,m,-6954.9,12600.7,,,,,,high,"mean Secchi depth of the lake (m) during milfoil growing season; SE in separate column; stars * p<0.10 ** p<0.05 *** p<0.01; table printed rotated, columns reconstructed from PDF positions; minus signs rendered as '2' in the PDF text layer","property sales price, land + house (2006 USD)",level,IV,1258.0,,none,"lakefront single-family sales, 41 lakes in King County WA, 1995-2006; two-stage least squares with milfoil presence endogenous; top-3 model by AICc (AICc 23.998, relative likelihood 0.963); n_obs from text (1,258 transactions), not printed in table",monetary,-6954.9,12600.7,"b (currency per unit, as reported)",reported,-0.015621974513515663,0.028303471545594736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5519455268358107,1248.0,m,,2014.0,United States,North America,High income,1995.0,2006.0,single_family,transaction,journal,False,False,unknown,,2000.5,iv,journal,False,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,Number of bedrooms,bedrooms,A_structural,level,count,0.03,0.00072,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",semi_elasticity,3.0,0.07200000000000001,100*b/1 (per unit count),reported,0.14807464661758551,0.0035537915188220527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.666666666666664,77444.0,count,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,"Lot size, sq. ft",lot_size,A_structural,level,1000 sqft,0.07,0.003,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports lot size in thousands of sq ft and the magnitude (0.07 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",semi_elasticity,0.07534737291696807,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.08355292463937246,0.0035808396274016768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.333333333333336,77444.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,"Living area, sq. ft",living_area,A_structural,level,1000 sqft,0.18,0.004,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports living area in thousands of sq ft and the magnitude (0.18-0.23 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",semi_elasticity,0.193750387500775,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.15962967878181286,0.0035473261951513963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.0,77444.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,Year property built,age,A_structural,other,years,-0.00012,6.8e-05,,,.,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Printed as 'Year property built' but Table 3 defines it as years since construction relative to 2019 (mean 42.67/46), i.e. building age in years; mapped to age. Written in the table as a x 10^-4 / 10^-5 scientific notation.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",other,-0.00012,6.8e-05,not harmonised,reported,-0.006341174630143141,0.0035933322904144467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7647058823529411,77444.0,years,year_built_not_age;nonstandard_x_transform,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,False,False,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,"Euclidean distance to coastline, ft",water_proximity,E_environment,level,1000 ft,-0.01,0.00094,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft (mean ~1.9-2.1); unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",semi_elasticity,-3.280839895013123,0.3083989501312336,100*b/0.3048 (X per 1000ft->km),reported,-0.03819980624534107,0.003590781787062061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.638297872340425,77444.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,"Euclidean distance to major road, ft",highway_distance,D_location,level,1000 ft,0.01,0.0006,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft; unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",semi_elasticity,3.280839895013123,0.19685039370078738,100*b/0.3048 (X per 1000ft->km),reported,0.05978295620628476,0.003586977372377085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.666666666666668,77444.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,FEMA 100-year floodplain,flood_risk,E_environment,binary,binary,0.05,0.004,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. 1 if property lies in a FEMA 100-year floodplain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",pct_premium,5.1262686240893585,0.42050843855040965,100*(exp(b - se^2/2) - 1),reported,0.04487231263223542,0.0035897850105788332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,Number of bedrooms,bedrooms,A_structural,level,count,0.04,0.002,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",semi_elasticity,4.0,0.2,100*b/1 (per unit count),reported,0.08258119788141761,0.004129059894070881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,58254.0,count,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,"Lot size, sq. ft",lot_size,A_structural,level,1000 sqft,0.001,8.4e-05,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports lot size in thousands of sq ft and the magnitude (0.07 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",semi_elasticity,0.0010763910416709723,9.041684750036166e-05,100*b/92.903 (X per 1000sqft->m2),reported,0.04926405911555707,0.004138180965706794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.904761904761905,58254.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,"Living area, sq. ft",living_area,A_structural,level,1000 sqft,0.23,0.002,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports living area in thousands of sq ft and the magnitude (0.18-0.23 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",semi_elasticity,0.2475699395843236,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4301387872772204,0.0037403372806714815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),115.0,58254.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,Year property built,age,A_structural,other,years,-0.00014,4.2e-05,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Printed as 'Year property built' but Table 3 defines it as years since construction relative to 2019 (mean 42.67/46), i.e. building age in years; mapped to age. Written in the table as a x 10^-4 / 10^-5 scientific notation.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",other,-0.00014,4.2e-05,not harmonised,reported,-0.013809388753735942,0.004142816626120783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.333333333333333,58254.0,years,year_built_not_age;nonstandard_x_transform,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,False,False,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,"Euclidean distance to coastline, ft",water_proximity,E_environment,level,1000 ft,-0.03,0.001,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft (mean ~1.9-2.1); unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",semi_elasticity,-9.84251968503937,0.32808398950131235,100*b/0.3048 (X per 1000ft->km),reported,-0.12334717183492869,0.004111572394497623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,58254.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,"Euclidean distance to major road, ft",highway_distance,D_location,level,1000 ft,0.01,0.002,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft; unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",semi_elasticity,3.280839895013123,0.6561679790026247,100*b/0.3048 (X per 1000ft->km),reported,0.020711614722242564,0.004142322944448513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,58254.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,FEMA 100-year floodplain,flood_risk,E_environment,binary,binary,-0.03,0.004,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. 1 if property lies in a FEMA 100-year floodplain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",pct_premium,-2.9562229984706034,0.3881782134194033,100*(exp(b - se^2/2) - 1),reported,-0.031059096042471132,0.004141212805662818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.5,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,SLR exposure less than 2 ft,flood_risk,E_environment,binary,binary,-0.12,0.02,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",pct_premium,-11.325692963295964,1.773840873434315,100*(exp(b - se^2/2) - 1),reported,-0.021555417766905696,0.003592569627817616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,SLR exposure less than 2 ft,flood_risk,E_environment,binary,binary,0.02,0.03,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",pct_premium,1.974235270363689,3.0606040200802673,100*(exp(b - se^2/2) - 1),reported,0.0027621305956689606,0.00414319589350344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666667,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,SLR exposure 2 to 3 ft,flood_risk,E_environment,binary,binary,-0.02,0.01,,,*,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",pct_premium,-1.9850335401682129,0.9801986733067553,100*(exp(b - se^2/2) - 1),reported,-0.007186623481896806,0.003593311740948403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,SLR exposure 2 to 3 ft,flood_risk,E_environment,binary,binary,-0.07,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",pct_premium,-6.765279861957419,0.9323938199059483,100*(exp(b - se^2/2) - 1),reported,-0.028990291948412183,0.004141470278344598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.000000000000001,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,SLR exposure 3 to 4 ft,flood_risk,E_environment,binary,binary,0.03,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",pct_premium,3.040301251486577,1.030454533953517,100*(exp(b - se^2/2) - 1),reported,0.010779587266368252,0.003593195755456084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,SLR exposure 3 to 4 ft,flood_risk,E_environment,binary,binary,-0.03,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",pct_premium,-2.9602987515132573,0.9704455335485083,100*(exp(b - se^2/2) - 1),reported,-0.01242867504356977,0.004142891681189923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,SLR exposure 4 to 5 ft,flood_risk,E_environment,binary,binary,0.06,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",pct_premium,6.1783456045305885,1.0618365465453596,100*(exp(b - se^2/2) - 1),reported,0.021555417766905696,0.003592569627817616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,SLR exposure 4 to 5 ft,flood_risk,E_environment,binary,binary,-0.01,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",pct_premium,-0.9999667504977716,0.9900498337491681,100*(exp(b - se^2/2) - 1),reported,-0.004143176137422553,0.004143176137422553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c1,4,1,6,SLR exposure 5 to 6 ft,flood_risk,E_environment,binary,binary,0.04,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.45; log-likelihood 14,156.1; year/month and city dummies included",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.014372133578535827,0.003593033394633957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T4-c2,4,2,6,SLR exposure 5 to 6 ft,flood_risk,E_environment,binary,binary,-0.01,0.005,,,*,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; overall model without owner-type interactions; spatial lag rho = 0.47; log-likelihood 25,291.4; year/month and city dummies included",pct_premium,-0.9962541796406543,0.49502491687458405,100*(exp(b - se^2/2) - 1),reported,-0.008286138918938086,0.004143069459469042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,Number of bedrooms,bedrooms,A_structural,level,count,0.03,0.001,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",semi_elasticity,3.0,0.1,100*b/1 (per unit count),reported,0.1071811429604603,0.0035727047653486767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.0,77444.0,count,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,"Lot size, sq. ft",lot_size,A_structural,level,1000 sqft,0.07,0.003,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports lot size in thousands of sq ft and the magnitude (0.07 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",semi_elasticity,0.07534737291696807,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.08355292463937246,0.0035808396274016768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.333333333333336,77444.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,"Living area, sq. ft",living_area,A_structural,level,1000 sqft,0.18,0.004,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports living area in thousands of sq ft and the magnitude (0.18-0.23 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",semi_elasticity,0.193750387500775,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.15962967878181286,0.0035473261951513963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.0,77444.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,Year property built,age,A_structural,other,years,-0.00012,6.8e-05,,,.,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Printed as 'Year property built' but Table 3 defines it as years since construction relative to 2019 (mean 42.67/46), i.e. building age in years; mapped to age. Written in the table as a x 10^-4 / 10^-5 scientific notation.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",other,-0.00012,6.8e-05,not harmonised,reported,-0.006341174630143141,0.0035933322904144467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7647058823529411,77444.0,years,year_built_not_age;nonstandard_x_transform,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,False,False,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,"Euclidean distance to coastline, ft",water_proximity,E_environment,level,1000 ft,-0.01,0.001,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft (mean ~1.9-2.1); unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",semi_elasticity,-3.280839895013123,0.32808398950131235,100*b/0.3048 (X per 1000ft->km),reported,-0.03591086779904764,0.0035910867799047643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,77444.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,"Euclidean distance to major road, ft",highway_distance,D_location,level,1000 ft,0.01,0.001,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft; unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",semi_elasticity,3.280839895013123,0.32808398950131235,100*b/0.3048 (X per 1000ft->km),reported,0.03591086779904764,0.0035910867799047643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,77444.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,FEMA 100-year floodplain,flood_risk,E_environment,binary,binary,0.05,0.004,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. 1 if property lies in a FEMA 100-year floodplain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,5.1262686240893585,0.42050843855040965,100*(exp(b - se^2/2) - 1),reported,0.04487231263223542,0.0035897850105788332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,Number of bedrooms,bedrooms,A_structural,level,count,0.04,0.002,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",semi_elasticity,4.0,0.2,100*b/1 (per unit count),reported,0.08258119788141761,0.004129059894070881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,58254.0,count,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,"Lot size, sq. ft",lot_size,A_structural,level,1000 sqft,0.001,8.4e-05,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports lot size in thousands of sq ft and the magnitude (0.07 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",semi_elasticity,0.0010763910416709723,9.041684750036166e-05,100*b/92.903 (X per 1000sqft->m2),reported,0.04926405911555707,0.004138180965706794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.904761904761905,58254.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,"Living area, sq. ft",living_area,A_structural,level,1000 sqft,0.23,0.002,,,***,,,medium,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says sq. ft but Table 3 reports living area in thousands of sq ft and the magnitude (0.18-0.23 per unit) is only plausible per 1,000 sq ft; unit scaling uncertain.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",semi_elasticity,0.2475699395843236,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4301387872772204,0.0037403372806714815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),115.0,58254.0,1000sqft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,Year property built,age,A_structural,other,years,0.00014,4.2e-05,,,***,,,low,"Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Printed as 'Year property built' but Table 3 defines it as years since construction relative to 2019 (mean 42.67/46), i.e. building age in years; mapped to age. Written in the table as a x 10^-4 / 10^-5 scientific notation. NOTE: printed WITHOUT a minus sign in Table 5 (1.4 x 10^-4***) whereas Table 4 prints -1.40 x 10^-4 for the same specification; likely a typographical omission of the minus sign.","Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",other,0.00014,4.2e-05,not harmonised,reported,0.013809388753735942,0.004142816626120783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.333333333333333,58254.0,years,year_built_not_age;nonstandard_x_transform,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,False,False,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,"Euclidean distance to coastline, ft",water_proximity,E_environment,level,1000 ft,-0.03,0.001,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft (mean ~1.9-2.1); unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",semi_elasticity,-9.84251968503937,0.32808398950131235,100*b/0.3048 (X per 1000ft->km),reported,-0.12334717183492869,0.004111572394497623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,58254.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,"Euclidean distance to major road, ft",highway_distance,D_location,level,1000 ft,0.01,0.002,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Table header says ft but Table 3 reports distance in thousands of ft; unit scaling uncertain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",semi_elasticity,3.280839895013123,0.6561679790026247,100*b/0.3048 (X per 1000ft->km),reported,0.020711614722242564,0.004142322944448513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,58254.0,1000ft,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,FEMA 100-year floodplain,flood_risk,E_environment,binary,binary,-0.03,0.004,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. 1 if property lies in a FEMA 100-year floodplain.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-2.9562229984706034,0.3881782134194033,100*(exp(b - se^2/2) - 1),reported,-0.031059096042471132,0.004141212805662818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.5,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure less than 2 ft & PHOs,flood_risk,E_environment,binary,binary,-0.1,0.03,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,-9.556966720110871,2.7145122541078783,100*(exp(b - se^2/2) - 1),reported,-0.011977155957252834,0.0035931467871758503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure less than 2 ft & PHOs,flood_risk,E_environment,binary,binary,-0.15,0.05,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-14.036723639745773,4.303539882125289,100*(exp(b - se^2/2) - 1),reported,-0.012428675043569768,0.004142891681189923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure less than 2 ft & NPHOs,flood_risk,E_environment,binary,binary,-0.13,0.03,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,-12.229962312952514,2.6342862927616837,100*(exp(b - se^2/2) - 1),reported,-0.015569532211288454,0.003592968971835797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.333333333333334,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure less than 2 ft & NPHOs,flood_risk,E_environment,binary,binary,0.14,0.04,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,14.935394780762001,4.601095195428909,100*(exp(b - se^2/2) - 1),reported,0.01449971648152996,0.004142776137579987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5000000000000004,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 2 to 3 ft & PHOs,flood_risk,E_environment,binary,binary,-0.01,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,-0.9999667504977716,0.9900498337491681,100*(exp(b - se^2/2) - 1),reported,-0.00359338133763527,0.0035933813376352697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 2 to 3 ft & PHOs,flood_risk,E_environment,binary,binary,-0.09,0.02,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-8.625158268842092,1.8278623705424566,100*(exp(b - se^2/2) - 1),reported,-0.01864121293674717,0.004142491763721593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 2 to 3 ft & NPHOs,flood_risk,E_environment,binary,binary,-0.03,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,-2.9602987515132573,0.9704455335485083,100*(exp(b - se^2/2) - 1),reported,-0.010779587266368252,0.003593195755456084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 2 to 3 ft & NPHOs,flood_risk,E_environment,binary,binary,-0.05,0.02,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-4.896080236086586,1.9024588490014283,100*(exp(b - se^2/2) - 1),reported,-0.010357473641028283,0.004142989456411313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 3 to 4 ft & PHOs,flood_risk,E_environment,binary,binary,0.05,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,5.121853413527222,1.0512710963760241,100*(exp(b - se^2/2) - 1),reported,0.017964123386709174,0.0035928246773418346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 3 to 4 ft & PHOs,flood_risk,E_environment,binary,binary,-0.06,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-5.828255346524447,0.9417645335842487,100*(exp(b - se^2/2) - 1),reported,-0.024851592442693186,0.004141932073782198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 3 to 4 ft & NPHOs,flood_risk,E_environment,binary,binary,-0.01,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,-0.9999667504977716,0.9900498337491681,100*(exp(b - se^2/2) - 1),reported,-0.00359338133763527,0.0035933813376352697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 3 to 4 ft & NPHOs,flood_risk,E_environment,binary,binary,0.003,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,0.2954355531886499,1.003004504503377,100*(exp(b - se^2/2) - 1),reported,0.0012429625494092154,0.004143208498030718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 4 to 5 ft & PHOs,flood_risk,E_environment,binary,binary,0.08,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,8.323290467566103,1.0832870676749586,100*(exp(b - se^2/2) - 1),reported,0.028735365246930312,0.003591920655866289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 4 to 5 ft & PHOs,flood_risk,E_environment,binary,binary,-0.002,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-0.20479001851185785,0.9980019986673331,100*(exp(b - se^2/2) - 1),reported,-0.0008286420552216176,0.004143210276108088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 4 to 5 ft & NPHOs,flood_risk,E_environment,binary,binary,0.002,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,0.19519024864151913,1.0020020013340003,100*(exp(b - se^2/2) - 1),reported,0.000718680721885832,0.0035934036094291593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 4 to 5 ft & NPHOs,flood_risk,E_environment,binary,binary,-0.01,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-0.9999667504977716,0.9900498337491681,100*(exp(b - se^2/2) - 1),reported,-0.004143176137422553,0.004143176137422553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 5 to 6 ft & PHOs,flood_risk,E_environment,binary,binary,0.05,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,5.121853413527222,1.0512710963760241,100*(exp(b - se^2/2) - 1),reported,0.017964123386709174,0.0035928246773418346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 5 to 6 ft & PHOs,flood_risk,E_environment,binary,binary,-0.003,0.01,,,,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with primary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-0.3045353475167323,0.997004495503373,100*(exp(b - se^2/2) - 1),reported,-0.0012429625494092154,0.004143208498030718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c1,5,1,7,SLR exposure 5 to 6 ft & NPHOs,flood_risk,E_environment,binary,binary,0.01,0.01,,,.,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,77454.0,0.79,se,"Pinellas County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.45; log-likelihood 14,083.7; year/month and city dummies included",pct_premium,0.9999665838355432,1.010050167084168,100*(exp(b - se^2/2) - 1),reported,0.00359338133763527,0.0035933813376352697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,77444.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92,35e9cf817db8c35d9d40580566f6516793f3cc92:T5-c2,5,2,7,SLR exposure 5 to 6 ft & NPHOs,flood_risk,E_environment,binary,binary,-0.02,0.01,,,***,,,medium,Dependent-variable transformation not stated in the paper (see model notes); coefficient recorded exactly as printed. Dummy = 1 if the property centroid falls within the NOAA sea-level-rise inundation layer for the stated scenario band (elevation exposure); mapped to flood_risk as nearest leaf. Interaction of the SLR exposure dummy with nonprimary homeowner (homestead-exemption) status; effect for that buyer group.,"Housing price (unadjusted sale price, USD); transformation not stated - authors interpret coefficients as percentage discounts and intercept of 2.50 is consistent with log10(price in 1,000 USD)",log,spatial_lag,58264.0,0.81,se,"Miami-Dade County, detached single-family homes within 5,000-ft coastal band, Jan 2004 - Mar 2019; SLR exposure dummies interacted with primary (PHO) / nonprimary (NPHO) homeowner status; spatial lag rho = 0.47; log-likelihood 25,265.1; year/month and city dummies included",pct_premium,-1.9850335401682129,0.9801986733067553,100*(exp(b - se^2/2) - 1),reported,-0.008286138918938086,0.004143069459469042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,58254.0,binary,,2020.0,United States,North America,High income,2004.0,2019.0,single_family,transaction,journal,True,True,unknown,,2011.5,spatial,journal,True,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,ln (FA),living_area,A_structural,log,m2,-0.43255,,7.81141,0.0,,,,medium,"Floor area, log; coefficient printed with a leading dash (negative) while zcalc printed without sign (absolute value); z-statistic recorded in t","ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",elasticity,-0.43255,0.05537412579803134,b (log-log),|b/t|,0.5031582297904399,0.06441324034846971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.81141,180.0,m2,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,True,True,True
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,ln (DA),cbd_distance,D_location,log,m,-0.1177,,5.35113,0.0,,,,medium,"Distance to Beira-Mar Avenue (seaside avenue in the Centro used as valuation pole), log of metres; could alternatively be read as water proximity; coefficient printed with a leading dash (negative) while zcalc printed without sign (absolute value); z-statistic recorded in t","ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",elasticity,-0.1177,0.021995354252279425,b (log-log),|b/t|,0.37046940095074155,0.06923199416772562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.35113,180.0,m,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,True,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,NB,bedrooms,A_structural,level,count,0.05794,,2.76309,0.00573,,,,high,; z-statistic recorded in t,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",semi_elasticity,5.794,2.0969277149857586,100*b/1 (per unit count),|b/t|,0.20171513788160145,0.07300346274699755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.76309,180.0,count,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,True,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,BT,bathrooms,A_structural,level,count,0.03583,,2.12263,0.03379,,,,high,; z-statistic recorded in t,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",semi_elasticity,3.583,1.6880002638236529,100*b/1 (per unit count),|b/t|,0.1562678263387226,0.07361990848085753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.12263,180.0,count,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,True,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,NG,parking,A_structural,level,count,0.1852,,8.92755,0.0,,,,high,Number of parking spaces; z-statistic recorded in t,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",semi_elasticity,18.52,2.0744773202054314,100*b/1 (per unit count),|b/t|,0.553981619623975,0.06205304026569159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.92755,180.0,count,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,True,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,SP,pool,C_exterior,binary,binary,0.09129,,3.47056,0.00052,,,,high,Swimming pool (building); z-statistic recorded in t,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",pct_premium,9.520772320878468,2.8818435052579914,100*(exp(b - se^2/2) - 1),|b/t|,0.25043688651135276,0.07216036792660344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.47056,180.0,binary,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,True,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T4-c1,4,1,9,CQ,condition,A_structural,level,"ordinal (1 low, 2 medium, 3 high construction quality)",0.20171,,12.3279,0.0,,,,high,Construction quality code; z-statistic recorded in t,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,spatial_error,190.0,0.8169,t,"Training partition (190 apartments, Centro/Agronomica/Trindade); spatial error (CAR) model, distance-band weights 525 m; z-statistics and p-values printed",semi_elasticity,20.171,1.636207302135806,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.6766047267169323,0.05488402134320786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.3279,180.0,ordinal,unit_not_harmonisable,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,True,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,ln (FA),living_area,A_structural,log,m2,-0.517,,,,,,,medium,"Floor area, log; GWR average of local coefficients (median -0.5074); no SE reported","ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",elasticity,-0.517,,b (log-log),,,,,,180.0,m2,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,ln (DA),cbd_distance,D_location,log,m,-0.08873,,,,,,,medium,"Distance to Beira-Mar Avenue (seaside avenue in the Centro used as valuation pole), log of metres; could alternatively be read as water proximity; GWR average of local coefficients (median -0.10434); no SE reported","ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",elasticity,-0.08873,,b (log-log),,,,,,180.0,m,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,NB,bedrooms,A_structural,level,count,0.06497,,,,,,,medium,GWR average of local coefficients (median 0.06628); no SE reported,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",semi_elasticity,6.497,,100*b/1 (per unit count),,,,,,180.0,count,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,BT,bathrooms,A_structural,level,count,0.06173,,,,,,,medium,GWR average of local coefficients (median 0.06011); no SE reported,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",semi_elasticity,6.173,,100*b/1 (per unit count),,,,,,180.0,count,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,NG,parking,A_structural,level,count,0.19071,,,,,,,medium,Number of parking spaces; GWR average of local coefficients (median 0.19394); no SE reported,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",semi_elasticity,19.070999999999998,,100*b/1 (per unit count),,,,,,180.0,count,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,SP,pool,C_exterior,binary,binary,0.09732,,,,,,,medium,Swimming pool (building); GWR average of local coefficients (median 0.09732); no SE reported; variable treated as global (fixed) in GWR,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",pct_premium,10.221302536183853,,100*(exp(b) - 1),,,,,,180.0,binary,,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d,36eac2e74fd054fe7faa7ad4134c3aa524d5616d:T5-c1,5,1,10,CQ,condition,A_structural,level,"ordinal (1 low, 2 medium, 3 high construction quality)",0.20735,,,,,,,medium,Construction quality code; GWR average of local coefficients (median 0.22325); no SE reported,"ln(UV): natural log of unit value (price per m2, R$/m2) of apartments",log,GWR,190.0,0.9239,none,"GWR (fixed Gaussian kernel, bandwidth 278.42 m); average of the 190 local coefficients printed (median also printed); pool variable fixed as global; no SE printed",semi_elasticity,20.735,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,180.0,ordinal,unit_not_harmonisable,2023.0,Brazil,South America,Upper middle income,2020.0,2020.0,condo_apartment,listing,journal,False,False,unknown,,2020.0,spatial,journal,False,False,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,DISTLRT1,transit_distance,D_location,dummy,binary,0.122668,,4.6,0.0,,,,high,Dummy: within 1000 m of nearest LRT station. t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",pct_premium,13.010713358773929,3.0147235140793778,100*(exp(b - se^2/2) - 1),|b/t|,0.1216369489560853,0.02644281499045333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,1409.0,binary,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,DISTLRT2,transit_distance,D_location,dummy,binary,0.098065,,4.6,0.0,,,,high,Dummy: 1001-2000 m from nearest LRT station. t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",pct_premium,10.278385631147025,2.3515016583287394,100*(exp(b - se^2/2) - 1),|b/t|,0.1216369489560853,0.02644281499045333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,1409.0,binary,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,FLOORAREA,living_area,A_structural,level,sqft,0.000436,,15.8,0.0,,,,high,Floor area in square feet. t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,0.46930649416854386,0.029702942668895182,100*b/0.092903 (X per sqft->m2),|b/t|,0.38795470176891544,0.024554095048665535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.8,1409.0,sqft,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,LOTAREA,lot_size,A_structural,level,sqft,0.000214,,16.5,0.0,,,,high,Lot (land) area in square feet. t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,0.23034768291758803,0.013960465631368973,100*b/0.092903 (X per sqft->m2),|b/t|,0.402409102160797,0.024388430433987692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.5,1409.0,sqft,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,BEDROOMS,bedrooms,A_structural,dummy,binary,0.041764,,2.3,0.02,,,,high,Dummy: four or more bedrooms. t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",pct_premium,4.24765065788375,1.8932681364504789,100*(exp(b - se^2/2) - 1),|b/t|,0.06115875003530312,0.0265907608849144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3,1409.0,binary,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,FREEHOLD,other,other,binary,binary,0.188286,,8.8,0.0,,,,high,Freehold tenure dummy. t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",pct_premium,20.690243073843995,2.5828960489074544,100*(exp(b - se^2/2) - 1),|b/t|,0.2282490508960566,0.025937392147279154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.8,1409.0,binary,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,False,False,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,SHOPCENTRE,commercial_distance,D_location,level,m,-7.7e-05,,-5.4,0.0,,,,high,Distance to nearest shopping centre (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,-7.7,1.425925925925926,100*b/0.001 (X per m->km),|b/t|,-0.14239349741052285,0.026369166187133863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.4,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,HOSPITAL,hospital_distance,D_location,level,m,0.0001,,10.3,0.0,,,,high,Distance to nearest hospital (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,10.0,0.9708737864077669,100*b/0.001 (X per m->km),|b/t|,0.26461716290343273,0.025690986689653663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.3,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,True
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,PARK,green_space,E_environment,level,m,-2.5e-05,,-2.3,0.02,,,,high,Distance to nearest recreational park (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,-2.5,1.0869565217391304,100*b/0.001 (X per m->km),|b/t|,-0.06115875003530312,0.0265907608849144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,PRIMARYSCH,school_distance,D_location,level,m,6.6e-05,,3.9,0.0,,,,high,Distance to nearest primary school (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,6.6000000000000005,1.6923076923076923,100*b/0.001 (X per m->km),|b/t|,0.10334217354767049,0.026497993217351412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,HP_SECONDARYSCH,school_distance,D_location,level,m,-6.5e-05,,-7.4,0.0,,,,high,Distance to nearest high-performing secondary school (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,-6.5,0.8783783783783783,100*b/0.001 (X per m->km),|b/t|,-0.19341795277140675,0.02613756118532523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.4,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,CBD,cbd_distance,D_location,level,m,-2.3e-05,,-3.5,0.0,,,,high,Distance to central business district (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,-2.3,0.6571428571428571,100*b/0.001 (X per m->km),|b/t|,-0.09283950270748297,0.026525572202137988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,FOREST,green_space,E_environment,level,m,-3.2e-05,,-3.0,0.0,,,,high,Distance to nearest forest (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,-3.1999999999999997,1.0666666666666664,100*b/0.001 (X per m->km),|b/t|,-0.07966785775922171,0.026555952586407236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e,3777ce734c4a5c9380dfa227ede8503f4f25b44e:T2-c1,2,1,5,INDUSTRIAL,hazard_site,E_environment,level,m,3.3e-05,,6.0,0.0,,,,high,Distance to nearest industrial area (m). t-ratio and p-value (Sig.) printed; no stars used.,ln sale price (MYR),log,OLS,1419.0,0.62,t,"Double-storey terraced houses, Greater Kuala Lumpur, 2009-2010; R2 from text (62%), not printed in table",semi_elasticity,3.3000000000000003,0.55,100*b/0.001 (X per m->km),|b/t|,0.15784009252939693,0.026306682088232818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,1409.0,m,,2021.0,Malaysia,Asia,Upper middle income,2009.0,2010.0,single_family,transaction,journal,False,True,OLS,,2009.5,ols,journal,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(AREA),living_area,A_structural,log,m2,0.89,,8.35,0.0,,,,high,Floor space 50-200 m2,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.89,0.1065868263473054,b (log-log),|b/t|,0.539302056525832,0.06458707263782419,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.35,170.0,m2,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(DECORATION),condition,A_structural,log,index,0.04,,1.59,0.11,,,,high,"Decoration grade 1-4 (ordinal), entered in log",ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.04,0.025157232704402514,b (log-log),|b/t|,0.1210506719446535,0.07613249807839843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,170.0,index,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(BEDROOM),bedrooms,A_structural,log,count,0.07,,0.89,0.37,,,,high,,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.07,0.07865168539325844,b (log-log),|b/t|,0.06810141212888834,0.07651844059425657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.89,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(HALL),other,other,log,count,0.07,,0.8,0.42,,,,high,Number of halls (living/dining rooms),ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.07,0.08750000000000001,b (log-log),|b/t|,0.06124202828995005,0.07655253536243756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,False,False,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(BATHROOM),bathrooms,A_structural,log,count,0.06,,1.1,0.27,,,,high,,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.06,0.054545454545454536,b (log-log),|b/t|,0.08406749786442289,0.07642499805856624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(FLOORLEVEL),floor_level,A_structural,log,count,-0.01,,-0.84,0.4,,,,high,,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,-0.01,0.011904761904761906,b (log-log),|b/t|,-0.06429177288589208,0.07653782486415724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.84,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(YEAR),age,A_structural,log,years,-0.16,,-5.22,0.0,,,,high,"YEAR = house age in years (mean 8.6), not year built",ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,-0.16,0.030651340996168584,b (log-log),|b/t|,-0.3716753494852454,0.07120217423088991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.22,170.0,years,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(GREENCOVERAGE),green_space,E_environment,log,share,0.05,,2.79,0.01,,,,high,Green coverage rate of the estate in percent,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.05,0.017921146953405017,b (log-log),|b/t|,0.20924627937267332,0.07499866644181839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.79,170.0,share,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(RING),other,other,log,index,0.01,,0.12,0.91,,,,high,Ring-road zone ordinal 1-4,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,0.01,0.08333333333333334,b (log-log),|b/t|,0.009203190092253693,0.07669325076878078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12,170.0,index,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,False,False,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(DIST_CBD),cbd_distance,D_location,log,m,-0.09,,-2.04,0.04,,,,high,Distance to Yan Shi Kou CBD in metres,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",elasticity,-0.09,0.044117647058823525,b (log-log),|b/t|,-0.1545802354626539,0.07577462522679113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.04,170.0,m,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T2-c1,II,1,2,LOG(UNDERGROUND),transit_distance,D_location,other,index,-0.08,,-1.9,0.06,,,,high,Log of ordinal walking-distance zone to nearest metro station (1 = <400 m ... 5 = >1600 m),ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Preliminary regression with all 11 features; R2 0.72, adj 0.70",other,-0.08,0.04210526315789474,not harmonised,|b/t|,-0.14420032274544917,0.07589490670813115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9,170.0,index,nonstandard_x_transform,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,False,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(AREA),living_area,A_structural,log,m2,0.92,,9.06,0.0,,,,high,Floor space 50-200 m2,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,0.92,0.10154525386313466,b (log-log),|b/t|,0.570631709690132,0.06298363241612935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.06,170.0,m2,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(DECORATION),condition,A_structural,log,index,0.05,,1.68,0.09,,,,high,"Decoration grade 1-4 (ordinal), entered in log",ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,0.05,0.029761904761904764,b (log-log),|b/t|,0.1277936474313566,0.0760676472805694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.68,170.0,index,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(BEDROOM),bedrooms,A_structural,log,count,0.07,,0.94,0.35,,,,high,,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,0.07,0.07446808510638299,b (log-log),|b/t|,0.07190807475538405,0.07649795186742984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.94,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(BATHROOM),bathrooms,A_structural,log,count,0.06,,1.06,0.29,,,,high,,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,0.06,0.056603773584905655,b (log-log),|b/t|,0.0810309463891698,0.0764442890463866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.06,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(FLOORLEVEL),floor_level,A_structural,log,count,-0.01,,-0.84,0.4,,,,high,,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,-0.01,0.011904761904761906,b (log-log),|b/t|,-0.06429177288589208,0.07653782486415724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.84,170.0,count,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(YEAR),age,A_structural,log,years,-0.17,,-5.38,0.0,,,,high,"YEAR = house age in years (mean 8.6), not year built",ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,-0.17,0.03159851301115242,b (log-log),|b/t|,-0.3814313774053503,0.07089802554002794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.38,170.0,years,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(GREENCOVERAGE),green_space,E_environment,log,share,0.05,,2.76,0.01,,,,high,Green coverage rate of the estate in percent,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,0.05,0.01811594202898551,b (log-log),|b/t|,0.2070933165634045,0.07503381034905961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.76,170.0,share,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(DIST_CBD),cbd_distance,D_location,log,m,-0.09,,-3.83,0.0,,,,high,Distance to Yan Shi Kou CBD in metres,ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",elasticity,-0.09,0.02349869451697128,b (log-log),|b/t|,-0.28183952149450586,0.07358734242676393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.83,170.0,m,,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,True,True,False
37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f,37a309783a11809263623703621db3a26e46597f:T3-c1,III,1,3,LOG(UNDERGROUND),transit_distance,D_location,other,index,-0.07,,-1.85,0.07,,,,high,Log of ordinal walking-distance zone to nearest metro station (1 = <400 m ... 5 = >1600 m),ln(PRICE) (asking price),log,OLS,180.0,0.72,none,"Stepwise regression (RING and HALL dropped); R2 0.72, adj 0.70",other,-0.07,0.03783783783783784,not harmonised,|b/t|,-0.14048145837659992,0.07593592344681076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.85,170.0,index,nonstandard_x_transform,2015.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,conference,False,False,unknown,,2014.0,ols,other,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.833,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.833,0.007,"b (currency per unit, as reported)",reported,0.22001453035685395,0.0018488615996374283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),118.99999999999999,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.563,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.563,0.006,"b (currency per unit, as reported)",reported,0.17509519607549312,0.001866023404001703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.83333333333333,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.338,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.338,0.006,"b (currency per unit, as reported)",reported,0.10616532994120055,0.0018845916557609563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.333333333333336,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Bathrooms,bathrooms,A_structural,level,count,0.187,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.187,0.002,"b (currency per unit, as reported)",reported,0.17449215959245623,0.001866226305801671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.523,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.523,0.01,"b (currency per unit, as reported)",reported,0.09864092533646607,0.00188605975786742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.300000000000004,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.199,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.199,0.005,"b (currency per unit, as reported)",reported,-0.07521935745559735,0.0018899336044119934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.800000000000004,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.886,0.028,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.886,0.028,"b (currency per unit, as reported)",reported,-0.05986523820384577,0.0018919036904149905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.642857142857142,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.419,0.028,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.419,0.028,"b (currency per unit, as reported)",reported,-0.028350455067499718,0.0018945411500954465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.964285714285714,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.239,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.239,0.029,"b (currency per unit, as reported)",reported,-0.015618005600311556,0.001895071809242825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.241379310344827,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.125,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.125,0.029,"b (currency per unit, as reported)",reported,-0.008169136784899781,0.0018952397340967495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.310344827586206,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.028,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.028,0.029,"b (currency per unit, as reported)",reported,-0.0018299446374563028,0.0018952998030797423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9655172413793103,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Log. Population density,density,F_neighbourhood,log,per_ha,-0.006,,,,***,,,high,"Log of people per hectare at project location; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.006,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Log. Distance to nearest park,green_space,E_environment,log,m,0.015,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,0.015,0.001,not harmonised,reported,0.028418062673378708,0.0018945375115585804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.013,0.001,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,23,Roads Index,other,other,log,share,-0.018,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.018,0.002,not harmonised,reported,-0.01705524572010622,0.0018950273022340246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,24,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.038,0.003,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.038,0.003,not harmonised,reported,-0.02400025582680508,0.001894757038958296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.666666666666666,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,24,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.049,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.049,0.003,not harmonised,reported,-0.030941792886611117,0.0018943954828537421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.333333333333332,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,24,Log. Distance to PLMB viaduct,transit_distance,D_location,log,m,-0.02,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 1 (PLMB) viaduct; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.02,0.003,not harmonised,reported,-0.012634344666643153,0.0018951516999964728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.666666666666667,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,24,Log. Homicides,crime,F_neighbourhood,log,count,-0.027,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.027,0.002,not harmonised,reported,-0.025578218860107724,0.001894682878526498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-BaseModel,4,Base Model,24,Log. Thefts,crime,F_neighbourhood,log,count,-0.033,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.033,0.004,not harmonised,reported,-0.015634338435087304,0.0018950713254651276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.25,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.834,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.834,0.007,"b (currency per unit, as reported)",reported,0.2202658462816298,0.0018487541054813055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),119.14285714285714,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.563,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.563,0.006,"b (currency per unit, as reported)",reported,0.17509519607549312,0.001866023404001703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.83333333333333,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.338,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.338,0.006,"b (currency per unit, as reported)",reported,0.10616532994120055,0.0018845916557609563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.333333333333336,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Bathrooms,bathrooms,A_structural,level,count,0.186,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.186,0.002,"b (currency per unit, as reported)",reported,0.17358723679256835,0.0018665294278770788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.524,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.524,0.01,"b (currency per unit, as reported)",reported,0.09882769095478244,0.0018860246365416497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.4,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.2,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.2,0.005,"b (currency per unit, as reported)",reported,-0.0755951894906458,0.0018898797372661447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.889,0.028,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.889,0.028,"b (currency per unit, as reported)",reported,-0.0600672120301775,0.0018918806938638582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.75,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.423,0.028,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.423,0.028,"b (currency per unit, as reported)",reported,-0.028620883147367226,0.0018945265440337643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.107142857142856,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.245,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.245,0.029,"b (currency per unit, as reported)",reported,-0.016009990154362812,0.0018950600590878432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.448275862068964,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.128,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.128,0.029,"b (currency per unit, as reported)",reported,-0.008365182509002807,0.0018952366621959485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.413793103448276,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.031,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.031,0.029,"b (currency per unit, as reported)",reported,-0.00202600936847528,0.0018952990866381654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0689655172413792,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Log. Population density,density,F_neighbourhood,log,per_ha,-0.006,,,,***,,,high,"Log of people per hectare at project location; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.006,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Log. Distance to nearest park,green_space,E_environment,log,m,0.015,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,0.015,0.001,not harmonised,reported,0.028418062673378708,0.0018945375115585804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.013,0.001,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,23,Roads Index,other,other,log,share,-0.017,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.017,0.002,not harmonised,reported,-0.016107985146545472,0.0018950570760641733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.5,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.039,0.003,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.039,0.003,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.046,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.046,0.003,not harmonised,reported,-0.02904904806478714,0.001894503134660031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.333333333333332,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Log. Distance to PLMB viaduct,transit_distance,D_location,log,m,-0.02,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 1 (PLMB) viaduct; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.02,0.003,not harmonised,reported,-0.012634344666643153,0.0018951516999964728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.666666666666667,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Log. Homicides,crime,F_neighbourhood,log,count,-0.029,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.029,0.002,not harmonised,reported,-0.027471521122668485,0.0018945876636323096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Log. Thefts,crime,F_neighbourhood,log,count,-0.031,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.031,0.004,not harmonised,reported,-0.01468701375548572,0.0018950985490949316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Urban Renewal,other,other,dummy,binary,0.064,0.01,,,***,,,high,Printed name Urban Renewal: project within urban renewal partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.064,0.01,"b (currency per unit, as reported)",reported,0.012129046776090986,0.0018951635587642164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model2,4,Model 2,24,Development,other,other,dummy,binary,0.158,0.012,,,***,,,high,Printed name Development: project within urban development partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.158,0.012,"b (currency per unit, as reported)",reported,0.024947055928309234,0.0018947131084791824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.166666666666666,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.833,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.833,0.007,"b (currency per unit, as reported)",reported,0.22001453035685395,0.0018488615996374283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),118.99999999999999,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.563,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.563,0.006,"b (currency per unit, as reported)",reported,0.17509519607549312,0.001866023404001703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.83333333333333,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.338,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.338,0.006,"b (currency per unit, as reported)",reported,0.10616532994120055,0.0018845916557609563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.333333333333336,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Bathrooms,bathrooms,A_structural,level,count,0.187,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.187,0.002,"b (currency per unit, as reported)",reported,0.17449215959245623,0.001866226305801671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.522,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.522,0.01,"b (currency per unit, as reported)",reported,0.09845414929394622,0.0018860948140602724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.2,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.198,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.198,0.005,"b (currency per unit, as reported)",reported,-0.074843493362308,0.0018899872061188885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.6,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.885,0.028,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.885,0.028,"b (currency per unit, as reported)",reported,-0.05979791195987371,0.001891911338843462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.607142857142858,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.42,0.028,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.42,0.028,"b (currency per unit, as reported)",reported,-0.028418062673378708,0.0018945375115585804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.0,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.24,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.24,0.029,"b (currency per unit, as reported)",reported,-0.015683336864272483,0.001895069871099592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.275862068965516,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.128,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.128,0.029,"b (currency per unit, as reported)",reported,-0.008365182509002807,0.0018952366621959485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.413793103448276,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.026,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.026,0.029,"b (currency per unit, as reported)",reported,-0.0016992346981380786,0.001895300240230934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8965517241379309,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Log. Population density,density,F_neighbourhood,log,per_ha,-0.006,,,,***,,,high,"Log of people per hectare at project location; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.006,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Log. Distance to nearest park,green_space,E_environment,log,m,0.015,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,0.015,0.001,not harmonised,reported,0.028418062673378708,0.0018945375115585804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.013,0.001,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,23,Roads Index,other,other,log,share,-0.018,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.018,0.002,not harmonised,reported,-0.01705524572010622,0.0018950273022340246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.039,0.003,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.039,0.003,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.05,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.05,0.003,not harmonised,reported,-0.03157263487183453,0.0018943580923100718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.666666666666668,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Log. Distance to PLMB viaduct,transit_distance,D_location,log,m,-0.019,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 1 (PLMB) viaduct; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.019,0.003,not harmonised,reported,-0.012002720836446538,0.0018951664478599798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.333333333333333,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Log. Homicides,crime,F_neighbourhood,log,count,-0.027,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.027,0.002,not harmonised,reported,-0.025578218860107724,0.001894682878526498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Log. Thefts,crime,F_neighbourhood,log,count,-0.034,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.034,0.004,not harmonised,reported,-0.016107985146545472,0.0018950570760641733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Buffer 500mts built trunk,transit_distance,D_location,dummy,binary,0.012,0.002,,,***,,,high,Within 500 m of an operating BRT (TransMilenio) trunk corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.012,0.002,"b (currency per unit, as reported)",reported,0.011371082638446396,0.0018951804397410658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Buffer 500mts trunk under construction,transit_distance,D_location,dummy,binary,-0.019,0.003,,,***,,,high,Within 500 m of a BRT trunk under construction; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.019,0.003,"b (currency per unit, as reported)",reported,-0.012002720836446538,0.0018951664478599798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.333333333333333,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model3,4,Model 3,24,Buffer 500mts Carrera Séptima Corridor,transit_distance,D_location,dummy,binary,0.003,0.003,,,,,,high,Within 500 m of the Carrera Séptima (7th Street) corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.003,0.003,"b (currency per unit, as reported)",reported,0.00189529957236118,0.0018952995723611797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.834,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.834,0.007,"b (currency per unit, as reported)",reported,0.2202658462816298,0.0018487541054813055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),119.14285714285714,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.564,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.564,0.006,"b (currency per unit, as reported)",reported,0.17539664047944345,0.001865921707228122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.99999999999999,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.339,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.339,0.006,"b (currency per unit, as reported)",reported,0.10647587277234258,0.0018845287216343821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Bathrooms,bathrooms,A_structural,level,count,0.186,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.186,0.002,"b (currency per unit, as reported)",reported,0.17358723679256835,0.0018665294278770788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.523,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.523,0.01,"b (currency per unit, as reported)",reported,0.09864092533646607,0.00188605975786742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.300000000000004,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.199,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.199,0.005,"b (currency per unit, as reported)",reported,-0.07521935745559735,0.0018899336044119934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.800000000000004,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.888,0.028,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.888,0.028,"b (currency per unit, as reported)",reported,-0.05999988823990321,0.0018918883679248757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.714285714285715,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.423,0.028,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.423,0.028,"b (currency per unit, as reported)",reported,-0.028620883147367226,0.0018945265440337643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.107142857142856,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.245,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.245,0.029,"b (currency per unit, as reported)",reported,-0.016009990154362812,0.0018950600590878432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.448275862068964,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.131,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.131,0.029,"b (currency per unit, as reported)",reported,-0.008561227268522674,0.0018952335174592177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.517241379310345,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.029,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.029,0.029,"b (currency per unit, as reported)",reported,-0.00189529957236118,0.0018952995723611797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Log. Population density,density,F_neighbourhood,log,per_ha,-0.006,,,,***,,,high,"Log of people per hectare at project location; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.006,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Log. Distance to nearest park,green_space,E_environment,log,m,0.015,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,0.015,0.001,not harmonised,reported,0.028418062673378708,0.0018945375115585804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.013,0.001,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,23,Roads Index,other,other,log,share,-0.016,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.016,0.002,not harmonised,reported,-0.015160681198544152,0.001895085149818019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.04,0.003,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.04,0.003,not harmonised,reported,-0.025262641170541063,0.0018946980877905795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.333333333333334,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.047,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.047,0.003,not harmonised,reported,-0.029679998731818962,0.001894468004158657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.666666666666666,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Log. Distance to PLMB viaduct,transit_distance,D_location,log,m,-0.019,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 1 (PLMB) viaduct; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.019,0.003,not harmonised,reported,-0.012002720836446538,0.0018951664478599798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.333333333333333,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Log. Homicides,crime,F_neighbourhood,log,count,-0.028,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.028,0.002,not harmonised,reported,-0.02652490567186004,0.0018946361194185743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Log. Thefts,crime,F_neighbourhood,log,count,-0.032,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.032,0.004,not harmonised,reported,-0.015160681198544152,0.001895085149818019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Buffer 500mts built trunk,transit_distance,D_location,dummy,binary,0.012,0.002,,,***,,,high,Within 500 m of an operating BRT (TransMilenio) trunk corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.012,0.002,"b (currency per unit, as reported)",reported,0.011371082638446396,0.0018951804397410658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Buffer 500mts trunk under construction,transit_distance,D_location,dummy,binary,-0.017,0.003,,,***,,,high,Within 500 m of a BRT trunk under construction; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.017,0.003,"b (currency per unit, as reported)",reported,-0.010739430828340432,0.001895193675589488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.666666666666667,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Buffer 500mts Carrera Séptima Corridor,transit_distance,D_location,dummy,binary,0.006,0.003,,,**,,,high,Within 500 m of the Carrera Séptima (7th Street) corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.006,0.003,"b (currency per unit, as reported)",reported,0.0037905787202268354,0.0018952893601134177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Urban Renewal,other,other,dummy,binary,0.063,0.01,,,***,,,high,Printed name Urban Renewal: project within urban renewal partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.063,0.01,"b (currency per unit, as reported)",reported,0.01193955765072933,0.0018951678810681474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.3,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T4-Model4,4,Model 4,24,Development,other,other,dummy,binary,0.157,0.012,,,***,,,high,Printed name Development: project within urban development partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"PLMB (Metro Line 1) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.157,0.012,"b (currency per unit, as reported)",reported,0.024789260504353657,0.0018947205481034644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.083333333333334,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.824,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.824,0.007,"b (currency per unit, as reported)",reported,0.21775072009060287,0.0018498240784395879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),117.71428571428571,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.555,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.555,0.006,"b (currency per unit, as reported)",reported,0.17268187408586522,0.001866831071198543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.333,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.333,0.006,"b (currency per unit, as reported)",reported,0.10461215150147189,0.0018849036306571513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Bathrooms,bathrooms,A_structural,level,count,0.185,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.185,0.002,"b (currency per unit, as reported)",reported,0.17268187408586522,0.001866831071198543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.564,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,0.564,0.01,"b (currency per unit, as reported)",reported,0.10628955080667338,0.001884566503664422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.39999999999999,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.196,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.196,0.005,"b (currency per unit, as reported)",reported,-0.07409166962736664,0.0018900936129430267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.2,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.842,0.029,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.842,0.029,"b (currency per unit, as reported)",reported,-0.05494601054883075,0.001892439793249515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.034482758620687,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.372,0.029,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.372,0.029,"b (currency per unit, as reported)",reported,-0.02430498027122982,0.0018947430856603893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.827586206896552,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.21,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.21,0.029,"b (currency per unit, as reported)",reported,-0.013723315326407295,0.0018951244974562459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.2413793103448265,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.1,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.1,0.029,"b (currency per unit, as reported)",reported,-0.006535387933191609,0.0018952625006255668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4482758620689657,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.005,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",monetary,-0.005,0.029,"b (currency per unit, as reported)",reported,-0.0003267763578082035,0.0018953028752875804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.17241379310344826,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Log. Population density,density,F_neighbourhood,log,per_ha,-0.007,,,,***,,,high,"Log of people per hectare; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.007,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Log. Distance to nearest park,green_space,E_environment,log,m,0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,0.013,0.001,not harmonised,reported,0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.01,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.01,0.001,not harmonised,reported,-0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,37,Roads Index,other,other,log,share,-0.02,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.02,0.002,not harmonised,reported,-0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,38,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.111,0.004,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.111,0.004,not harmonised,reported,-0.05252206454918955,0.0018926870107816053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-27.75,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,38,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.051,0.002,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.051,0.002,not harmonised,reported,-0.048273879461766526,0.0018930933122261384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.499999999999996,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,38,Log. Distance to L2MB,transit_distance,D_location,log,m,0.064,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 2 (L2MB) route; positive sign; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,0.064,0.003,not harmonised,reported,0.04040011982519715,0.0018937556168061166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.333333333333332,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,38,Log. Homicides,crime,F_neighbourhood,log,count,-0.021,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.021,0.002,not harmonised,reported,-0.019896741718969026,0.001894927782758955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-BaseModel,7,Base Model,38,Log. Thefts,crime,F_neighbourhood,log,count,-0.021,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.473,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; year dummies + year x isochrone interactions + 13 attributes; robust SE",other,-0.021,0.004,not harmonised,reported,-0.009949848075073892,0.001895209157156932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.25,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.824,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.824,0.007,"b (currency per unit, as reported)",reported,0.21775072009060287,0.0018498240784395879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),117.71428571428571,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.556,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.556,0.006,"b (currency per unit, as reported)",reported,0.17298371040924876,0.001866730687869591,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.66666666666667,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.334,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.334,0.006,"b (currency per unit, as reported)",reported,0.10492284880453227,0.0018848415952909985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.66666666666667,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Bathrooms,bathrooms,A_structural,level,count,0.184,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.184,0.002,"b (currency per unit, as reported)",reported,0.17177607348377552,0.0018671312335192991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.563,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.563,0.01,"b (currency per unit, as reported)",reported,0.10610321764403743,0.0018846042210308607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.29999999999999,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.197,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.197,0.005,"b (currency per unit, as reported)",reported,-0.07446759736735088,0.0018900405423185503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.4,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.845,0.029,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.845,0.029,"b (currency per unit, as reported)",reported,-0.055141185988629195,0.0018924194007931916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.137931034482754,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.375,0.029,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.375,0.029,"b (currency per unit, as reported)",reported,-0.02450087098490048,0.001894734022832304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.93103448275862,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.215,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.215,0.029,"b (currency per unit, as reported)",reported,-0.014049997178903706,0.0018951158985498022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.413793103448276,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.102,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.102,0.029,"b (currency per unit, as reported)",reported,-0.006666089940567717,0.0018952608654555277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5172413793103443,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.006,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,-0.006,0.029,"b (currency per unit, as reported)",reported,-0.00039213162015780466,0.0018953028307627225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.20689655172413793,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Log. Population density,density,F_neighbourhood,log,per_ha,-0.006,,,,***,,,high,"Log of people per hectare; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.006,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Log. Distance to nearest park,green_space,E_environment,log,m,0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,0.013,0.001,not harmonised,reported,0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.01,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.01,0.001,not harmonised,reported,-0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,37,Roads Index,other,other,log,share,-0.018,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.018,0.002,not harmonised,reported,-0.01705524572010622,0.0018950273022340246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.115,0.004,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.115,0.004,not harmonised,reported,-0.05440924567939779,0.0018924955018920968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.75,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.047,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.047,0.003,not harmonised,reported,-0.029679998731818962,0.001894468004158657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.666666666666666,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Log. Distance to L2MB,transit_distance,D_location,log,m,0.067,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 2 (L2MB) route; positive sign; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,0.067,0.003,not harmonised,reported,0.0422905641735609,0.001893607351054965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.333333333333336,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Log. Homicides,crime,F_neighbourhood,log,count,-0.023,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.023,0.002,not harmonised,reported,-0.021790808819598856,0.001894852940834683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Log. Thefts,crime,F_neighbourhood,log,count,-0.018,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",other,-0.018,0.004,not harmonised,reported,-0.008528553209880049,0.001895234046640011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Urban Renewal,other,other,dummy,binary,0.079,0.01,,,***,,,high,Printed name Urban Renewal: project within urban renewal partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.079,0.01,"b (currency per unit, as reported)",reported,0.014971215428261546,0.001895090560539436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.9,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model2,7,Model 2,38,Development,other,other,dummy,binary,0.177,0.012,,,***,,,high,Printed name Development: project within urban development partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan dummies; robust SE",monetary,0.177,0.012,"b (currency per unit, as reported)",reported,0.027944801294194632,0.0018945627996064158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.749999999999998,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.823,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.823,0.007,"b (currency per unit, as reported)",reported,0.2174989678016177,0.0018499304673284618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),117.57142857142857,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.554,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.554,0.006,"b (currency per unit, as reported)",reported,0.1723799891073949,0.001866931289971786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.33333333333334,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.333,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.333,0.006,"b (currency per unit, as reported)",reported,0.10461215150147189,0.0018849036306571513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Bathrooms,bathrooms,A_structural,level,count,0.186,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.186,0.002,"b (currency per unit, as reported)",reported,0.17358723679256835,0.0018665294278770788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.556,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.556,0.01,"b (currency per unit, as reported)",reported,0.10479857356492299,0.0018848664310237948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.6,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.193,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.193,0.005,"b (currency per unit, as reported)",reported,-0.07296369750466292,0.0018902512306907492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.6,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.84,0.029,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.84,0.029,"b (currency per unit, as reported)",reported,-0.054815890088451974,0.0018924533482917946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.965517241379306,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.373,0.029,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.373,0.029,"b (currency per unit, as reported)",reported,-0.024370277487972967,0.001894740072791464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.86206896551724,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.213,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.213,0.029,"b (currency per unit, as reported)",reported,-0.013919324971945571,0.0018951193623775663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.344827586206896,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.107,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.107,0.029,"b (currency per unit, as reported)",reported,-0.006992843449660488,0.0018952566358892914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6896551724137927,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.004,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.004,0.029,"b (currency per unit, as reported)",reported,-0.000261421091271312,0.0018953029117170122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13793103448275862,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Log. Population density,density,F_neighbourhood,log,per_ha,-0.007,,,,***,,,high,"Log of people per hectare; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.007,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Log. Distance to nearest park,green_space,E_environment,log,m,0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,0.013,0.001,not harmonised,reported,0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.01,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.01,0.001,not harmonised,reported,-0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,37,Roads Index,other,other,log,share,-0.021,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.021,0.002,not harmonised,reported,-0.019896741718969026,0.001894927782758955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.5,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.125,0.004,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.125,0.004,not harmonised,reported,-0.059124604784762634,0.0018919873531124045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.25,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.044,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.044,0.003,not harmonised,reported,-0.027787043309389314,0.0018945711347310896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.666666666666666,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Log. Distance to L2MB,transit_distance,D_location,log,m,0.072,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 2 (L2MB) route; positive sign; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,0.072,0.003,not harmonised,reported,0.045440285664788,0.0018933452360328335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.999999999999996,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Log. Homicides,crime,F_neighbourhood,log,count,-0.023,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.023,0.002,not harmonised,reported,-0.021790808819598856,0.001894852940834683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Log. Thefts,crime,F_neighbourhood,log,count,-0.015,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",other,-0.015,0.004,not harmonised,reported,-0.007107206654016691,0.0018952551077377842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.75,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Buffer 500mts built trunk,transit_distance,D_location,dummy,binary,0.02,0.002,,,***,,,high,Within 500 m of an operating BRT trunk corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,0.02,0.002,"b (currency per unit, as reported)",reported,0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Buffer 500mts trunk under construction,transit_distance,D_location,dummy,binary,-0.022,0.003,,,***,,,high,Within 500 m of a BRT trunk under construction; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.022,0.003,"b (currency per unit, as reported)",reported,-0.013897546201269349,0.0018951199365367293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.333333333333333,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model3,7,Model 3,38,Buffer 500mts Carrera Séptima Corridor,transit_distance,D_location,dummy,binary,-0.019,0.003,,,***,,,high,Within 500 m of the Carrera Séptima corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds BRT 500 m buffer dummies; robust SE",monetary,-0.019,0.003,"b (currency per unit, as reported)",reported,-0.012002720836446538,0.0018951664478599798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.333333333333333,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Bedrooms: 1,bedrooms,A_structural,dummy,count,0.823,0.007,,,***,,,high,1 bedroom vs reference 4 bedrooms; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.823,0.007,"b (currency per unit, as reported)",reported,0.2174989678016177,0.0018499304673284618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),117.57142857142857,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Bedrooms: 2,bedrooms,A_structural,dummy,count,0.554,0.006,,,***,,,high,2 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.554,0.006,"b (currency per unit, as reported)",reported,0.1723799891073949,0.001866931289971786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.33333333333334,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Bedrooms: 3,bedrooms,A_structural,dummy,count,0.333,0.006,,,***,,,high,3 bedrooms vs reference 4; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.333,0.006,"b (currency per unit, as reported)",reported,0.10461215150147189,0.0018849036306571513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Bathrooms,bathrooms,A_structural,level,count,0.185,0.002,,,***,,,high,SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.185,0.002,"b (currency per unit, as reported)",reported,0.17268187408586522,0.001866831071198543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.5,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Micro-apartments,dwelling_type,A_structural,dummy,binary,0.555,0.01,,,***,,,high,Housing typology micro-apartment vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.555,0.01,"b (currency per unit, as reported)",reported,0.1046121515014719,0.0018849036306571513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.50000000000001,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Affordable housing,dwelling_type,A_structural,dummy,binary,-0.194,0.005,,,***,,,high,Housing typology affordable (VIS) vs market housing; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.194,0.005,"b (currency per unit, as reported)",reported,-0.07333971953921477,0.0018901989571962572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.8,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Socioeconomic Level: 2,income,F_neighbourhood,dummy,index,-0.843,0.029,,,***,,,high,Socioeconomic stratum 2 vs reference 1 (ordinal 1-6); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.843,0.029,"b (currency per unit, as reported)",reported,-0.05501106972968477,0.0018924330037495356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.068965517241377,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Socioeconomic Level: 3,income,F_neighbourhood,dummy,index,-0.376,0.029,,,***,,,high,Stratum 3 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.376,0.029,"b (currency per unit, as reported)",reported,-0.02456616726341743,0.001894730985742302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.96551724137931,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Socioeconomic Level: 4,income,F_neighbourhood,dummy,index,-0.217,0.029,,,***,,,high,Stratum 4 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.217,0.029,"b (currency per unit, as reported)",reported,-0.014180668666003086,0.001895112402369076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.482758620689655,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Socioeconomic Level: 5,income,F_neighbourhood,dummy,index,-0.108,0.029,,,***,,,high,Stratum 5 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.108,0.029,"b (currency per unit, as reported)",reported,-0.007058193886036424,0.0018952557656949658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7241379310344827,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Socioeconomic Level: 6,income,F_neighbourhood,dummy,index,-0.005,0.029,,,,,,high,Stratum 6 vs 1; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.005,0.029,"b (currency per unit, as reported)",reported,-0.0003267763578082035,0.0018953028752875804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.17241379310344826,278383.0,index,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Log. Population density,density,F_neighbourhood,log,per_ha,-0.006,,,,***,,,high,"Log of people per hectare; SE printed 0.000 (rounded), stored null","Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.006,,not harmonised,,,,,,278383.0,per_ha,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Log. Distance to nearest park,green_space,E_environment,log,m,0.013,0.001,,,***,,,high,Log Euclidean distance (m) to nearest park; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,0.013,0.001,not harmonised,reported,0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Log. Distance to nearest equipment,public_services,F_neighbourhood,log,m,-0.01,0.001,,,***,,,high,Log Euclidean distance (m) to nearest public facility; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.01,0.001,not harmonised,reported,-0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,37,Roads Index,other,other,log,share,-0.02,0.002,,,***,,,high,Printed name Roads Index: log of percentage of road space in cadastral sector; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.02,0.002,not harmonised,reported,-0.018949626553203914,0.0018949626553203913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,278383.0,share,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Log. Distance to CBD.72,cbd_distance,D_location,log,m,-0.128,0.004,,,***,,,high,Log Euclidean distance (m) to CBD at Calle 72 / Carrera 7; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.128,0.004,not harmonised,reported,-0.06053845555951275,0.0018918267362347733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Log. Distance to nearest activity center,employment_access,D_location,log,m,-0.039,0.003,,,***,,,high,Log Euclidean distance (m) to nearest economic activity centre; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.039,0.003,not harmonised,reported,-0.02463146322752492,0.00189472794057884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Log. Distance to L2MB,transit_distance,D_location,log,m,0.074,0.003,,,***,,,high,Log Euclidean distance (m) to future Metro Line 2 (L2MB) route; positive sign; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,0.074,0.003,not harmonised,reported,0.04669980018109904,0.0018932351424769882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.666666666666664,278383.0,m,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Log. Homicides,crime,F_neighbourhood,log,count,-0.024,0.002,,,***,,,high,Log number of homicides by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.024,0.002,not harmonised,reported,-0.02273775566491255,0.001894812972076046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Log. Thefts,crime,F_neighbourhood,log,count,-0.012,0.004,,,***,,,high,Log number of thefts by locality (yearly); SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",other,-0.012,0.004,not harmonised,reported,-0.005685817020201936,0.0018952723400673121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,278383.0,count,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Buffer 500mts built trunk,transit_distance,D_location,dummy,binary,0.019,0.002,,,***,,,high,Within 500 m of an operating BRT trunk corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.019,0.002,"b (currency per unit, as reported)",reported,0.018002460371396373,0.0018949958285680395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.5,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Buffer 500mts trunk under construction,transit_distance,D_location,dummy,binary,-0.02,0.003,,,***,,,high,Within 500 m of a BRT trunk under construction; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.02,0.003,"b (currency per unit, as reported)",reported,-0.012634344666643153,0.0018951516999964728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.666666666666667,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Buffer 500mts Carrera Séptima Corridor,transit_distance,D_location,dummy,binary,-0.018,0.003,,,***,,,high,Within 500 m of the Carrera Séptima corridor; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,-0.018,0.003,"b (currency per unit, as reported)",reported,-0.011371082638446394,0.0018951804397410658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,True,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Urban Renewal,other,other,dummy,binary,0.076,0.01,,,***,,,high,Printed name Urban Renewal: project within urban renewal partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.076,0.01,"b (currency per unit, as reported)",reported,0.014402808523061212,0.0018951063846133175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26,37e8b014df3e5f26ac93ad70c5f3f3c607c04c26:T7-Model4,7,Model 4,38,Development,other,other,dummy,binary,0.172,0.012,,,***,,,high,Printed name Development: project within urban development partial plan; SE printed with a spurious leading minus sign (Excel formatting artefact; note says robust SE in parentheses); absolute value stored,"Ln price per square meter of new housing unit (annual mean, COP)",per_m2,OLS,278393.0,0.474,se,"L2MB (Metro Line 2) isochrone DiD specification, 2007-2023; adds partial-plan and BRT buffer dummies; robust SE",monetary,0.172,0.012,"b (currency per unit, as reported)",reported,0.02715599072481746,0.0018946040040570325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.333333333333332,278383.0,binary,,2025.0,Colombia,South America,Upper middle income,2007.0,2023.0,condo_apartment,listing,journal,True,False,robust,,2015.0,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Finishing2,renovation,A_structural,binary,binary,1.099,,,,*,,,medium,"Interior finishing works: super or medium finishing vs demi-finishing (reference); improvement attribute; PRINTED VALUE IS exp(beta) (ln = 0.0944); Wald chi-square = 9.4; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,200.11633595049295,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,PhCond: Very good,condition,A_structural,dummy,binary,1.154,,,,*,,,medium,"Overall physical condition of the block: very good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.1432); Wald chi-square = 12.9; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,217.0850980592909,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,PhCond: Good,condition,A_structural,dummy,binary,1.078,,,,*,,,medium,"Overall physical condition of the block: good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0751); Wald chi-square = 4.4; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,193.8796077398218,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Heating,heating,A_structural,binary,binary,1.062,,,,**,,,medium,"Individual boiler heating vs district/central heating (reference); PRINTED VALUE IS exp(beta) (ln = 0.0602); Wald chi-square = 4.6; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,189.21495078388926,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Location: Central and interm.,cbd_distance,D_location,dummy,binary,1.27,,,,*,,,medium,"Location ring within the city: central or intermediate district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.239); Wald chi-square = 56.9; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,256.08525623555204,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Location: Urban periphery district,cbd_distance,D_location,dummy,binary,1.191,,,,*,,,medium,"Location ring within the city: urban periphery district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1748); Wald chi-square = 27.8; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,229.0369932679089,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Rooms,bedrooms,A_structural,dummy,binary,1.44,,,,*,,,medium,"Two-room flat vs one-room flat (reference); number of rooms, not bedrooms; PRINTED VALUE IS exp(beta) (ln = 0.3646); Wald chi-square = 218.4; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,322.0695816996552,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Garage,garage,A_structural,binary,binary,1.118,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.1115); Wald chi-square = 8.1; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,205.8730620510393,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,NrFloors,storeys,A_structural,dummy,binary,1.061,,,,**,,,medium,"Block with more than 5 floors vs <=5 floors (reference); building height; PRINTED VALUE IS exp(beta) (ln = 0.0592); Wald chi-square = 4.9; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,188.9258803923903,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Type,other,other,binary,binary,1.121,,,,*,,,medium,"'Detached' flat = all rooms have direct access to a hall (layout), vs non-detached (reference); not a dwelling-type variable; PRINTED VALUE IS exp(beta) (ln = 0.1142); Wald chi-square = 20.1; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,206.79205904343334,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c1,2,1 (M1),10,Balcony,terrace_balcony,C_exterior,binary,binary,1.09,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0862); Wald chi-square = 12.2; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.54,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square",pct_premium,197.42740725630657,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Finishing2,renovation,A_structural,binary,binary,1.084,,,,*,,,medium,"Interior finishing works: super or medium finishing vs demi-finishing (reference); improvement attribute; PRINTED VALUE IS exp(beta) (ln = 0.0807); Wald chi-square = 7.4; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.084,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,PhCond: Very good,condition,A_structural,dummy,binary,1.149,,,,*,,,medium,"Overall physical condition of the block: very good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.1389); Wald chi-square = 13.3; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.149,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,PhCond: Good,condition,A_structural,dummy,binary,1.057,,,,,,,medium,"Overall physical condition of the block: good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0554); Wald chi-square = 2.9; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.057,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Heating,heating,A_structural,binary,binary,1.062,,,,**,,,medium,"Individual boiler heating vs district/central heating (reference); PRINTED VALUE IS exp(beta) (ln = 0.0602); Wald chi-square = 4.9; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.062,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Location: Central and interm.,cbd_distance,D_location,dummy,binary,1.25,,,,*,,,medium,"Location ring within the city: central or intermediate district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.2231); Wald chi-square = 52.4; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.25,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Location: Urban periphery district,cbd_distance,D_location,dummy,binary,1.177,,,,*,,,medium,"Location ring within the city: urban periphery district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.163); Wald chi-square = 24.8; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.177,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Rooms,bedrooms,A_structural,dummy,binary,1.419,,,,*,,,medium,"Two-room flat vs one-room flat (reference); number of rooms, not bedrooms; PRINTED VALUE IS exp(beta) (ln = 0.35); Wald chi-square = 213.5; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.419,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Garage,garage,A_structural,binary,binary,1.115,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.1089); Wald chi-square = 8.1; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.115,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,NrFloors,storeys,A_structural,dummy,binary,1.046,,,,,,,medium,"Block with more than 5 floors vs <=5 floors (reference); building height; PRINTED VALUE IS exp(beta) (ln = 0.045); Wald chi-square = 3.06; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.046,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Type,other,other,binary,binary,1.12,,,,*,,,medium,"'Detached' flat = all rooms have direct access to a hall (layout), vs non-detached (reference); not a dwelling-type variable; PRINTED VALUE IS exp(beta) (ln = 0.1133); Wald chi-square = 21.1; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.12,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c2,2,2 (M2),10,Balcony,terrace_balcony,C_exterior,binary,binary,1.08,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.077); Wald chi-square = 10.3; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.5,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.08,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Finishing2,renovation,A_structural,binary,binary,1.096,,,,*,,,medium,"Interior finishing works: super or medium finishing vs demi-finishing (reference); improvement attribute; PRINTED VALUE IS exp(beta) (ln = 0.0917); Wald chi-square = 8.3; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.096,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,PhCond: Very good,condition,A_structural,dummy,binary,1.183,,,,*,,,medium,"Overall physical condition of the block: very good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.1681); Wald chi-square = 16.3; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.183,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,PhCond: Good,condition,A_structural,dummy,binary,1.081,,,,**,,,medium,"Overall physical condition of the block: good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0779); Wald chi-square = 4.5; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.081,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Heating,heating,A_structural,binary,binary,1.067,,,,**,,,medium,"Individual boiler heating vs district/central heating (reference); PRINTED VALUE IS exp(beta) (ln = 0.0649); Wald chi-square = 4.6; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.067,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Location: Central and interm.,cbd_distance,D_location,dummy,binary,1.26,,,,*,,,medium,"Location ring within the city: central or intermediate district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.2311); Wald chi-square = 44.8; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.26,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Location: Urban periphery district,cbd_distance,D_location,dummy,binary,1.199,,,,*,,,medium,"Location ring within the city: urban periphery district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1815); Wald chi-square = 23.6; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.199,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Rooms,bedrooms,A_structural,dummy,binary,1.424,,,,*,,,medium,"Two-room flat vs one-room flat (reference); number of rooms, not bedrooms; PRINTED VALUE IS exp(beta) (ln = 0.3535); Wald chi-square = 184.9; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.424,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Garage,garage,A_structural,binary,binary,1.114,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.108); Wald chi-square = 5.1; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.114,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,NrFloors,storeys,A_structural,dummy,binary,1.058,,,,,,,medium,"Block with more than 5 floors vs <=5 floors (reference); building height; PRINTED VALUE IS exp(beta) (ln = 0.0564); Wald chi-square = 3.5; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.058,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Type,other,other,binary,binary,1.12,,,,*,,,medium,"'Detached' flat = all rooms have direct access to a hall (layout), vs non-detached (reference); not a dwelling-type variable; PRINTED VALUE IS exp(beta) (ln = 0.1133); Wald chi-square = 16.8; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.12,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c3,2,3 (M3),10,Balcony,terrace_balcony,C_exterior,binary,binary,1.081,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0779); Wald chi-square = 8.4; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.45,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square,other,1.081,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Finishing2,renovation,A_structural,binary,binary,1.095,,,,*,,,medium,"Interior finishing works: super or medium finishing vs demi-finishing (reference); improvement attribute; PRINTED VALUE IS exp(beta) (ln = 0.0908); Wald chi-square = 10.5; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,198.91826833933632,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,PhCond: Very good,condition,A_structural,dummy,binary,1.15,,,,*,,,medium,"Overall physical condition of the block: very good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.1398); Wald chi-square = 15.7; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,215.81929096897673,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,PhCond: Good,condition,A_structural,dummy,binary,1.091,,,,*,,,medium,"Overall physical condition of the block: good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0871); Wald chi-square = 7.0; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,197.7249834268501,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Location: Central and interm.,cbd_distance,D_location,dummy,binary,1.217,,,,*,,,medium,"Location ring within the city: central or intermediate district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1964); Wald chi-square = 45.7; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,237.70413975343524,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Location: Urban periphery district,cbd_distance,D_location,dummy,binary,1.138,,,,*,,,medium,"Location ring within the city: urban periphery district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1293); Wald chi-square = 17.6; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,212.05210778255727,,100*(exp(b) - 1),,,,,,473.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Area,living_area,A_structural,level,m2,1.017,,,,*,,,medium,"Floor area in m2; PRINTED VALUE IS exp(beta) (ln = 0.0169); Wald chi-square = 375.1; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",semi_elasticity,101.69999999999999,,100*b/1 (X per m2->m2),,,,,,473.0,m2,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Type,other,other,binary,binary,1.041,,,,**,,,medium,"'Detached' flat = all rooms have direct access to a hall (layout), vs non-detached (reference); not a dwelling-type variable; PRINTED VALUE IS exp(beta) (ln = 0.0402); Wald chi-square = 2.8; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,183.2047646446072,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Balcony,terrace_balcony,C_exterior,binary,binary,1.067,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0649); Wald chi-square = 8.1; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,190.66464675754412,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Elevator,accessibility,B_interior,binary,binary,1.062,,,,**,,,medium,"Existence of an elevator; PRINTED VALUE IS exp(beta) (ln = 0.0602); Wald chi-square = 6.1; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,189.21495078388926,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c4,2,4 (M4),10,Floor2,floor_level,A_structural,dummy,binary,1.066,,,,*,,,medium,"Intermediate floor vs ground or last floor (reference); PRINTED VALUE IS exp(beta) (ln = 0.0639); Wald chi-square = 8.1; stars: * 1%, ** 5%",sale price of the flat (EUR),log,OLS,483.0,0.61,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square",pct_premium,190.374127394678,,100*(exp(b) - 1),,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Finishing2,renovation,A_structural,binary,binary,1.077,,,,*,,,medium,"Interior finishing works: super or medium finishing vs demi-finishing (reference); improvement attribute; PRINTED VALUE IS exp(beta) (ln = 0.0742); Wald chi-square = 8.4; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.077,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,PhCond: Very good,condition,A_structural,dummy,binary,1.127,,,,*,,,medium,"Overall physical condition of the block: very good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.1196); Wald chi-square = 41.7; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.127,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,PhCond: Good,condition,A_structural,dummy,binary,1.056,,,,*,,,medium,"Overall physical condition of the block: good vs unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0545); Wald chi-square = 15.4; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.056,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Location: Central and interm.,cbd_distance,D_location,dummy,binary,1.192,,,,*,,,medium,"Location ring within the city: central or intermediate district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1756); Wald chi-square = 41.9; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.192,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Location: Urban periphery district,cbd_distance,D_location,dummy,binary,1.117,,,,*,,,medium,"Location ring within the city: urban periphery district vs suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1106); Wald chi-square = 14.5; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.117,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Area,living_area,A_structural,level,m2,1.018,,,,*,,,medium,"Floor area in m2; PRINTED VALUE IS exp(beta) (ln = 0.0178); Wald chi-square = 438.6; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.018,,not harmonised,,,,,,473.0,m2,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Balcony,terrace_balcony,C_exterior,binary,binary,1.061,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0592); Wald chi-square = 7.3; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.061,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Elevator,accessibility,B_interior,binary,binary,1.057,,,,**,,,medium,"Existence of an elevator; PRINTED VALUE IS exp(beta) (ln = 0.0554); Wald chi-square = 6.2; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.057,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T2-c5,2,5 (M5),10,Floor2,floor_level,A_structural,dummy,binary,1.061,,,,*,,,medium,"Intermediate floor vs ground or last floor (reference); PRINTED VALUE IS exp(beta) (ln = 0.0592); Wald chi-square = 8.1; stars: * 1%, ** 5%",sale price of the flat (EUR),other,other,483.0,0.62,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square,other,1.061,,not harmonised,,,,,,473.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Finishing: Superior,renovation,A_structural,dummy,binary,1.106,,,,**,,,medium,"Superior finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.1007); Wald chi-square = 5.7; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,202.224520339094,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Finishing: Medium,renovation,A_structural,dummy,binary,1.066,,,,,,,medium,"Medium finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.0639); Wald chi-square = None; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,190.374127394678,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,PhCond2,condition,A_structural,binary,binary,1.103,,,,*,,,medium,"Physical condition of the block very good vs good or unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.098); Wald chi-square = 9.4; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,201.31920542942723,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Location: Central,cbd_distance,D_location,dummy,binary,1.263,,,,*,,,medium,"Central district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.2335); Wald chi-square = 45.6; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,253.60136321004734,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Location: Intermediate,cbd_distance,D_location,dummy,binary,1.064,,,,**,,,medium,"Intermediate district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.062); Wald chi-square = 4.6; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,189.79395950117143,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Rooms,bedrooms,A_structural,dummy,binary,1.298,,,,*,,,medium,"Two-room flat vs one-room flat (reference); number of rooms, not bedrooms; PRINTED VALUE IS exp(beta) (ln = 0.2608); Wald chi-square = 94.9; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,266.1965407987391,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Garage,garage,A_structural,binary,binary,1.29,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.2546); Wald chi-square = 30.5; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,263.27865557528094,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c1,3,1 (M1),12,Balcony,terrace_balcony,C_exterior,binary,binary,1.08,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.077); Wald chi-square = 8.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.5,none,"M1: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,194.4679551065524,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Finishing: Superior,renovation,A_structural,dummy,binary,1.096,,,,**,,,medium,"Superior finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.0917); Wald chi-square = 4.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.096,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Finishing: Medium,renovation,A_structural,dummy,binary,1.065,,,,,,,medium,"Medium finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.063); Wald chi-square = 3.3; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.065,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,PhCond2,condition,A_structural,binary,binary,1.131,,,,*,,,medium,"Physical condition of the block very good vs good or unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.1231); Wald chi-square = 15.7; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.131,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Location: Central,cbd_distance,D_location,dummy,binary,1.266,,,,*,,,medium,"Central district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.2359); Wald chi-square = 45.1; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.266,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Location: Intermediate,cbd_distance,D_location,dummy,binary,1.051,,,,*,,,medium,"Intermediate district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.0497); Wald chi-square = 3.7; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.051,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Rooms,bedrooms,A_structural,dummy,binary,1.302,,,,*,,,medium,"Two-room flat vs one-room flat (reference); number of rooms, not bedrooms; PRINTED VALUE IS exp(beta) (ln = 0.2639); Wald chi-square = 100.5; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.302,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Garage,garage,A_structural,binary,binary,1.3,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.2624); Wald chi-square = 33.1; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.3,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c2,3,2 (M2),12,Balcony,terrace_balcony,C_exterior,binary,binary,1.077,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0742); Wald chi-square = 8.1; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.51,none,M2: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.077,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Finishing: Superior,renovation,A_structural,dummy,binary,1.1,,,,*,,,medium,"Superior finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.0953); Wald chi-square = 5.5; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.1,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Finishing: Medium,renovation,A_structural,dummy,binary,1.059,,,,,,,medium,"Medium finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.0573); Wald chi-square = 2.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.059,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,PhCond2,condition,A_structural,binary,binary,1.14,,,,*,,,medium,"Physical condition of the block very good vs good or unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.131); Wald chi-square = 16.7; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.14,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Location: Central,cbd_distance,D_location,dummy,binary,1.245,,,,*,,,medium,"Central district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.2191); Wald chi-square = 36.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.245,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Location: Intermediate,cbd_distance,D_location,dummy,binary,1.041,,,,,,,medium,"Intermediate district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.0402); Wald chi-square = 1.9; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.041,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Rooms,bedrooms,A_structural,dummy,binary,1.299,,,,*,,,medium,"Two-room flat vs one-room flat (reference); number of rooms, not bedrooms; PRINTED VALUE IS exp(beta) (ln = 0.2616); Wald chi-square = 100.3; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.299,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Garage,garage,A_structural,binary,binary,1.274,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.2422); Wald chi-square = 18.2; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.274,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c3,3,3 (M3),12,Balcony,terrace_balcony,C_exterior,binary,binary,1.074,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0714); Wald chi-square = 7.4; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.49,none,M3: In.Gauss-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses number of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.074,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Finishing: Superior,renovation,A_structural,dummy,binary,1.118,,,,*,,,medium,"Superior finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.1115); Wald chi-square = 7.9; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,205.8730620510393,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Finishing: Medium,renovation,A_structural,dummy,binary,1.066,,,,**,,,medium,"Medium finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.0639); Wald chi-square = 3.9; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,190.374127394678,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,PhCond2,condition,A_structural,binary,binary,1.084,,,,**,,,medium,"Physical condition of the block very good vs good or unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0807); Wald chi-square = 5.6; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,195.64818581475447,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Location: Central,cbd_distance,D_location,dummy,binary,1.199,,,,*,,,medium,"Central district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1815); Wald chi-square = 27.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,231.67984653190578,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Location: Intermediate,cbd_distance,D_location,dummy,binary,1.061,,,,**,,,medium,"Intermediate district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.0592); Wald chi-square = 4.7; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,188.9258803923903,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Area,living_area,A_structural,level,m2,1.01,,,,*,,,medium,"Floor area in m2; PRINTED VALUE IS exp(beta) (ln = 0.01); Wald chi-square = 159.6; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",semi_elasticity,101.0,,100*b/1 (X per m2->m2),,,,,,458.0,m2,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Garage,garage,A_structural,binary,binary,1.182,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.1672); Wald chi-square = 14.1; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,226.08894643851917,,100*(exp(b) - 1),,,,,,458.0,binary,implausible_magnitude,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c4,3,4 (M4),12,Balcony,terrace_balcony,C_exterior,binary,binary,1.067,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0649); Wald chi-square = 6.9; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),log,OLS,468.0,0.55,none,"M4: Log-linear GLM estimated by maximum likelihood (normal distribution, identity link on log price; equivalent to OLS log-linear); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent)",pct_premium,190.66464675754412,,100*(exp(b) - 1),,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,ols,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Finishing: Superior,renovation,A_structural,dummy,binary,1.114,,,,*,,,medium,"Superior finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.108); Wald chi-square = 7.6; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.114,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Finishing: Medium,renovation,A_structural,dummy,binary,1.06,,,,*,,,medium,"Medium finishing works vs demi-finishing (reference); PRINTED VALUE IS exp(beta) (ln = 0.0583); Wald chi-square = 3.2; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.06,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,PhCond2,condition,A_structural,binary,binary,1.09,,,,*,,,medium,"Physical condition of the block very good vs good or unsatisfactory (reference); PRINTED VALUE IS exp(beta) (ln = 0.0862); Wald chi-square = 8.2; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.09,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Location: Central,cbd_distance,D_location,dummy,binary,1.192,,,,*,,,medium,"Central district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.1756); Wald chi-square = 29.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.192,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Location: Intermediate,cbd_distance,D_location,dummy,binary,1.061,,,,*,,,medium,"Intermediate district vs urban periphery/suburban areas (reference); categorical, not a distance; PRINTED VALUE IS exp(beta) (ln = 0.0592); Wald chi-square = 4.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.061,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Area,living_area,A_structural,level,m2,1.01,,,,*,,,medium,"Floor area in m2; PRINTED VALUE IS exp(beta) (ln = 0.01); Wald chi-square = 149.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.01,,not harmonised,,,,,,458.0,m2,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Garage,garage,A_structural,binary,binary,1.183,,,,*,,,medium,"Existence of a garage; PRINTED VALUE IS exp(beta) (ln = 0.1681); Wald chi-square = 14.8; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.183,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc,38bde6e6f3fb947d8b6029d64eef6268c93759cc:T3-c5,3,5 (M5),12,Balcony,terrace_balcony,C_exterior,binary,binary,1.072,,,,*,,,medium,"Existence of a balcony; PRINTED VALUE IS exp(beta) (ln = 0.0695); Wald chi-square = 8.2; stars: * 1%, ** 5%",monthly rental price of the flat (EUR),other,other,468.0,0.56,none,M5: Gamma-log GLM estimated by maximum likelihood (log link on level price; coefficients are log-link semi-elasticities); uses floor area instead of rooms; parentheses = Wald chi-square; RENT model (price_type = rent),other,1.072,,not harmonised,,,,,,458.0,binary,,2016.0,Romania,Europe,High income,2011.0,2012.0,condo_apartment,transaction,journal,False,True,unknown,,2011.5,other,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Secondary Water,other,other,binary,binary,0.0368,0.003,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,3.74808343122075,0.31124565089593986,100*(exp(b - se^2/2) - 1),reported,0.0449369237091587,0.003663336171942285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.266666666666666,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Secondary Water,other,other,binary,binary,0.0395,0.004,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,4.028217659425604,0.41611619955398316,100*(exp(b - se^2/2) - 1),reported,0.06577268143206652,0.00666052470198142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.875,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Secondary Water,other,other,binary,binary,0.0304,0.005,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,3.085391246001201,0.5154333991072267,100*(exp(b - se^2/2) - 1),reported,0.056914554024520975,0.009360946385612003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.08,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Secondary Water,other,other,binary,binary,0.0198,0.01,,,**,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,1.994632157038434,1.019997320161417,100*(exp(b - se^2/2) - 1),reported,0.04542526685613803,0.022942053967746477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9800000000000002,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.151,0.007,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.1625350472923168,0.007534737291696806,100*b/92.903 (X per 1000sqft->m2),reported,0.07885697025255631,0.0036556211375357232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.57142857142857,74365.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.106,0.011,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,0.11409745041712305,0.011840301458380692,100*b/92.903 (X per 1000sqft->m2),reported,0.0641898678314615,0.006661212699491287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.636363636363637,22444.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.1007,0.015,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.1083925778962669,0.016145865625064582,100*b/92.903 (X per 1000sqft->m2),reported,0.06282085609750315,0.009357625039350021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.713333333333334,11375.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.1576,0.027,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.16963922816734522,0.02906255812511625,100*b/92.903 (X per 1000sqft->m2),reported,0.13286353590544833,0.02276215399395371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.837037037037037,1896.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0078,0.001,,,***,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,other,0.0078,0.001,not harmonised,reported,0.0285912228252614,0.0036655413878540254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8,74365.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0143,0.002,,,***,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",other,0.0143,0.002,not harmonised,reported,0.04767183404384829,0.0066673893767619985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.15,22444.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0146,0.003,,,***,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,other,0.0146,0.003,not harmonised,reported,0.04558313971113511,0.009366398570781188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.866666666666666,11375.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0091,0.006,,,,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,other,0.0091,0.006,not harmonised,reported,0.03481029360236861,0.022951841935627656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5166666666666666,1896.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Acreage,lot_size,A_structural,level,acres,0.0011,0.001,,,*,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,2.718159196138819e-05,2.4710538146716536e-05,100*b/4046.86 (X per acre->m2),reported,0.004033711755824943,0.0036670106871135844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,74365.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Acreage,lot_size,A_structural,level,acres,0.0275,0.018,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,0.0006795397990347047,0.00044478968664089753,100*b/4046.86 (X per acre->m2),reported,0.010197353558835318,0.006674631420328571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.527777777777778,22444.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Acreage,lot_size,A_structural,level,acres,0.0877,0.027,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.00216711419546704,0.0006671845299613464,100*b/4046.86 (X per acre->m2),reported,0.03044099283584113,0.009371799390737862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.248148148148148,11375.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Acreage,lot_size,A_structural,level,acres,0.1766,0.036,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.00436388103671014,0.0008895793732817951,100*b/4046.86 (X per acre->m2),reported,0.11195159675168062,0.02282139005130522,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.905555555555556,1896.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,House Age,age,A_structural,level,years,-0.0039,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,-0.38999999999999996,,100*b/1 (per year),,,,,,74365.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,House Age,age,A_structural,level,years,-0.0037,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,-0.37,,100*b/1 (per year),,,,,,22444.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,House Age,age,A_structural,level,years,-0.0039,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,-0.38999999999999996,,100*b/1 (per year),,,,,,11375.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,House Age,age,A_structural,level,years,-0.0038,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,-0.38,,100*b/1 (per year),,,,,,1896.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Bathrooms,bathrooms,A_structural,level,count,0.0287,0.001,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,2.87,0.1,100*b/1 (per unit count),reported,0.10466600252341897,0.003646899042627839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.7,74365.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Bathrooms,bathrooms,A_structural,level,count,0.0238,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,2.3800000000000003,0.2,100*b/1 (per unit count),reported,0.07918283540906831,0.006654019782274648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.9,22444.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Bathrooms,bathrooms,A_structural,level,count,0.0256,0.003,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,2.56,0.3,100*b/1 (per unit count),reported,0.07975489666253238,0.009346276952640514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.533333333333333,11375.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Bathrooms,bathrooms,A_structural,level,count,0.0263,0.008,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,2.63,0.8,100*b/1 (per unit count),reported,0.07528566969559436,0.02290058393782338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2875,1896.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Bedrooms,bedrooms,A_structural,level,count,0.0697,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,6.97,0.2,100*b/1 (per unit count),reported,0.12676539398456219,0.0036374575031438215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.85,74365.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Bedrooms,bedrooms,A_structural,level,count,0.0603,0.003,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,6.03,0.3,100*b/1 (per unit count),reported,0.13297557206122876,0.006615700102548695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.099999999999998,22444.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Bedrooms,bedrooms,A_structural,level,count,0.057,0.004,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,5.7,0.4,100*b/1 (per unit count),reported,0.13243321416294637,0.009293558888627814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.25,11375.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Bedrooms,bedrooms,A_structural,level,count,0.0492,0.009,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,4.92,0.8999999999999999,100*b/1 (per unit count),reported,0.12456828366310785,0.02278688115788558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.466666666666667,1896.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0074,0.0,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.74,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,74365.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0087,0.001,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,0.8699999999999999,0.1,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.057974638422213294,0.006663751542783138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.7,22444.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0114,0.001,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,1.1400000000000001,0.1,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10628263052992257,0.00932303776578268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.4,11375.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0095,0.002,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,0.95,0.2,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10844402634922926,0.022830321336679844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,1896.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0796,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,8.285167379378677,0.21657076789986002,100*(exp(b - se^2/2) - 1),reported,0.14441819999280453,0.0036285979897689577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.800000000000004,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0703,0.004,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,7.282139937076226,0.4291319927905151,100*(exp(b - se^2/2) - 1),reported,0.11651373907861831,0.006629515737047983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.575,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0813,0.005,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,8.468269801883444,0.5423481283186504,100*(exp(b - se^2/2) - 1),reported,0.15071465227269995,0.0092690438052091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.259999999999998,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0763,0.012,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,7.920860567071841,1.2951435737852433,100*(exp(b - se^2/2) - 1),reported,0.14449158991705763,0.02272475857149006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.358333333333333,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Deck,terrace_balcony,C_exterior,binary,binary,0.0214,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,2.1628578907640916,0.20432612443336837,100*(exp(b - se^2/2) - 1),reported,0.03920716411853827,0.0036642209456577827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.7,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Deck,terrace_balcony,C_exterior,binary,binary,0.016,0.003,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,1.6124112837298865,0.3048386056218285,100*(exp(b - se^2/2) - 1),reported,0.03557734786504881,0.006670752724696653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333333,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Deck,terrace_balcony,C_exterior,binary,binary,0.0067,0.005,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,0.6709911258702128,0.5033612476056212,100*(exp(b - se^2/2) - 1),reported,0.012563042259106847,0.009375404670975258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.34,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Deck,terrace_balcony,C_exterior,binary,binary,0.0208,0.009,,,**,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,2.0976477265611804,0.9189160448852525,100*(exp(b - se^2/2) - 1),reported,0.053001821126033466,0.022933480294918325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3111111111111113,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Swimming Pool,pool,C_exterior,binary,binary,-0.1137,0.005,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,-10.748548307639306,0.44626283671239825,100*(exp(b - se^2/2) - 1),reported,-0.08310007785490389,0.0036543569857037774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.74,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Swimming Pool,pool,C_exterior,binary,binary,-0.0552,0.008,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,-5.373441113100275,0.7570366958818675,100*(exp(b - se^2/2) - 1),reported,-0.04600857884373564,0.006667909977352992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.8999999999999995,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Swimming Pool,pool,C_exterior,binary,binary,-0.0477,0.013,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,-4.66607904579045,1.2394457011416191,100*(exp(b - se^2/2) - 1),reported,-0.034382896846332736,0.009370600817658817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6692307692307695,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Swimming Pool,pool,C_exterior,binary,binary,0.0418,0.047,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,4.153490987483277,4.900623827344107,100*(exp(b - se^2/2) - 1),reported,0.020420608903668824,0.022960971733790306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8893617021276595,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Fireplace,fireplace,B_interior,binary,binary,0.0741,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,7.691233773415362,0.21538289831219662,100*(exp(b - se^2/2) - 1),reported,0.13462699404911257,0.0036336570593552657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.05,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Fireplace,fireplace,B_interior,binary,binary,0.0625,0.004,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,6.448594299625188,0.4257977835671437,100*(exp(b - se^2/2) - 1),reported,0.10373386830897514,0.006638967571774409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.625,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Fireplace,fireplace,B_interior,binary,binary,0.0499,0.005,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,5.115283503000723,0.5255829872612834,100*(exp(b - se^2/2) - 1),reported,0.0931669238053422,0.009335363106747714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.98,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Fireplace,fireplace,B_interior,binary,binary,0.0145,0.013,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,1.4519904397412242,1.318987325436818,100*(exp(b - se^2/2) - 1),reported,0.025607256189165702,0.02295822968683821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1153846153846154,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Elevation ('000ft),other,other,level,1000 ft,0.0421,0.006,,,***,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,4.21,0.6,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.025721887758392844,0.003665827233975227,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.016666666666667,74365.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Elevation ('000ft),other,other,level,1000 ft,0.1027,0.012,,,***,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",semi_elasticity,10.27,1.2,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.05703370307150156,0.006664113309230952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.558333333333334,22444.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Elevation ('000ft),other,other,level,1000 ft,0.1011,0.015,,,***,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,10.11,1.5,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.06306940212667704,0.009357478060337841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.74,11375.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Elevation ('000ft),other,other,level,1000 ft,0.0764,0.044,,,*,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,semi_elasticity,7.64,4.3999999999999995,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.03984524386923783,0.02294752264720503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7363636363636363,1896.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,View,view,E_environment,binary,binary,0.059,0.002,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,6.077311919179906,0.2121550481480318,100*(exp(b - se^2/2) - 1),reported,0.1075502270984841,0.0036457704101181053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.499999999999996,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,View,view,E_environment,binary,binary,0.0577,0.004,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,5.938865375085034,0.4237588515575924,100*(exp(b - se^2/2) - 1),reported,0.09584330306284213,0.00664424977905318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.425,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,View,view,E_environment,binary,binary,0.0498,0.005,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,5.104772500209309,0.5255304315903846,100*(exp(b - se^2/2) - 1),reported,0.09298183235010433,0.009335525336355859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.959999999999999,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,View,view,E_environment,binary,binary,0.029,0.012,,,**,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,2.9350478572533145,1.2353095133701568,100*(exp(b - se^2/2) - 1),reported,0.055415305455486924,0.02293047122296011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4166666666666665,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Cul-de-sac,other,other,binary,binary,0.0152,0.003,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,1.5311538620053078,0.3045948322596771,100*(exp(b - se^2/2) - 1),reported,0.01857646590820962,0.0036664077450413723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.066666666666666,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Cul-de-sac,other,other,binary,binary,0.018,0.005,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,1.8150249432132615,0.5090814881948968,100*(exp(b - se^2/2) - 1),reported,0.024022987651546313,0.006673052125429532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5999999999999996,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Cul-de-sac,other,other,binary,binary,0.0246,0.006,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,2.4886628365149877,0.6149430458942958,100*(exp(b - se^2/2) - 1),reported,0.03841381940233525,0.009369224244472014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.1,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Cul-de-sac,other,other,binary,binary,0.0487,0.014,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,4.980244620895169,1.4698674646459948,100*(exp(b - se^2/2) - 1),reported,0.07963432578576461,0.02289282466120543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4785714285714286,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c1,2,1,10,Corner Lot,other,other,binary,binary,0.0187,0.003,,,***,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7417,se,All houses (full sample); boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,1.8871355049997263,0.30566278199442326,100*(exp(b - se^2/2) - 1),reported,0.022851916819973717,0.0036660829122952487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.233333333333333,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c2,2,2,10,Corner Lot,other,other,binary,binary,0.0232,0.004,,,***,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7502,se,"Houses within 1,000 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment",pct_premium,2.346302558486646,0.4093884853287289,100*(exp(b - se^2/2) - 1),reported,0.038685894035690144,0.006669981730291404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c3,2,3,10,Corner Lot,other,other,binary,binary,0.0163,0.006,,,***,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,1.6415274102486332,0.6098601418452482,100*(exp(b - se^2/2) - 1),reported,0.025463600296204827,0.00937310440351098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7166666666666663,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T2-c4,2,4,10,Corner Lot,other,other,binary,binary,-0.01,0.013,,,,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7733,se,Houses within 100 ft of the secondary-water boundary; boundary-segment FE + month/year FE; SE clustered by boundary segment,pct_premium,-1.0033821927281639,1.2870647838739184,100*(exp(b - se^2/2) - 1),reported,-0.017663213688895077,0.0229621777955636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7692307692307693,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Secondary Water,other,other,binary,binary,0.0078,0.003,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,0.7825964019428033,0.3023491497739411,100*(exp(b - se^2/2) - 1),reported,0.009533872033508917,0.003666873859041891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5999999999999996,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Secondary Water,other,other,binary,binary,0.0248,0.004,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,2.5101877157438723,0.41004403120210375,100*(exp(b - se^2/2) - 1),reported,0.04134947196561255,0.006669269671872993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.199999999999999,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Secondary Water,other,other,binary,binary,0.0235,0.005,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,2.3765503597852344,0.5118891503733146,100*(exp(b - se^2/2) - 1),reported,0.04402515250269361,0.009367053723977364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Secondary Water,other,other,binary,binary,0.0201,0.011,,,**,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,2.024163877558194,1.1223337017881287,100*(exp(b - se^2/2) - 1),reported,0.0419278060576768,0.022945565504201234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8272727272727274,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.1632,0.007,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,0.17566701800070267,0.007534737291696806,100*b/92.903 (X per 1000sqft->m2),reported,0.08518368078067474,0.003653711798190706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.314285714285717,74365.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.12,0.01,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,0.12916692500051666,0.010763910416709722,100*b/92.903 (X per 1000sqft->m2),reported,0.07984401296867373,0.006653667747389478,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,22444.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.109,0.015,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,0.11732662354213597,0.016145865625064582,100*b/92.903 (X per 1000sqft->m2),reported,0.06797572352783131,0.009354457366215319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.266666666666667,11375.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,House Size ('000 sq ft),living_area,A_structural,level,1000 sqft,0.1617,0.025,,,***,,,high,House size in thousands of square feet,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,0.1740524314381962,0.026909776041774306,100*b/92.903 (X per 1000sqft->m2),reported,0.14693038302688433,0.022716509435201662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.468,1896.0,1000sqft,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0068,0.001,,,***,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),other,0.0068,0.001,not harmonised,reported,0.024928126613029223,0.0036659009725042977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.8,74365.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0132,0.002,,,***,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",other,0.0132,0.002,not harmonised,reported,0.04401216860170161,0.006668510394197214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6,22444.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0144,0.003,,,***,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),other,0.0144,0.003,not harmonised,reported,0.04495998410315154,0.009366663354823238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,11375.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,House Size Squared,living_area,A_structural,squared,1000 sqft squared,0.0094,0.006,,,,,,high,Square of house size in thousands of sq ft,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),other,0.0094,0.006,not harmonised,reported,0.03595642566413533,0.022950909998384256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5666666666666667,1896.0,1000 sqft sqd,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Acreage,lot_size,A_structural,level,acres,0.0017,0.001,,,**,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,4.2007914849418104e-05,2.4710538146716536e-05,100*b/4046.86 (X per acre->m2),reported,0.006233847754358952,0.0036669692672699717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,74365.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Acreage,lot_size,A_structural,level,acres,0.0343,0.021,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,0.000847571458432377,0.0005189213010810472,100*b/4046.86 (X per acre->m2),reported,0.010901816957120362,0.006674581810481856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.633333333333333,22444.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Acreage,lot_size,A_structural,level,acres,0.0975,0.027,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,0.002409277469304862,0.0006671845299613464,100*b/4046.86 (X per acre->m2),reported,0.03383890936766155,0.009370774901813967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.611111111111111,11375.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Acreage,lot_size,A_structural,level,acres,0.1895,0.033,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,0.004682646978802783,0.0008154477588416456,100*b/4046.86 (X per acre->m2),reported,0.13074705811412263,0.022768616980295763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.742424242424242,1896.0,acre,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Age,age,A_structural,level,years,-0.0038,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,-0.38,,100*b/1 (per year),,,,,,74365.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Age,age,A_structural,level,years,-0.0038,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,-0.38,,100*b/1 (per year),,,,,,22444.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Age,age,A_structural,level,years,-0.0039,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,-0.38999999999999996,,100*b/1 (per year),,,,,,11375.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Age,age,A_structural,level,years,-0.0041,0.0,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,-0.41000000000000003,,100*b/1 (per year),,,,,,1896.0,years,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Bathrooms,bathrooms,A_structural,level,count,0.0246,0.001,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,2.46,0.1,100*b/1 (per unit count),reported,0.08984437424982356,0.003652210335358681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.6,74365.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Bathrooms,bathrooms,A_structural,level,count,0.0206,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,2.06,0.2,100*b/1 (per unit count),reported,0.06859036041942462,0.006659258293148021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.3,22444.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Bathrooms,bathrooms,A_structural,level,count,0.0226,0.003,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,2.26,0.3,100*b/1 (per unit count),reported,0.07045807996410608,0.009352842473111427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.533333333333332,11375.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Bathrooms,bathrooms,A_structural,level,count,0.0231,0.008,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,2.31,0.8,100*b/1 (per unit count),reported,0.06616830585517718,0.022915430599195565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8874999999999997,1896.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Bedrooms,bedrooms,A_structural,level,count,0.0677,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,6.77,0.2,100*b/1 (per unit count),reported,0.12318393485257591,0.0036391118124837795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.849999999999994,74365.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Bedrooms,bedrooms,A_structural,level,count,0.0598,0.003,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,5.9799999999999995,0.3,100*b/1 (per unit count),reported,0.13189221478830498,0.006616666293727675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.933333333333334,22444.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Bedrooms,bedrooms,A_structural,level,count,0.0572,0.004,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,5.72,0.4,100*b/1 (per unit count),reported,0.13288970014088444,0.009292986023838072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.3,11375.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Bedrooms,bedrooms,A_structural,level,count,0.0471,0.008,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,4.71,0.8,100*b/1 (per unit count),reported,0.13399164593466745,0.022758665976164324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8875,1896.0,count,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0114,0.0,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,1.1400000000000001,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,74365.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0113,0.001,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,1.13,0.1,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0752136049979282,0.006656071238754707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.299999999999999,22444.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0115,0.001,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,1.15,0.1,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10720426562302683,0.009322110054176247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.5,11375.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Downtown Ogden (min),cbd_distance,D_location,level,minutes,0.0096,0.002,,,***,,,high,Travel time in minutes to downtown Ogden (CBD) via Google Maps API; positive sign as printed,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,0.96,0.2,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10957190790298739,0.022827480813122372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,1896.0,minutes,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0681,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,7.047020941906745,0.21409447007232543,100*(exp(b - se^2/2) - 1),reported,0.12390061646283641,0.003638784624459219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.05,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0629,0.004,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,6.491182254368133,0.4259681367489356,100*(exp(b - se^2/2) - 1),reported,0.10439055308071665,0.006638508939950185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.725,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0796,0.005,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,8.284030391090402,0.5414269197496501,100*(exp(b - se^2/2) - 1),reported,0.1476325821207363,0.009273403399543738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.92,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Air Conditioning,air_conditioning,A_structural,binary,binary,0.0771,0.012,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,8.007231799411985,1.2961801032007558,100*(exp(b - se^2/2) - 1),reported,0.14597445567316694,0.022719759637846997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.425,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Deck,terrace_balcony,C_exterior,binary,binary,0.0233,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,2.3571518415596904,0.2047147131121362,100*(exp(b - se^2/2) - 1),reported,0.04268209057763694,0.0036636987620289217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.65,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Deck,terrace_balcony,C_exterior,binary,binary,0.0174,0.003,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,1.7547682861771774,0.3052656785509942,100*(exp(b - se^2/2) - 1),reported,0.038685894035690144,0.006669981730291404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Deck,terrace_balcony,C_exterior,binary,binary,0.0084,0.005,,,*,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,0.8422773628341762,0.5042176894958977,100*(exp(b - se^2/2) - 1),reported,0.01574996911532522,0.00937498161626501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.68,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Deck,terrace_balcony,C_exterior,binary,binary,0.0211,0.009,,,**,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,2.128281615732752,0.9191917610540755,100*(exp(b - se^2/2) - 1),reported,0.05376407643433173,0.022932544450662822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3444444444444446,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Swimming Pool,pool,C_exterior,binary,binary,-0.1057,0.005,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,-10.03167301626573,0.44984725797425185,100*(exp(b - se^2/2) - 1),reported,-0.07728934766508967,0.003656071318121555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.14,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Swimming Pool,pool,C_exterior,binary,binary,-0.07,0.008,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,-6.763601621890825,0.7459150559247586,100*(exp(b - se^2/2) - 1),reported,-0.05830669649668273,0.006663622456763741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.75,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Swimming Pool,pool,C_exterior,binary,binary,-0.0723,0.012,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,-6.981519730472529,1.1163021340945987,100*(exp(b - se^2/2) - 1),reported,-0.05640134722678249,0.009361219456727384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.025,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Swimming Pool,pool,C_exterior,binary,binary,0.0417,0.042,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,4.166250571637797,4.378842960797328,100*(exp(b - se^2/2) - 1),reported,0.022795794255025488,0.022959792774845816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9928571428571428,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Fireplace,fireplace,B_interior,binary,binary,0.075,0.002,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,7.788199511848548,0.2155768301769263,100*(exp(b - se^2/2) - 1),reported,0.13623197155371328,0.003632852574765688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.5,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Fireplace,fireplace,B_interior,binary,binary,0.0642,0.004,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,6.629710815354017,0.42652225542581085,100*(exp(b - se^2/2) - 1),reported,0.10652383156845875,0.00663699885161737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.049999999999997,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Fireplace,fireplace,B_interior,binary,binary,0.0493,0.006,,,***,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,5.051655464172411,0.6303212784659353,100*(exp(b - se^2/2) - 1),reported,0.07681303941396385,0.009348442930705539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.216666666666667,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Fireplace,fireplace,B_interior,binary,binary,0.0118,0.012,,,,,,high,,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,1.1797042638690192,1.2142438735780814,100*(exp(b - se^2/2) - 1),reported,0.0225772415617691,0.02295990667298553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9833333333333333,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Elevation ('000ft),other,other,level,1000 ft,0.0379,0.007,,,***,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,3.7900000000000005,0.7000000000000001,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.01985049298097823,0.0036663179648244743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.414285714285715,74365.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Elevation ('000ft),other,other,level,1000 ft,0.0545,0.014,,,***,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",semi_elasticity,5.45,1.4000000000000001,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.025975969567293866,0.006672726127378241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.892857142857143,22444.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Elevation ('000ft),other,other,level,1000 ft,0.0565,0.018,,,***,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,5.65,1.7999999999999998,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.029417938532152795,0.00937208661201328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1388888888888893,11375.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Elevation ('000ft),other,other,level,1000 ft,0.0539,0.046,,,,,,high,Elevation of the property in thousands of feet (ArcMap),"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),semi_elasticity,5.390000000000001,4.6,100*b (unit as reported: 1000ft; no harmonised unit defined for this characteristic),reported,0.02690014237115827,0.022957449890042305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1717391304347826,1896.0,1000ft,unit_not_harmonisable,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,View,view,E_environment,binary,binary,0.0615,0.002,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,6.342836966993337,0.21268609930575993,100*(exp(b - se^2/2) - 1),reported,0.11205137092783046,0.0036439470220432668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.75,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,View,view,E_environment,binary,binary,0.0555,0.004,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,5.706056055411235,0.42282760682896914,100*(exp(b - se^2/2) - 1),reported,0.09222065507274665,0.0066465336989366955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.875,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,View,view,E_environment,binary,binary,0.0496,0.005,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,5.083753647664602,0.5254253360139745,100*(exp(b - se^2/2) - 1),reported,0.09261162053756967,0.009335848844513071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.92,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,View,view,E_environment,binary,binary,0.0355,0.011,,,***,,,high,Mountain view mentioned in property description,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,3.6074963696576257,1.1397514129408712,100*(exp(b - se^2/2) - 1),reported,0.07391403507958459,0.022902940447195225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.227272727272727,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Cul-de-sac,other,other,binary,binary,0.0108,0.003,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,1.0853981666944623,0.3032575591560291,100*(exp(b - se^2/2) - 1),reported,0.013200195687018105,0.0036667210241716954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Cul-de-sac,other,other,binary,binary,0.016,0.005,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,1.6115983876912043,0.5080643427030475,100*(exp(b - se^2/2) - 1),reported,0.02135506010916079,0.006673456284112748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Cul-de-sac,other,other,binary,binary,0.0229,0.006,,,***,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,2.314580121925225,0.6138985308056549,100*(exp(b - se^2/2) - 1),reported,0.035762726893014615,0.00937014678419597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8166666666666664,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Cul-de-sac,other,other,binary,binary,0.036,0.014,,,**,,,high,Lot located in a cul-de-sac,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,3.6554259195826333,1.4513181850872932,100*(exp(b - se^2/2) - 1),reported,0.058952105585743604,0.022925818838900294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.571428571428571,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c1,3,1,11,Corner Lot,other,other,binary,binary,0.0159,0.003,,,***,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7343,se,All houses (full sample); neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,1.6022505506466223,0.30480812328540846,100*(exp(b - se^2/2) - 1),reported,0.019431645131362182,0.0036663481379928644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c2,3,2,11,Corner Lot,other,other,binary,binary,0.0239,0.004,,,***,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7431,se,"Houses within 1,000 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE')",pct_premium,2.4179700509735502,0.40967515759204537,100*(exp(b - se^2/2) - 1),reported,0.039851314206950206,0.006669676017899616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9750000000000005,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c3,3,3,11,Corner Lot,other,other,binary,binary,0.0186,0.006,,,***,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7419,se,Houses within 500 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,1.875571971362855,0.6112644344889736,100*(exp(b - se^2/2) - 1),reported,0.029053778082523504,0.00937218647823339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0999999999999996,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T3-c4,3,4,11,Corner Lot,other,other,binary,binary,-0.0136,0.014,,,,,,high,Corner lot indicator,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; neighbourhood (Neighborhood Scout) FE + month/year FE; SE clustered by neighbourhood (table footer still prints 'Boundary FE'),pct_premium,-1.3604609306223425,1.381088887050446,100*(exp(b - se^2/2) - 1),reported,-0.022304046146940783,0.02296004750420375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9714285714285713,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T4-c1,4,1,12,Secondary Water,other,other,binary,binary,0.0363,0.003,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,53027.0,0.743,se,All houses (full sample); non-summer month sales only (June-August excluded); full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,3.696222355854406,0.3110900669697148,100*(exp(b - se^2/2) - 1),reported,0.052478203040877545,0.0043370415736262435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.1,53017.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T4-c2,4,2,12,Secondary Water,other,other,binary,binary,0.0404,0.004,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,16041.0,0.7491,se,"Houses within 1,000 ft of the secondary-water boundary; non-summer month sales only (June-August excluded); full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE",pct_premium,4.121885199389519,0.41649087271121216,100*(exp(b - se^2/2) - 1),reported,0.07951767480751153,0.007873037109654606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.1,16031.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T4-c3,4,3,12,Secondary Water,other,other,binary,binary,0.0343,0.006,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,8103.0,0.7489,se,Houses within 500 ft of the secondary-water boundary; non-summer month sales only (June-August excluded); full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,3.4876400728235613,0.6209370172026598,100*(exp(b - se^2/2) - 1),reported,0.06341806760437015,0.011093539522630346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.716666666666666,8093.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T4-c4,4,4,12,Secondary Water,other,other,binary,binary,0.022,0.011,,,*,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,1379.0,0.7985,se,Houses within 100 ft of the secondary-water boundary; non-summer month sales only (June-August excluded); full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,2.218194059227385,1.124468162917482,100*(exp(b - se^2/2) - 1),reported,0.05397525801500045,0.026987629007500225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1369.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c1,5,1,13,Secondary Water,other,other,binary,binary,0.0371,0.003,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7406,se,All houses (full sample); adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,3.7792125253807596,0.3113390385986637,100*(exp(b - se^2/2) - 1),reported,0.04530250852710755,0.0036632756221380764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.366666666666667,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c2,5,2,13,Secondary Water,other,other,binary,binary,0.0403,0.004,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7483,se,"Houses within 1,000 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE",pct_premium,4.11147353146164,0.41644922570632587,100*(exp(b - se^2/2) - 1),reported,0.06709884816133249,0.006659935301372953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.075000000000001,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c3,5,3,13,Secondary Water,other,other,binary,binary,0.0348,0.006,,,***,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7441,se,Houses within 500 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,3.539396830971242,0.6212475633413261,100*(exp(b - se^2/2) - 1),reported,0.054301403595138395,0.009362310964679035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c4,5,4,13,Secondary Water,other,other,binary,binary,0.021,0.01,,,**,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7688,se,Houses within 100 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,2.11709918114531,1.0212220516375285,100*(exp(b - se^2/2) - 1),reported,0.04817210687638552,0.02293909851256453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c1,5,1,13,Summer,transaction_timing,G_market,binary,binary,0.0199,0.004,,,***,,,high,Sale occurred in June-August,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7406,se,All houses (full sample); adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,2.009116423163304,0.408039729997436,100*(exp(b - se^2/2) - 1),reported,0.01824049138290121,0.0036664304287238603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9750000000000005,74365.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c2,5,2,13,Summer,transaction_timing,G_market,binary,binary,0.0257,0.006,,,***,,,high,Sale occurred in June-August,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7483,se,"Houses within 1,000 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE",pct_premium,2.6014623940178394,0.6156198554217748,100*(exp(b - se^2/2) - 1),reported,0.028579479006553016,0.006672251908144674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.283333333333333,22444.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c3,5,3,13,Summer,transaction_timing,G_market,binary,binary,0.0352,0.008,,,***,,,high,Sale occurred in June-August,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7441,se,Houses within 500 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,3.579370752618538,0.8286614827641267,100*(exp(b - se^2/2) - 1),reported,0.04121997349081335,0.00936817579336667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.4,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c4,5,4,13,Summer,transaction_timing,G_market,binary,binary,0.0699,0.02,,,***,,,high,Sale occurred in June-August,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7688,se,Houses within 100 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,7.218647705788239,2.1448018715969064,100*(exp(b - se^2/2) - 1),reported,0.08000802031355601,0.022892137428771393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.495,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c1,5,1,13,Secondary*Summer,other,other,other,binary x binary,-0.0013,0.005,,,,,,high,Interaction secondary water access x summer sale,"Natural log of the real house price (sold price, $)",log,OLS,74375.0,0.7406,se,All houses (full sample); adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,-0.0013,0.005,not harmonised,reported,-0.0009534301018863317,0.0036670388534089677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26,74365.0,binary x binary,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c2,5,2,13,Secondary*Summer,other,other,other,binary x binary,-0.003,0.007,,,,,,high,Interaction secondary water access x summer sale,"Natural log of the real house price (sold price, $)",log,OLS,22454.0,0.7483,se,"Houses within 1,000 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE",other,-0.003,0.007,not harmonised,reported,-0.0028606933581222073,0.006674951168951817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42857142857142855,22444.0,binary x binary,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c3,5,3,13,Secondary*Summer,other,other,other,binary x binary,-0.0136,0.01,,,,,,high,Interaction secondary water access x summer sale,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7441,se,Houses within 500 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,-0.0136,0.01,not harmonised,reported,-0.01275052009236821,0.00937538242085898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3599999999999999,11375.0,binary x binary,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T5-c4,5,4,13,Secondary*Summer,other,other,other,binary x binary,-0.0226,0.025,,,,,,high,Interaction secondary water access x summer sale,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7688,se,Houses within 100 ft of the secondary-water boundary; adds Summer dummy and Secondary*Summer interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,-0.0226,0.025,not harmonised,reported,-0.020756574811383596,0.02296081284445088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9039999999999999,1896.0,binary x binary,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c1,6,1,14,Secondary Water,other,other,binary,binary,0.0275,0.012,,,*,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design; main effect in interacted specification,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; adds Secondary*Acres interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,2.7807610295170093,1.2334579381287034,100*(exp(b - se^2/2) - 1),reported,0.021482039623825156,0.00937398092676007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2916666666666665,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c2,6,2,14,Secondary Water,other,other,binary,binary,0.0223,0.011,,,**,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design; main effect in interacted specification,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7734,se,Houses within 100 ft of the secondary-water boundary; adds Secondary*Acres interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,2.2488641177239144,1.124805553972485,100*(exp(b - se^2/2) - 1),reported,0.04650748182146454,0.022940910315520625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0272727272727273,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c3,6,3,14,Secondary Water,other,other,binary,binary,0.075,0.016,,,**,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design; main effect in interacted specification,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7466,se,Houses within 500 ft of the secondary-water boundary; adds Secondary*Sqft interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,7.774619054296861,1.7246146414154104,100*(exp(b - se^2/2) - 1),reported,0.04390829037027802,0.00936710194565931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6875,11375.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c4,6,4,14,Secondary Water,other,other,binary,binary,0.0756,0.032,,,*,,,high,Access to unmetered secondary (irrigation) water right; binary treatment variable of a spatial RDD design; main effect in interacted specification,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; adds Secondary*Sqft interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,pct_premium,7.797900886359921,3.4512994413859817,100*(exp(b - se^2/2) - 1),reported,0.054176925571990046,0.02293203198814394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3625,1896.0,binary,,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c1,6,1,14,Secondary*Acres,other,other,other,binary x acres,0.0115,0.047,,,,,,high,Interaction secondary water x lot acreage,"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7462,se,Houses within 500 ft of the secondary-water boundary; adds Secondary*Acres interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,0.0115,0.047,not harmonised,reported,0.002294157007740796,0.009376119944679776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24468085106382978,11375.0,binary x acres,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c2,6,2,14,Secondary*Acres,other,other,other,binary x acres,-0.0314,0.065,,,,,,high,Interaction secondary water x lot acreage,"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7734,se,Houses within 100 ft of the secondary-water boundary; adds Secondary*Acres interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,-0.0314,0.065,not harmonised,reported,-0.011093546284524525,0.022964347404270516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.483076923076923,1896.0,binary x acres,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c3,6,3,14,Secondary*Sqft,other,other,other,binary x 1000 sqft,-0.0231,0.008,,,**,,,high,Interaction secondary water x house size ('000 sq ft),"Natural log of the real house price (sold price, $)",log,OLS,11385.0,0.7466,se,Houses within 500 ft of the secondary-water boundary; adds Secondary*Sqft interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,-0.0231,0.008,not harmonised,reported,-0.027063700817443824,0.009372710239807385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8874999999999997,11375.0,binary x 1000 sqft,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5,38bf59597966e99f31288b3b3bfbe1d0b22ac0b5:T6-c4,6,4,14,Secondary*Sqft,other,other,other,binary x 1000 sqft,-0.0327,0.015,,,*,,,high,Interaction secondary water x house size ('000 sq ft),"Natural log of the real house price (sold price, $)",log,OLS,1906.0,0.7741,se,Houses within 100 ft of the secondary-water boundary; adds Secondary*Sqft interaction; full Table 2 controls included but not printed; boundary FE + month/year FE; clustered SE,other,-0.0327,0.015,not harmonised,reported,-0.050002730423118195,0.022937032304182654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.18,1896.0,binary x 1000 sqft,nonstandard_x_transform,2024.0,United States,North America,High income,1999.0,2020.0,single_family,transaction,journal,True,True,clustered,,2009.5,ols,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Bathrooms,bathrooms,A_structural,level,count,0.324,0.036,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.324,0.036,not harmonised,reported,0.11849287583000466,0.013165875092222739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.000000000000002,5688.0,count,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Bathrooms,bathrooms,A_structural,level,count,0.323,0.036,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.323,0.036,not harmonised,reported,0.11813226855766155,0.013166444792804382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.972222222222223,5688.0,count,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Bathrooms,bathrooms,A_structural,level,count,0.583,0.131,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.583,0.131,not harmonised,reported,0.12598642200793256,0.02830912741516152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.450381679389313,1228.0,count,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Bathrooms,bathrooms,A_structural,level,count,0.584,0.132,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.584,0.132,not harmonised,reported,0.1252580862328329,0.028311759217010183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.424242424242424,1228.0,count,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Bathrooms,bathrooms,A_structural,level,count,1.08,0.167,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.08,0.167,not harmonised,reported,0.15797676588718526,0.024427888799222162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.467065868263473,1634.0,count,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Bathrooms,bathrooms,A_structural,level,count,1.11,0.171,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.11,0.171,not harmonised,reported,0.15855218668077753,0.02442560713730897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.491228070175438,1634.0,count,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,"Living Space (1,000s square feet)",living_area,A_structural,level,1000 sqft,1.092,0.033,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.092,0.033,not harmonised,reported,0.4017885214462972,0.01214196081293755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.09090909090909,5688.0,1000sqft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,"Living Space (1,000s square feet)",living_area,A_structural,level,1000 sqft,1.091,0.033,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.091,0.033,not harmonised,reported,0.4014799125673192,0.012143755375546777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.060606060606055,5688.0,1000sqft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,"Living Space (1,000s square feet)",living_area,A_structural,level,1000 sqft,1.247,0.139,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.247,0.139,not harmonised,reported,0.24800911183480578,0.027644961142773056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.971223021582734,1228.0,1000sqft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,"Living Space (1,000s square feet)",living_area,A_structural,level,1000 sqft,1.249,0.14,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.249,0.14,not harmonised,reported,0.24671655597757267,0.02765437777170551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.92142857142857,1228.0,1000sqft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,"Living Space (1,000s square feet)",living_area,A_structural,level,1000 sqft,1.338,0.221,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.338,0.221,not harmonised,reported,0.14812232255677027,0.02446564520556519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.054298642533937,1634.0,1000sqft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,"Living Space (1,000s square feet)",living_area,A_structural,level,1000 sqft,1.371,0.226,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.371,0.226,not harmonised,reported,0.1484111976857198,0.024464573797937764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.066371681415929,1634.0,1000sqft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Lot size (100s acres),lot_size,A_structural,level,100 acres,3.137,0.58,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,3.137,0.58,not harmonised,reported,0.07153075563501253,0.01322532300551714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.408620689655173,5688.0,100 acres,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Lot size (100s acres),lot_size,A_structural,level,100 acres,3.133,0.579,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,3.133,0.579,not harmonised,reported,0.07156276613448088,0.013225292560441885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.411053540587219,5688.0,100 acres,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Lot size (100s acres),lot_size,A_structural,level,100 acres,5.604,0.686,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,5.604,0.686,not harmonised,reported,0.22703024206873307,0.027791353686500878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.169096209912535,1228.0,100 acres,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Lot size (100s acres),lot_size,A_structural,level,100 acres,5.609,0.687,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,5.609,0.687,not harmonised,reported,0.22690864443743278,0.027792162369141803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.164483260553128,1228.0,100 acres,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Lot size (100s acres),lot_size,A_structural,level,100 acres,11.074,1.563,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,11.074,1.563,not harmonised,reported,0.17264296972800608,0.024367072574035892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0850927703135,1634.0,100 acres,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Lot size (100s acres),lot_size,A_structural,level,100 acres,11.376,1.602,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,11.376,1.602,not harmonised,reported,0.17302191285823892,0.024365427601872255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.101123595505618,1634.0,100 acres,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Age (50 years),age,A_structural,level,50 years,-0.218,0.023,,,***,,,high,"Age measured in units of 50 years; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.218,0.023,not harmonised,reported,-0.12469412688483097,0.013155802377757398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.478260869565217,5688.0,50 years,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Age (50 years),age,A_structural,level,50 years,-0.218,0.023,,,***,,,high,"Age measured in units of 50 years; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.218,0.023,not harmonised,reported,-0.12469412688483097,0.013155802377757398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.478260869565217,5688.0,50 years,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Age (50 years),age,A_structural,level,50 years,-0.27,0.051,,,***,,,high,"Age measured in units of 50 years; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.27,0.051,not harmonised,reported,-0.14938052539505564,0.028216321463510507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.294117647058824,1228.0,50 years,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Age (50 years),age,A_structural,level,50 years,-0.269,0.051,,,***,,,high,"Age measured in units of 50 years; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.269,0.051,not harmonised,reported,-0.14883954297640226,0.02821864941188295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.274509803921569,1228.0,50 years,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Age (50 years),age,A_structural,level,50 years,-0.589,0.076,,,***,,,high,"Age measured in units of 50 years; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.589,0.076,not harmonised,reported,-0.18829421964721826,0.024296028341576553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,1634.0,50 years,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Age (50 years),age,A_structural,level,50 years,-0.601,0.078,,,***,,,high,"Age measured in units of 50 years; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.601,0.078,not harmonised,reported,-0.18724234292338734,0.024301002908526145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.705128205128205,1634.0,50 years,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Age squared,age,A_structural,squared,(50 years) squared,0.005,0.001,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.005,0.001,not harmonised,reported,0.06615122520811101,0.0132302450416222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,5688.0,sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Age squared,age,A_structural,squared,(50 years) squared,0.005,0.001,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.005,0.001,not harmonised,reported,0.06615122520811101,0.0132302450416222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,5688.0,sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Age squared,age,A_structural,squared,(50 years) squared,0.004,0.001,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.004,0.001,not harmonised,reported,0.11340959542474854,0.028352398856187136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,1228.0,sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Age squared,age,A_structural,squared,(50 years) squared,0.004,0.001,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.004,0.001,not harmonised,reported,0.11340959542474854,0.028352398856187136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,1228.0,sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Age squared,age,A_structural,squared,(50 years) squared,0.012,0.002,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.012,0.002,not harmonised,reported,0.14682263526971892,0.02447043921161982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,1634.0,sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Age squared,age,A_structural,squared,(50 years) squared,0.012,0.002,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.012,0.002,not harmonised,reported,0.14682263526971892,0.02447043921161982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,1634.0,sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Cabin (0/1),dwelling_type,A_structural,binary,binary,-0.317,0.25,,,,,,high,"Cabin dwelling indicator; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.317,0.25,not harmonised,reported,-0.016810401546392986,0.013257414468764184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.268,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Cabin (0/1),dwelling_type,A_structural,binary,binary,-0.311,0.249,,,,,,high,"Cabin dwelling indicator; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.311,0.249,not harmonised,reported,-0.016558527033499075,0.01325747019723881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.248995983935743,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Cabin (0/1),dwelling_type,A_structural,binary,binary,0.717,0.368,,,*,,,high,"Cabin dwelling indicator; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.717,0.368,not harmonised,reported,0.055513922768906,0.02849250150482205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9483695652173914,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Cabin (0/1),dwelling_type,A_structural,binary,binary,0.714,0.369,,,*,,,high,"Cabin dwelling indicator; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.714,0.369,not harmonised,reported,0.05513299761302586,0.028493103808412527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9349593495934958,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Cabin (0/1),dwelling_type,A_structural,binary,binary,1.685,0.783,,,**,,,high,"Cabin dwelling indicator; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.685,0.783,not harmonised,reported,0.05316154054468641,0.024703552668539738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.151979565772669,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Cabin (0/1),dwelling_type,A_structural,binary,binary,1.744,0.81,,,**,,,high,"Cabin dwelling indicator; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.744,0.81,not harmonised,reported,0.053188806434786524,0.024703516750101545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.153086419753086,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Distance to water (miles),water_proximity,E_environment,level,miles,-0.171,0.041,,,***,,,high,"Minimum distance from home to coastline; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.171,0.041,not harmonised,reported,-0.0552165661578953,0.013239059722068467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.170731707317073,5688.0,miles,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Distance to water (miles),water_proximity,E_environment,level,miles,-0.171,0.041,,,***,,,high,"Minimum distance from home to coastline; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.171,0.041,not harmonised,reported,-0.0552165661578953,0.013239059722068467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.170731707317073,5688.0,miles,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Distance to water (miles),water_proximity,E_environment,level,miles,-0.237,0.172,,,,,,high,"Minimum distance from home to coastline; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.237,0.172,not harmonised,reported,-0.039290290571300894,0.02851447248212554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.377906976744186,1228.0,miles,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Distance to water (miles),water_proximity,E_environment,level,miles,-0.244,0.173,,,,,,high,"Minimum distance from home to coastline; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.244,0.173,not harmonised,reported,-0.04021546242495659,0.028513422129170032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4104046242774568,1228.0,miles,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Distance to water (miles),water_proximity,E_environment,level,miles,-0.396,0.252,,,,,,high,"Minimum distance from home to coastline; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.396,0.252,not harmonised,reported,-0.038845498788608476,0.024719862865478122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5714285714285714,1634.0,miles,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Distance to water (miles),water_proximity,E_environment,level,miles,-0.301,0.259,,,,,,high,"Minimum distance from home to coastline; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.301,0.259,not harmonised,reported,-0.028738314382217387,0.02472831702655915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.162162162162162,1634.0,miles,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Distance sq to water (miles),water_proximity,E_environment,squared,miles squared,0.018,0.006,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.018,0.006,not harmonised,reported,0.03974643167585821,0.013248810558619404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,5688.0,miles sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Distance sq to water (miles),water_proximity,E_environment,squared,miles squared,0.018,0.006,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.018,0.006,not harmonised,reported,0.03974643167585821,0.013248810558619404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,5688.0,miles sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Distance sq to water (miles),water_proximity,E_environment,squared,miles squared,0.067,0.031,,,**,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.067,0.031,not harmonised,reported,0.06155870689910274,0.02848238677421171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1612903225806455,1228.0,miles sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Distance sq to water (miles),water_proximity,E_environment,squared,miles squared,0.068,0.031,,,**,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.068,0.031,not harmonised,reported,0.0624739338182055,0.028480763946534857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1935483870967745,1228.0,miles sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Distance sq to water (miles),water_proximity,E_environment,squared,miles squared,0.112,0.043,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.112,0.043,not harmonised,reported,0.06430190410348209,0.02468733818258687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.604651162790698,1634.0,miles sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Distance sq to water (miles),water_proximity,E_environment,squared,miles squared,0.101,0.044,,,**,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.101,0.044,not harmonised,reported,0.0566948446320633,0.02469874419614638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.295454545454546,1634.0,miles sqd,nonstandard_x_transform,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,View of water (% 2-mile buffer),view,E_environment,level,share (% water within 2-mile buffer),2.282,0.141,,,***,,,high,"Percentage of water within a 2-mile buffer around the home, proxy for water view; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,2.282,0.141,not harmonised,reported,0.20981688123953376,0.012964145598060585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.18439716312057,5688.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,View of water (% 2-mile buffer),view,E_environment,level,share (% water within 2-mile buffer),2.289,0.142,,,***,,,high,"Percentage of water within a 2-mile buffer around the home, proxy for water view; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,2.289,0.142,not harmonised,reported,0.2090150775056003,0.012966422457752399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.119718309859156,5688.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,View of water (% 2-mile buffer),view,E_environment,level,share (% water within 2-mile buffer),3.396,0.473,,,***,,,high,"Percentage of water within a 2-mile buffer around the home, proxy for water view; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,3.396,0.473,not harmonised,reported,0.20071425799744674,0.027955784461952973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.17970401691332,1228.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,View of water (% 2-mile buffer),view,E_environment,level,share (% water within 2-mile buffer),3.397,0.474,,,***,,,high,"Percentage of water within a 2-mile buffer around the home, proxy for water view; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,3.397,0.474,not harmonised,reported,0.20036443337019313,0.02795782791211997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666667,1228.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,View of water (% 2-mile buffer),view,E_environment,level,share (% water within 2-mile buffer),5.458,0.751,,,***,,,high,"Percentage of water within a 2-mile buffer around the home, proxy for water view; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,5.458,0.751,not harmonised,reported,0.17695359857478052,0.024348140807926015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.267643142476698,1634.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,View of water (% 2-mile buffer),view,E_environment,level,share (% water within 2-mile buffer),6.14,0.789,,,***,,,high,"Percentage of water within a 2-mile buffer around the home, proxy for water view; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,6.14,0.789,not harmonised,reported,0.18904402199348588,0.024292464715449574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.782002534854245,1634.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Waterfront (0/1),waterfront,C_exterior,binary,binary,1.333,0.101,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.333,0.101,not harmonised,reported,0.1723768398812772,0.013060810823712677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.198019801980196,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Waterfront (0/1),waterfront,C_exterior,binary,binary,1.333,0.101,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.333,0.101,not harmonised,reported,0.1723768398812772,0.013060810823712677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.198019801980196,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Waterfront (0/1),waterfront,C_exterior,binary,binary,1.545,0.172,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.545,0.172,not harmonised,reported,0.24830316219584303,0.02764281158426214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.982558139534884,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Waterfront (0/1),waterfront,C_exterior,binary,binary,1.549,0.172,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,1.549,0.172,not harmonised,reported,0.24890623881600438,0.027638394497322628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.005813953488373,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Waterfront (0/1),waterfront,C_exterior,binary,binary,2.848,0.297,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,2.848,0.297,not harmonised,reported,0.23081765365908027,0.024070520764307178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.58922558922559,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Waterfront (0/1),waterfront,C_exterior,binary,binary,2.941,0.304,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,2.941,0.304,not harmonised,reported,0.2327559202357456,0.024059095461294342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.674342105263158,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,16,Govt. Beach/Dock (0/1),green_space,E_environment,binary,binary,0.221,0.121,,,*,,,high,"Home within 1,000 ft of a government-managed or conserved beach, dock, park or land; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.221,0.121,not harmonised,reported,0.024210278985166523,0.013255401616312891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8264462809917357,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,16,Govt. Beach/Dock (0/1),green_space,E_environment,binary,binary,0.219,0.121,,,*,,,high,"Home within 1,000 ft of a government-managed or conserved beach, dock, park or land; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.219,0.121,not harmonised,reported,0.02399130812208834,0.013255471610834197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8099173553719008,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,16,Govt. Beach/Dock (0/1),green_space,E_environment,binary,binary,0.118,0.269,,,,,,high,"Home within 1,000 ft of a government-managed or conserved beach, dock, park or land; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.118,0.269,not harmonised,reported,0.012516892438265747,0.02853427174486005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4386617100371747,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,16,Govt. Beach/Dock (0/1),green_space,E_environment,binary,binary,0.115,0.27,,,,,,high,"Home within 1,000 ft of a government-managed or conserved beach, dock, park or land; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.115,0.27,not harmonised,reported,0.012153540594177448,0.028534399655894875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42592592592592593,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,16,Govt. Beach/Dock (0/1),green_space,E_environment,binary,binary,0.865,0.309,,,***,,,high,"Home within 1,000 ft of a government-managed or conserved beach, dock, park or land; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.865,0.309,not harmonised,reported,0.06908642044655591,0.024679426494781248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7993527508090614,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,16,Govt. Beach/Dock (0/1),green_space,E_environment,binary,binary,0.886,0.316,,,***,,,high,"Home within 1,000 ft of a government-managed or conserved beach, dock, park or land; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.886,0.316,not harmonised,reported,0.06919558872711143,0.02467923932027902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8037974683544302,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,Elevation (100s feet),other,other,level,100 ft,-0.101,0.027,,,***,,,high,"Elevation of the home (proxy for view); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.101,0.027,not harmonised,reported,-0.04953866107337386,0.013243008405753405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.740740740740741,5688.0,100ft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,Elevation (100s feet),other,other,level,100 ft,-0.101,0.027,,,***,,,high,"Elevation of the home (proxy for view); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.101,0.027,not harmonised,reported,-0.04953866107337386,0.013243008405753405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.740740740740741,5688.0,100ft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,Elevation (100s feet),other,other,level,100 ft,-0.328,0.122,,,***,,,high,"Elevation of the home (proxy for view); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.328,0.122,not harmonised,reported,-0.07649629837094012,0.02845289146723992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6885245901639347,1228.0,100ft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,Elevation (100s feet),other,other,level,100 ft,-0.329,0.122,,,***,,,high,"Elevation of the home (proxy for view); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.329,0.122,not harmonised,reported,-0.07672814784991751,0.028452383093282477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.69672131147541,1228.0,100ft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,Elevation (100s feet),other,other,level,100 ft,0.029,0.081,,,,,,high,"Elevation of the home (proxy for view); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.029,0.081,not harmonised,reported,0.008856658905934355,0.024737564530368374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35802469135802467,1634.0,100ft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,Elevation (100s feet),other,other,level,100 ft,0.038,0.083,,,,,,high,"Elevation of the home (proxy for view); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.038,0.083,not harmonised,reported,0.011325349788736783,0.024736948222767185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4578313253012048,1634.0,100ft,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,Seasonal homes (% census tract),other,other,level,share (%),-0.007,0.003,,,**,,,high,"Proportion of seasonal housing units in census tract (ACS); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.007,0.003,not harmonised,reported,-0.030923542658064997,0.013252946853456425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3333333333333335,5688.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,Seasonal homes (% census tract),other,other,level,share (%),-0.007,0.003,,,**,,,high,"Proportion of seasonal housing units in census tract (ACS); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.007,0.003,not harmonised,reported,-0.030923542658064997,0.013252946853456425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3333333333333335,5688.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,Seasonal homes (% census tract),other,other,level,share (%),-0.024,0.057,,,,,,high,"Proportion of seasonal housing units in census tract (ACS); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.024,0.057,not harmonised,reported,-0.012014504254358955,0.02853444760410252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42105263157894735,1228.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,Seasonal homes (% census tract),other,other,level,share (%),-0.025,0.057,,,,,,high,"Proportion of seasonal housing units in census tract (ACS); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.025,0.057,not harmonised,reported,-0.012515031758542067,0.028534272409475907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4385964912280702,1228.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,Seasonal homes (% census tract),other,other,level,share (%),-0.068,0.063,,,,,,high,"Proportion of seasonal housing units in census tract (ACS); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.068,0.063,not harmonised,reported,-0.026692396540102616,0.024729720323918597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0793650793650795,1634.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,Seasonal homes (% census tract),other,other,level,share (%),-0.087,0.067,,,,,,high,"Proportion of seasonal housing units in census tract (ACS); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.087,0.067,not harmonised,reported,-0.03210661094792175,0.024725780844951237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.298507462686567,1634.0,share,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,"Median income ($10,000s)",income,F_neighbourhood,level,10000 USD,0.004,0.015,,,,,,high,"Median household income in the home's census tract (ACS 2012-2014); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.004,0.015,not harmonised,reported,0.003535788049832444,0.013259205186871665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26666666666666666,5688.0,10000 usd,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,"Median income ($10,000s)",income,F_neighbourhood,level,10000 USD,0.005,0.015,,,,,,high,"Median household income in the home's census tract (ACS 2012-2014); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.005,0.015,not harmonised,reported,0.004419719522007512,0.013259158566022534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33333333333333337,5688.0,10000 usd,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,"Median income ($10,000s)",income,F_neighbourhood,level,10000 USD,-1.245,0.45,,,***,,,high,"Median household income in the home's census tract (ACS 2012-2014); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-1.245,0.45,not harmonised,reported,-0.0787060867418512,0.02844798315970524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.766666666666667,1228.0,10000 usd,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,"Median income ($10,000s)",income,F_neighbourhood,level,10000 USD,-1.241,0.451,,,***,,,high,"Median household income in the home's census tract (ACS 2012-2014); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-1.241,0.451,not harmonised,reported,-0.07828188455781314,0.028448936289745146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7516629711751666,1228.0,10000 usd,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,"Median income ($10,000s)",income,F_neighbourhood,level,10000 USD,0.613,0.343,,,*,,,high,"Median household income in the home's census tract (ACS 2012-2014); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.613,0.343,not harmonised,reported,0.04416886957972503,0.024714391950808625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7871720116618075,1634.0,10000 usd,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,"Median income ($10,000s)",income,F_neighbourhood,level,10000 USD,0.639,0.352,,,*,,,high,"Median household income in the home's census tract (ACS 2012-2014); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.639,0.352,not harmonised,reported,0.044863656372622525,0.0247136260456387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8153409090909092,1634.0,10000 usd,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,"Spending per student ($1,000s)",school_quality,F_neighbourhood,level,1000 USD per student,0.048,0.032,,,,,,high,"School district expenditure per student 2014-2015 (proxy for school quality); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.048,0.032,not harmonised,reported,0.019884999542396455,0.013256666361597636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,5688.0,1000 usd per student,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,"Spending per student ($1,000s)",school_quality,F_neighbourhood,level,1000 USD per student,0.049,0.032,,,,,,high,"School district expenditure per student 2014-2015 (proxy for school quality); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.049,0.032,not harmonised,reported,0.020299101405597507,0.013256556019982043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.53125,5688.0,1000 usd per student,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,"Spending per student ($1,000s)",school_quality,F_neighbourhood,level,1000 USD per student,0.043,0.049,,,,,,high,"School district expenditure per student 2014-2015 (proxy for school quality); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.043,0.049,not harmonised,reported,0.025034392602874298,0.028527563663740485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8775510204081631,1228.0,1000 usd per student,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,"Spending per student ($1,000s)",school_quality,F_neighbourhood,level,1000 USD per student,0.042,0.049,,,,,,high,"School district expenditure per student 2014-2015 (proxy for school quality); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.042,0.049,not harmonised,reported,0.024452549678020203,0.028527974624356897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8571428571428572,1228.0,1000 usd per student,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,"Spending per student ($1,000s)",school_quality,F_neighbourhood,level,1000 USD per student,0.141,0.073,,,*,,,high,"School district expenditure per student 2014-2015 (proxy for school quality); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.141,0.073,not harmonised,reported,0.04772819440544545,0.024710341784379558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9315068493150684,1634.0,1000 usd per student,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,"Spending per student ($1,000s)",school_quality,F_neighbourhood,level,1000 USD per student,0.136,0.074,,,*,,,high,"School district expenditure per student 2014-2015 (proxy for school quality); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.136,0.074,not harmonised,reported,0.04541849716299745,0.024713005809278023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8378378378378382,1634.0,1000 usd per student,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,Sale in winter (0/1),transaction_timing,G_market,binary,binary,-0.144,0.041,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.144,0.041,not harmonised,reported,-0.046518791754297004,0.013244933763376232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.512195121951219,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,Sale in winter (0/1),transaction_timing,G_market,binary,binary,-0.144,0.041,,,***,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.144,0.041,not harmonised,reported,-0.046518791754297004,0.013244933763376232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.512195121951219,5688.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,Sale in winter (0/1),transaction_timing,G_market,binary,binary,-0.065,0.149,,,,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.065,0.149,not harmonised,reported,-0.012447847397012346,0.028534296340843684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43624161073825507,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,Sale in winter (0/1),transaction_timing,G_market,binary,binary,-0.067,0.149,,,,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.067,0.149,not harmonised,reported,-0.012830795972325804,0.0285341582071126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4496644295302014,1228.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,Sale in winter (0/1),transaction_timing,G_market,binary,binary,-0.054,0.195,,,,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.054,0.195,not harmonised,reported,-0.006850510424170703,0.024737954309505317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2769230769230769,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,Sale in winter (0/1),transaction_timing,G_market,binary,binary,-0.06,0.201,,,,,,high,"coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.06,0.201,not harmonised,reported,-0.007384435909174019,0.024737860295732966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.29850746268656714,1634.0,binary,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,True,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,Neighborhood attitudes,other,other,level,index (time-weighted mean number of concerns at lease hearings),-0.005,0.058,,,,,,high,"House-specific time-weighted average number of concerns raised at public aquaculture lease hearings for leases within 2 miles; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.005,0.058,not harmonised,reported,-0.0011430413282478014,0.013259279407674495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08620689655172413,5688.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,Neighborhood attitudes,other,other,level,index (time-weighted mean number of concerns at lease hearings),0.012,0.055,,,,,,high,"House-specific time-weighted average number of concerns raised at public aquaculture lease hearings for leases within 2 miles; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.012,0.055,not harmonised,reported,0.002892923473297579,0.013259232585947236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2181818181818182,5688.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,Neighborhood attitudes,other,other,level,index (time-weighted mean number of concerns at lease hearings),-0.778,0.512,,,,,,high,"House-specific time-weighted average number of concerns raised at public aquaculture lease hearings for leases within 2 miles; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.778,0.512,not harmonised,reported,-0.04332140566560904,0.02850971683906405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.51953125,1228.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,Neighborhood attitudes,other,other,level,index (time-weighted mean number of concerns at lease hearings),-0.77,0.512,,,,,,high,"House-specific time-weighted average number of concerns raised at public aquaculture lease hearings for leases within 2 miles; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.77,0.512,not harmonised,reported,-0.042876764535538604,0.028510264210643853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.50390625,1228.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,Neighborhood attitudes,other,other,level,index (time-weighted mean number of concerns at lease hearings),0.0,0.335,,,,,,high,"House-specific time-weighted average number of concerns raised at public aquaculture lease hearings for leases within 2 miles; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.0,0.335,not harmonised,reported,0.0,0.02473853479976467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,1634.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,Neighborhood attitudes,other,other,level,index (time-weighted mean number of concerns at lease hearings),-0.061,0.336,,,,,,high,"House-specific time-weighted average number of concerns raised at public aquaculture lease hearings for leases within 2 miles; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.061,0.336,not harmonised,reported,-0.004491176795918396,0.024738285302107886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18154761904761904,1634.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c1,3,1,17,Aquaculture index (base),other,other,level,index (sum over leases within 2 miles of acres/distance),-0.885,2.922,,,,,,high,"Marine aquaculture index Q = sum_k a_ik/d_ik over active leases within 2-mile buffer (environmental (dis)amenity, coastal mariculture); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-0.885,2.922,not harmonised,reported,-0.004015871087959568,0.01325918115143261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.30287474332648867,5688.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c3,3,3,17,Aquaculture index (base),other,other,level,index (sum over leases within 2 miles of acres/distance),0.694,0.215,,,***,,,high,"Marine aquaculture index Q = sum_k a_ik/d_ik over active leases within 2-mile buffer (environmental (dis)amenity, coastal mariculture); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,0.694,0.215,not harmonised,reported,0.09172487743327622,0.028416208426735434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.227906976744186,1228.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c5,3,5,17,Aquaculture index (base),other,other,level,index (sum over leases within 2 miles of acres/distance),-23.392,10.882,,,**,,,high,"Marine aquaculture index Q = sum_k a_ik/d_ik over active leases within 2-mile buffer (environmental (dis)amenity, coastal mariculture); coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (base form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-23.392,10.882,not harmonised,reported,-0.05310304219844106,0.024703629668409527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1496048520492557,1634.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c2,3,2,17,Aquaculture index (alternate),other,other,level,index (base index x share of water in buffer),-7.894,9.837,,,,,,high,"Alternate aquaculture index scaled by portion of water within 2-mile buffer; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,5698.0,0.733,se,"Casco Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-7.894,9.837,not harmonised,reported,-0.010639716927044249,0.013258537548940241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8024804310257193,5688.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c4,3,4,17,Aquaculture index (alternate),other,other,level,index (base index x share of water in buffer),4.342,1.367,,,***,,,high,"Alternate aquaculture index scaled by portion of water within 2-mile buffer; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1238.0,0.598,se,"Damariscotta River region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,4.342,1.367,not harmonised,reported,0.09027040573782266,0.028420001069461902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1762984637893195,1228.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29,38d51e7656f94829f9f43fba3ef936f46b613e29:T3-c6,3,6,17,Aquaculture index (alternate),other,other,level,index (base index x share of water in buffer),-57.609,24.721,,,**,,,high,"Alternate aquaculture index scaled by portion of water within 2-mile buffer; coefficient on Box-Cox transformed price (lambda ~0.10-0.14), not directly a log or level effect","Box-Cox transformed sales price (lambda estimated, 0.099-0.143)",boxcox,other,1644.0,0.59,se,"Penobscot Bay region, single-family detached homes 2012-2014; Aquaculture index (alternate form); municipality and sale-year fixed effects; Box-Cox ML with robust SE; adjusted R2",other,-57.609,24.721,not harmonised,reported,-0.05755430080034025,0.024697527644729315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.330366894543101,1634.0,index,,2017.0,United States,North America,High income,2012.0,2014.0,single_family,transaction,journal,True,True,robust,,2013.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Bedroom,bedrooms,A_structural,other,standardised (per 1 SD); count,-6173.81,,,,***,,,medium,"number of bedrooms; dependent variable is price per m2; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-6173.81,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Bedroom,bedrooms,A_structural,other,standardised (per 1 SD); count,-6140.76,,,,***,,,medium,"number of bedrooms; dependent variable is price per m2; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-6140.76,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Bedroom,bedrooms,A_structural,other,standardised (per 1 SD); count,-6290.89,,,,***,,,medium,"number of bedrooms; dependent variable is price per m2; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-6290.89,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Bedroom,bedrooms,A_structural,other,standardised (per 1 SD); count,-6250.43,,,,***,,,medium,"number of bedrooms; dependent variable is price per m2; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-6250.43,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Bedroom,bedrooms,A_structural,other,standardised (per 1 SD); count,-6254.2,,,,***,,,medium,"number of bedrooms; dependent variable is price per m2; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-6254.2,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Bedroom,bedrooms,A_structural,other,standardised (per 1 SD); count,-6251.12,,,,***,,,medium,"number of bedrooms; dependent variable is price per m2; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-6251.12,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Living room,other,other,other,standardised (per 1 SD); count (living rooms),6539.21,,,,***,,,medium,"number of living rooms; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,6539.21,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Living room,other,other,other,standardised (per 1 SD); count (living rooms),6622.03,,,,***,,,medium,"number of living rooms; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,6622.03,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Living room,other,other,other,standardised (per 1 SD); count (living rooms),6573.25,,,,***,,,medium,"number of living rooms; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,6573.25,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Living room,other,other,other,standardised (per 1 SD); count (living rooms),6667.46,,,,***,,,medium,"number of living rooms; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,6667.46,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Living room,other,other,other,standardised (per 1 SD); count (living rooms),6666.75,,,,***,,,medium,"number of living rooms; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,6666.75,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Living room,other,other,other,standardised (per 1 SD); count (living rooms),6666.42,,,,***,,,medium,"number of living rooms; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,6666.42,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Orientation West,sunlight,E_environment,other,standardised (per 1 SD); binary (west orientation),-920.33,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-920.33,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Orientation West,sunlight,E_environment,other,standardised (per 1 SD); binary (west orientation),-915.16,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-915.16,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Orientation West,sunlight,E_environment,other,standardised (per 1 SD); binary (west orientation),-915.22,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-915.22,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Orientation West,sunlight,E_environment,other,standardised (per 1 SD); binary (west orientation),-911.98,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-911.98,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Orientation West,sunlight,E_environment,other,standardised (per 1 SD); binary (west orientation),-912.2,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-912.2,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Orientation West,sunlight,E_environment,other,standardised (per 1 SD); binary (west orientation),-910.92,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-910.92,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Orientation South,sunlight,E_environment,other,standardised (per 1 SD); binary (south orientation),1346.1,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,1346.1,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Orientation South,sunlight,E_environment,other,standardised (per 1 SD); binary (south orientation),1323.73,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,1323.73,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Orientation South,sunlight,E_environment,other,standardised (per 1 SD); binary (south orientation),1400.0,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,1400.0,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Orientation South,sunlight,E_environment,other,standardised (per 1 SD); binary (south orientation),1385.72,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,1385.72,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Orientation South,sunlight,E_environment,other,standardised (per 1 SD); binary (south orientation),1385.95,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1385.95,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Orientation South,sunlight,E_environment,other,standardised (per 1 SD); binary (south orientation),1387.04,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1387.04,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Orientation East,sunlight,E_environment,other,standardised (per 1 SD); binary (east orientation),-686.05,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-686.05,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Orientation East,sunlight,E_environment,other,standardised (per 1 SD); binary (east orientation),-690.0,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-690.0,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Orientation East,sunlight,E_environment,other,standardised (per 1 SD); binary (east orientation),-690.47,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-690.47,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Orientation East,sunlight,E_environment,other,standardised (per 1 SD); binary (east orientation),-694.39,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-694.39,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Orientation East,sunlight,E_environment,other,standardised (per 1 SD); binary (east orientation),-694.22,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-694.22,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Orientation East,sunlight,E_environment,other,standardised (per 1 SD); binary (east orientation),-693.49,,,,***,,,medium,"orientation dummy, standardised; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-693.49,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Floor level 1,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 1; category 3 reference),900.32,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,900.32,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Floor level 1,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 1; category 3 reference),892.71,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,892.71,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Floor level 1,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 1; category 3 reference),865.8,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,865.8,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Floor level 1,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 1; category 3 reference),866.91,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,866.91,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Floor level 1,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 1; category 3 reference),913.8,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,913.8,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Floor level 1,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 1; category 3 reference),999.5,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,999.5,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Floor level 2,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 2; category 3 reference),917.9,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,917.9,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Floor level 2,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 2; category 3 reference),905.75,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,905.75,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Floor level 2,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 2; category 3 reference),900.98,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,900.98,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Floor level 2,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 2; category 3 reference),898.09,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,898.09,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Floor level 2,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 2; category 3 reference),779.25,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,779.25,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Floor level 2,floor_level,A_structural,other,standardised (per 1 SD); binary (floor level category 2; category 3 reference),1129.92,,,,***,,,medium,"floor-level category dummy (definition in Table S1); standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1129.92,,not harmonised,,,,,,86021.0,standardised ; binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Housing age,age,A_structural,other,standardised (per 1 SD); years,-5238.5,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-5238.5,,not harmonised,,,,,,86021.0,standardised ; years,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Housing age,age,A_structural,other,standardised (per 1 SD); years,-5198.79,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-5198.79,,not harmonised,,,,,,86021.0,standardised ; years,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Housing age,age,A_structural,other,standardised (per 1 SD); years,-5112.7,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-5112.7,,not harmonised,,,,,,86021.0,standardised ; years,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Housing age,age,A_structural,other,standardised (per 1 SD); years,-5151.89,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-5151.89,,not harmonised,,,,,,86021.0,standardised ; years,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Housing age,age,A_structural,other,standardised (per 1 SD); years,-5156.02,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-5156.02,,not harmonised,,,,,,86021.0,standardised ; years,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Housing age,age,A_structural,other,standardised (per 1 SD); years,-5154.19,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-5154.19,,not harmonised,,,,,,86021.0,standardised ; years,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Distance to the closest large market,commercial_distance,D_location,other,standardised (per 1 SD); m (distance),-4271.23,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-4271.23,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Distance to the closest large market,commercial_distance,D_location,other,standardised (per 1 SD); m (distance),-3916.23,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-3916.23,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Distance to the closest large market,commercial_distance,D_location,other,standardised (per 1 SD); m (distance),-2350.93,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2350.93,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Distance to the closest large market,commercial_distance,D_location,other,standardised (per 1 SD); m (distance),-2120.37,,,,**,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-2120.37,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Distance to the closest large market,commercial_distance,D_location,other,standardised (per 1 SD); m (distance),-2117.26,,,,**,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-2117.26,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Distance to the closest large market,commercial_distance,D_location,other,standardised (per 1 SD); m (distance),-2118.85,,,,**,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-2118.85,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Distance to the closest metro station,transit_distance,D_location,other,standardised (per 1 SD); m (distance),-21670.74,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-21670.74,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Distance to the closest metro station,transit_distance,D_location,other,standardised (per 1 SD); m (distance),-21768.2,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-21768.2,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Distance to the closest metro station,transit_distance,D_location,other,standardised (per 1 SD); m (distance),-23116.68,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-23116.68,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Distance to the closest metro station,transit_distance,D_location,other,standardised (per 1 SD); m (distance),-22334.51,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-22334.51,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Distance to the closest metro station,transit_distance,D_location,other,standardised (per 1 SD); m (distance),-22343.39,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-22343.39,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Distance to the closest metro station,transit_distance,D_location,other,standardised (per 1 SD); m (distance),-22333.11,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-22333.11,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Distance to the closest general hospital,hospital_distance,D_location,other,standardised (per 1 SD); m (distance),2229.17,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2229.17,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Distance to the closest general hospital,hospital_distance,D_location,other,standardised (per 1 SD); m (distance),2856.12,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,2856.12,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Distance to the closest general hospital,hospital_distance,D_location,other,standardised (per 1 SD); m (distance),-1723.88,,,,*,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-1723.88,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Distance to the closest general hospital,hospital_distance,D_location,other,standardised (per 1 SD); m (distance),-1460.87,,,,,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-1460.87,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Distance to the closest general hospital,hospital_distance,D_location,other,standardised (per 1 SD); m (distance),-1465.88,,,,,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-1465.88,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Distance to the closest general hospital,hospital_distance,D_location,other,standardised (per 1 SD); m (distance),-1469.55,,,,,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-1469.55,,not harmonised,,,,,,86021.0,standardised ; m,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Access to bus stops,transit_distance,D_location,other,standardised (per 1 SD); count (bus stops within 1 km),-3610.06,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-3610.06,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Access to bus stops,transit_distance,D_location,other,standardised (per 1 SD); count (bus stops within 1 km),-4752.34,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-4752.34,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Access to bus stops,transit_distance,D_location,other,standardised (per 1 SD); count (bus stops within 1 km),-6570.91,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-6570.91,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Access to bus stops,transit_distance,D_location,other,standardised (per 1 SD); count (bus stops within 1 km),-7665.9,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-7665.9,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Access to bus stops,transit_distance,D_location,other,standardised (per 1 SD); count (bus stops within 1 km),-7662.77,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-7662.77,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Access to bus stops,transit_distance,D_location,other,standardised (per 1 SD); count (bus stops within 1 km),-7657.24,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-7657.24,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Access to schools,school_distance,D_location,other,standardised (per 1 SD); count (schools within 1 km),69.09,,,,,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,69.09,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Access to schools,school_distance,D_location,other,standardised (per 1 SD); count (schools within 1 km),286.43,,,,,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,286.43,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Access to schools,school_distance,D_location,other,standardised (per 1 SD); count (schools within 1 km),-1027.85,,,,*,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-1027.85,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Access to schools,school_distance,D_location,other,standardised (per 1 SD); count (schools within 1 km),-841.78,,,,,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-841.78,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Access to schools,school_distance,D_location,other,standardised (per 1 SD); count (schools within 1 km),-845.59,,,,,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-845.59,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Access to schools,school_distance,D_location,other,standardised (per 1 SD); count (schools within 1 km),-841.96,,,,,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-841.96,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Access to sports and recreation facilities,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); count (facilities within 1 km),2827.09,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2827.09,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Access to sports and recreation facilities,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); count (facilities within 1 km),1052.16,,,,*,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,1052.16,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Access to sports and recreation facilities,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); count (facilities within 1 km),4942.46,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,4942.46,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Access to sports and recreation facilities,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); count (facilities within 1 km),3091.51,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,3091.51,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Access to sports and recreation facilities,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); count (facilities within 1 km),3088.2,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3088.2,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Access to sports and recreation facilities,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); count (facilities within 1 km),3084.58,,,,***,,,medium,"standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3084.58,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Access to groceries,commercial_distance,D_location,other,standardised (per 1 SD); count (grocery facilities within 1 km),-2572.96,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2572.96,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Access to groceries,commercial_distance,D_location,other,standardised (per 1 SD); count (grocery facilities within 1 km),-2784.05,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-2784.05,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Access to groceries,commercial_distance,D_location,other,standardised (per 1 SD); count (grocery facilities within 1 km),-3102.49,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-3102.49,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Access to groceries,commercial_distance,D_location,other,standardised (per 1 SD); count (grocery facilities within 1 km),-3147.55,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-3147.55,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Access to groceries,commercial_distance,D_location,other,standardised (per 1 SD); count (grocery facilities within 1 km),-3142.16,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-3142.16,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Access to groceries,commercial_distance,D_location,other,standardised (per 1 SD); count (grocery facilities within 1 km),-3140.57,,,,***,,,medium,"accessibility count, not a distance; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-3140.57,,not harmonised,,,,,,86021.0,standardised ; count,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Compact high-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-19946.58,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-19946.58,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Compact high-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-17672.54,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-17672.54,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-572.35,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-572.35,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),264.71,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,264.71,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-154.86,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-154.86,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),1011.49,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,1011.49,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),1008.41,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1008.41,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),1002.44,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1002.44,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-4899.85,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-4899.85,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-4032.52,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-4032.52,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-4043.78,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-4043.78,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-4035.11,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-4035.11,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Open high-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),4979.54,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,4979.54,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Open high-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),2792.81,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2792.81,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),58.09,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,58.09,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),607.44,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,607.44,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),611.06,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,611.06,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),603.85,,,,,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,603.85,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Open low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-1812.12,,,,**,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-1812.12,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Open low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-1687.91,,,,**,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-1687.91,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Open low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-2690.12,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2690.12,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Open low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-2243.61,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-2243.61,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Open low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-2233.03,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-2233.03,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Open low-rise,other,other,other,standardised (per 1 SD); share (land-use form within 1 km),-2238.77,,,,***,,,medium,"3D land-use configuration, immediate neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-2238.77,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),108.05,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,108.05,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),520.7,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,520.7,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),141.62,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,141.62,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),593.94,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,593.94,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),595.07,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,595.07,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),595.5,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,595.5,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),724.41,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,724.41,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),175.24,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,175.24,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),-50.9,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-50.9,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),-752.5,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-752.5,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),-750.44,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-750.44,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),-762.2,,,,,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-762.2,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Low plants,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),20507.32,,,,***,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,20507.32,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Low plants,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),19220.42,,,,***,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,19220.42,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Low plants,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),12942.21,,,,***,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,12942.21,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Low plants,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),11962.61,,,,***,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,11962.61,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Low plants,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),11956.61,,,,***,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,11956.61,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Low plants,green_space,E_environment,other,standardised (per 1 SD); share (land cover within 1 km),11969.94,,,,***,,,medium,"vegetation cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,11969.94,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover within 1 km),-1496.31,,,,,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-1496.31,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover within 1 km),-3171.33,,,,**,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-3171.33,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover within 1 km),-2537.86,,,,,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2537.86,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover within 1 km),-3155.12,,,,*,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-3155.12,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover within 1 km),-3143.27,,,,*,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-3143.27,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover within 1 km),-3155.25,,,,*,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-3155.25,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover within 1 km),2970.19,,,,***,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2970.19,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover within 1 km),2703.36,,,,***,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,2703.36,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover within 1 km),1834.6,,,,*,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,1834.6,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover within 1 km),1905.95,,,,**,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,1905.95,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover within 1 km),1901.01,,,,**,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1901.01,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover within 1 km),1913.4,,,,**,,,medium,"surface cover form; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,1913.4,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Street compact high-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-104.83,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-104.83,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Street compact high-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-2082.45,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2082.45,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Street compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-4320.72,,,,***,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-4320.72,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Street compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-4827.65,,,,***,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-4827.65,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Street compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-2383.07,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2383.07,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Street compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-5214.76,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-5214.76,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Street compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-5220.12,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-5220.12,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Street compact mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-5212.35,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-5212.35,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Street compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-658.7,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-658.7,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Street compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-7657.79,,,,*,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-7657.79,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Street compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-7655.13,,,,*,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-7655.13,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Street compact low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),-7284.43,,,,*,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-7284.43,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Street open high-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),2143.25,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2143.25,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Street open high-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),6274.07,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,6274.07,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Street open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),3047.79,,,,***,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,3047.79,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Street open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),7455.27,,,,*,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,7455.27,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Street open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),7357.44,,,,*,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,7357.44,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Street open mid-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),7446.51,,,,*,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,7446.51,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Street open low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),2574.2,,,,***,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2574.2,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Street open low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),2570.14,,,,***,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,2570.14,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Street open low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),3012.18,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,3012.18,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Street open low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),3391.09,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,3391.09,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Street open low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),3390.41,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3390.41,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Street open low-rise,other,other,other,standardised (per 1 SD); share (land-use form in street/township),3390.83,,,,,,,medium,"broader neighbourhood; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3390.83,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Street scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),-2235.18,,,,***,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-2235.18,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Street scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),-2297.6,,,,***,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-2297.6,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Street scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),3754.35,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,3754.35,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Street scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),3277.48,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,3277.48,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Street scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),3269.97,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3269.97,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Street scattered trees,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),3273.02,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3273.02,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,8,Street bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),2077.03,,,,***,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2077.03,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,8,Street bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),2894.19,,,,***,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,2894.19,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,8,Street bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),68.38,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,68.38,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,8,Street bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),3992.72,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,3992.72,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,8,Street bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),3996.56,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3996.56,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,8,Street bush or scrub,green_space,E_environment,other,standardised (per 1 SD); share (land cover in street/township),4004.25,,,,,,,medium,"broader neighbourhood vegetation; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,4004.25,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,9,Street bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover in street/township),-564.73,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-564.73,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,9,Street bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover in street/township),-900.99,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-900.99,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,9,Street bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover in street/township),2570.8,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,2570.8,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,9,Street bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover in street/township),2515.03,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,2515.03,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,9,Street bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover in street/township),2516.2,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,2516.2,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,9,Street bare rock or paved,other,other,other,standardised (per 1 SD); share (land cover in street/township),2509.32,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,2509.32,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,9,Street bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover in street/township),853.65,,,,*,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,853.65,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,9,Street bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover in street/township),1479.36,,,,***,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,1479.36,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,9,Street bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover in street/township),-836.82,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-836.82,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,9,Street bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover in street/township),346.08,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,346.08,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,9,Street bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover in street/township),353.63,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,353.63,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,9,Street bare soil or sand,other,other,other,standardised (per 1 SD); share (land cover in street/township),353.38,,,,,,,medium,"broader neighbourhood; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,353.38,,not harmonised,,,,,,86021.0,standardised ; share,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,9,Metro station density,other,other,other,"standardised (per 1 SD); density (metro stations per area, street/township)",466.53,,,,,,,medium,"broader neighbourhood transit density; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,466.53,,not harmonised,,,,,,86021.0,standardised ; density,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,9,Metro station density,other,other,other,"standardised (per 1 SD); density (metro stations per area, street/township)",90.53,,,,,,,medium,"broader neighbourhood transit density; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,90.53,,not harmonised,,,,,,86021.0,standardised ; density,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,9,Metro station density,other,other,other,"standardised (per 1 SD); density (metro stations per area, street/township)",3043.01,,,,,,,medium,"broader neighbourhood transit density; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,3043.01,,not harmonised,,,,,,86021.0,standardised ; density,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,9,Metro station density,other,other,other,"standardised (per 1 SD); density (metro stations per area, street/township)",3883.69,,,,,,,medium,"broader neighbourhood transit density; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,3883.69,,not harmonised,,,,,,86021.0,standardised ; density,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,9,Metro station density,other,other,other,"standardised (per 1 SD); density (metro stations per area, street/township)",3888.82,,,,,,,medium,"broader neighbourhood transit density; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3888.82,,not harmonised,,,,,,86021.0,standardised ; density,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,9,Metro station density,other,other,other,"standardised (per 1 SD); density (metro stations per area, street/township)",3887.46,,,,,,,medium,"broader neighbourhood transit density; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,3887.46,,not harmonised,,,,,,86021.0,standardised ; density,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c1,1,RE-ESF 1,9,Function mix,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); index (entropy of 7 facility types at street level),-234.27,,,,,,,medium,"functional mix entropy; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.767,none,"RE-ESF 1; single-level RE-ESF; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-234.27,,not harmonised,,,,,,86021.0,standardised ; index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c2,1,RE-ESF 2,9,Function mix,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); index (entropy of 7 facility types at street level),-140.8,,,,,,,medium,"functional mix entropy; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.766,none,"RE-ESF 2; single-level RE-ESF; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-140.8,,not harmonised,,,,,,86021.0,standardised ; index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c3,1,SFML 1,9,Function mix,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); index (entropy of 7 facility types at street level),-1020.64,,,,,,,medium,"functional mix entropy; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 1; two-level SFML; excludes compact low-rise, open mid-rise and street counterparts; adjusted R2; all regressors standardised",other,-1020.64,,not harmonised,,,,,,86021.0,standardised ; index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c4,1,SFML 2,9,Function mix,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); index (entropy of 7 facility types at street level),-1173.29,,,,,,,medium,"functional mix entropy; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 2; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adjusted R2; all regressors standardised",other,-1173.29,,not harmonised,,,,,,86021.0,standardised ; index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c5,1,SFML 3,9,Function mix,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); index (entropy of 7 facility types at street level),-1166.92,,,,,,,medium,"functional mix entropy; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 3; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-1166.92,,not harmonised,,,,,,86021.0,standardised ; index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3,3b145fc34a20579d69b10544f4da1eace43b32a3:T1-c6,1,SFML 4,9,Function mix,neighbourhood_amenities,F_neighbourhood,other,standardised (per 1 SD); index (entropy of 7 facility types at street level),-1153.67,,,,,,,medium,"functional mix entropy; page 9; standardised regressor; significance letter mapped (a=p<0.001 ***, b=p<0.01 **, c=p<0.05 *); sign read from page image",housing price per square metre,per_m2,other,86031.0,0.779,none,"SFML 4; two-level SFML; excludes compact high-rise, open high-rise and street counterparts; adds cross-level interactions (not extracted); adjusted R2; all regressors standardised",other,-1153.67,,not harmonised,,,,,,86021.0,standardised ; index,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,True,False,unknown,,2015.0,other,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,LOT,living_area,A_structural,log,m2 (printed 'Lot size (m2)'; ln),0.976,,65.585,,**,,,medium,"Printed label 'Lot size (m2)' but mean ln = 4.52 (~92 m2) for a sample of apartments/houses, i.e. this is effectively the dwelling floor area; mapped to living_area, flag","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,elasticity,0.976,0.01488145155142182,b (log-log),|b/t|,0.929429289479521,0.014171369817481448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),65.585,678.0,m2 '; ln),,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,LOT,living_area,A_structural,log,m2 (printed 'Lot size (m2)'; ln),0.967,,64.224,,**,,,medium,"Printed label 'Lot size (m2)' but mean ln = 4.52 (~92 m2) for a sample of apartments/houses, i.e. this is effectively the dwelling floor area; mapped to living_area, flag","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,elasticity,0.967,0.015056676631788737,b (log-log),|b/t|,0.926730845659194,0.014429665633706932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),64.224,678.0,m2 '; ln),,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,LOT,living_area,A_structural,log,m2 (printed 'Lot size (m2)'; ln),0.953,,59.367,,**,,,medium,"Printed label 'Lot size (m2)' but mean ln = 4.52 (~92 m2) for a sample of apartments/houses, i.e. this is effectively the dwelling floor area; mapped to living_area, flag","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,elasticity,0.953,0.01605268920444018,b (log-log),|b/t|,0.9157866470331151,0.015425853538718727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.367,678.0,m2 '; ln),,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,LOT,living_area,A_structural,log,m2 (printed 'Lot size (m2)'; ln),0.961,,63.918,,**,,,medium,"Printed label 'Lot size (m2)' but mean ln = 4.52 (~92 m2) for a sample of apartments/houses, i.e. this is effectively the dwelling floor area; mapped to living_area, flag","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,elasticity,0.961,0.015034888450827623,b (log-log),|b/t|,0.926103666852024,0.014488933740918431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),63.918,678.0,m2 '; ln),,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,LOT,living_area,A_structural,log,m2 (printed 'Lot size (m2)'; ln),0.949,,61.395,,**,,,medium,"Printed label 'Lot size (m2)' but mean ln = 4.52 (~92 m2) for a sample of apartments/houses, i.e. this is effectively the dwelling floor area; mapped to living_area, flag","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,elasticity,0.949,0.01545728479517876,b (log-log),|b/t|,0.9206245315806453,0.014995105978999024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),61.395,678.0,m2 '; ln),,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,FEE,other,other,other,z-score of property management fee (yuan/m2/month),0.061,,6.817,,**,,,high,"Property management fees, standardized (z-score); taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,0.061,0.008948217691066452,not harmonised,|b/t|,0.2532693551351385,0.037152611872544886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.817,678.0,z-score of property management fee,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,FEE,other,other,other,z-score of property management fee (yuan/m2/month),0.049,,5.086,,**,,,high,"Property management fees, standardized (z-score); taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,0.049,0.009634290208415257,not harmonised,|b/t|,0.1917038926421176,0.03769246807749068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.086,678.0,z-score of property management fee,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,FEE,other,other,other,z-score of property management fee (yuan/m2/month),0.056,,6.316,,**,,,high,"Property management fees, standardized (z-score); taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,0.056,0.008866371120962635,not harmonised,|b/t|,0.23572877973073875,0.03732247937472114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.316,678.0,z-score of property management fee,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,FEE,other,other,other,z-score of property management fee (yuan/m2/month),0.049,,5.336,,**,,,high,"Property management fees, standardized (z-score); taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,0.049,0.009182908545727137,not harmonised,|b/t|,0.20075578488666992,0.037622898217142035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.336,678.0,z-score of property management fee,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,FEE,other,other,other,z-score of property management fee (yuan/m2/month),0.049,,5.035,,**,,,high,"Property management fees, standardized (z-score); taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,0.049,0.009731876861966237,not harmonised,|b/t|,0.18985120185707235,0.03770629629733314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.035,678.0,z-score of property management fee,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,TRSP,transit_distance,D_location,other,z-score of number of bus lines within 100 m,-0.047,,-8.016,,**,,,high,"Bus accessibility count (z-scored), higher = more bus lines; sign negative","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,-0.047,0.005863273453093812,not harmonised,|b/t|,-0.2942258154584295,0.036704817297708275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.016,678.0,z-score of number of bus lines within 100 m,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,TRSP,transit_distance,D_location,other,z-score of number of bus lines within 100 m,-0.044,,-7.563,,**,,,high,"Bus accessibility count (z-scored), higher = more bus lines; sign negative","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,-0.044,0.005817797170435012,not harmonised,|b/t|,-0.27892770642973785,0.036880564118701296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.563,678.0,z-score of number of bus lines within 100 m,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,TRSP,transit_distance,D_location,other,z-score of number of bus lines within 100 m,-0.048,,-8.367,,**,,,high,"Bus accessibility count (z-scored), higher = more bus lines; sign negative","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,-0.048,0.005736823234134098,not harmonised,|b/t|,-0.30592645076095526,0.036563457722117276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.367,678.0,z-score of number of bus lines within 100 m,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,TRSP,transit_distance,D_location,other,z-score of number of bus lines within 100 m,-0.045,,-7.897,,**,,,high,"Bus accessibility count (z-scored), higher = more bus lines; sign negative","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,-0.045,0.005698366468279093,not harmonised,|b/t|,-0.2902283945209585,0.036751727810682346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.897,678.0,z-score of number of bus lines within 100 m,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,TRSP,transit_distance,D_location,other,z-score of number of bus lines within 100 m,-0.047,,-8.248,,**,,,high,"Bus accessibility count (z-scored), higher = more bus lines; sign negative","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,-0.047,0.005698351115421921,not harmonised,|b/t|,-0.30197476406571955,0.036611877311556684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.248,678.0,z-score of number of bus lines within 100 m,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,LFAC,neighbourhood_amenities,F_neighbourhood,other,z-score of ordinal 1-5 quality of living facilities,0.051,,7.211,,**,,,high,"Quality of living facilities (field assessment), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,0.051,0.007072528082096796,not harmonised,|b/t|,0.2668913543014815,0.03701169800325634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.211,678.0,z-score of ordinal 1-5 quality of living facilities,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,LFAC,neighbourhood_amenities,F_neighbourhood,other,z-score of ordinal 1-5 quality of living facilities,0.056,,7.744,,**,,,high,"Quality of living facilities (field assessment), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,0.056,0.007231404958677686,not harmonised,|b/t|,0.2850664630167804,0.03681126846807598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.744,678.0,z-score of ordinal 1-5 quality of living facilities,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,LFAC,neighbourhood_amenities,F_neighbourhood,other,z-score of ordinal 1-5 quality of living facilities,0.057,,7.876,,**,,,high,"Quality of living facilities (field assessment), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,0.057,0.0072371762315896395,not harmonised,|b/t|,0.28952138039233705,0.03675995180197271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.876,678.0,z-score of ordinal 1-5 quality of living facilities,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,LFAC,neighbourhood_amenities,F_neighbourhood,other,z-score of ordinal 1-5 quality of living facilities,0.058,,8.103,,**,,,high,"Quality of living facilities (field assessment), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,0.058,0.007157842774281131,not harmonised,|b/t|,0.2971385734118297,0.036670192942346004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.103,678.0,z-score of ordinal 1-5 quality of living facilities,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,LFAC,neighbourhood_amenities,F_neighbourhood,other,z-score of ordinal 1-5 quality of living facilities,0.061,,8.657,,**,,,high,"Quality of living facilities (field assessment), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,0.061,0.0070463208963844285,not harmonised,|b/t|,0.31549042295273677,0.036443389505918536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.657,678.0,z-score of ordinal 1-5 quality of living facilities,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,AGE,age,A_structural,other,z-score of age in years,-0.071,,-8.911,,**,,,high,"Age of property at sale in years, entered as z-score (sd units)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,-0.071,0.007967680395017393,not harmonised,|b/t|,-0.3237890039010841,0.036335877443730694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.911,678.0,z-score of age in years,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,AGE,age,A_structural,other,z-score of age in years,-0.073,,-9.27,,**,,,high,"Age of property at sale in years, entered as z-score (sd units)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,-0.073,0.007874865156418555,not harmonised,|b/t|,-0.33539155813137855,0.03618031910802358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.27,678.0,z-score of age in years,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,AGE,age,A_structural,other,z-score of age in years,-0.072,,-9.168,,**,,,high,"Age of property at sale in years, entered as z-score (sd units)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,-0.072,0.007853403141361256,not harmonised,|b/t|,-0.3321102176076093,0.036224936475524584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.168,678.0,z-score of age in years,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,AGE,age,A_structural,other,z-score of age in years,-0.08,,-9.89,,**,,,high,"Age of property at sale in years, entered as z-score (sd units)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,-0.08,0.008088978766430738,not harmonised,|b/t|,-0.35507330061717507,0.03590225486523509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.89,678.0,z-score of age in years,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,AGE,age,A_structural,other,z-score of age in years,-0.078,,-9.726,,**,,,high,"Age of property at sale in years, entered as z-score (sd units)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,-0.078,0.008019740900678593,not harmonised,|b/t|,-0.34991156548015456,0.03597692427309834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.726,678.0,z-score of age in years,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,PUBS,green_space,E_environment,binary,binary (public open space within 200 m),0.075,,5.99,,**,,,high,Existence of public space reachable within 200 m,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,pct_premium,7.7799663094412,1.3496045294882697,100*(exp(b - se^2/2) - 1),|b/t|,0.2241889404728295,0.037427202082275374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.99,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,PUBS,green_space,E_environment,binary,binary (public open space within 200 m),0.087,,6.66,,**,,,high,Existence of public space reachable within 200 m,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,pct_premium,9.08036064115494,1.425045212244597,100*(exp(b - se^2/2) - 1),|b/t|,0.24779851943149875,0.037206984899624436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.66,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,PUBS,green_space,E_environment,binary,binary (public open space within 200 m),0.072,,5.746,,**,,,high,Existence of public space reachable within 200 m,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,pct_premium,7.457098030735443,1.346592147103978,100*(exp(b - se^2/2) - 1),|b/t|,0.21548933314361052,0.037502494455901585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.746,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,PUBS,green_space,E_environment,binary,binary (public open space within 200 m),0.086,,6.824,,**,,,high,Existence of public space reachable within 200 m,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,pct_premium,8.971978749590793,1.3734370491535912,100*(exp(b - se^2/2) - 1),|b/t|,0.2535127171905987,0.03715016371491774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.824,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,PUBS,green_space,E_environment,binary,binary (public open space within 200 m),0.081,,5.933,,**,,,high,Existence of public space reachable within 200 m,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,pct_premium,8.426984362481038,1.4804322033019992,100*(exp(b - se^2/2) - 1),|b/t|,0.22216136349888452,0.03744503008577187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.933,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,MANA,other,other,other,z-score of ordinal 1-5 quality of community management,0.035,,4.263,,**,,,high,"Quality of community (estate) management, z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,0.035,0.008210180623973728,not harmonised,|b/t|,0.1615684996300686,0.03790018757449416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.263,678.0,z-score of ordinal 1-5 quality of community management,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,MANA,other,other,other,z-score of ordinal 1-5 quality of community management,0.033,,4.052,,**,,,high,"Quality of community (estate) management, z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,0.033,0.008144126357354394,not harmonised,|b/t|,0.15376543872776294,0.03794803522402838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.052,678.0,z-score of ordinal 1-5 quality of community management,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,MANA,other,other,other,z-score of ordinal 1-5 quality of community management,0.033,,4.061,,**,,,high,"Quality of community (estate) management, z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,0.033,0.008126077320856932,not harmonised,|b/t|,0.1540988696346813,0.03794604029418402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.061,678.0,z-score of ordinal 1-5 quality of community management,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,MANA,other,other,other,z-score of ordinal 1-5 quality of community management,0.036,,4.42,,**,,,high,"Quality of community (estate) management, z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,0.036,0.008144796380090497,not harmonised,|b/t|,0.16735505372123097,0.03786313432607035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.42,678.0,z-score of ordinal 1-5 quality of community management,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,MANA,other,other,other,z-score of ordinal 1-5 quality of community management,0.035,,4.289,,**,,,high,"Quality of community (estate) management, z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,0.035,0.00816041035206342,not harmonised,|b/t|,0.16252795153181876,0.03789413651942615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.289,678.0,z-score of ordinal 1-5 quality of community management,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,DECORATION,condition,A_structural,other,z-score of ordinal 1-5 quality of decoration,0.026,,4.941,,**,,,high,"Quality of interior decoration (1 none ... 5 luxury), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,0.026,0.005262092693786683,not harmonised,|b/t|,0.1864311312394543,0.037731457445750724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.941,678.0,z-score of ordinal 1-5 quality of decoration,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,DECORATION,condition,A_structural,other,z-score of ordinal 1-5 quality of decoration,0.027,,5.108,,**,,,high,"Quality of interior decoration (1 none ... 5 luxury), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,0.027,0.0052858261550509005,not harmonised,|b/t|,0.19250246157306367,0.037686464677577074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.108,678.0,z-score of ordinal 1-5 quality of decoration,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,DECORATION,condition,A_structural,other,z-score of ordinal 1-5 quality of decoration,0.027,,5.055,,**,,,high,"Quality of interior decoration (1 none ... 5 luxury), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,0.027,0.005341246290801187,not harmonised,|b/t|,0.19057799017494811,0.03770088826408469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.055,678.0,z-score of ordinal 1-5 quality of decoration,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,DECORATION,condition,A_structural,other,z-score of ordinal 1-5 quality of decoration,0.026,,5.002,,**,,,high,"Quality of interior decoration (1 none ... 5 luxury), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,0.026,0.005197920831667333,not harmonised,|b/t|,0.18865131899415782,0.037715177727740475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.002,678.0,z-score of ordinal 1-5 quality of decoration,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,DECORATION,condition,A_structural,other,z-score of ordinal 1-5 quality of decoration,0.026,,5.032,,**,,,high,"Quality of interior decoration (1 none ... 5 luxury), z-scored","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,0.026,0.0051669316375198724,not harmonised,|b/t|,0.189742156654402,0.03770710585341852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.032,678.0,z-score of ordinal 1-5 quality of decoration,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,CENV,other,other,other,z-score of ordinal 1-5 quality of community environment,-0.008,,-1.374,,,,,high,"Quality of community environment (observation/interviews), z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,other,-0.008,0.005822416302765647,not harmonised,|b/t|,-0.0526948392924434,0.038351411420992285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.374,678.0,z-score of ordinal 1-5 quality of community environment,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,CENV,other,other,other,z-score of ordinal 1-5 quality of community environment,-0.011,,-1.924,,,,,high,"Quality of community environment (observation/interviews), z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,other,-0.011,0.005717255717255717,not harmonised,|b/t|,-0.07368988096409117,0.038300353931440316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.924,678.0,z-score of ordinal 1-5 quality of community environment,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,CENV,other,other,other,z-score of ordinal 1-5 quality of community environment,-0.016,,-2.598,,**,,,high,"Quality of community environment (observation/interviews), z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,other,-0.016,0.006158583525789069,not harmonised,|b/t|,-0.09928262513881508,0.038215021223562386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.598,678.0,z-score of ordinal 1-5 quality of community environment,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,CENV,other,other,other,z-score of ordinal 1-5 quality of community environment,-0.021,,-3.297,,**,,,high,"Quality of community environment (observation/interviews), z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,other,-0.021,0.006369426751592357,not harmonised,|b/t|,-0.1256175287478627,0.03810055467026469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.297,678.0,z-score of ordinal 1-5 quality of community environment,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,CENV,other,other,other,z-score of ordinal 1-5 quality of community environment,-0.024,,-3.741,,**,,,high,"Quality of community environment (observation/interviews), z-scored; taxonomy other","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,other,-0.024,0.006415396952686447,not harmonised,|b/t|,-0.14221198702292567,0.03801443117426508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.741,678.0,z-score of ordinal 1-5 quality of community environment,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,False,False,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,DEXE,green_space,E_environment,log,m (ln distance to Huafa Exercise Park),-0.033,,-3.145,,**,,,high,Log distance to Huafa Exercise Park (small park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,elasticity,-0.033,0.010492845786963434,b (log-log),|b/t|,-0.11991149792471875,0.03812766229720787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.145,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,DEXE,green_space,E_environment,log,m (ln distance to Huafa Exercise Park),-0.033,,-3.14,,**,,,high,Log distance to Huafa Exercise Park (small park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,elasticity,-0.033,0.010509554140127388,b (log-log),|b/t|,-0.1197235943174113,0.03812853322210551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.14,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,DEXE,green_space,E_environment,log,m (ln distance to Huafa Exercise Park),-0.053,,-4.474,,**,,,high,Log distance to Huafa Exercise Park (small park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,elasticity,-0.053,0.011846222619579794,b (log-log),|b/t|,-0.16934137454491416,0.03785010606725842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.474,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,DEXE,green_space,E_environment,log,m (ln distance to Huafa Exercise Park),-0.028,,-2.639,,**,,,high,Log distance to Huafa Exercise Park (small park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,elasticity,-0.028,0.010610079575596818,b (log-log),|b/t|,-0.10083363304204487,0.038209031088308025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.639,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,DEXE,green_space,E_environment,log,m (ln distance to Huafa Exercise Park),-0.041,,-3.237,,**,,,high,Log distance to Huafa Exercise Park (small park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,elasticity,-0.041,0.012666048810627124,b (log-log),|b/t|,-0.12336660478593083,0.03811140092243769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.237,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,DCEM,green_space,E_environment,log,m (ln distance to Martyrs' Park),-0.069,,-4.95,,**,,,high,Log distance to Martyrs' Park (Martyrs' Cemetery park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,elasticity,-0.069,0.01393939393939394,b (log-log),|b/t|,-0.18675888047049533,0.03772906676171622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.95,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,DCEM,green_space,E_environment,log,m (ln distance to Martyrs' Park),-0.086,,-5.751,,**,,,high,Log distance to Martyrs' Park (Martyrs' Cemetery park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,elasticity,-0.086,0.01495392105720744,b (log-log),|b/t|,-0.21566812751198106,0.03750097852755713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.751,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,DCEM,green_space,E_environment,log,m (ln distance to Martyrs' Park),-0.015,,-0.757,,,,,high,Log distance to Martyrs' Park (Martyrs' Cemetery park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,elasticity,-0.015,0.019815059445178335,b (log-log),|b/t|,-0.02906013156392789,0.03838854896159563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.757,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,DCEM,green_space,E_environment,log,m (ln distance to Martyrs' Park),-0.039,,-2.503,,*,,,high,Log distance to Martyrs' Park (Martyrs' Cemetery park),"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,elasticity,-0.039,0.01558130243707551,b (log-log),|b/t|,-0.09568606246792946,0.03822855072630022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.503,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,PARKING,parking,A_structural,binary,binary (parking space exists),-0.06,,-4.013,,**,,,high,Existence of parking space,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,pct_premium,-5.834072372068344,1.408070571020556,100*(exp(b - se^2/2) - 1),|b/t|,-0.15231996571133083,0.03795663237262169,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.013,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,PARKING,parking,A_structural,binary,binary (parking space exists),-0.058,,-3.875,,**,,,high,Existence of parking space,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,pct_premium,-5.645575114833656,1.4124308890666921,100*(exp(b - se^2/2) - 1),|b/t|,-0.14719741851382875,0.03798643058421387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.875,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,PARKING,parking,A_structural,binary,binary (parking space exists),-0.051,,-3.339,,**,,,high,Existence of parking space,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,pct_premium,-4.983217116560034,1.4514588858087385,100*(exp(b - se^2/2) - 1),|b/t|,-0.12719202415071965,0.038092849401233794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.339,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,PARKING,parking,A_structural,binary,binary (parking space exists),-0.068,,-4.556,,**,,,high,Existence of parking space,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,pct_premium,-6.584358171850524,1.3944186172794235,100*(exp(b - se^2/2) - 1),|b/t|,-0.17235369059323366,0.03783004622327341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.556,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,PARKING,parking,A_structural,binary,binary (parking space exists),-0.059,,-3.928,,**,,,high,Existence of parking space,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,pct_premium,-5.739956754809872,1.4159801267889225,100*(exp(b - se^2/2) - 1),|b/t|,-0.149166198301158,0.03797510140049847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.928,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,POOL,pool,C_exterior,binary,binary (swimming pool exists),0.07,,3.476,,**,,,high,Existence of swimming pool,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,pct_premium,7.229072939158887,2.1598266020654537,100*(exp(b - se^2/2) - 1),|b/t|,0.13232114089618924,0.03806707160419714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.476,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,POOL,pool,C_exterior,binary,binary (swimming pool exists),0.092,,4.321,,**,,,high,Existence of swimming pool,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,pct_premium,9.611634690829085,2.3343106602970414,100*(exp(b - se^2/2) - 1),|b/t|,0.1637081825763987,0.03788664257727348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.321,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,POOL,pool,C_exterior,binary,binary (swimming pool exists),0.083,,4.117,,**,,,high,Existence of swimming pool,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,pct_premium,8.632102496913241,2.1905020672699482,100*(exp(b - se^2/2) - 1),|b/t|,0.15617236408217272,0.03793353511833197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.117,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,POOL,pool,C_exterior,binary,binary (swimming pool exists),0.081,,4.079,,**,,,high,Existence of swimming pool,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,pct_premium,8.415711627222455,2.153322937531444,100*(exp(b - se^2/2) - 1),|b/t|,0.15476557343108813,0.03794203810519444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.079,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,POOL,pool,C_exterior,binary,binary (swimming pool exists),0.086,,4.12,,**,,,high,Existence of swimming pool,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,pct_premium,8.956893170555391,2.2748384522874043,100*(exp(b - se^2/2) - 1),|b/t|,0.1562833861522708,0.03793286071657058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.12,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m1,3,Model 1,7,LIFT,accessibility,B_interior,binary,binary (lift exists),0.025,,1.424,,,,,high,Existence of lift/elevator,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.917,t,Controlled variables only (no landscape variables); adjusted R2 = 0.916,pct_premium,2.5157121668929694,1.8000616582240678,100*(exp(b - se^2/2) - 1),|b/t|,0.054606791953899794,0.03834746625976109,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.424,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,LIFT,accessibility,B_interior,binary,binary (lift exists),0.039,,2.183,,*,,,high,Existence of lift/elevator,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,pct_premium,3.960456523954714,1.857583548435921,100*(exp(b - se^2/2) - 1),|b/t|,0.08354451749207704,0.03827050732573387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.183,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,LIFT,accessibility,B_interior,binary,binary (lift exists),0.026,,1.541,,,,,high,Existence of lift/elevator,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,pct_premium,2.6194874725627093,1.7316589656268326,100*(exp(b - se^2/2) - 1),|b/t|,0.05907837849540077,0.03833768883543204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.541,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,LIFT,accessibility,B_interior,binary,binary (lift exists),0.034,,1.966,,*,,,high,Existence of lift/elevator,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,pct_premium,3.4429905289713014,1.7892104083802651,100*(exp(b - se^2/2) - 1),|b/t|,0.07528947424689622,0.03829576513066949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.966,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,LIFT,accessibility,B_interior,binary,binary (lift exists),0.033,,1.821,,,,,high,Existence of lift/elevator,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,pct_premium,3.3380842284194356,1.872991092529549,100*(exp(b - se^2/2) - 1),|b/t|,0.06976468517116435,0.03831119449267675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.821,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m2,3,Model 2,7,DPAR,green_space,E_environment,log,m (ln distance to Xiangshan Park),0.056,,3.027,,**,,,high,"Log distance to Xiangshan Park (1,400-acre main park)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DPAR (log distance to Xiangshan Park); adjusted R2 = 0.917,elasticity,0.056,0.01850016518004625,b (log-log),|b/t|,0.11547357806352808,0.03814786193046847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.027,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,DPAR,green_space,E_environment,log,m (ln distance to Xiangshan Park),-0.007,,0.408,,,,,high,"Log distance to Xiangshan Park (1,400-acre main park); printed t '(0.408)' without minus sign despite negative coefficient (likely sign typo)","ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,elasticity,-0.007,0.01715686274509804,b (log-log),|b/t|,0.01566722239508963,0.03840005488992556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.408,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m3,3,Model 3,7,DSEA,water_proximity,E_environment,log,m (ln distance to seaside),-0.055,,-3.656,,**,,,high,Log distance to seaside,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.919,t,Controls + DSEA (log distance to seaside); adjusted R2 = 0.917,elasticity,-0.055,0.015043763676148797,b (log-log),|b/t|,-0.13904394221717414,0.038031712860277386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.656,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,DSEA,water_proximity,E_environment,log,m (ln distance to seaside),-0.04,,-3.085,,**,,,high,Log distance to seaside,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,elasticity,-0.04,0.012965964343598056,b (log-log),|b/t|,-0.11765580961572299,0.03813802580736564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.085,678.0,m,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m4,3,Model 4,7,SEAVIEW,view,E_environment,binary,binary (sea view),0.089,,4.5,,**,,,high,Existence of sea view from property,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + SEAVIEW; adjusted R2 = 0.918,pct_premium,9.286689224636667,2.161870631400964,100*(exp(b - se^2/2) - 1),|b/t|,0.17029701733693423,0.03784378163042982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb,3b4b454985f862f1a1d6165d8c81255275be3ddb:T3-m5,3,Model 5,7,SEAVIEW,view,E_environment,binary,binary (sea view),0.072,,3.265,,**,,,high,Existence of sea view from property,"ln sales (listing) price of property, RMB",log,OLS,688.0,0.92,t,Controls + all three landscape variables; adjusted R2 = 0.918,pct_premium,7.43940767795217,2.3698372058987616,100*(exp(b - se^2/2) - 1),|b/t|,0.12441727511385159,0.03810636297514597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.265,678.0,binary,,2022.0,China,Asia,Upper middle income,2015.0,2015.0,mixed,listing,journal,False,False,OLS,,2015.0,ols,journal,True,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Broker involvement in the transaction process,other,other,binary,binary,0.468,0.202,2.32,0.023,*,,,high,"1 = transaction facilitated by a broker/agent, 0 otherwise; printed name 'Broker involvement in the transaction process' (transaction characteristic, no taxonomy leaf)","Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",pct_premium,56.45496202704927,32.255307534278465,100*(exp(b - se^2/2) - 1),reported,0.23389949195437656,0.10081874653205887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.32,93.0,binary,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,False,False,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,External wall finishing material,roof_materials,A_structural,other,ordinal,0.383,0.183,2.089,0.04,*,,,medium,"Ordinal material code (1 = quartz paint, 2 = plastered with cement, 3 = granite paint per Table 2; only quartz paint and plastered observed); text interprets as quartz paint premium of 38.3% vs plaster, direction of coding ambiguous","Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",other,0.383,0.183,not harmonised,reported,0.211709044625879,0.1013446838802676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.089,93.0,ordinal,nonstandard_x_transform,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,False,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Floor finishing material,flooring,B_interior,other,ordinal,0.035,0.025,1.391,0.168,,,,medium,"Ordinal code 1 = ceramic tile, 2 = cement screed; coding direction ambiguous","Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",other,0.035,0.025,not harmonised,reported,0.1427625263887991,0.1026330168143775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.391,93.0,ordinal,nonstandard_x_transform,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,False,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Ceiling finishing material,roof_materials,A_structural,other,ordinal,0.023,0.023,1.008,0.013,*,,,medium,"Ceiling finish code 1 = gypsum, 2 = plastic; printed t = 1.008 is inconsistent with printed p = .013 (likely typo in table); text interprets as gypsum premium 2.3% vs plastic","Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",other,0.023,0.023,not harmonised,reported,0.10395837909065095,0.1031333125899315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.008,93.0,ordinal,nonstandard_x_transform,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,False,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Direction of the house (East=1 Non-East=0),sunlight,E_environment,binary,binary,0.456,0.16,0.285,0.036,*,,,medium,"East-facing aspect dummy; printed t = .285 is inconsistent with B/SE = 2.85 and with p = .036 (t likely misprinted, should read 2.85)","Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",pct_premium,55.76838399581712,25.24400551626365,100*(exp(b - se^2/2) - 1),reported,0.029540226089115846,0.10364991610216087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.285,93.0,binary,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,True,True,True
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Land grade,other,other,other,ordinal,-0.009,0.011,-0.865,0.389,,,,high,Municipal land grade 1-3 (ordinal); printed name 'Land grade',"Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",other,-0.009,0.011,not harmonised,reported,-0.08933766158638391,0.10328053362587737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.865,93.0,ordinal,nonstandard_x_transform,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,False,False,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Accessible road type,other,other,other,ordinal,-0.001,0.008,-0.094,0.925,,,,high,Ordinal road type code (asphalt / cobblestone / gravel / earthen); printed name 'Accessible road type',"Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",other,-0.001,0.008,not harmonised,reported,-0.009746882912122746,0.10369024374598665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.094,93.0,ordinal,nonstandard_x_transform,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,False,False,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Log_ Floor Area in square meter,living_area,A_structural,log,m2,0.601,0.175,3.441,0.001,*,,,high,Log of floor area (m2); elasticity,"Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",elasticity,0.601,0.175,b (log-log),reported,0.33606259007601225,0.09766422263179665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.441,93.0,m2,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,True,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Log_ Gross plot size in square meter,lot_size,A_structural,log,m2,0.335,0.11,3.052,0.003,*,,,high,Log of gross plot area (m2); elasticity,"Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",elasticity,0.335,0.11,b (log-log),reported,0.30172792298363493,0.09886236008638102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.052,93.0,m2,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,True,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Log_ Age of the House in years,age,A_structural,log,years,-0.187,0.054,-3.434,0.001,*,,,high,Log of house age in years (range 1-15),"Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",elasticity,-0.187,0.054,b (log-log),reported,-0.3354559416439194,0.0976866457903085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.434,93.0,years,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,True,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Log_ Number of rooms,bedrooms,A_structural,log,count,0.148,0.066,2.24,0.028,*,,,high,"Log of number of rooms (all rooms, 3-5)","Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",elasticity,0.148,0.066,b (log-log),reported,0.2262538542191667,0.10100618491927084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.24,93.0,count,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,True,True,False
3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a,3b820e938ca4b455736f08f285e1b5bb43f7674a:T9-c1,9,1,14,Log_ Distance of the property from the main road in meter,highway_distance,D_location,log,m,-0.097,0.031,-3.115,0.002,*,,,high,Log of distance from the property to the main road in metres (234-817 m),"Log_ Residential Real Estate Price (sales price, birr)",log,OLS,103.0,0.88,se,"Full survey sample (103 valid of 134 sampled owners); SE, t and p printed in separate columns",elasticity,-0.097,0.031,b (log-log),reported,-0.30737320843726085,0.09867518729928117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.115,93.0,m,,2024.0,Ethiopia,Africa,Low income,2024.0,2024.0,single_family,transaction,journal,False,False,OLS,,2024.0,ols,journal,True,True,True
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Building period:1960-1970,age,A_structural,dummy,binary,-370.48,25.94,,,***,,,high,"built 1960-1970 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-370.48,25.94,"b (currency per unit, as reported)",reported,-0.48283139288178045,0.03380653835929978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.28218966846569,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Building period:1971-1980,age,A_structural,dummy,binary,-311.17,23.36,,,***,,,high,"built 1971-1980 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-311.17,23.36,"b (currency per unit, as reported)",reported,-0.457314001649066,0.03433125005149012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.320633561643836,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Building period:1981-1990,age,A_structural,dummy,binary,-232.93,23.37,,,***,,,high,"built 1981-1990 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-232.93,23.37,"b (currency per unit, as reported)",reported,-0.3591078441937145,0.036029495208032926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.967051775780915,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Building period:1991-2000,age,A_structural,dummy,binary,-58.64,21.64,,,***,,,high,"built 1991-2000 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-58.64,21.64,"b (currency per unit, as reported)",reported,-0.10404280012960104,0.03839505789230161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.709796672828096,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Terrace,dwelling_type,A_structural,dummy,binary,-285.65,35.17,,,***,,,high,terraced house vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-285.65,35.17,"b (currency per unit, as reported)",reported,-0.2991837313284019,0.036836309577524574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.121978959340346,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Corner,dwelling_type,A_structural,dummy,binary,-281.36,31.77,,,***,,,high,corner house vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-281.36,31.77,"b (currency per unit, as reported)",reported,-0.3235036472535099,0.036528685219093006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.85615360402896,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Semidetached,dwelling_type,A_structural,dummy,binary,-122.89,47.96,,,**,,,high,semi-detached vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-122.89,47.96,"b (currency per unit, as reported)",reported,-0.09843775349866589,0.038417077531092976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.562343619683069,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,168,Duplex,dwelling_type,A_structural,dummy,binary,-151.08,30.6,,,***,,,high,duplex vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),monetary,-151.08,30.6,"b (currency per unit, as reported)",reported,-0.1872300482291358,0.037921892214797165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.9372549019607845,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Building period:1960-1970,age,A_structural,dummy,binary,-389.5,36.67,,,***,,,high,"built 1960-1970 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-389.5,36.67,"b (currency per unit, as reported)",reported,-0.37939174517793617,0.03571834478992277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.621761658031087,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Building period:1971-1980,age,A_structural,dummy,binary,-261.5,33.96,,,***,,,high,"built 1971-1980 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-261.5,33.96,"b (currency per unit, as reported)",reported,-0.28494120462979566,0.03700421915574707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.700235571260306,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Building period:1981-1990,age,A_structural,dummy,binary,-173.08,32.59,,,***,,,high,"built 1981-1990 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-173.08,32.59,"b (currency per unit, as reported)",reported,-0.20084465783562339,0.03781793043022282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.310831543418226,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Building period:1991-2000,age,A_structural,dummy,binary,-49.34,26.55,,,*,,,high,"built 1991-2000 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-49.34,26.55,"b (currency per unit, as reported)",reported,-0.07155806500419727,0.038505606523336795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8583804143126177,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Terrace,dwelling_type,A_structural,dummy,binary,-264.34,35.24,,,***,,,high,terraced house vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-264.34,35.24,"b (currency per unit, as reported)",reported,-0.2781506229098197,0.03708113774435215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.501135073779794,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Corner,dwelling_type,A_structural,dummy,binary,-274.54,31.18,,,***,,,high,corner house vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-274.54,31.18,"b (currency per unit, as reported)",reported,-0.32182921772115913,0.03655072123750907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.805003207184093,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Semidetached,dwelling_type,A_structural,dummy,binary,-149.5,47.57,,,***,,,high,semi-detached vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-149.5,47.57,"b (currency per unit, as reported)",reported,-0.12044083959436239,0.03832355009701551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1427370191297035,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLSD,6.1,OLSD,168,Duplex,dwelling_type,A_structural,dummy,binary,-147.24,30.17,,,***,,,high,duplex vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007; land price interacted with 9 postcode dummies,monetary,-147.24,30.17,"b (currency per unit, as reported)",reported,-0.18514630185692146,0.03793713615201929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.880344713291349,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Building period:1960-1970,age,A_structural,dummy,binary,-398.4,41.75,,,***,,,high,"built 1960-1970 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-398.4,41.75,"b (currency per unit, as reported)",reported,-0.3456753275184799,0.03622476135516199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.54251497005988,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Building period:1971-1980,age,A_structural,dummy,binary,-323.5,41.75,,,***,,,high,"built 1971-1980 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-323.5,41.75,"b (currency per unit, as reported)",reported,-0.286581032585439,0.03698534191790442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.748502994011976,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Building period:1981-1990,age,A_structural,dummy,binary,-226.14,42.87,,,***,,,high,"built 1981-1990 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-226.14,42.87,"b (currency per unit, as reported)",reported,-0.1995443503267414,0.03782818739943134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.275017494751575,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Building period:1991-2000,age,A_structural,dummy,binary,-115.13,37.26,,,***,,,high,"built 1991-2000 vs built after 2000 (reference); vintage dummy, not years; coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses",transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-115.13,37.26,"b (currency per unit, as reported)",reported,-0.11844491513901238,0.03833281975227657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.089908749329039,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Terrace,dwelling_type,A_structural,dummy,binary,-187.28,37.32,,,***,,,high,terraced house vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-187.28,37.32,"b (currency per unit, as reported)",reported,-0.19019022931450325,0.03789993249688842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.018220793140407,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Corner,dwelling_type,A_structural,dummy,binary,-192.85,34.07,,,***,,,high,corner house vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-192.85,34.07,"b (currency per unit, as reported)",reported,-0.21348011829060076,0.03771463640218184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.660405048429704,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Semidetached,dwelling_type,A_structural,dummy,binary,-96.93,48.73,,,**,,,high,semi-detached vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-96.93,48.73,"b (currency per unit, as reported)",reported,-0.07656386924151355,0.03849125501020277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.989123743074082,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-MGWR,6.1,MGWR,168,Duplex,dwelling_type,A_structural,dummy,binary,-104.56,31.03,,,***,,,high,duplex vs detached (reference); coefficient in EUR per m2 of living space (dep var is price per m2 of living space); SE in parentheses,transaction price per m2 of living space (EUR),per_m2,GWR,681.0,,se,"Year 2007; mixed GWR: property-specific land prices (local), structure characteristics global",monetary,-104.56,31.03,"b (currency per unit, as reported)",reported,-0.12899675113712566,0.03828203125272579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.369642281662907,671.0,binary,,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,spatial,other,False,True,False
3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e,3bbf88ea5271189e3329337f57932674d7aa341e:T6.1-OLS,6.1,OLS,169,Land price (ratio of plot size to structure size),lot_size,A_structural,other,m2,214.87,,,,,,,medium,"From Table 6.2 (2007, OLS column): estimated land price = coefficient on plot size / living space ratio, EUR per m2 of land; no SE printed",transaction price per m2 of living space (EUR),per_m2,OLS,681.0,,se,Year 2007 sales in city 'A'; common land price (model 10 without postcode dummies),other,214.87,,not harmonised,,,,,,671.0,m2,nonstandard_x_transform,2017.0,Netherlands,Europe,High income,1998.0,2007.0,single_family,transaction,thesis,False,True,OLS,,2002.5,ols,other,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,M2,living_area,A_structural,level,m2,0.013,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,1.3,0.1,100*b/1 (X per m2->m2),reported,0.5064081016355528,0.03895446935658099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,HEAT_D,heating,A_structural,binary,binary,-0.148,0.029,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,pct_premium,-13.793146110483123,2.501050233331932,100*(exp(b - se^2/2) - 1),reported,-0.22465694699957137,0.04402061799315926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.1034482758620685,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,HEAT_D,heating,A_structural,binary,binary,-0.129,0.024,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",pct_premium,-12.127914250718486,2.1095375173093998,100*(exp(b - se^2/2) - 1),reported,-0.23596117845519124,0.04389975413119837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.375,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,HEAT_D,heating,A_structural,binary,binary,-0.131,0.024,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",pct_premium,-12.303482795149778,2.1053226585385048,100*(exp(b - se^2/2) - 1),reported,-0.23941131516645855,0.04386161499232829,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.458333333333333,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,HEAT_D,heating,A_structural,binary,binary,-0.123,0.023,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",pct_premium,-11.597019424900001,2.033806423889888,100*(exp(b - se^2/2) - 1),reported,-0.23483419475688816,0.0439120851984425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3478260869565215,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,HEAT_D,heating,A_structural,binary,binary,-0.163,0.034,,,*,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,pct_premium,-15.089973340224084,2.888610040609607,100*(exp(b - se^2/2) - 1),reported,-0.21166885575562677,0.04415178586313687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.794117647058823,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,CORRI_D,other,other,binary,binary,0.178,0.041,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,pct_premium,19.382149247043667,4.898783817062684,100*(exp(b - se^2/2) - 1),reported,0.1924606639415419,0.04433082708765853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.341463414634146,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,CORRI_D,other,other,binary,binary,0.139,0.033,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",pct_premium,14.849857224653974,3.7921095300118974,100*(exp(b - se^2/2) - 1),reported,0.18693013973189468,0.044379097921960604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.212121212121212,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,CORRI_D,other,other,binary,binary,0.146,0.033,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",pct_premium,15.656626623817926,3.8187474205676275,100*(exp(b - se^2/2) - 1),reported,0.19599063460036556,0.04429925302611003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.424242424242424,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,CORRI_D,other,other,binary,binary,0.136,0.033,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",pct_premium,14.505823960900543,3.7807502488630442,100*(exp(b - se^2/2) - 1),reported,0.18303227379243694,0.04441224290551779,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.121212121212121,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,CORRI_D,other,other,binary,binary,0.167,0.049,,,*,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,pct_premium,18.033642054446155,5.790595900010971,100*(exp(b - se^2/2) - 1),reported,0.1521720474279343,0.04464928337705857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4081632653061225,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,BATH,bathrooms,A_structural,level,count,-0.026,0.034,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-2.6,3.4000000000000004,100*b/1 (per unit count),reported,-0.03452529498447397,0.04514846267200443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7647058823529411,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,BATH,bathrooms,A_structural,level,count,0.001,0.029,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,0.1,2.9000000000000004,100*b/1 (per unit count),reported,0.0015577703563043883,0.04517534033282726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.034482758620689655,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,BATH,bathrooms,A_structural,level,count,0.004,0.029,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,0.4,2.9000000000000004,100*b/1 (per unit count),reported,0.006230968023481229,0.04517451817023891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13793103448275862,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,BATH,bathrooms,A_structural,level,count,0.005,0.028,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,0.5,2.8000000000000003,100*b/1 (per unit count),reported,0.008066772370759135,0.04517392527625116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17857142857142858,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,BATH,bathrooms,A_structural,level,count,-0.034,0.035,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-3.4000000000000004,3.5000000000000004,100*b/1 (per unit count),reported,-0.04384247255167036,0.0451319570384842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9714285714285714,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,YEARS,age,A_structural,level,years,-0.031,0.006,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-3.1,0.6,100*b/1 (per year),reported,-0.22729691823301026,0.0439929519160665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.166666666666667,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,YEARS,age,A_structural,level,years,-0.029,0.005,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,-2.9000000000000004,0.5,100*b/1 (per year),reported,-0.2534612582355012,0.04370021693715538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,YEARS,age,A_structural,level,years,-0.031,0.005,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,-3.1,0.5,100*b/1 (per year),reported,-0.2697080117954873,0.043501292225078605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.2,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,YEARS,age,A_structural,level,years,-0.03,0.005,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,-3.0,0.5,100*b/1 (per year),reported,-0.2616124321181684,0.04360207201969473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,YEARS,age,A_structural,level,years,-0.033,0.006,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-3.3000000000000003,0.6,100*b/1 (per year),reported,-0.24113299761177795,0.043842363202141445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,BRAND_D,other,other,binary,binary,0.122,0.029,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,pct_premium,12.927914004822272,3.2762868951629245,100*(exp(b - se^2/2) - 1),reported,0.18670636152891548,0.04438102036343073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.206896551724138,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,BRAND_D,other,other,binary,binary,0.116,0.024,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",pct_premium,12.267249589049456,2.695190093119811,100*(exp(b - se^2/2) - 1),reported,0.2133218121810068,0.044135547347794506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.833333333333333,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,BRAND_D,other,other,binary,binary,0.111,0.024,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",pct_premium,11.707314345743747,2.6817477764507,100*(exp(b - se^2/2) - 1),reported,0.20451979842622262,0.044220496957021106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.625,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,BRAND_D,other,other,binary,binary,0.112,0.024,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",pct_premium,11.819077532369192,2.6844308655481086,100*(exp(b - se^2/2) - 1),reported,0.20628424925175867,0.04420376769680543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.666666666666667,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,BRAND_D,other,other,binary,binary,0.113,0.027,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,pct_premium,11.922390147727956,3.023006218961182,100*(exp(b - se^2/2) - 1),reported,0.1857761277551087,0.04438898627776935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.185185185185185,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,SCHOOL,school_quality,F_neighbourhood,level,share,0.782,0.215,,,**,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,78.2,21.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.16213819616342162,0.044577637052603133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6372093023255814,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,SCHOOL,school_quality,F_neighbourhood,level,share,0.55,0.202,,,**,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,55.00000000000001,20.200000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12208225451955368,0.04483748256899971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.722772277227723,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,SCHOOL,school_quality,F_neighbourhood,level,share,0.513,0.202,,,*,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,51.300000000000004,20.200000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11397993848201358,0.044880989421767534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5396039603960396,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,SCHOOL,school_quality,F_neighbourhood,level,share,0.52,0.203,,,*,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,52.0,20.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11495310532917943,0.04487592381119889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.561576354679803,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,SCHOOL,school_quality,F_neighbourhood,level,share,0.697,0.205,,,,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,69.69999999999999,20.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1518159739926168,0.044651757056652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,**,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,*,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,*,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,*,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,**,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,GRDP,income,F_neighbourhood,level,currency,0.005,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,0.5,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.22032632461961588,0.044065264923923174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,GRDP,income,F_neighbourhood,level,currency,0.002,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,0.2,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.0899842541331695,0.04499212706658476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,GRDP,income,F_neighbourhood,level,currency,0.002,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,0.2,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.0899842541331695,0.04499212706658476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,GRDP,income,F_neighbourhood,level,currency,0.002,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,0.2,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.0899842541331695,0.04499212706658476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,GRDP,income,F_neighbourhood,level,currency,0.005,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,0.5,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.22032632461961588,0.044065264923923174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,SO2,air_pollution,E_environment,level,ppm,-54.235,12.619,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-5423.5,1261.9,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.19059928396486586,0.044347236366786065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.29788414295903,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,SO2,air_pollution,E_environment,level,ppm,-36.103,12.49,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,-3610.3,1249.0,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.12948257130946705,0.04479509502410446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.890552441953563,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,SO2,air_pollution,E_environment,level,ppm,-36.375,12.493,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,-3637.5,1249.3,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.13041076477086028,0.04478959956789985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.911630513087329,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,SO2,air_pollution,E_environment,level,ppm,-35.1,12.514,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,-3510.0,1251.3999999999999,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.12570547526126083,0.0448170460803253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8048585584145758,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,SO2,air_pollution,E_environment,level,ppm,-52.734,12.636,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-5273.400000000001,1263.6,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.18526730430396407,0.04439332607397295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.173314339981007,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,NO2,air_pollution,E_environment,level,ppm,-41.089,6.793,,,**,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-4108.9,679.3000000000001,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.2635899524890425,0.04357775918757004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.048726630354777,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,NO2,air_pollution,E_environment,level,ppm,-8.081,8.254,,,,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,-808.0999999999999,825.4,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.04418534411010804,0.045131274629975474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9790404652289799,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,NO2,air_pollution,E_environment,level,ppm,-4.468,8.157,,,,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,-446.8,815.7,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.024737268445987195,0.04516157088494126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5477503984307956,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,NO2,air_pollution,E_environment,level,ppm,-4.761,8.338,,,,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,-476.1,833.8,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.025786583818524934,0.04516037300543182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5710002398656753,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,NO2,air_pollution,E_environment,level,ppm,-42.995,6.658,,,**,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-4299.5,665.8000000000001,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.2800530785710466,0.04336767989594205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.4576449384199455,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,O3,air_pollution,E_environment,level,ppm,-4.916,4.919,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-491.6,491.9,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.045101900743826084,0.045129424279674625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.999390119943078,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,O3,air_pollution,E_environment,level,ppm,-1.486,5.683,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,-148.6,568.3,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.011811711086769367,0.045172243678405326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26148161182474045,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,O3,air_pollution,E_environment,level,ppm,-1.301,5.706,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,-130.1,570.6,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.010299697083922456,0.04517299889382133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22800560813179108,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,O3,air_pollution,E_environment,level,ppm,-0.173,5.784,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,-17.299999999999997,578.4,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.0013511992091523855,0.04517535390599652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02991009681881051,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,O3,air_pollution,E_environment,level,ppm,-3.981,4.808,,,*,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-398.09999999999997,480.79999999999995,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.03737886179821447,0.0451438250504434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8279950083194676,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,PM10,air_pollution,E_environment,level,ug/m3,-0.014,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,-1.4000000000000001,0.3,100*b/1 (per ug/m3),reported,-0.20628424925175867,0.04420376769680543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.666666666666667,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,PM10,air_pollution,E_environment,level,ug/m3,-0.012,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,-1.2,0.3,100*b/1 (per ug/m3),reported,-0.17782168978975482,0.044455422447438706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,PM10,air_pollution,E_environment,level,ug/m3,-0.012,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,-1.2,0.3,100*b/1 (per ug/m3),reported,-0.17782168978975482,0.044455422447438706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,PM10,air_pollution,E_environment,level,ug/m3,-0.01,0.004,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,-1.0,0.4,100*b/1 (per ug/m3),reported,-0.1122250348108084,0.04489001392432335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,PM10,air_pollution,E_environment,level,ug/m3,-0.013,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,-1.3,0.3,100*b/1 (per ug/m3),reported,-0.19211356472533145,0.04433389955199957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.333333333333333,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c1,4,1,8,CO,air_pollution,E_environment,level,ppm,0.473,0.212,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,Base OLS semi-log,semi_elasticity,47.3,21.2,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.10028416282044683,0.04494765860028484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.231132075471698,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c2,4,2,8,CO,air_pollution,E_environment,level,ppm,0.191,0.275,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.854,se,"Spatial error model, 5 km weight matrix, ML",semi_elasticity,19.1,27.500000000000004,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.031360932105671945,0.04515317449769521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6945454545454545,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c3,4,3,8,CO,air_pollution,E_environment,level,ppm,0.152,0.275,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.737,se,"Spatial error model, 5 km, GM 2-step",semi_elasticity,15.2,27.500000000000004,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.024961892488209302,0.0451613186464313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5527272727272726,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c4,4,4,8,CO,air_pollution,E_environment,level,ppm,0.142,0.278,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.734,se,"Spatial error model, 5 km, GM iterated",semi_elasticity,14.2,27.800000000000004,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.023069060921068193,0.04516337278913351,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5107913669064748,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T4-c5,4,5,8,CO,air_pollution,E_environment,level,ppm,0.413,0.218,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,OLS,500.0,0.786,se,OLS with White heteroskedasticity-robust variance,semi_elasticity,41.3,21.8,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.08527284879200353,0.045010849967691946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8944954128440366,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,ols,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,M2,living_area,A_structural,level,m2,0.01,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,1.0,0.1,100*b/1 (X per m2->m2),reported,0.41169348479630913,0.04116934847963091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,M2,living_area,A_structural,level,m2,0.011,0.001,,,**,,,high,"Area of apartment, m2. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,1.0999999999999999,0.1,100*b/1 (X per m2->m2),reported,0.44501236673450423,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,490.0,m2,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,HEAT_D,heating,A_structural,binary,binary,-0.039,0.022,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",pct_premium,-3.8482006365534005,2.1158515601220067,100*(exp(b - se^2/2) - 1),reported,-0.07982807977514488,0.045031224488543256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.772727272727273,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,HEAT_D,heating,A_structural,binary,binary,-0.063,0.023,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",pct_premium,-6.130484401819302,2.1595699894850062,100*(exp(b - se^2/2) - 1),reported,-0.12280468437548113,0.04483345620057247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.739130434782609,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,HEAT_D,heating,A_structural,binary,binary,-0.171,0.026,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",pct_premium,-15.74632520819833,2.1913360910262116,100*(exp(b - se^2/2) - 1),reported,-0.28480979474697277,0.04330441323638182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.5769230769230775,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,HEAT_D,heating,A_structural,binary,binary,-0.173,0.026,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",pct_premium,-15.91466416271443,2.1869577985960205,100*(exp(b - se^2/2) - 1),reported,-0.28786632917207705,0.043263147736843956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.653846153846153,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,True
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,HEAT_D,heating,A_structural,binary,binary,-0.162,0.041,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",pct_premium,-15.027329098337693,3.4868089386149554,100*(exp(b - se^2/2) - 1),reported,-0.17572049305527582,0.044472470464606846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.951219512195122,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,HEAT_D,heating,A_structural,binary,binary,-0.178,0.028,,,**,,,high,"Heat type dummy: 1 = individual heating, 0 = central heating. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",pct_premium,-16.338559364568084,2.343438785768551,100*(exp(b - se^2/2) - 1),reported,-0.27602904475986434,0.04342029917570899,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.357142857142857,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,True
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,CORRI_D,other,other,binary,binary,0.098,0.029,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",pct_premium,10.249908675678077,3.198592076814673,100*(exp(b - se^2/2) - 1),reported,0.15091324750762505,0.04465800181348088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3793103448275863,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,CORRI_D,other,other,binary,binary,0.1,0.031,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",pct_premium,10.464001101014953,3.4260298460345076,100*(exp(b - se^2/2) - 1),reported,0.14420394014096957,0.044703221443700564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2258064516129035,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,CORRI_D,other,other,binary,binary,0.152,0.037,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",pct_premium,16.33636414160218,4.307392874798816,100*(exp(b - se^2/2) - 1),reported,0.1824697021415055,0.04441696696865595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.108108108108108,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,CORRI_D,other,other,binary,binary,0.161,0.037,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",pct_premium,17.388117208353382,4.346334384611325,100*(exp(b - se^2/2) - 1),reported,0.19288271429207288,0.044327083408737235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.351351351351352,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,CORRI_D,other,other,binary,binary,0.167,0.041,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",pct_premium,18.07614181508368,4.845192487764282,100*(exp(b - se^2/2) - 1),reported,0.18096892207537446,0.04442949583886438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.073170731707317,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,CORRI_D,other,other,binary,binary,0.178,0.04,,,**,,,high,"Corridor type dummy: 1 = stairway type, 0 = corridor type; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",pct_premium,19.38698432199777,4.779301284939203,100*(exp(b - se^2/2) - 1),reported,0.1970874772737928,0.044289320735683785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.449999999999999,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,BATH,bathrooms,A_structural,level,count,-0.034,0.023,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,-3.4000000000000004,2.3,100*b/1 (per unit count),reported,-0.06663260333867135,0.045074996376160026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4782608695652175,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,BATH,bathrooms,A_structural,level,count,0.024,0.026,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,2.4,2.6,100*b/1 (per unit count),reported,0.04166415515893598,0.0451361680888473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9230769230769231,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,BATH,bathrooms,A_structural,level,count,0.003,0.032,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,0.3,3.2,100*b/1 (per unit count),reported,0.004235155312339974,0.045174989998293065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09375,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,BATH,bathrooms,A_structural,level,count,0.005,0.032,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,0.5,3.2,100*b/1 (per unit count),reported,0.007058479650613727,0.045174269763927855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15625,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,BATH,bathrooms,A_structural,level,count,-0.036,0.034,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-3.5999999999999996,3.4000000000000004,100*b/1 (per unit count),reported,-0.0477781449812862,0.04512380359343698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0588235294117645,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,BATH,bathrooms,A_structural,level,count,-0.018,0.033,,,,,,high,Number of bathrooms. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-1.7999999999999998,3.3000000000000003,100*b/1 (per unit count),reported,-0.024633647168757707,0.045161686476055805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5454545454545454,490.0,count,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,YEARS,age,A_structural,level,years,-0.024,0.004,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,-2.4,0.4,100*b/1 (per year),reported,-0.2616124321181684,0.04360207201969473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,YEARS,age,A_structural,level,years,-0.026,0.005,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,-2.6,0.5,100*b/1 (per year),reported,-0.22868689199227302,0.043978248460052514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.199999999999999,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,YEARS,age,A_structural,level,years,-0.032,0.006,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,-3.2,0.6,100*b/1 (per year),reported,-0.23423274901586713,0.043918640440475096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.333333333333333,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,YEARS,age,A_structural,level,years,-0.033,0.006,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,-3.3000000000000003,0.6,100*b/1 (per year),reported,-0.24113299761177795,0.043842363202141445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,YEARS,age,A_structural,level,years,-0.033,0.006,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-3.3000000000000003,0.6,100*b/1 (per year),reported,-0.24113299761177795,0.043842363202141445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,YEARS,age,A_structural,level,years,-0.034,0.006,,,**,,,high,Apartment age in years. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-3.4000000000000004,0.6,100*b/1 (per year),reported,-0.24799686959560396,0.04376415345804775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.666666666666667,490.0,years,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,True
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,YEARS2,age,A_structural,squared,years,0.001,0.001,,,**,,,high,Apartment age squared (labelled re-development index). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",other,0.001,0.001,not harmonised,reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,years,nonstandard_x_transform,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,SIZE,other,other,level,count,0.001,0.001,,,**,,,high,Size of complex: total number of apartment units in the complex; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,0.1,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,count,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,BRAND_D,other,other,binary,binary,0.101,0.021,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",pct_premium,10.603273465566154,2.3231809477031895,100*(exp(b - se^2/2) - 1),reported,0.2123184489937072,0.04414542008780051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.809523809523809,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,BRAND_D,other,other,binary,binary,0.098,0.022,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",pct_premium,10.269590040886257,2.426518127238717,100*(exp(b - se^2/2) - 1),reported,0.19728096107143958,0.044287562689506836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.454545454545455,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,BRAND_D,other,other,binary,binary,0.129,0.026,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",pct_premium,13.730564988357763,2.9579943228309022,100*(exp(b - se^2/2) - 1),reported,0.21871287798934388,0.04408166533118559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.961538461538462,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,BRAND_D,other,other,binary,binary,0.124,0.026,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",pct_premium,13.163331429050329,2.943241264597855,100*(exp(b - se^2/2) - 1),reported,0.21061892029595036,0.04416203167495733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.769230769230769,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,BRAND_D,other,other,binary,binary,0.114,0.029,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",pct_premium,12.028094768758768,3.250181162164044,100*(exp(b - se^2/2) - 1),reported,0.1748503276130241,0.044479469305067534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9310344827586206,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,BRAND_D,other,other,binary,binary,0.101,0.028,,,**,,,high,Well-known developer brand dummy; no taxonomy leaf. Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",pct_premium,10.5843066306194,3.097574596937586,100*(exp(b - se^2/2) - 1),reported,0.16083271533123394,0.0445872874185599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.607142857142857,490.0,binary,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,False,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,SCHOOL,school_quality,F_neighbourhood,level,share,0.527,0.244,,,*,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,52.7,24.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09711028861813324,0.044961879360198305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1598360655737707,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,SCHOOL,school_quality,F_neighbourhood,level,share,0.58,0.24,,,*,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,57.99999999999999,24.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.10852901233751422,0.044908556829316236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4166666666666665,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,SCHOOL,school_quality,F_neighbourhood,level,share,0.822,0.19,,,**,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,82.19999999999999,19.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.19181390530714612,0.04433654745542307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.326315789473684,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,SCHOOL,school_quality,F_neighbourhood,level,share,0.758,0.186,,,**,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,75.8,18.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.18105908384477568,0.044428746167715416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.075268817204301,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,SCHOOL,school_quality,F_neighbourhood,level,share,0.681,0.213,,,**,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,68.10000000000001,21.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.14295064626945156,0.04471143561731744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1971830985915495,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,SCHOOL,school_quality,F_neighbourhood,level,share,0.917,0.211,,,**,,,high,"College entrance rate of the district (share, mean 0.73). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,91.7,21.099999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.19265310015949974,0.04432912119264389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.345971563981043,490.0,share,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,SUBWAY,transit_distance,D_location,level,m,0.001,0.001,,,,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,*,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,**,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,**,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,**,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,SUBWAY,transit_distance,D_location,level,m,-0.001,0.001,,,**,,,high,Distance to nearest subway station in metres; coefficient printed to 3 decimals (-0.001). Table legend: * 5% significance; ** 1% significance; ( ) standard error.,"ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04512936824065242,0.04512936824065242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,490.0,m,implausible_magnitude,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,GRDP,income,F_neighbourhood,level,currency,0.003,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,0.3,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.13429844431075358,0.04476614810358452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,GRDP,income,F_neighbourhood,level,currency,0.004,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,0.4,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.17782168978975482,0.044455422447438706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,GRDP,income,F_neighbourhood,level,currency,0.004,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,0.4,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.17782168978975482,0.044455422447438706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,GRDP,income,F_neighbourhood,level,currency,0.003,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,0.3,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.13429844431075358,0.04476614810358452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,GRDP,income,F_neighbourhood,level,currency,0.005,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,0.5,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.22032632461961588,0.044065264923923174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,GRDP,income,F_neighbourhood,level,currency,0.005,0.001,,,**,,,high,"Gross regional domestic product per capita, million won (printed GRDPC in Table 1). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,0.5,0.1,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.22032632461961588,0.044065264923923174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,490.0,currency,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,SO2,air_pollution,E_environment,level,ppm,6.352,13.978,,,,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,635.2,1397.8,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.020524657477986572,0.04516587881412095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.45442838746601805,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,SO2,air_pollution,E_environment,level,ppm,-15.283,13.802,,,,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,-1528.3,1380.2,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.04996039507673201,0.04511898009874076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.107303289378351,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,SO2,air_pollution,E_environment,level,ppm,-51.402,11.155,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,-5140.2,1115.5,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.2037984163882041,0.04422729338956493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.607978484984312,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,SO2,air_pollution,E_environment,level,ppm,-55.429,11.014,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,-5542.900000000001,1101.3999999999999,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.22169225345395804,0.04405128145089923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0325948792445985,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,SO2,air_pollution,E_environment,level,ppm,-53.455,12.472,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-5345.5,1247.2,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.19009135197197086,0.04435168537638052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.286000641436819,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,SO2,air_pollution,E_environment,level,ppm,-43.373,12.306,,,**,,,high,"Sulfur dioxide concentration, ppm (spatially interpolated). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-4337.3,1230.6,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.15724181916713295,0.04461341910107067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.524540874370226,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,NO2,air_pollution,E_environment,level,ppm,-8.522,9.304,,,,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,-852.2,930.4,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.041343030941622776,0.045136770697120195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9159501289767842,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,NO2,air_pollution,E_environment,level,ppm,-21.45,8.51,,,*,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,-2145.0,851.0,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.113136386751754,0.04488534504696627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5205640423031728,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,NO2,air_pollution,E_environment,level,ppm,-24.52,6.394,,,**,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,-2452.0,639.4,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.17069805944919578,0.04451237325114837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.834845167344385,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,NO2,air_pollution,E_environment,level,ppm,-22.118,6.338,,,**,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,-2211.7999999999997,633.8,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.1557272594879652,0.044624259455408426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4897443988639947,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,NO2,air_pollution,E_environment,level,ppm,-44.275,6.963,,,**,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-4427.5,696.3,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.2760878850312433,0.04341953570801914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.358609794628752,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,NO2,air_pollution,E_environment,level,ppm,-26.219,7.19,,,**,,,high,"Nitrogen dioxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-2621.9,719.0,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.16254543892994774,0.04457461024090638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6465924895688455,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,O3,air_pollution,E_environment,level,ppm,-3.978,6.379,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,-397.8,637.9,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.02816059757227083,0.045157479113503166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6236087160997023,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,O3,air_pollution,E_environment,level,ppm,1.04,5.813,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,104.0,581.3,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.008082036211370804,0.04517391970836392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17890934113194565,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,O3,air_pollution,E_environment,level,ppm,-4.31,4.749,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,-430.99999999999994,474.9,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.040964942906923055,0.045137474214611964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9075594862076226,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,O3,air_pollution,E_environment,level,ppm,-4.758,4.731,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,-475.8,473.09999999999997,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.04538639424906522,0.04512884220099359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.00570703868104,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,O3,air_pollution,E_environment,level,ppm,-4.596,4.951,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-459.6,495.09999999999997,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.041899371088310364,0.045135723728943554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9282973136740054,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,O3,air_pollution,E_environment,level,ppm,4.066,4.958,,,,,,high,"Ozone concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,406.59999999999997,495.8,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.03702243442065788,0.04514442446080222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8200887454618797,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,PM10,air_pollution,E_environment,level,ug/m3,-0.006,0.003,,,,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,-0.6,0.3,100*b/1 (per ug/m3),reported,-0.0899842541331695,0.04499212706658476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,PM10,air_pollution,E_environment,level,ug/m3,-0.007,0.003,,,*,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,-0.7000000000000001,0.3,100*b/1 (per ug/m3),reported,-0.10482848367219184,0.04492649300236792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3333333333333335,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,PM10,air_pollution,E_environment,level,ug/m3,-0.021,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,-2.1,0.3,100*b/1 (per ug/m3),reported,-0.30151134457776363,0.0430730492253948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,PM10,air_pollution,E_environment,level,ug/m3,-0.022,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,-2.1999999999999997,0.3,100*b/1 (per ug/m3),reported,-0.31447831100338325,0.0428834060459159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.333333333333333,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,PM10,air_pollution,E_environment,level,ug/m3,-0.013,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,-1.3,0.3,100*b/1 (per ug/m3),reported,-0.19211356472533145,0.04433389955199957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.333333333333333,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,PM10,air_pollution,E_environment,level,ug/m3,-0.011,0.003,,,**,,,high,"PM10 concentration, ug/m3 (printed μm/m3). Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-1.0999999999999999,0.3,100*b/1 (per ug/m3),reported,-0.16341640569676505,0.044568110644572285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6666666666666665,490.0,ug/m3,,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,True,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c1,5,1,9,CO,air_pollution,E_environment,level,ppm,0.245,0.294,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.893,se,"Spatial error model, 1.7 km weight matrix, ML",semi_elasticity,24.5,29.4,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.03761951424040615,0.04514341708848738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c2,5,2,9,CO,air_pollution,E_environment,level,ppm,0.089,0.271,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.765,se,"Spatial error model, 1.7 km, GM 2-step",semi_elasticity,8.9,27.1,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.01483456733220994,0.045170424123920166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.32841328413284127,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c3,5,3,9,CO,air_pollution,E_environment,level,ppm,0.144,0.217,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.822,se,"Spatial error model, 10 km, ML",semi_elasticity,14.399999999999999,21.7,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.029964680959366278,0.04515510950126725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6635944700460829,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c4,5,4,9,CO,air_pollution,E_environment,level,ppm,0.178,0.217,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_error,500.0,0.768,se,"Spatial error model, 10 km, GM 2-step",semi_elasticity,17.8,21.7,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.037030898765543524,0.04514441029282553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8202764976958525,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c5,5,5,9,CO,air_pollution,E_environment,level,ppm,0.454,0.213,,,*,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.79,se,"Spatial lag model, 20 km, ML",semi_elasticity,45.4,21.3,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,0.095846040199635,0.0449674153359521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.131455399061033,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687,3e2f420e36b96c93f3636cc35296dc7c69414687:T5-c6,5,6,9,CO,air_pollution,E_environment,level,ppm,-0.114,0.224,,,,,,high,"Carbon monoxide concentration, ppm. Table legend: * 5% significance; ** 1% significance; ( ) standard error.","ln PART (apartment price, million won)",log,spatial_lag,500.0,0.806,se,"Spatial lag model, 20 km, IV (2SLS)",semi_elasticity,-11.4,22.400000000000002,100*b (unit as reported: ppm; pollution unit not ug/m3),reported,-0.02298497532247986,0.045163460282767444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5089285714285714,490.0,ppm,unit_not_harmonisable,2013.0,South Korea,Asia,High income,2008.0,2008.0,condo_apartment,listing,journal,False,False,OLS,,2008.0,spatial,journal,False,True,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c1,2,1,17,Number of Local Lodging,other,other,level,count,0.0394,0.0166,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,181626.0,0.51,se,"7-digit postcode FE + quarter FE, SE clustered at full postcode",semi_elasticity,3.94,1.66,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005569347865975113,0.002346476512060581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.373493975903614,181616.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c2,2,2,17,Number of Local Lodging,other,other,level,count,0.0394,0.0201,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,181626.0,0.51,se,"7-digit postcode FE + quarter FE, SE clustered at postcode sector",semi_elasticity,3.94,2.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.004599583603993787,0.0023464880822404856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9601990049751243,181616.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c3,2,3,17,Number of Local Lodging,other,other,level,count,0.0394,0.0196,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,181626.0,0.51,se,"7-digit postcode FE + quarter FE, SE clustered at civil parish",semi_elasticity,3.94,1.96,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.004716917342240306,0.002346486799693147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.010204081632653,181616.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c4,2,4,17,Number of Local Lodging,other,other,level,count,0.0605,0.0258,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,185647.0,0.323,se,4-digit postcode FE + quarter FE,semi_elasticity,6.05,2.58,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005442481555320266,0.0023209260186324446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3449612403100772,185637.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c5,2,5,17,Number of Local Lodging,other,other,level,count,0.0431,0.0196,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,185653.0,0.328,se,civil parish FE + quarter FE,semi_elasticity,4.31,1.96,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005103595593088778,0.002320892659502089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1989795918367347,185643.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c6,2,6,17,Number of Local Lodging,other,other,level,count,0.0233,0.0125,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,181626.0,0.517,se,7-digit postcode FE + quarter FE + civil parish time trends,semi_elasticity,2.33,1.25,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0043738582150817384,0.002346490458734838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.864,181616.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c7,2,7,17,Number of Local Lodging,other,other,level,count,0.028,0.0128,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,185653.0,0.342,se,civil parish FE + quarter FE + civil parish time trends (preferred),semi_elasticity,2.8000000000000003,1.28,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0050769533811955946,0.0023208929742608428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1875,185643.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T2-c8,2,8,17,Number of Local Lodging,other,other,level,count,0.0492,0.0159,,,***,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density). Col 8 dependent variable is residualized log price (x_transform level).,Residualized log(prices),other,FE_panel,185653.0,0.178,se,"residualized log prices (residuals from quarter FE, parish FE, parish trends) as outcome",other,0.0492,0.0159,not harmonised,reported,0.007181538447590484,0.0023208630348920465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.094339622641509,185643.0,count,,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c1,3,1,20,Number of LL,other,other,level,count,0.0233,0.0128,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,144579.0,0.372,se,"Drop before 2014 (2014 onwards only); civil parish FE, quarter FE, parish time trends",semi_elasticity,2.33,1.28,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.004787440461057157,0.0026300102103661636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8203125,144569.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c2,3,2,20,Number of LL,other,other,level,count,0.0314,0.0128,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,177182.0,0.341,se,"Only 1 transaction per day; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.1399999999999997,1.28,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005827935801260392,0.00237571905274309,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4531249999999996,177172.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c3,3,3,20,Number of LL,other,other,level,count,0.0342,0.0128,,,***,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,167032.0,0.304,se,"No new built (drop first transaction after new-building evaluation); civil parish FE, quarter FE, parish time trends",semi_elasticity,3.42,1.28,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.006537625144516476,0.002446830463444763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.671875,167022.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c4,3,4,20,Number of LL,other,other,level,count,0.0306,0.0178,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,178327.0,0.34,se,"Dropping 95th percentile of LL per parish-quarter; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.06,1.78,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.004071003266587531,0.0023680999393875177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7191011235955056,178317.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c5,3,5,20,Number of LL,other,other,level,count,-0.00316,0.0519,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,41067.0,0.293,se,"Drop after 2013 (placebo, pre-2014 period only); civil parish FE, quarter FE, parish time trends",semi_elasticity,-0.316,5.19,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0003004872843778677,0.004935218373168144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06088631984585742,41057.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c6,3,6,20,Number of LL,other,other,level,count,0.0342,0.012,,,***,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density). Log of price per m2 as dependent variable.,Log(prices/m2),per_m2,FE_panel,185653.0,0.226,se,"Full sample, price per square meter outcome; civil parish FE, quarter FE, parish time trends",monetary,0.0342,0.012,"b (currency per unit, as reported)",reported,0.006614485523435558,0.002320872113486161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.85,185643.0,count,,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T3-c7,3,7,20,LL neighbourhood postcodes,other,other,other,count,0.00775,0.0544,,,,,,high,"Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density). Average number of new STR in neighbouring postcodes (same 6-digit prefix), not own postcode.",Log(prices),log,FE_panel,185653.0,0.341,se,"SUTVA test: quarterly average of local lodgings in all postcodes sharing the same first 6 digits; civil parish FE, quarter FE, parish time trends",other,0.00775,0.0544,not harmonised,reported,0.0003306461651157999,0.002320922759006389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.14246323529411764,185643.0,count,nonstandard_x_transform,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T4-c1,4,1,21,Number of Local Lodging t−1,other,other,other,count,0.0734,0.0237,,,***,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density). One-quarter lag of the STR count.,Log(prices),log,FE_panel,183316.0,0.343,se,"Lagged STR count (t-1); civil parish FE, quarter FE, parish time trends",other,0.0734,0.0237,not harmonised,reported,0.007233492099050696,0.0023356098466962055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.09704641350211,183306.0,count,nonstandard_x_transform,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T4-c2,4,2,21,Number of Local Lodging t,other,other,level,count,0.028,0.0128,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density). Identical to Table 2 column 7.,Log(prices),log,FE_panel,185653.0,0.342,se,Baseline (same as Table 2 col 7),semi_elasticity,2.8000000000000003,1.28,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0050769533811955946,0.0023208929742608428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1875,185643.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T4-c3,4,3,21,Number of Local Lodging t+1,other,other,other,count,0.028,0.0172,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density). One-quarter lead of the STR count (falsification test).,Log(prices),log,FE_panel,172640.0,0.337,se,"Lead STR count (t+1), falsification; civil parish FE, quarter FE, parish time trends",other,0.028,0.0172,not harmonised,reported,0.0039180332359831345,0.0024067918449610687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.627906976744186,172630.0,count,nonstandard_x_transform,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T5-c1,5,1,22,Number of LL,other,other,level,count,0.0201,0.0149,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,127347.0,0.356,se,"Lisbon metropolitan area; civil parish FE, quarter FE, parish time trends",semi_elasticity,2.01,1.49,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.003780327093083209,0.002802332024225862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.348993288590604,127337.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T5-c2,5,2,22,Number of LL,other,other,level,count,0.0303,0.0165,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,33999.0,0.333,se,"Lisbon municipality; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.0300000000000002,1.6500000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.00996019743751821,0.005423869891717836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8363636363636364,33989.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T5-c3,5,3,22,Number of LL,other,other,level,count,0.0576,0.0298,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,6973.0,0.37,se,"Lisbon historical center; civil parish FE, quarter FE, parish time trends",semi_elasticity,5.76,2.98,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.02315749298079547,0.011980786299092102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9328859060402683,6963.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T5-c4,5,4,22,Number of LL,other,other,level,count,0.0394,0.023,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,58306.0,0.309,se,"Porto metropolitan area; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.94,2.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0070947660149016005,0.004141614678749666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7130434782608694,58296.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T5-c5,5,5,22,Number of LL,other,other,level,count,0.0134,0.0122,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,14176.0,0.372,se,"Porto municipality; civil parish FE, quarter FE, parish time trends",semi_elasticity,1.34,1.22,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.009227899524733603,0.00840152046281716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0983606557377048,14166.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T5-c6,5,6,22,Number of LL,other,other,level,count,0.0185,0.00966,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,5087.0,0.41,se,"Porto historical center; civil parish FE, quarter FE, parish time trends",semi_elasticity,1.8499999999999999,0.966,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.026867930003305764,0.01402941642334777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9151138716356106,5077.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T10-c1,10,1,32,Number of Local Lodging,other,other,level,count,0.0344,0.0155,,,**,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,93116.0,0.359,se,"Civil parishes below median unemployment rate; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.44,1.55,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.007273214947428877,0.003277175339684524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2193548387096773,93106.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T10-c2,10,2,32,Number of Local Lodging,other,other,level,count,0.0309,0.0203,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,92537.0,0.271,se,"Civil parishes above median unemployment rate; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.09,2.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0050040629374732025,0.003287458823000195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.522167487684729,92527.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T10-c3,10,3,32,Number of Local Lodging,other,other,level,count,0.0721,0.0423,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,94201.0,0.328,se,"Civil parishes below median population density; civil parish FE, quarter FE, parish time trends",semi_elasticity,7.21,4.2299999999999995,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005553713803813775,0.003258281468811688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7044917257683216,94191.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T10-c4,10,4,32,Number of Local Lodging,other,other,level,count,0.0185,0.0114,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,91452.0,0.36,se,"Civil parishes above median population density; civil parish FE, quarter FE, parish time trends",semi_elasticity,1.8499999999999999,1.1400000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005366458376694915,0.003306898675368759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6228070175438596,91442.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T10-c5,10,5,32,Number of Local Lodging,other,other,level,count,0.0106,0.0123,,,,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,114199.0,0.336,se,"Non-coastal civil parishes; civil parish FE, quarter FE, parish time trends",semi_elasticity,1.06,1.23,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0025502761338590334,0.002959282683628878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8617886178861789,114189.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a,3fff47f2ad1427c42ba357bc9b2363011924b07a:T10-c6,10,6,32,Number of Local Lodging,other,other,level,count,0.0357,0.019,,,*,,,high,Number of new short-term rental (local lodging) establishments opening in the property's postcode and quarter (neighbourhood STR density).,Log(prices),log,FE_panel,54318.0,0.388,se,"Coastal civil parishes; civil parish FE, quarter FE, parish time trends",semi_elasticity,3.5700000000000003,1.9,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.00806247896721722,0.004290955192636615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.878947368421053,54308.0,count,unit_not_harmonisable,2026.0,Portugal,Europe,High income,2010.0,2017.0,mixed,transaction,journal,True,True,clustered,,2013.5,panel,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Floor1,floor_level,A_structural,dummy,binary,-1444.32,,-1.69,,,,,high,first-floor dummy,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-1444.32,854.6272189349112,"b (currency per unit, as reported)",|b/t|,-0.09479194389095359,0.056089907627783195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.69,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Floor1,floor_level,A_structural,dummy,binary,-0.02,,-0.7,,,,,high,first-floor dummy,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,-2.020132614629566,2.800567638019301,100*(exp(b - se^2/2) - 1),|b/t|,-0.03940989166038625,0.05629984522912321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Floor1,floor_level,A_structural,dummy,binary,-1498.37,,-1.8,,,,,high,first-floor dummy,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-1498.37,832.4277777777777,"b (currency per unit, as reported)",|b/t|,-0.10090091909944687,0.05605606616635937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Floor1,floor_level,A_structural,dummy,binary,-0.02,,-0.62,,,,,high,first-floor dummy,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,-2.0311182948745676,3.1619312042153394,100*(exp(b - se^2/2) - 1),|b/t|,-0.034911747316117805,0.05630926986470614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.62,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,FloorT,floor_level,A_structural,dummy,binary,-1570.3,,-1.77,,,,,high,top-floor dummy,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-1570.3,887.1751412429378,"b (currency per unit, as reported)",|b/t|,-0.09923593687436472,0.05606550105896311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.77,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,FloorT,floor_level,A_structural,dummy,binary,-0.06,,-2.27,,*,,,high,top-floor dummy,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,-5.856438413534015,2.4892454632182783,100*(exp(b - se^2/2) - 1),|b/t|,-0.12686655213055853,0.055888348956193185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.27,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,FloorT,floor_level,A_structural,dummy,binary,-1851.6,,-2.15,,*,,,high,top-floor dummy,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-1851.6,861.2093023255813,"b (currency per unit, as reported)",|b/t|,-0.12025960910186856,0.05593470190784585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.15,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,FloorT,floor_level,A_structural,dummy,binary,-0.06,,-2.13,,*,,,high,top-floor dummy,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,-5.860903425439989,2.6528578410823904,100*(exp(b - se^2/2) - 1),|b/t|,-0.11915687218698962,0.05594219351501859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.13,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Build,storeys,A_structural,dummy,binary,3284.04,,4.75,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,3284.04,691.3768421052631,"b (currency per unit, as reported)",|b/t|,0.2585333134654985,0.054428065992736537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Build,storeys,A_structural,dummy,binary,0.15,,6.53,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,16.152775503309847,2.6688382298505733,100*(exp(b - se^2/2) - 1),|b/t|,0.34529444509793267,0.052878169233986624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.53,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Build,storeys,A_structural,dummy,binary,3115.5,,4.64,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,3115.5,671.4439655172414,"b (currency per unit, as reported)",|b/t|,0.2529334954763207,0.054511529197482915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.64,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Build,storeys,A_structural,dummy,binary,0.15,,6.48,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,16.15230076732388,2.689431117426581,100*(exp(b - se^2/2) - 1),|b/t|,0.3429625796607519,0.05292632402172097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.48,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Phone,other,other,binary,binary,1311.96,,1.55,,,,,high,Phone: wired telephone connection dummy,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,1311.96,846.4258064516129,"b (currency per unit, as reported)",|b/t|,0.08700145717344539,0.056129972369964753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.55,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Phone,other,other,binary,binary,0.08,,2.87,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,8.286629707679527,3.0196155196514525,100*(exp(b - se^2/2) - 1),|b/t|,0.15963254229022042,0.055621094874641265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.87,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Phone,other,other,binary,binary,1098.97,,1.35,,,,,high,Phone: wired telephone connection dummy,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,1098.97,814.0518518518518,"b (currency per unit, as reported)",|b/t|,0.07584479122297429,0.05618132683183281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.35,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Phone,other,other,binary,binary,0.07,,2.61,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,7.212251859820729,2.876458723670313,100*(exp(b - se^2/2) - 1),|b/t|,0.14549206992518549,0.05574408809394079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.61,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Area,living_area,A_structural,level,m2,478.44,,7.59,,*,,,high,total area of the apartment,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,478.44,63.03557312252965,"b (currency per unit, as reported)",|b/t|,0.3932019202306444,0.0518052595824301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.59,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Area,living_area,A_structural,log,m2,0.65,,7.24,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,0.65,0.08977900552486189,b (log-log),|b/t|,0.377710091428499,0.052169902131008146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.24,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Area,living_area,A_structural,level,m2,461.5,,7.24,,*,,,high,total area of the apartment,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,461.5,63.74309392265193,"b (currency per unit, as reported)",|b/t|,0.377710091428499,0.052169902131008146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.24,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Area,living_area,A_structural,log,m2,0.67,,7.46,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,0.67,0.08981233243967829,b (log-log),|b/t|,0.38748588575837034,0.05194180774240889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.46,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Balc,terrace_balcony,C_exterior,level,count,-528.53,,-0.52,,,,,high,rank 0-2: balcony and/or loggia,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-528.53,1016.4038461538461,"b (currency per unit, as reported)",|b/t|,-0.029286113740986164,0.05631944950189647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.52,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Balc,terrace_balcony,C_exterior,level,count,-0.01,,-0.34,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",semi_elasticity,-1.0,2.941176470588235,100*b/1 (per unit count),|b/t|,-0.01915331561482207,0.05633328122006491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Balc,terrace_balcony,C_exterior,level,count,-915.68,,-0.92,,,,,high,rank 0-2: balcony and/or loggia,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-915.68,995.3043478260869,"b (currency per unit, as reported)",|b/t|,-0.05176662622782489,0.05626807198676618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.92,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Balc,terrace_balcony,C_exterior,level,count,-0.07,,-0.14,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",semi_elasticity,-7.000000000000001,50.0,100*b/1 (per unit count),|b/t|,-0.007887860981163957,0.05634186415117112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Cond2,condition,A_structural,dummy,binary,1312.06,,1.17,,,,,high,condition 2 satisfactory (ref: repair needed),"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,1312.06,1121.4188034188035,"b (currency per unit, as reported)",|b/t|,0.06577925788406055,0.0562215879350945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.17,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Cond2,condition,A_structural,dummy,binary,0.02,,0.6,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,1.9634718913845939,3.4006711334225193,100*(exp(b - se^2/2) - 1),|b/t|,0.03378686891997429,0.05631144819995716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Cond2,condition,A_structural,dummy,binary,1614.12,,1.46,,,,,high,condition 2 satisfactory (ref: repair needed),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,1614.12,1105.5616438356165,"b (currency per unit, as reported)",|b/t|,0.08198475373036565,0.05615394091120936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.46,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Cond2,condition,A_structural,dummy,binary,0.02,,0.64,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,1.970331643888068,3.1881291875836117,100*(exp(b - se^2/2) - 1),|b/t|,0.036036493039342664,0.056307020373972914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.64,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Cond3,condition,A_structural,dummy,binary,1967.68,,1.43,,,,,high,condition 3 cosmetic repair needed,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,1967.68,1376.0,"b (currency per unit, as reported)",|b/t|,0.08031111430421749,0.05616161839455769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.43,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Cond3,condition,A_structural,dummy,binary,0.02,,0.42,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,1.9045304637745009,4.858101619175028,100*(exp(b - se^2/2) - 1),|b/t|,0.023657695904667257,0.05632784739206491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Cond3,condition,A_structural,dummy,binary,1990.26,,1.43,,,,,high,condition 3 cosmetic repair needed,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,1990.26,1391.79020979021,"b (currency per unit, as reported)",|b/t|,0.08031111430421749,0.05616161839455769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.43,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Cond3,condition,A_structural,dummy,binary,0.02,,0.33,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,1.8329411488157143,6.183038424404581,100*(exp(b - se^2/2) - 1),|b/t|,0.01859018043623746,0.05633388010981049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Cond4,condition,A_structural,dummy,binary,2026.7,,1.75,,,,,high,condition 4 normal,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,2026.7,1158.1142857142856,"b (currency per unit, as reported)",|b/t|,0.09812548460529193,0.056071705488738244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Cond4,condition,A_structural,dummy,binary,0.04,,1.11,,,,,high,condition 4 normal,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,4.01351972430084,3.7506694565491467,100*(exp(b - se^2/2) - 1),|b/t|,0.062419459360192775,0.05623374717134484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.11,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Cond4,condition,A_structural,dummy,binary,2204.5,,1.94,,,,,high,condition 4 normal,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,2204.5,1136.340206185567,"b (currency per unit, as reported)",|b/t|,0.10865941647774756,0.05601000849368431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.94,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Cond4,condition,A_structural,dummy,binary,0.04,,1.25,,,,,high,condition 4 normal,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,4.0278015473872,3.3305944774156426,100*(exp(b - se^2/2) - 1),|b/t|,0.0702554919986499,0.05620439359891992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Cond5,condition,A_structural,dummy,binary,2266.53,,2.1,,*,,,high,condition 5 good,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,2266.53,1079.3,"b (currency per unit, as reported)",|b/t|,0.11750194089630359,0.055953305188715996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Cond5,condition,A_structural,dummy,binary,0.05,,1.58,,,,,high,condition 5 good,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,5.074483458728696,3.326807267012735,100*(exp(b - se^2/2) - 1),|b/t|,0.08867224096627026,0.05612167149763941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.58,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Cond5,condition,A_structural,dummy,binary,2321.35,,2.17,,*,,,high,condition 5 good,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,2321.35,1069.7465437788019,"b (currency per unit, as reported)",|b/t|,0.12136190104110074,0.05592714333691278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.17,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Cond5,condition,A_structural,dummy,binary,0.06,,1.78,,,,,high,condition 5 good,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,6.123347781333988,3.579224314197841,100*(exp(b - se^2/2) - 1),|b/t|,0.09979102427069753,0.05606237318578512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.78,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Cond6,condition,A_structural,dummy,binary,4600.19,,2.76,,*,,,high,condition 6 perfect,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,4600.19,1666.7355072463768,"b (currency per unit, as reported)",|b/t|,0.1536614949357458,0.055674454686864425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.76,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Cond6,condition,A_structural,dummy,binary,0.13,,2.5,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,13.728972771593462,5.921907593288034,100*(exp(b - se^2/2) - 1),|b/t|,0.13948208816291768,0.05579283526516707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Cond6,condition,A_structural,dummy,binary,4615.75,,2.79,,*,,,high,condition 6 perfect,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,4615.75,1654.3906810035842,"b (currency per unit, as reported)",|b/t|,0.15529166144041276,0.055660093706241136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.79,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Cond6,condition,A_structural,dummy,binary,0.14,,2.75,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,14.878416151119955,5.8559393396367945,100*(exp(b - se^2/2) - 1),|b/t|,0.15311782647725627,0.05567920962809319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,10,Cond7,condition,A_structural,dummy,binary,9713.48,,6.18,,*,,,high,condition 7 Euro-renovation,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,9713.48,1571.7605177993528,"b (currency per unit, as reported)",|b/t|,0.32883860726543435,0.05321013062547482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.18,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,10,Cond7,condition,A_structural,dummy,binary,0.3,,6.08,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,34.82165949434486,6.660487537381594,100*(exp(b - se^2/2) - 1),|b/t|,0.3240805567107715,0.05330272314321901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.08,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,10,Cond7,condition,A_structural,dummy,binary,9401.38,,5.79,,*,,,high,condition 7 Euro-renovation,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,9401.38,1623.727115716753,"b (currency per unit, as reported)",|b/t|,0.3101431235810363,0.05356530631796827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.79,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,10,Cond7,condition,A_structural,dummy,binary,0.31,,6.36,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,36.18064657152178,6.64562555630464,100*(exp(b - se^2/2) - 1),|b/t|,0.3373402020612933,0.05304091227378825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.36,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,DCBD,cbd_distance,D_location,level,km,-828.21,,-6.72,,*,,,high,road-network distance to CBD (central square),"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-828.21,123.24553571428572,"b (currency per unit, as reported)",|b/t|,-0.3540972051209995,0.05269303647633922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.72,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,DCBD,cbd_distance,D_location,log,km,-0.24,,-10.24,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,-0.24,0.0234375,b (log-log),|b/t|,-0.4997455633034749,0.04880327766635496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.24,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,DCBD,cbd_distance,D_location,level,km,-1935.77,,-7.66,,*,,,high,road-network distance to CBD (central square),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-1935.77,252.71148825065274,"b (currency per unit, as reported)",|b/t|,-0.3962610622146086,0.051731209166397986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.66,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,DCBD,cbd_distance,D_location,log,km,-0.23,,-6.82,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,-0.23,0.03372434017595308,b (log-log),|b/t|,-0.3586928960287861,0.05259426627988066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.82,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,DSCent,commercial_distance,D_location,level,km,-47.62,,-0.23,,,,,high,distance to nearest secondary centre (business/commercial concentration),"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-47.62,207.04347826086953,"b (currency per unit, as reported)",|b/t|,-0.012957943895590567,0.05633888650256768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,DSCent,commercial_distance,D_location,log,km,-0.05,,-4.91,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,-0.05,0.010183299389002037,b (log-log),|b/t|,-0.26663208597198074,0.0543038871633362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.91,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,DSCent,commercial_distance,D_location,level,km,-196.67,,-0.87,,,,,high,distance to nearest secondary centre (business/commercial concentration),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-196.67,226.0574712643678,"b (currency per unit, as reported)",|b/t|,-0.04896015993215738,0.05627604589903147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.87,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,DSCent,commercial_distance,D_location,log,km,-0.04,,-3.47,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,-0.04,0.011527377521613832,b (log-log),|b/t|,-0.19187943904785607,0.05529666831350319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.47,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,DStop,transit_distance,D_location,level,km,-2864.5,,-1.36,,,,,high,distance to nearest public transport stop,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-2864.5,2106.25,"b (currency per unit, as reported)",|b/t|,-0.07640333691061968,0.056178924198985056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.36,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,DStop,transit_distance,D_location,log,km,-0.01,,-0.34,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,-0.01,0.029411764705882353,b (log-log),|b/t|,-0.01915331561482207,0.05633328122006491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,DStop,transit_distance,D_location,level,km,-5975.94,,-2.9,,*,,,high,distance to nearest public transport stop,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-5975.94,2060.6689655172413,"b (currency per unit, as reported)",|b/t|,-0.16125800242919325,0.05560620773420457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,DStop,transit_distance,D_location,log,km,-0.0,,-0.26,,,,,low,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes); printed as -0.00/0.00","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,-0.0,0.0,b (log-log),|b/t|,-0.014647768770770208,0.05633757219527003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,DRail,transit_distance,D_location,level,km,-37.42,,-0.41,,,,,high,distance to the main railway station,"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-37.42,91.26829268292684,"b (currency per unit, as reported)",|b/t|,-0.023094721526247846,0.05632858908840939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.41,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,DRail,transit_distance,D_location,log,km,-0.11,,-5.44,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,-0.11,0.020220588235294115,b (log-log),|b/t|,-0.29305240920200204,0.05386992816213273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.44,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,DRail,transit_distance,D_location,level,km,303.53,,2.3,,*,,,high,distance to the main railway station,"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,303.53,131.9695652173913,"b (currency per unit, as reported)",|b/t|,0.1285156882226032,0.05587638618374054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,DRail,transit_distance,D_location,log,km,-0.11,,-4.25,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,-0.11,0.02588235294117647,b (log-log),|b/t|,-0.2328767123287671,0.0547945205479452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.25,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,DWater,water_proximity,E_environment,level,km,-1702.6,,-3.79,,*,,,high,"straight-line distance to water (ponds, river) merged with main parks/green areas","asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,-1702.6,449.2348284960422,"b (currency per unit, as reported)",|b/t|,-0.20883393885147344,0.05510130312703785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.79,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,DWater,water_proximity,E_environment,log,km,-0.01,,-0.37,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,-0.01,0.02702702702702703,b (log-log),|b/t|,-0.02084260964313135,0.05633137741386851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,DWater,water_proximity,E_environment,level,km,-299.82,,-0.58,,,,,high,"straight-line distance to water (ponds, river) merged with main parks/green areas","asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,-299.82,516.9310344827586,"b (currency per unit, as reported)",|b/t|,-0.03266186210584726,0.05631355535490907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.58,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,DWater,water_proximity,E_environment,log,km,-0.01,,-0.93,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,-0.01,0.01075268817204301,b (log-log),|b/t|,-0.05232777447866215,0.05626642417060446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.93,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,DNuis,hazard_site,E_environment,level,km,2816.35,,3.8,,*,,,high,"distance to nearest nuisance (contaminating factories, slagheaps)","asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,2816.35,741.1447368421053,"b (currency per unit, as reported)",|b/t|,0.20936083029259847,0.05509495534015749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,DNuis,hazard_site,E_environment,log,km,0.03,,1.54,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",elasticity,0.03,0.01948051948051948,b (log-log),|b/t|,0.08644436534965663,0.0561327047725043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.54,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,DNuis,hazard_site,E_environment,level,km,3184.94,,4.18,,*,,,high,"distance to nearest nuisance (contaminating factories, slagheaps)","asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,3184.94,761.9473684210527,"b (currency per unit, as reported)",|b/t|,0.22924426648709187,0.054843125953849735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.18,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,DNuis,hazard_site,E_environment,log,km,0.03,,1.29,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",elasticity,0.03,0.023255813953488372,b (log-log),|b/t|,0.07249203560053111,0.05619537643452024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.29,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LIN,3,LIN,11,Prest,other,other,binary,binary,4761.57,,5.97,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"asking price, USD",level,OLS,325.0,0.69,t,"Whole sample, linear (adjusted R2)",monetary,4761.57,797.5829145728643,"b (currency per unit, as reported)",|b/t|,0.318818155555106,0.05340337613988375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.97,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOG,3,LOG,11,Prest,other,other,binary,binary,0.09,,2.74,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear (adjusted R2)",pct_premium,9.358418689723802,3.5940031216594495,100*(exp(b - se^2/2) - 1),|b/t|,0.15257401894998437,0.0556839485218921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.74,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,Prest,other,other,binary,binary,2453.18,,2.77,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",monetary,2453.18,885.624548736462,"b (currency per unit, as reported)",|b/t|,0.15420502387933557,0.055669683710951476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.77,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,Prest,other,other,binary,binary,0.08,,2.01,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",pct_premium,8.242937946568961,4.3115903191043135,100*(exp(b - se^2/2) - 1),|b/t|,0.11253132203094983,0.05598573235370639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,N·DCBD,cbd_distance,D_location,other,km,737.47,,2.91,,*,,,high,interaction North direction x DCBD (ref East),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",other,737.47,253.42611683848796,not harmonised,|b/t|,0.16179953166798686,0.0556012136316106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.91,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,N·DCBD,cbd_distance,D_location,other,km,0.01,,0.35,,,,,high,interaction North direction x DCBD (ref East),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",other,0.01,0.028571428571428574,not harmonised,|b/t|,0.019716432565542064,0.056332664472977335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,S·DCBD,cbd_distance,D_location,other,km,-61.76,,-0.52,,,,,high,interaction South direction x DCBD (ref East),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",other,-61.76,118.76923076923076,not harmonised,|b/t|,-0.029286113740986164,0.05631944950189647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.52,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,S·DCBD,cbd_distance,D_location,other,km,-0.03,,-1.56,,,,,high,interaction South direction x DCBD (ref East),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",other,-0.03,0.01923076923076923,not harmonised,|b/t|,-0.08755846738834223,0.05612722268483477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.56,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LINDIR,3,LINDIR,11,W·DCBD,cbd_distance,D_location,other,km,746.62,,5.01,,*,,,high,interaction West direction x DCBD (ref East),"asking price, USD",level,OLS,325.0,0.71,t,"Whole sample, linear with direction x DCBD interactions (adjusted R2)",other,746.62,149.02594810379242,not harmonised,|b/t|,0.2716654106143959,0.054224632857164855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.01,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T3-LOGDIR,3,LOGDIR,11,W·DCBD,cbd_distance,D_location,other,km,-0.04,,-1.74,,,,,high,interaction West direction x DCBD (ref East),"ln(asking price, USD)",log,OLS,325.0,0.77,t,"Whole sample, log-linear with direction x DCBD interactions (adjusted R2)",other,-0.04,0.022988505747126436,not harmonised,|b/t|,-0.09757012072883484,0.056074782028066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.74,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Floor1,floor_level,A_structural,dummy,binary,-7739.64,,-2.28,,*,,,high,first-floor dummy,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-7739.64,3394.5789473684213,"b (currency per unit, as reported)",|b/t|,-0.272126753312237,0.1193538391720338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.28,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Floor1,floor_level,A_structural,dummy,binary,-0.2,,-2.24,,*,,,high,first-floor dummy,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,-18.452618729611135,7.310096009624838,100*(exp(b - se^2/2) - 1),|b/t|,-0.2676975571988046,0.11950783803518063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.24,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Floor1,floor_level,A_structural,dummy,binary,-240.1,,-0.33,,,,,high,first-floor dummy,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-240.1,727.5757575757575,"b (currency per unit, as reported)",|b/t|,-0.021296577291135096,0.06453508270040938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Floor1,floor_level,A_structural,dummy,binary,-0.01,,-0.22,,,,,high,first-floor dummy,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,-1.0972416891766867,4.500226517041673,100*(exp(b - se^2/2) - 1),|b/t|,-0.014199507224627194,0.06454321465739633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,FloorT,floor_level,A_structural,dummy,binary,-2934.34,,-1.48,,,,,high,top-floor dummy,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-2934.34,1982.6621621621623,"b (currency per unit, as reported)",|b/t|,-0.18055441069973094,0.12199622344576415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.48,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,FloorT,floor_level,A_structural,dummy,binary,-0.1,,-1.84,,,,,high,top-floor dummy,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,-9.6497898592384,4.91759466323891,100*(exp(b - se^2/2) - 1),|b/t|,-0.22250280594789706,0.12092543801516145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.84,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,FloorT,floor_level,A_structural,dummy,binary,-987.3,,-1.38,,,,,high,top-floor dummy,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-987.3,715.4347826086956,"b (currency per unit, as reported)",|b/t|,-0.08872728702514433,0.06429513552546691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.38,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,FloorT,floor_level,A_structural,dummy,binary,-0.05,,-1.6,,,,,high,top-floor dummy,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,-4.923492961478814,2.9725919515647314,100*(exp(b - se^2/2) - 1),|b/t|,-0.10273309938750283,0.06420818711718926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Build,storeys,A_structural,dummy,binary,6897.53,,2.65,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,6897.53,2602.841509433962,"b (currency per unit, as reported)",|b/t|,0.3122567087244522,0.1178327202733782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Build,storeys,A_structural,dummy,binary,0.16,,2.22,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,17.04669926753386,8.457736007148183,100*(exp(b - se^2/2) - 1),|b/t|,0.2654765583058811,0.1195840352729194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.22,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Build,storeys,A_structural,dummy,binary,3741.82,,5.77,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,3741.82,648.4956672443675,"b (currency per unit, as reported)",|b/t|,0.3490291009694522,0.06049031212642153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.77,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Build,storeys,A_structural,dummy,binary,0.2,,7.71,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,22.099188565629202,3.168359943346744,100*(exp(b - se^2/2) - 1),|b/t|,0.4455500510754867,0.05778859287619801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.71,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Phone,other,other,binary,binary,-186.15,,-0.06,,,,,high,Phone: wired telephone connection dummy,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-186.15,3102.5,"b (currency per unit, as reported)",|b/t|,-0.007441877995430625,0.12403129992384374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Phone,other,other,binary,binary,0.04,,0.5,,,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,3.7485502986466246,8.326486193539106,100*(exp(b - se^2/2) - 1),|b/t|,0.061898446059017294,0.12379689211803457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Phone,other,other,binary,binary,998.89,,1.5,,,,,high,Phone: wired telephone connection dummy,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,998.89,665.9266666666666,"b (currency per unit, as reported)",|b/t|,0.09637388493048533,0.06424925662032355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Phone,other,other,binary,binary,0.06,,2.18,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,6.1434445739406485,2.922485907923008,100*(exp(b - se^2/2) - 1),|b/t|,0.1393455211137805,0.06391996381366077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.18,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Area,living_area,A_structural,level,m2,566.02,,4.8,,*,,,high,total area of the apartment,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,566.02,117.92083333333333,"b (currency per unit, as reported)",|b/t|,0.5115654675833851,0.10657613907987187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,65.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Area,living_area,A_structural,log,m2,0.86,,4.81,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,0.86,0.1787941787941788,b (log-log),|b/t|,0.5123516766837174,0.10651802010056496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.81,65.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Area,living_area,A_structural,level,m2,363.13,,6.07,,*,,,high,total area of the apartment,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,363.13,59.82372322899506,"b (currency per unit, as reported)",|b/t|,0.36481310205196527,0.06010100528038967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.07,240.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Area,living_area,A_structural,log,m2,0.43,,3.53,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,0.43,0.12181303116147309,b (log-log),|b/t|,0.22216602379655923,0.06293655065058336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.53,240.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Balc,terrace_balcony,C_exterior,level,count,-2287.85,,-1.08,,,,,high,rank 0-2: balcony and/or loggia,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-2287.85,2118.3796296296296,"b (currency per unit, as reported)",|b/t|,-0.1327715430085721,0.12293661389682602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.08,65.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Balc,terrace_balcony,C_exterior,level,count,-0.05,,-0.8,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),semi_elasticity,-5.0,6.25,100*b/1 (per unit count),|b/t|,-0.09874285972226249,0.12342857465282811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,65.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Balc,terrace_balcony,C_exterior,level,count,-597.1,,-0.86,,,,,high,rank 0-2: balcony and/or loggia,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-597.1,694.3023255813954,"b (currency per unit, as reported)",|b/t|,-0.055427422571162684,0.06445049136181707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.86,240.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Balc,terrace_balcony,C_exterior,level,count,-0.0,,-0.08,,,,,low,rank 0-2: balcony and/or loggia; printed as -0.00/0.00,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),semi_elasticity,-0.0,0.0,100*b/1 (per unit count),|b/t|,-0.0051639089432829865,0.06454886179103733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08,240.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Cond2,condition,A_structural,dummy,binary,-205.56,,-0.06,,,,,high,condition 2 satisfactory (ref: repair needed),"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-205.56,3426.0,"b (currency per unit, as reported)",|b/t|,-0.007441877995430625,0.12403129992384374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Cond2,condition,A_structural,dummy,binary,-0.01,,-0.11,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,-1.403284018019324,9.000453034083346,100*(exp(b - se^2/2) - 1),|b/t|,-0.013642551057226953,0.12402319142933593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Cond2,condition,A_structural,dummy,binary,828.44,,0.77,,,,,high,condition 2 satisfactory (ref: repair needed),"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,828.44,1075.896103896104,"b (currency per unit, as reported)",|b/t|,0.049642005880663385,0.06447013750735504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.77,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Cond2,condition,A_structural,dummy,binary,0.02,,0.46,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,1.9237522110731486,4.435658000116329,100*(exp(b - se^2/2) - 1),|b/t|,0.02967979136219078,0.06452128556997995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Cond3,condition,A_structural,dummy,binary,1792.23,,0.38,,,,,high,condition 3 cosmetic repair needed,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,1792.23,4716.394736842105,"b (currency per unit, as reported)",|b/t|,0.047080932105365736,0.12389718975096246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.38,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Cond3,condition,A_structural,dummy,binary,0.04,,0.33,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,3.3192797170577437,12.61588817202895,100*(exp(b - se^2/2) - 1),|b/t|,0.040897217467322146,0.12393096202218833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Cond3,condition,A_structural,dummy,binary,1349.33,,0.92,,,,,high,condition 3 cosmetic repair needed,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,1349.33,1466.6630434782608,"b (currency per unit, as reported)",|b/t|,0.059281303943399984,0.06443619993847824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.92,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Cond3,condition,A_structural,dummy,binary,0.02,,0.31,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,1.8080340084591917,6.581944129204875,100*(exp(b - se^2/2) - 1),|b/t|,0.020006408906503785,0.06453680292420577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Cond4,condition,A_structural,dummy,binary,1239.57,,0.38,,,,,high,condition 4 normal,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,1239.57,3262.0263157894733,"b (currency per unit, as reported)",|b/t|,0.047080932105365736,0.12389718975096246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.38,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Cond4,condition,A_structural,dummy,binary,0.01,,0.07,,,,,high,condition 4 normal,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,-0.020406080941326454,14.429288101202397,100*(exp(b - se^2/2) - 1),|b/t|,0.00868210417886961,0.12403005969813728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.07,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Cond4,condition,A_structural,dummy,binary,473.89,,0.46,,,,,high,condition 4 normal,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,473.89,1030.195652173913,"b (currency per unit, as reported)",|b/t|,0.02967979136219078,0.06452128556997995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Cond4,condition,A_structural,dummy,binary,0.04,,1.04,,,,,high,condition 4 normal,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,4.00412283685363,4.003118362278417,100*(exp(b - se^2/2) - 1),|b/t|,0.06698095059175603,0.06440476018438081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.04,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Cond5,condition,A_structural,dummy,binary,-751.75,,-0.23,,,,,high,condition 5 good,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-751.75,3268.478260869565,"b (currency per unit, as reported)",|b/t|,-0.02851638733944164,0.12398429278018101,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Cond5,condition,A_structural,dummy,binary,-0.04,,-0.43,,,,,high,condition 5 good,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,-4.335859298392286,8.937576178161146,100*(exp(b - se^2/2) - 1),|b/t|,-0.05325923864269965,0.12385869451790617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Cond5,condition,A_structural,dummy,binary,1899.72,,1.9,,,,,high,condition 5 good,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,1899.72,999.8526315789475,"b (currency per unit, as reported)",|b/t|,0.12173236095005825,0.0640696636579254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Cond5,condition,A_structural,dummy,binary,0.09,,2.21,,*,,,high,condition 5 good,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,9.32673471051051,4.455913372555155,100*(exp(b - se^2/2) - 1),|b/t|,0.14122512888266253,0.06390277325007354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.21,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Cond6,condition,A_structural,dummy,binary,9885.78,,2.28,,*,,,high,condition 6 perfect,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,9885.78,4335.868421052633,"b (currency per unit, as reported)",|b/t|,0.272126753312237,0.1193538391720338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.28,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Cond6,condition,A_structural,dummy,binary,0.22,,1.85,,,,,high,condition 6 perfect,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,23.729697990754484,14.818209769147229,100*(exp(b - se^2/2) - 1),|b/t|,0.2236517286513659,0.12089282629803562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.85,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Cond6,condition,A_structural,dummy,binary,2603.74,,2.01,,*,,,high,condition 6 perfect,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,2603.74,1295.3930348258707,"b (currency per unit, as reported)",|b/t|,0.12866649196706728,0.06401318008311806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Cond6,condition,A_structural,dummy,binary,0.12,,2.25,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,12.589444026416663,6.01331654175667,100*(exp(b - se^2/2) - 1),|b/t|,0.1437288955267324,0.06387950912299217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,Cond7,condition,A_structural,dummy,binary,7147.54,,1.44,,,,,high,condition 7 Euro-renovation,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,7147.54,4963.569444444444,"b (currency per unit, as reported)",|b/t|,0.1758274524959495,0.1221023975666316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.44,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,Cond7,condition,A_structural,dummy,binary,0.15,,1.17,,,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),pct_premium,15.232509450161679,14.895310804208759,100*(exp(b - se^2/2) - 1),|b/t|,0.14361623985874467,0.12274892295619201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.17,65.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Cond7,condition,A_structural,dummy,binary,9219.21,,6.42,,*,,,high,condition 7 Euro-renovation,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,9219.21,1436.0140186915887,"b (currency per unit, as reported)",|b/t|,0.382837706603342,0.05963204152700031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.42,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Cond7,condition,A_structural,dummy,binary,0.33,,5.79,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,38.8710745246923,7.927797623368695,100*(exp(b - se^2/2) - 1),|b/t|,0.35009085379741806,0.06046474158850053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.79,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,DCBD,cbd_distance,D_location,level,km,-6260.55,,-2.55,,*,,,high,road-network distance to CBD (central square),"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-6260.55,2455.117647058824,"b (currency per unit, as reported)",|b/t|,-0.3015640499017416,0.11826041172617319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.55,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,DCBD,cbd_distance,D_location,log,km,-0.13,,-2.06,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,-0.13,0.06310679611650485,b (log-log),|b/t|,-0.24755824824699635,0.12017390691601763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.06,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,DCBD,cbd_distance,D_location,level,km,-612.39,,-5.64,,*,,,high,road-network distance to CBD (central square),"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-612.39,108.57978723404256,"b (currency per unit, as reported)",|b/t|,-0.34209498550681156,0.06065513927425738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.64,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,DCBD,cbd_distance,D_location,log,km,-0.31,,-10.65,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,-0.31,0.029107981220657275,b (log-log),|b/t|,-0.5665033866172269,0.053192806255138676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.65,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,DSCent,commercial_distance,D_location,level,km,-1136.12,,-0.28,,,,,high,distance to nearest secondary centre (business/commercial concentration),"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-1136.12,4057.571428571428,"b (currency per unit, as reported)",|b/t|,-0.034708799916743305,0.12395999970265467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.28,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,DSCent,commercial_distance,D_location,log,km,-0.12,,-0.44,,,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,-0.12,0.2727272727272727,b (log-log),|b/t|,-0.05449418913505426,0.12385042985239605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.44,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,True
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,DSCent,commercial_distance,D_location,level,km,-794.8,,-3.75,,*,,,high,distance to nearest secondary centre (business/commercial concentration),"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-794.8,211.94666666666666,"b (currency per unit, as reported)",|b/t|,-0.23526698345822997,0.06273786225552799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.75,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,DSCent,commercial_distance,D_location,log,km,-0.04,,-3.46,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,-0.04,0.011560693641618498,b (log-log),|b/t|,-0.21797179565242103,0.06299762880127775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.46,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,DStop,transit_distance,D_location,level,km,3523.89,,0.4,,,,,high,distance to nearest public transport stop,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,3523.89,8809.724999999999,"b (currency per unit, as reported)",|b/t|,0.049552943083144424,0.12388235770786106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,DStop,transit_distance,D_location,log,km,0.01,,0.09,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,0.01,0.11111111111111112,b (log-log),|b/t|,0.011162430629402012,0.12402700699335571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,DStop,transit_distance,D_location,level,km,2757.43,,1.58,,,,,high,distance to nearest public transport stop,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,2757.43,1745.2088607594935,"b (currency per unit, as reported)",|b/t|,0.10146223837729833,0.06421660656791033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.58,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,DStop,transit_distance,D_location,log,km,0.01,,0.54,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,0.01,0.018518518518518517,b (log-log),|b/t|,0.03483569385611229,0.06451054417798571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.54,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,DRail,transit_distance,D_location,level,km,-5185.56,,-1.84,,,,,high,distance to the main railway station,"asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-5185.56,2818.2391304347825,"b (currency per unit, as reported)",|b/t|,-0.22250280594789706,0.12092543801516145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.84,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,DRail,transit_distance,D_location,log,km,-0.83,,-1.48,,,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,-0.83,0.5608108108108107,b (log-log),|b/t|,-0.18055441069973094,0.12199622344576415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.48,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,DRail,transit_distance,D_location,level,km,-217.81,,-2.43,,*,,,high,distance to the main railway station,"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-217.81,89.63374485596707,"b (currency per unit, as reported)",|b/t|,-0.1549610970159754,0.06376999877200633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.43,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,DRail,transit_distance,D_location,log,km,-0.08,,-3.95,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,-0.08,0.020253164556962026,b (log-log),|b/t|,-0.24706688914300307,0.06254857952987418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.95,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,DWater,water_proximity,E_environment,level,km,8017.64,,1.0,,,,,high,"straight-line distance to water (ponds, river) merged with main parks/green areas","asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,8017.64,8017.64,"b (currency per unit, as reported)",|b/t|,0.12309149097933272,0.12309149097933274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,DWater,water_proximity,E_environment,log,km,0.07,,0.93,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,0.07,0.07526881720430108,b (log-log),|b/t|,0.11459243030745948,0.12321766699726827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.93,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,DWater,water_proximity,E_environment,level,km,-1442.2,,-3.28,,*,,,high,"straight-line distance to water (ponds, river) merged with main parks/green areas","asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,-1442.2,439.69512195121956,"b (currency per unit, as reported)",|b/t|,-0.207131475396291,0.06314984005984482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.28,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,DWater,water_proximity,E_environment,log,km,-0.04,,-2.03,,*,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,-0.04,0.01970443349753695,b (log-log),|b/t|,-0.12992524867030236,0.06400258555187309,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.03,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Cent,4,1 (LIN_Cent),13,DNuis,hazard_site,E_environment,level,km,-4839.16,,-0.83,,,,,high,"distance to nearest nuisance (contaminating factories, slagheaps)","asking price, USD",level,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (=1 for all) (adjusted R2),monetary,-4839.16,5830.313253012048,"b (currency per unit, as reported)",|b/t|,-0.10240757864460578,0.12338262487301904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.83,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Cent,4,2 (LOG_Cent),13,DNuis,hazard_site,E_environment,log,km,-0.12,,-0.54,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,75.0,0.47,t,Central administrative district only; Prest omitted (adjusted R2),elasticity,-0.12,0.2222222222222222,b (log-log),|b/t|,-0.06682902178712688,0.12375744775393865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.54,65.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,DNuis,hazard_site,E_environment,level,km,724.51,,0.99,,,,,high,"distance to nearest nuisance (contaminating factories, slagheaps)","asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,724.51,731.8282828282828,"b (currency per unit, as reported)",|b/t|,0.06377413906672551,0.06441832228962172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.99,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,DNuis,hazard_site,E_environment,log,km,0.03,,1.72,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),elasticity,0.03,0.01744186046511628,b (log-log),|b/t|,0.11034749723188901,0.0641555216464471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.72,240.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LIN_Out,4,3 (LIN_Out),13,Prest,other,other,binary,binary,2282.15,,3.01,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"asking price, USD",level,OLS,250.0,0.69,t,Outside the central district (adjusted R2),monetary,2282.15,758.1893687707642,"b (currency per unit, as reported)",|b/t|,0.19072798322273318,0.06336477847931336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.01,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:T4-LOG_Out,4,4 (LOG_Out),13,Prest,other,other,binary,binary,0.06,,2.02,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,OLS,250.0,0.73,t,Outside the central district (adjusted R2),pct_premium,6.1368238468817005,3.153969940233741,100*(exp(b - se^2/2) - 1),|b/t|,0.1292959484493264,0.06400789527194377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.02,240.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,ols,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Floor1,floor_level,A_structural,dummy,binary,-0.02,,-0.82,,,,,high,first-floor dummy,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,-2.009283559022046,2.390728471479891,100*(exp(b - se^2/2) - 1),|b/t|,-0.04615253351179462,0.05628357745340808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.82,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Floor1,floor_level,A_structural,dummy,binary,-0.03,,-0.99,,,,,high,first-floor dummy,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,-2.9999931457920237,2.9407440410560857,100*(exp(b - se^2/2) - 1),|b/t|,-0.05569360476701566,0.05625616643132895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.99,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Floor1,floor_level,A_structural,dummy,binary,-0.02,,-0.77,,,,,high,first-floor dummy,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,-2.0131916461883392,2.545970580017546,100*(exp(b - se^2/2) - 1),|b/t|,-0.04334381290868805,0.05629066611517928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.77,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Floor1,floor_level,A_structural,dummy,binary,-0.02,,-0.9,,,,,high,first-floor dummy,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,-2.0043321179932128,2.178219274015012,100*(exp(b - se^2/2) - 1),|b/t|,-0.05064418313919596,0.05627131459910662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,FloorT,floor_level,A_structural,dummy,binary,-0.06,,-2.51,,*,,,high,top-floor dummy,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,-5.850449929131418,2.25122996075916,100*(exp(b - se^2/2) - 1),|b/t|,-0.14002909794077215,0.055788485235367384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.51,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,FloorT,floor_level,A_structural,dummy,binary,-0.06,,-2.33,,*,,,high,top-floor dummy,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,-5.8547665050125435,2.4251447216761766,100*(exp(b - se^2/2) - 1),|b/t|,-0.13016375884216344,0.05586427418118602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.33,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,FloorT,floor_level,A_structural,dummy,binary,-0.06,,-2.41,,*,,,high,top-floor dummy,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,-5.852728534274143,2.34464199232593,100*(exp(b - se^2/2) - 1),|b/t|,-0.1345533081928202,0.05583124821278845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.41,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,FloorT,floor_level,A_structural,dummy,binary,-0.05,,-2.24,,*,,,high,top-floor dummy,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,-4.900751919441271,2.1232799654033796,100*(exp(b - se^2/2) - 1),|b/t|,-0.12521636333734257,0.055900162204170795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.24,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Build,storeys,A_structural,dummy,binary,0.14,,6.11,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,14.997188163934494,2.635651912275153,100*(exp(b - se^2/2) - 1),|b/t|,0.3255105763684502,0.053275053415458296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.11,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Build,storeys,A_structural,dummy,binary,0.16,,6.59,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,17.31650413163226,2.849191796034744,100*(exp(b - se^2/2) - 1),|b/t|,0.34808426333136583,0.05282007030824974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.59,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Build,storeys,A_structural,dummy,binary,0.14,,6.14,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,14.997482438029941,2.6227741342021473,100*(exp(b - se^2/2) - 1),|b/t|,0.3269383658767514,0.053247290859405765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.14,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Build,storeys,A_structural,dummy,binary,0.16,,6.58,,*,,,high,"nine-storey (newer, 1970s-80s) vs five-storey (1950s-60s) building; also proxies age","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,17.316398951903466,2.8535218747521225,100*(exp(b - se^2/2) - 1),|b/t|,0.3476199328318098,0.05282977702611092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.58,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Phone,other,other,binary,binary,0.07,,2.77,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,7.216577804580493,2.710309483313905,100*(exp(b - se^2/2) - 1),|b/t|,0.15420502387933557,0.055669683710951476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.77,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Phone,other,other,binary,binary,0.06,,2.54,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,6.154033487989885,2.508275306800062,100*(exp(b - se^2/2) - 1),|b/t|,0.14166935633949929,0.05577533714153515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.54,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Phone,other,other,binary,binary,0.07,,2.57,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,7.211042235411247,2.9212285092527304,100*(exp(b - se^2/2) - 1),|b/t|,0.14330844868706355,0.05576204229068621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.57,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Phone,other,other,binary,binary,0.06,,2.33,,*,,,high,Phone: wired telephone connection dummy,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,6.148454356803534,2.7343430383142304,100*(exp(b - se^2/2) - 1),|b/t|,0.13016375884216344,0.05586427418118602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.33,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Area,living_area,A_structural,log,m2,0.6,,6.4,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,0.6,0.09374999999999999,b (log-log),|b/t|,0.3392183793497032,0.05300287177339113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Area,living_area,A_structural,log,m2,0.6,,6.25,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,0.6,0.096,b (log-log),|b/t|,0.332154431909686,0.05314470910554976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Area,living_area,A_structural,log,m2,0.62,,6.56,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,0.62,0.09451219512195122,b (log-log),|b/t|,0.34669050432024484,0.052849162243939755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.56,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Area,living_area,A_structural,log,m2,0.61,,6.44,,*,,,medium,"total area of the apartment; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,0.61,0.09472049689440994,b (log-log),|b/t|,0.34109250876411906,0.052964675273931526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.44,315.0,m2,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Balc,terrace_balcony,C_exterior,level,count,-0.01,,-0.31,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",semi_elasticity,-1.0,3.225806451612903,100*b/1 (per unit count),|b/t|,-0.017463857536251477,0.056335024310488625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Balc,terrace_balcony,C_exterior,level,count,-0.01,,-0.08,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",semi_elasticity,-1.0,12.5,100*b/1 (per unit count),|b/t|,-0.004507443568881748,0.056343044611021846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Balc,terrace_balcony,C_exterior,level,count,-0.01,,-0.14,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",semi_elasticity,-1.0,7.142857142857142,100*b/1 (per unit count),|b/t|,-0.007887860981163957,0.05634186415117112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Balc,terrace_balcony,C_exterior,level,count,0.01,,0.11,,,,,high,rank 0-2: balcony and/or loggia,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",semi_elasticity,1.0,9.090909090909092,100*b/1 (per unit count),|b/t|,0.00619767883436825,0.05634253485789318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11,315.0,count,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Cond2,condition,A_structural,dummy,binary,0.01,,0.34,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,0.9613388640344889,2.9707357855416703,100*(exp(b - se^2/2) - 1),|b/t|,0.01915331561482207,0.05633328122006491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Cond2,condition,A_structural,dummy,binary,0.01,,0.26,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,0.9303364776549916,3.8848083349391076,100*(exp(b - se^2/2) - 1),|b/t|,0.014647768770770208,0.05633757219527003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Cond2,condition,A_structural,dummy,binary,0.01,,0.38,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,0.9700487265232871,2.658026755484652,100*(exp(b - se^2/2) - 1),|b/t|,0.021405668700097802,0.05633070710552054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.38,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Cond2,condition,A_structural,dummy,binary,0.01,,0.3,,,,,high,condition 2 satisfactory (ref: repair needed),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,0.9489183945532886,3.3668338902805597,100*(exp(b - se^2/2) - 1),|b/t|,0.016900670885446462,0.05633556961815487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Cond3,condition,A_structural,dummy,binary,0.02,,0.29,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,1.7778060074763014,7.035871310529352,100*(exp(b - se^2/2) - 1),|b/t|,0.016337468147964496,0.05633609706194654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.29,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Cond3,condition,A_structural,dummy,binary,0.01,,0.21,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,0.890563443946979,4.8097627004008,100*(exp(b - se^2/2) - 1),|b/t|,0.011831331401985824,0.056339673342789644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Cond3,condition,A_structural,dummy,binary,0.01,,0.25,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,0.9242450080380493,4.040200668336672,100*(exp(b - se^2/2) - 1),|b/t|,0.014084507042253521,0.056338028169014086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Cond3,condition,A_structural,dummy,binary,0.01,,0.16,,,,,high,condition 3 cosmetic repair needed,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,0.8079338115755785,6.312813544276049,100*(exp(b - se^2/2) - 1),|b/t|,0.009014612416486226,0.05634132760303891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Cond4,condition,A_structural,dummy,binary,0.03,,0.82,,,,,high,condition 4 normal,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,2.976513863966801,3.769955612025062,100*(exp(b - se^2/2) - 1),|b/t|,0.04615253351179462,0.05628357745340808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.82,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Cond4,condition,A_structural,dummy,binary,0.03,,0.71,,,,,high,condition 4 normal,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,2.9535078226555322,4.354033242057114,100*(exp(b - se^2/2) - 1),|b/t|,0.0399719969007544,0.05629858718416112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.71,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Cond4,condition,A_structural,dummy,binary,0.03,,0.91,,,,,high,condition 4 normal,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,2.9894724061419797,3.397102859187419,100*(exp(b - se^2/2) - 1),|b/t|,0.051205428939339034,0.05626970213114179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.91,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Cond4,condition,A_structural,dummy,binary,0.03,,0.8,,,,,high,condition 4 normal,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,2.973025027015286,3.8642045023256886,100*(exp(b - se^2/2) - 1),|b/t|,0.045029172875303725,0.05628646609412965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Cond5,condition,A_structural,dummy,binary,0.05,,1.31,,,,,high,condition 5 good,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,5.050563375831252,4.0124851006718485,100*(exp(b - se^2/2) - 1),|b/t|,0.0736098995681557,0.056190763029126484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.31,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Cond5,condition,A_structural,dummy,binary,0.05,,1.33,,,,,high,condition 5 good,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,5.0528474024937875,3.9521469788572334,100*(exp(b - se^2/2) - 1),|b/t|,0.07472748614816546,0.05618607981065071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.33,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Cond5,condition,A_structural,dummy,binary,0.05,,1.44,,,,,high,condition 5 good,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,5.063756393345464,3.6502468624167506,100*(exp(b - se^2/2) - 1),|b/t|,0.08086907029713811,0.0561590765952348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.44,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Cond5,condition,A_structural,dummy,binary,0.05,,1.46,,,,,high,condition 5 good,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,5.065479704887821,3.600243480739809,100*(exp(b - se^2/2) - 1),|b/t|,0.08198475373036565,0.05615394091120936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.46,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Cond6,condition,A_structural,dummy,binary,0.12,,2.56,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,12.625882673412292,5.285141491778324,100*(exp(b - se^2/2) - 1),|b/t|,0.14276221489679658,0.055766490194061165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.56,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Cond6,condition,A_structural,dummy,binary,0.13,,2.65,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,13.74588834966497,5.586705276686824,100*(exp(b - se^2/2) - 1),|b/t|,0.1476735670200202,0.05572587434717744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Cond6,condition,A_structural,dummy,binary,0.13,,2.8,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,13.760160835220248,5.2874174940071725,100*(exp(b - se^2/2) - 1),|b/t|,0.15583476916873645,0.055655274703120164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Cond6,condition,A_structural,dummy,binary,0.14,,2.88,,*,,,high,condition 6 perfect,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,14.891552985644463,5.591608744444856,100*(exp(b - se^2/2) - 1),|b/t|,0.16017450730260027,0.05561614836895843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.88,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,Cond7,condition,A_structural,dummy,binary,0.3,,5.92,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,34.81266848689699,6.840500714067583,100*(exp(b - se^2/2) - 1),|b/t|,0.3164163372364734,0.053448705614269156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.92,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,Cond7,condition,A_structural,dummy,binary,0.3,,5.83,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,34.80728288314119,6.946100210511164,100*(exp(b - se^2/2) - 1),|b/t|,0.31207770805439095,0.05352962402305162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.83,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,Cond7,condition,A_structural,dummy,binary,0.31,,6.01,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,36.16125770440826,7.032642019650169,100*(exp(b - se^2/2) - 1),|b/t|,0.3207352005971049,0.053366921896356896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.01,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,Cond7,condition,A_structural,dummy,binary,0.3,,5.94,,*,,,high,condition 7 Euro-renovation,"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,34.813832193436056,6.817468725131328,100*(exp(b - se^2/2) - 1),|b/t|,0.31737779808081695,0.053430605737511266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.94,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,DCBD,cbd_distance,D_location,log,km,-0.19,,-9.11,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,-0.19,0.020856201975850714,b (log-log),|b/t|,-0.456647570261186,0.05012596819551988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.11,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,DCBD,cbd_distance,D_location,log,km,-0.23,,-11.18,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,-0.23,0.020572450805008947,b (log-log),|b/t|,-0.5329901905052631,0.047673541190095085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.18,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,DCBD,cbd_distance,D_location,log,km,-0.18,,-7.25,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,-0.18,0.02482758620689655,b (log-log),|b/t|,-0.37815723008406554,0.05215961794262974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.25,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,DCBD,cbd_distance,D_location,log,km,-0.21,,-7.72,,*,,,medium,"road-network distance to CBD (central square); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,-0.21,0.027202072538860103,b (log-log),|b/t|,-0.3988727260325916,0.05166745155862586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.72,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,DSCent,commercial_distance,D_location,log,km,-0.05,,-4.26,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,-0.05,0.011737089201877935,b (log-log),|b/t|,-0.23339484236062255,0.05478752168089732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.26,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,DSCent,commercial_distance,D_location,log,km,-0.05,,-4.04,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,-0.05,0.012376237623762377,b (log-log),|b/t|,-0.2219506852121695,0.054938288418853835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.04,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,DSCent,commercial_distance,D_location,log,km,-0.04,,-3.26,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,-0.04,0.012269938650306749,b (log-log),|b/t|,-0.18065791593248273,0.05541653862959593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.26,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,DSCent,commercial_distance,D_location,log,km,-0.04,,-2.84,,*,,,medium,"distance to nearest secondary centre (business/commercial concentration); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,-0.04,0.014084507042253521,b (log-log),|b/t|,-0.15800578272253007,0.055635838986806366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.84,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,DStop,transit_distance,D_location,log,km,-0.01,,-0.32,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,-0.01,0.03125,b (log-log),|b/t|,-0.01802702756496147,0.056334461140504596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.32,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,DStop,transit_distance,D_location,log,km,-0.01,,-0.62,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,-0.01,0.016129032258064516,b (log-log),|b/t|,-0.034911747316117805,0.05630926986470614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.62,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,DStop,transit_distance,D_location,log,km,-0.01,,-0.22,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,-0.01,0.045454545454545456,b (log-log),|b/t|,-0.012394643549183727,0.05633928885992604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,DStop,transit_distance,D_location,log,km,-0.01,,-0.52,,,,,medium,"distance to nearest public transport stop; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,-0.01,0.019230769230769232,b (log-log),|b/t|,-0.029286113740986164,0.05631944950189647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.52,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,DRail,transit_distance,D_location,log,km,-0.08,,-4.32,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,-0.08,0.018518518518518517,b (log-log),|b/t|,-0.23649943751613672,0.05474524016577239,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.32,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,DRail,transit_distance,D_location,log,km,-0.11,,-4.86,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,-0.11,0.022633744855967076,b (log-log),|b/t|,-0.26410718913075637,0.054343043031019826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.86,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,DRail,transit_distance,D_location,log,km,-0.08,,-3.56,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,-0.08,0.02247191011235955,b (log-log),|b/t|,-0.19666599409323496,0.05524325676776263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.56,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,DRail,transit_distance,D_location,log,km,-0.1,,-4.01,,*,,,medium,"distance to the main railway station; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,-0.1,0.02493765586034913,b (log-log),|b/t|,-0.22038286963327514,0.05495832160430801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.01,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,21,DWater,water_proximity,E_environment,log,km,-0.01,,-0.36,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,-0.01,0.02777777777777778,b (log-log),|b/t|,-0.020279530753310925,0.05633202987030813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,21,DWater,water_proximity,E_environment,log,km,-0.01,,-0.68,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,-0.01,0.014705882352941176,b (log-log),|b/t|,-0.03828556945996004,0.05630230802935301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.68,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,21,DWater,water_proximity,E_environment,log,km,-0.01,,-0.87,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,-0.01,0.011494252873563218,b (log-log),|b/t|,-0.04896015993215738,0.05627604589903147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.87,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,21,DWater,water_proximity,E_environment,log,km,-0.02,,-1.27,,,,,medium,"straight-line distance to water (ponds, river) merged with main parks/green areas; regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,-0.02,0.015748031496062992,b (log-log),|b/t|,-0.07137389836806626,0.05619991997485532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.27,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,22,DNuis,hazard_site,E_environment,log,km,0.02,,1.16,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",elasticity,0.02,0.01724137931034483,b (log-log),|b/t|,0.06521944369624494,0.05622365835883185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.16,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,22,DNuis,hazard_site,E_environment,log,km,0.02,,1.18,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",elasticity,0.02,0.01694915254237288,b (log-log),|b/t|,0.0663390099659505,0.0562194999711445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.18,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,22,DNuis,hazard_site,E_environment,log,km,0.02,,1.0,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",elasticity,0.02,0.02,b (log-log),|b/t|,0.0562543950463012,0.0562543950463012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,22,DNuis,hazard_site,E_environment,log,km,0.02,,0.91,,,,,medium,"distance to nearest nuisance (contaminating factories, slagheaps); regressor assumed logged in the log model (not stated; inferred from coefficient magnitudes, see study notes)","ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",elasticity,0.02,0.021978021978021976,b (log-log),|b/t|,0.051205428939339034,0.05626970213114179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.91,315.0,km,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,True,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SAR,Appendix,LOG SAR,22,Prest,other,other,binary,binary,0.09,,2.87,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial autoregressive (lag) model, rho 0.21 (3.74)* (adjusted R2)",pct_premium,9.363642157395535,3.4312085551731335,100*(exp(b - se^2/2) - 1),|b/t|,0.15963254229022042,0.055621094874641265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.87,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOG-SEM,Appendix,LOG SEM,22,Prest,other,other,binary,binary,0.1,,2.82,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model, lambda 0.22 (3.32)* (adjusted R2)",pct_premium,10.447627019718308,3.919045808778893,100*(exp(b - se^2/2) - 1),|b/t|,0.15692056030267532,0.05564558875981394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.82,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,22,Prest,other,other,binary,binary,0.07,,2.17,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",pct_premium,7.1950309651061195,3.4597038104974733,100*(exp(b - se^2/2) - 1),|b/t|,0.12136190104110074,0.05592714333691278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.17,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,22,Prest,other,other,binary,binary,0.08,,2.09,,*,,,high,Prest: prestigious-area dummy (used in place of unavailable income data),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",pct_premium,8.24937595619113,4.146553369090751,100*(exp(b - se^2/2) - 1),|b/t|,0.11695007881435227,0.05595697550925946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.09,315.0,binary,,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,False,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,22,N·DCBD,cbd_distance,D_location,other,km,0.01,,0.35,,,,,high,interaction North direction x DCBD (ref East),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",other,0.01,0.028571428571428574,not harmonised,|b/t|,0.019716432565542064,0.056332664472977335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,22,N·DCBD,cbd_distance,D_location,other,km,0.01,,0.12,,,,,high,interaction North direction x DCBD (ref East),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",other,0.01,0.08333333333333334,not harmonised,|b/t|,0.006761079500634237,0.05634232917195198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,22,S·DCBD,cbd_distance,D_location,other,km,-0.02,,-1.0,,,,,high,interaction South direction x DCBD (ref East),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",other,-0.02,0.02,not harmonised,|b/t|,-0.0562543950463012,0.0562543950463012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,22,S·DCBD,cbd_distance,D_location,other,km,-0.04,,-1.55,,,,,high,interaction South direction x DCBD (ref East),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",other,-0.04,0.025806451612903226,not harmonised,|b/t|,-0.08700145717344539,0.056129972369964753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.55,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SAR,Appendix,LOGDIR SAR,22,W·DCBD,cbd_distance,D_location,other,km,-0.03,,-1.6,,,,,high,interaction West direction x DCBD (ref East),"ln(asking price, USD)",log,spatial_lag,325.0,0.77,t,"Spatial lag model with direction interactions, rho 0.19 (3.31)* (adjusted R2)",other,-0.03,0.01875,not harmonised,|b/t|,-0.08978568203688475,0.05611605127305296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,True,False
416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba,416244dcac790a50e1100858c6071c54dd1650ba:A-LOGDIR-SEM,Appendix,LOGDIR SEM,22,W·DCBD,cbd_distance,D_location,other,km,-0.04,,-2.07,,*,,,high,interaction West direction x DCBD (ref East),"ln(asking price, USD)",log,spatial_error,325.0,0.78,t,"Spatial error model with direction interactions, lambda 0.21 (3.18)* (adjusted R2)",other,-0.04,0.019323671497584544,not harmonised,|b/t|,-0.11584603006858343,0.05596426573361519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.07,315.0,km,nonstandard_x_transform,2007.0,Ukraine,Europe,Lower middle income,2005.0,2005.0,condo_apartment,listing,journal,False,False,robust,,2005.0,spatial,journal,False,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T6-M1,6,Model 1,9,Bedrooms,bedrooms,A_structural,level,count,0.589,0.005,114.16,0.0,,0.579,0.599,high,studios coded 0,"ln(monthly rent, THB)",log,OLS,14849.0,0.492,se,Pooled three cities; city dummies + bedrooms (adj. R2 0.492),semi_elasticity,58.9,0.5,100*b/1 (per unit count),reported,0.683807319649105,0.005989902940163848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),114.16,14839.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,True
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T6-M2,6,Model 2,9,Bedrooms,bedrooms,A_structural,level,count,0.104,0.009,12.202,0.0,,0.087,0.12,high,,"ln(monthly rent, THB)",log,OLS,14849.0,0.662,se,Pooled three cities; adds ln(size) (adj. R2 0.662),semi_elasticity,10.4,0.8999999999999999,100*b/1 (per unit count),reported,0.09966915802033444,0.008168264056739422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.202,14839.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T6-M2,6,Model 2,9,ln(size),living_area,A_structural,log,m2,0.822,0.012,67.554,0.0,,0.798,0.846,high,"unit size in sqm, log","ln(monthly rent, THB)",log,OLS,14849.0,0.662,se,Pooled three cities; adds ln(size) (adj. R2 0.662),elasticity,0.822,0.012,b (log-log),reported,0.4849776705275907,0.007179111089315077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),67.554,14839.0,m2,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Garden,view,E_environment,binary,binary,-0.074,0.016,-4.728,0.0,,-0.105,-0.043,high,ref: city view; %change -7.1,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,-7.144716852817057,1.4858747101460597,100*(exp(b - se^2/2) - 1),reported,-0.04109540656009852,0.008691921861272952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.728,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Lake,view,E_environment,binary,binary,0.183,0.033,5.49,0.0,,0.117,0.248,high,ref: city view; %change +20.0,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,20.016074261220453,3.9626875466667415,100*(exp(b - se^2/2) - 1),reported,0.047704622264080176,0.00868936653261934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.49,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Mountain,view,E_environment,binary,binary,-0.079,0.017,-4.519,0.0,,-0.113,-0.045,high,ref: city view; %change -7.6,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,-7.6093589677367905,1.5708678715566475,100*(exp(b - se^2/2) - 1),reported,-0.039281662728273464,0.00869255647892752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.519,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Pool,view,E_environment,binary,binary,-0.136,0.013,-10.314,0.0,,-0.162,-0.11,high,ref: city view; %change -12.7,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,-12.723111959765642,1.134695422235335,100*(exp(b - se^2/2) - 1),reported,-0.08936528458881621,0.008664464280474715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.314,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,True
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: River,view,E_environment,binary,binary,-0.158,0.049,-3.205,0.001,,-0.255,-0.061,high,ref: city view; %change -14.6,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,-14.717464965665418,4.18386393164556,100*(exp(b - se^2/2) - 1),reported,-0.027870332294304807,0.00869589151148356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.205,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,True
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Sea,view,E_environment,binary,binary,0.159,0.015,10.491,0.0,,0.129,0.188,high,ref: city view; %change +17.2,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,17.2206066080139,1.7585069200210761,100*(exp(b - se^2/2) - 1),reported,0.09088633264497305,0.008663266861593084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.491,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Skyline,view,E_environment,binary,binary,0.044,0.014,3.206,0.001,,0.017,0.071,high,ref: city view; %change +4.5,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,4.487995163550607,1.4629752968438214,100*(exp(b - se^2/2) - 1),reported,0.027879021428076266,0.008695889403642005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.206,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,View: Unblocked Open,view,E_environment,binary,binary,-0.092,0.015,-6.075,0.0,,-0.121,-0.062,high,ref: city view; %change -8.8,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",pct_premium,-8.799745651253444,1.3681577243176355,100*(exp(b - se^2/2) - 1),reported,-0.052774423985483356,0.008687147981149522,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.075,13214.0,binary,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,Bedrooms,bedrooms,A_structural,level,count,0.088,0.009,9.521,0.0,,0.07,0.107,high,,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",semi_elasticity,8.799999999999999,0.8999999999999999,100*b/1 (per unit count),reported,0.08254311366447378,0.00866958446218609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.521,13214.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,ln(size),living_area,A_structural,log,m2,0.755,0.016,47.358,0.0,,0.723,0.786,high,"unit size in sqm, log","ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",elasticity,0.755,0.016,b (log-log),reported,0.38092005477507007,0.008043415152140507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.358,13214.0,m2,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T7-M3,7,Model 3,11,Bathrooms,bathrooms,A_structural,level,count,0.077,0.013,5.997,0.0,,0.052,0.102,high,,"ln(monthly rent, THB)",log,OLS,13224.0,0.668,se,"Pooled three cities, listings with bathrooms and view type; adds bathrooms + view dummies (ref: city view); adj. R2 0.668",semi_elasticity,7.7,1.3,100*b/1 (per unit count),reported,0.05209867755491866,0.00868745665414685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.997,13214.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-BKK,8,Bangkok (+ transit distance),12,Distance to BTS/MRT (km),transit_distance,D_location,level,km,-0.082,0.004,,0.0,,,,high,distance to nearest BTS skytrain / MRT station; %change -7.9,"ln(monthly rent, THB)",log,OLS,7195.0,0.674,se,Bangkok only; Model 3 covariates + distance to BTS/MRT; adj. R2 0.674; only listed coefficients printed,semi_elasticity,-8.200000000000001,0.4,100*b/1 (X per km->km),reported,-0.2350699493108347,0.011466826795650472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.5,7185.0,km,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-BKK,8,Bangkok (+ transit distance),12,ln(size),living_area,A_structural,log,m2,0.735,0.024,,0.0,,,,high,,"ln(monthly rent, THB)",log,OLS,7195.0,0.674,se,Bangkok only; Model 3 covariates + distance to BTS/MRT; adj. R2 0.674; only listed coefficients printed,elasticity,0.735,0.024,b (log-log),reported,0.3397979814429507,0.011095444292014716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.625,7185.0,m2,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-BKK,8,Bangkok (+ transit distance),12,Bathrooms,bathrooms,A_structural,level,count,0.086,0.015,,0.0,,,,high,,"ln(monthly rent, THB)",log,OLS,7195.0,0.674,se,Bangkok only; Model 3 covariates + distance to BTS/MRT; adj. R2 0.674; only listed coefficients printed,semi_elasticity,8.6,1.5,100*b/1 (per unit count),reported,0.06748428189236445,0.011770514283551938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.733333333333333,7185.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-BKK,8,Bangkok (+ transit distance),12,Bedrooms,bedrooms,A_structural,level,count,0.07,0.014,,0.0,,,,high,,"ln(monthly rent, THB)",log,OLS,7195.0,0.674,se,Bangkok only; Model 3 covariates + distance to BTS/MRT; adj. R2 0.674; only listed coefficients printed,semi_elasticity,7.000000000000001,1.4000000000000001,100*b/1 (per unit count),reported,0.058884687143838495,0.0117769374287677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,7185.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-PKT,8,Phuket & Pattaya (+ floor level),12,Floor level,floor_level,A_structural,level,count,0.002,0.001,,0.183,,,,high,"text reports 0.0017 per storey, p=0.18","ln(monthly rent, THB)",log,OLS,4422.0,0.703,se,Phuket and Pattaya only (Phuket reference); Model 3 covariates + floor level; adj. R2 0.703; only listed coefficients printed,semi_elasticity,0.2,0.1,100*b/1 (per unit count),reported,0.0300964632714423,0.01504823163572115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,4412.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-PKT,8,Phuket & Pattaya (+ floor level),12,ln(size),living_area,A_structural,log,m2,0.629,0.021,,0.0,,,,high,,"ln(monthly rent, THB)",log,OLS,4422.0,0.703,se,Phuket and Pattaya only (Phuket reference); Model 3 covariates + floor level; adj. R2 0.703; only listed coefficients printed,elasticity,0.629,0.021,b (log-log),reported,0.41107309444665124,0.013724220959268165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.95238095238095,4412.0,m2,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-PKT,8,Phuket & Pattaya (+ floor level),12,Bathrooms,bathrooms,A_structural,level,count,0.073,0.026,,0.005,,,,high,,"ln(monthly rent, THB)",log,OLS,4422.0,0.703,se,Phuket and Pattaya only (Phuket reference); Model 3 covariates + floor level; adj. R2 0.703; only listed coefficients printed,semi_elasticity,7.3,2.6,100*b/1 (per unit count),reported,0.042232240179363716,0.015041619789910365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8076923076923075,4412.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4,41a24683a4b966679145f8c822178e61a9a339a4:T8-PKT,8,Phuket & Pattaya (+ floor level),12,Bedrooms,bedrooms,A_structural,level,count,0.157,0.014,,0.0,,,,high,,"ln(monthly rent, THB)",log,OLS,4422.0,0.703,se,Phuket and Pattaya only (Phuket reference); Model 3 covariates + floor level; adj. R2 0.703; only listed coefficients printed,semi_elasticity,15.7,1.4000000000000001,100*b/1 (per unit count),reported,0.16647569846862992,0.014844966742425598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.214285714285714,4412.0,count,,2026.0,Thailand,Asia,Upper middle income,2026.0,2026.0,condo_apartment,rent,journal,False,True,robust,,2026.0,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Living Area [log],living_area,A_structural,log,m2,0.8,0.01,81.98,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",elasticity,0.8,0.01,b (log-log),reported,0.6487838848011535,0.007913928821677891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.98,9246.0,m2,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Age [years],age,A_structural,level,years,0.0,0.0,-20.11,0.0,***,,,medium,Coefficient and SE printed as 0.00 (rounded); t = -20.11,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,-0.2047100389505756,0.010179514617134539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.11,9246.0,years,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Age squared,age,A_structural,squared,years,0.0,0.0,19.59,0.0,***,,,medium,Printed as 0.00 (rounded),"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",other,0.0,0.0,not harmonised,|b/t|,0.1996303285915213,0.010190420040404354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.59,9246.0,years,nonstandard_x_transform,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Rooms,bedrooms,A_structural,level,count,0.04,0.0,10.05,0.0,***,,,high,Number of rooms,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,4.0,0.3980099502487562,100*b/1 (per unit count),|b/t|,0.10395128980496335,0.010343411920891875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.05,9246.0,count,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Floor,floor_level,A_structural,level,count,0.0,0.0,5.29,0.0,***,,,medium,Printed as 0.00 (rounded),"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,0.0,0.0,100*b/1 (per unit count),|b/t|,0.05493163156926957,0.010384051336345854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.29,9246.0,count,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Bathtub,bathroom_quality,B_interior,binary,binary,-0.04,0.0,-9.78,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,-3.92185968246187,0.39296091580872117,100*(exp(b - se^2/2) - 1),|b/t|,-0.10118755359441099,0.01034637562315041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.78,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Refurbished,renovation,A_structural,binary,binary,0.02,0.01,3.03,0.0,**,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,2.0150331234984886,1.0202013400267558,100*(exp(b - se^2/2) - 1),reported,0.03149562077022479,0.010394594313605541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.03,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Built-in kitchen,kitchen_quality,B_interior,binary,binary,0.11,0.01,21.65,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,11.622225795067266,1.1162780704588713,100*(exp(b - se^2/2) - 1),reported,0.21965581297032855,0.010145765033271527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.65,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Balcony,terrace_balcony,C_exterior,binary,binary,0.02,0.0,3.84,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,2.0187502754330566,0.5313548645972687,100*(exp(b - se^2/2) - 1),|b/t|,0.0399032480044534,0.010391470834493073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.84,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Parking,parking,A_structural,binary,binary,0.03,0.01,4.88,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,3.040301251486577,1.030454533953517,100*(exp(b - se^2/2) - 1),reported,0.05068556623675214,0.01038638652392462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.88,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Elevator,accessibility,B_interior,binary,binary,0.04,0.01,7.64,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.07920450456558127,0.010367081749421633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.64,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Terrace,terrace_balcony,C_exterior,binary,binary,0.04,0.01,5.99,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.06217400522651872,0.010379633593742691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.99,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,NUTS centroid [km],cbd_distance,D_location,level,km,-0.02,0.0,-13.02,0.0,***,,,high,Distance to NUTS-3 centroid (city centre proxy),"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-2.0,0.15360983102918588,100*b/1 (X per km->km),|b/t|,-0.13418031895913196,0.010305708061377263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.02,9246.0,km,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Bakery [km],neighbourhood_amenities,F_neighbourhood,level,km,-0.01,0.01,-1.22,0.22,,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-1.0,1.0,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.012686678463636039,0.010398916773472164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.22,9246.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Bar [km],neighbourhood_amenities,F_neighbourhood,level,km,-0.02,0.0,-3.73,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-2.0,0.5361930294906166,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.038761928933235615,0.010391938051805794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.73,9246.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Biergarten [km],neighbourhood_amenities,F_neighbourhood,level,km,-0.05,0.0,-14.71,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-5.0,0.3399048266485384,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.15122109768038314,0.010280156198530463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.71,9246.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Café [km],neighbourhood_amenities,F_neighbourhood,level,km,-0.02,0.01,-2.11,0.03,*,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-2.0,1.0,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.021938199219599223,0.010397250814975936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.11,9246.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,School [km],school_distance,D_location,level,km,-0.03,0.01,-2.35,0.02,*,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-3.0,1.0,100*b/1 (X per km->km),reported,-0.024432125895573968,0.010396649317265519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.35,9246.0,km,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Supermarket [km],commercial_distance,D_location,level,km,0.02,0.01,1.2,0.23,,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,2.0,1.0,100*b/1 (X per km->km),reported,0.012478732784139274,0.010398943986782727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,9246.0,km,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-A,6,Panel A,32,Bus station [km],transit_distance,D_location,level,km,-0.04,0.0,-17.16,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,"OLS including spatial controls (coordinates, distances); SE/t/p printed in columns",semi_elasticity,-4.0,0.2331002331002331,100*b/1 (X per km->km),|b/t|,-0.17568411633240608,0.01023800211727308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.16,9246.0,km,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,32,Living Area [log],living_area,A_structural,log,m2,0.84,0.01,76.62,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,elasticity,0.84,0.01,b (log-log),reported,0.6231817643615948,0.008133408566452555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),76.62,9246.0,m2,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,32,Age [years],age,A_structural,level,years,0.0,0.0,-18.16,0.0,***,,,medium,Printed as 0.00 (rounded),"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,semi_elasticity,0.0,0.0,100*b/1 (per year),|b/t|,-0.185578917587387,0.010219103391375937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.16,9246.0,years,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,32,Age squared,age,A_structural,squared,years,0.0,0.0,21.45,0.0,***,,,medium,Printed as 0.00 (rounded),"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,other,0.0,0.0,not harmonised,|b/t|,0.21772327575189887,0.01015026926582279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.45,9246.0,years,nonstandard_x_transform,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,False,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Rooms,bedrooms,A_structural,level,count,0.03,0.0,6.32,0.0,***,,,high,Number of rooms,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,semi_elasticity,3.0,0.4746835443037974,100*b/1 (per unit count),|b/t|,0.0655849339583329,0.010377362968090649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.32,9246.0,count,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Floor,floor_level,A_structural,level,count,0.01,0.0,6.43,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,semi_elasticity,1.0,0.15552099533437014,100*b/1 (per unit count),|b/t|,0.06672140544962463,0.010376579385633692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.43,9246.0,count,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Bathtub,bathroom_quality,B_interior,binary,binary,-0.05,0.0,-10.78,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,-4.878080737913026,0.4412010317721308,100*(exp(b - se^2/2) - 1),|b/t|,-0.11141139413611181,0.010335008732477904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.78,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Refurbished,renovation,A_structural,binary,binary,0.03,0.01,4.53,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,3.040301251486577,1.030454533953517,100*(exp(b - se^2/2) - 1),reported,0.047058691507471474,0.010388232120854629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.53,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Built-in kitchen,kitchen_quality,B_interior,binary,binary,0.15,0.01,26.57,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,16.177615246841537,1.161834242728283,100*(exp(b - se^2/2) - 1),reported,0.26634045455171157,0.010024104424227007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.57,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,True
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Balcony,terrace_balcony,C_exterior,binary,binary,0.02,0.01,2.94,0.0,**,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,2.0150331234984886,1.0202013400267558,100*(exp(b - se^2/2) - 1),reported,0.030560994388410576,0.010394896050479788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.94,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Parking,parking,A_structural,binary,binary,0.01,0.01,1.52,0.13,,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,0.9999665838355432,1.010050167084168,100*(exp(b - se^2/2) - 1),reported,0.01580565103702695,0.010398454629622996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Elevator,accessibility,B_interior,binary,binary,0.1,0.01,16.78,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,10.511566091118452,1.1051709180756477,100*(exp(b - se^2/2) - 1),reported,0.1719099123255177,0.010244929220829422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.78,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d,41c05e4c190bfd812f576bb4bc53a0c147331a7d:T6-B,6,Panel B,33,Terrace,terrace_balcony,C_exterior,binary,binary,0.03,0.01,4.06,0.0,***,,,high,,"log(rent) (monthly asking rent, EUR)",log,OLS,9256.0,,se,OLS excluding spatial controls; continued on page 33,pct_premium,3.040301251486577,1.030454533953517,100*(exp(b - se^2/2) - 1),reported,0.04218541322379711,0.010390495867930323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.06,9246.0,binary,,2022.0,Germany,Europe,High income,2019.0,2020.0,rental,rent,journal,False,False,unknown,random_forest;gradient_boosting,2019.5,ols,journal,True,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,energ_clas,energy_efficiency,A_structural,other,index,-0.042,0.003,,,***,,,high,Mean-centred standardized coefficient. Energy class (ordinal); negative sign = worse class lowers price,House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.042,0.003,not harmonised,reported,-0.13524203070138519,0.009660145050098943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,energ_clas,energy_efficiency,A_structural,other,index,-0.042,0.003,,,***,,,high,Mean-centred standardized coefficient. Energy class (ordinal); negative sign = worse class lowers price,House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.042,0.003,not harmonised,reported,-0.13524203070138519,0.009660145050098943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,energ_clas,energy_efficiency,A_structural,other,index,-0.046,0.003,,,***,,,high,Mean-centred standardized coefficient. Energy class (ordinal); negative sign = worse class lowers price,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.046,0.003,not harmonised,reported,-0.14785265454111232,0.009642564426594282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.333333333333332,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,energ_clas,energy_efficiency,A_structural,other,index,-0.03,0.005,,,***,,,high,Mean-centred standardized coefficient. Energy class (ordinal); negative sign = worse class lowers price,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.03,0.005,not harmonised,reported,-0.08207634827468742,0.013679391379114569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,5308.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,energ_clas,energy_efficiency,A_structural,other,index,-0.048,0.004,,,***,,,high,Mean-centred standardized coefficient. Energy class (ordinal); negative sign = worse class lowers price,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.048,0.004,not harmonised,reported,-0.16412198797244365,0.01367683233103697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,5202.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,sup_mq,living_area,A_structural,other,m2,0.405,0.003,,,***,,,high,Mean-centred standardized coefficient. Surface in m2,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.405,0.003,not harmonised,reported,0.7962558058824194,0.005898191154684588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),135.0,10520.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,sup_mq,living_area,A_structural,other,m2,0.405,0.003,,,***,,,high,Mean-centred standardized coefficient. Surface in m2,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.405,0.003,not harmonised,reported,0.7962558058824194,0.005898191154684588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),135.0,10520.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,sup_mq,living_area,A_structural,other,m2,0.439,0.004,,,***,,,high,Mean-centred standardized coefficient. Surface in m2,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.439,0.004,not harmonised,reported,0.7306110873479001,0.006657048631871527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),109.75,10520.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,sup_mq,living_area,A_structural,other,m2,0.531,0.006,,,***,,,high,Mean-centred standardized coefficient. Surface in m2,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.531,0.006,not harmonised,reported,0.7720429414591754,0.00872364905603588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),88.5,5308.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,sup_mq,living_area,A_structural,other,m2,0.35,0.004,,,***,,,high,Mean-centred standardized coefficient. Surface in m2,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.35,0.004,not harmonised,reported,0.7716442428708031,0.008818791347094892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),87.49999999999999,5202.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,cond_fees,other,other,other,currency,0.006,0.003,,,*,,,high,Mean-centred standardized coefficient. Condominium fees,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.006,0.003,not harmonised,reported,0.0194957333382473,0.00974786666912365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,10520.0,currency,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,cond_fees,other,other,other,currency,0.006,0.003,,,*,,,high,Mean-centred standardized coefficient. Condominium fees,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.006,0.003,not harmonised,reported,0.0194957333382473,0.00974786666912365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,10520.0,currency,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,cond_fees,other,other,other,currency,0.019,0.003,,,***,,,high,Mean-centred standardized coefficient. Condominium fees,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.019,0.003,not harmonised,reported,0.06163084217730712,0.00973118560694323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.333333333333333,10520.0,currency,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,cond_fees,other,other,other,currency,0.01,0.005,,,*,,,high,Mean-centred standardized coefficient. Condominium fees,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.01,0.005,not harmonised,reported,0.027441064997422587,0.013720532498711293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,5308.0,currency,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,cond_fees,other,other,other,currency,0.025,0.004,,,***,,,high,Mean-centred standardized coefficient. Condominium fees,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.025,0.004,not harmonised,reported,0.08633171094934794,0.013813073751895671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,5202.0,currency,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,kitch_type,kitchen_quality,B_interior,other,index,0.083,0.006,,,***,,,high,Mean-centred standardized coefficient. Kitchen type,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.083,0.006,not harmonised,reported,0.13366094014746427,0.00966223663716609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.833333333333334,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,kitch_type,kitchen_quality,B_interior,other,index,0.083,0.006,,,***,,,high,Mean-centred standardized coefficient. Kitchen type,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.083,0.006,not harmonised,reported,0.13366094014746427,0.00966223663716609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.833333333333334,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,kitch_type,kitchen_quality,B_interior,other,index,0.064,0.007,,,***,,,high,Mean-centred standardized coefficient. Kitchen type,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.064,0.007,not harmonised,reported,0.0887882370591895,0.009711213428348853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.142857142857142,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,kitch_type,kitchen_quality,B_interior,other,index,0.103,0.012,,,***,,,high,Mean-centred standardized coefficient. Kitchen type,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.103,0.012,not harmonised,reported,0.11700308184044386,0.01363142701053715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.583333333333332,5308.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,kitch_type,kitchen_quality,B_interior,other,index,0.052,0.008,,,***,,,high,Mean-centred standardized coefficient. Kitchen type,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.052,0.008,not harmonised,reported,0.08975768920072293,0.013808875261649681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,5202.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,elevat,accessibility,B_interior,other,binary,0.115,0.006,,,***,,,high,Mean-centred standardized coefficient. Presence of elevator,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.115,0.006,not harmonised,reported,0.18368989929827675,0.009583820832953569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.166666666666668,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,elevat,accessibility,B_interior,other,binary,0.115,0.006,,,***,,,high,Mean-centred standardized coefficient. Presence of elevator,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.115,0.006,not harmonised,reported,0.18368989929827675,0.009583820832953569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.166666666666668,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,elevat,accessibility,B_interior,other,binary,0.154,0.007,,,***,,,high,Mean-centred standardized coefficient. Presence of elevator,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.154,0.007,not harmonised,reported,0.2097236415287196,0.009532892796759982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,elevat,accessibility,B_interior,other,binary,0.206,0.013,,,***,,,high,Mean-centred standardized coefficient. Presence of elevator,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.206,0.013,not harmonised,reported,0.2125306653049125,0.013412129363902245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.846153846153847,5308.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,elevat,accessibility,B_interior,other,binary,0.077,0.008,,,***,,,high,Mean-centred standardized coefficient. Presence of elevator,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.077,0.008,not harmonised,reported,0.13227643887094126,0.013743006635941949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.625,5202.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,balcony,terrace_balcony,C_exterior,other,binary,0.011,0.006,,,*,,,high,Mean-centred standardized coefficient.,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.011,0.006,not harmonised,reported,0.01787163137533974,0.00974816256836713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8333333333333333,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,balcony,terrace_balcony,C_exterior,other,binary,0.012,0.006,,,*,,,high,Mean-centred standardized coefficient.,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.012,0.006,not harmonised,reported,0.0194957333382473,0.00974786666912365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,balcony,terrace_balcony,C_exterior,other,binary,-0.019,0.007,,,**,,,high,Mean-centred standardized coefficient.,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.019,0.007,not harmonised,reported,-0.02645426331973581,0.009746307538850037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.714285714285714,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,balcony,terrace_balcony,C_exterior,other,binary,0.008,0.011,,,,,,high,Mean-centred standardized coefficient.,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.008,0.011,not harmonised,reported,0.009981830887727678,0.013725017470625558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7272727272727273,5308.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,balcony,terrace_balcony,C_exterior,other,binary,-0.043,0.008,,,***,,,high,Mean-centred standardized coefficient.,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.043,0.008,not harmonised,reported,-0.07431742421869467,0.013826497529059475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.374999999999999,5202.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,garage,garage,A_structural,other,binary,0.169,0.006,,,***,,,high,Mean-centred standardized coefficient. Garage/box,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.169,0.006,not harmonised,reported,0.26481320005267817,0.009401652072876148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.166666666666668,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,garage,garage,A_structural,other,binary,0.168,0.006,,,***,,,high,Mean-centred standardized coefficient. Garage/box,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.168,0.006,not harmonised,reported,0.26335523572544683,0.009405544133051672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.0,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,garage,garage,A_structural,other,binary,0.121,0.007,,,***,,,high,Mean-centred standardized coefficient. Garage/box,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.121,0.007,not harmonised,reported,0.16618731302197493,0.00961414207565144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.285714285714285,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,garage,garage,A_structural,other,binary,0.07,0.012,,,***,,,high,Mean-centred standardized coefficient. Garage/box,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.07,0.012,not harmonised,reported,0.07981117837027768,0.0136819162920476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.833333333333334,5308.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,garage,garage,A_structural,other,binary,0.205,0.008,,,***,,,high,Mean-centred standardized coefficient. Garage/box,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.205,0.008,not harmonised,reported,0.3347845786713737,0.013064764045712145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.624999999999996,5202.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,furnish_type,other,other,other,index,-0.006,0.006,,,,,,high,Mean-centred standardized coefficient. Furnishing type,House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.006,0.006,not harmonised,reported,-0.009749256342902057,0.009749256342902057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,furnish_type,other,other,other,index,-0.006,0.006,,,,,,high,Mean-centred standardized coefficient. Furnishing type,House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.006,0.006,not harmonised,reported,-0.009749256342902057,0.009749256342902057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,furnish_type,other,other,other,index,0.017,0.007,,,*,,,high,Mean-centred standardized coefficient. Furnishing type,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.017,0.007,not harmonised,reported,0.023671256072596798,0.00974698779459868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.428571428571429,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,furnish_type,other,other,other,index,0.036,0.011,,,**,,,high,Mean-centred standardized coefficient. Furnishing type,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.036,0.011,not harmonised,reported,0.04487522401851714,0.013711874005658013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2727272727272725,5308.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,furnish_type,other,other,other,index,-0.023,0.008,,,**,,,high,Mean-centred standardized coefficient. Furnishing type,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.023,0.008,not harmonised,reported,-0.03982978083488858,0.013853836812135156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.875,5202.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,prop_status_2,condition,A_structural,other,binary,-0.079,0.009,,,***,,,high,Mean-centred standardized coefficient. Property status: renewed (ref. new),House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.079,0.009,not harmonised,reported,-0.08526918318076596,0.009714210742112577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.777777777777779,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,prop_status_2,condition,A_structural,other,binary,-0.079,0.009,,,***,,,high,Mean-centred standardized coefficient. Property status: renewed (ref. new),House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.079,0.009,not harmonised,reported,-0.08526918318076596,0.009714210742112577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.777777777777779,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,prop_status_2,condition,A_structural,other,binary,-0.045,0.011,,,***,,,high,Mean-centred standardized coefficient. Property status: renewed (ref. new),House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.045,0.011,not harmonised,reported,-0.039853529444503724,0.009741973864212022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.090909090909091,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,prop_status_2,condition,A_structural,other,binary,0.015,0.02,,,,,,high,Mean-centred standardized coefficient. Property status: renewed (ref. new),House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.015,0.02,not harmonised,reported,0.010293730551100163,0.013724974068133552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,5308.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,prop_status_2,condition,A_structural,other,binary,-0.1,0.012,,,***,,,high,Mean-centred standardized coefficient. Property status: renewed (ref. new),House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.1,0.012,not harmonised,reported,-0.11477675365969757,0.013773210439163706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.333333333333334,5202.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,prop_status_3,condition,A_structural,other,binary,-0.213,0.01,,,***,,,high,Mean-centred standardized coefficient. Property status: habitable (ref. new),House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.213,0.01,not harmonised,reported,-0.20333084228795145,0.009546048933706641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.3,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,prop_status_3,condition,A_structural,other,binary,-0.213,0.01,,,***,,,high,Mean-centred standardized coefficient. Property status: habitable (ref. new),House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.213,0.01,not harmonised,reported,-0.20333084228795145,0.009546048933706641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.3,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,prop_status_3,condition,A_structural,other,binary,-0.175,0.012,,,***,,,high,Mean-centred standardized coefficient. Property status: habitable (ref. new),House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.175,0.012,not harmonised,reported,-0.14076764383845222,0.009652638434636724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.583333333333332,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,prop_status_3,condition,A_structural,other,binary,-0.153,0.021,,,***,,,high,Mean-centred standardized coefficient. Property status: habitable (ref. new),House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.153,0.021,not harmonised,reported,-0.09950523414262706,0.013657581156831166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.285714285714285,5308.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,prop_status_3,condition,A_structural,other,binary,-0.221,0.012,,,***,,,high,Mean-centred standardized coefficient. Property status: habitable (ref. new),House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.221,0.012,not harmonised,reported,-0.24740595511837085,0.013433807517739592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.416666666666668,5202.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,prop_status_4,condition,A_structural,other,binary,-0.31,0.013,,,***,,,high,Mean-centred standardized coefficient. Property status: to be renewed (ref. new),House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.31,0.013,not harmonised,reported,-0.22645359419233965,0.009496441046775533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.846153846153847,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,prop_status_4,condition,A_structural,other,binary,-0.31,0.013,,,***,,,high,Mean-centred standardized coefficient. Property status: to be renewed (ref. new),House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.31,0.013,not harmonised,reported,-0.22645359419233965,0.009496441046775533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.846153846153847,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,prop_status_4,condition,A_structural,other,binary,-0.248,0.016,,,***,,,high,Mean-centred standardized coefficient. Property status: to be renewed (ref. new),House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.248,0.016,not harmonised,reported,-0.14942405479628196,0.009640261599760126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.5,10520.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,prop_status_4,condition,A_structural,other,binary,-0.205,0.024,,,***,,,high,Mean-centred standardized coefficient. Property status: to be renewed (ref. new),House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.205,0.024,not harmonised,reported,-0.11644282539181472,0.01363233077757831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.541666666666666,5308.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,prop_status_4,condition,A_structural,other,binary,-0.4,0.021,,,***,,,high,Mean-centred standardized coefficient. Property status: to be renewed (ref. new),House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.4,0.021,not harmonised,reported,-0.25533800233849396,0.013405245122770932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.047619047619047,5202.0,binary,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,dist_sub_st,transit_distance,D_location,other,m,-0.115,0.007,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest subway/urban rail station,House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.115,0.007,not harmonised,reported,-0.15815798803003217,0.009627007967045435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.42857142857143,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,dist_sub_st,transit_distance,D_location,other,m,-0.12,0.007,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest subway/urban rail station,House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.12,0.007,not harmonised,reported,-0.1648513402107825,0.009616328178962312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.142857142857142,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,dist_sub_st,transit_distance,D_location,other,m,-0.119,0.004,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest subway/urban rail station,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.119,0.004,not harmonised,reported,-0.27857243173492746,0.009363779217980758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.749999999999996,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,dist_sub_st,transit_distance,D_location,other,m,0.005,0.007,,,,,,high,Mean-centred standardized coefficient. Distance in metres to nearest subway/urban rail station,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.005,0.007,not harmonised,reported,0.009803601194738222,0.01372504167263351,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7142857142857143,5308.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,dist_sub_st,transit_distance,D_location,other,m,-0.096,0.004,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest subway/urban rail station,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.096,0.004,not harmonised,reported,-0.31573481518554336,0.01315561729939764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.0,5202.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,dist_tr_st,transit_distance,D_location,other,m,-0.028,0.005,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest train station,House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.028,0.005,not harmonised,reported,-0.054517233159524045,0.009735220207057867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.6,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,dist_tr_st,transit_distance,D_location,other,m,-0.029,0.006,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest train station,House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.029,0.006,not harmonised,reported,-0.04707140991817672,0.009738912396864148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.833333333333333,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,dist_tr_st,transit_distance,D_location,other,m,-0.029,0.004,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest train station,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.029,0.004,not harmonised,reported,-0.07050953912499619,0.009725453672413268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.25,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,dist_tr_st,transit_distance,D_location,other,m,-0.053,0.005,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest train station,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.053,0.005,not harmonised,reported,-0.14397656217485294,0.013582694544797446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.6,5308.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,dist_tr_st,transit_distance,D_location,other,m,-0.026,0.004,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest train station,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.026,0.004,not harmonised,reported,-0.08975768920072293,0.013808875261649681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.5,5202.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,dist_mall,commercial_distance,D_location,other,m,-0.001,0.005,,,,,,high,Mean-centred standardized coefficient. Distance in metres to nearest shopping mall,House price (log),log,OLS,10530.0,0.858,se,full sample,other,-0.001,0.005,not harmonised,reported,-0.0019499402328103947,0.009749701164051973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,dist_mall,commercial_distance,D_location,other,m,-0.003,0.005,,,,,,high,Mean-centred standardized coefficient. Distance in metres to nearest shopping mall,House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.003,0.005,not harmonised,reported,-0.005849731730142145,0.009749552883570243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,dist_mall,commercial_distance,D_location,other,m,0.018,0.004,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest shopping mall,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.018,0.004,not harmonised,reported,0.043831573123665056,0.00974034958303668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,dist_mall,commercial_distance,D_location,other,m,-0.066,0.005,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest shopping mall,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.066,0.005,not harmonised,reported,-0.17827683247793483,0.01350582064226779,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.200000000000001,5308.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,dist_mall,commercial_distance,D_location,other,m,0.003,0.004,,,,,,high,Mean-centred standardized coefficient. Distance in metres to nearest shopping mall,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.003,0.004,not harmonised,reported,0.010398066971066409,0.013864089294755211,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,5202.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c1,4,1 (Model 1 Base OLS),11,green_area,green_space,E_environment,other,m2,0.004,0.003,,,,,,high,Mean-centred standardized coefficient. Green area (m2) within 300 m radius,House price (log),log,OLS,10530.0,0.858,se,full sample,other,0.004,0.003,not harmonised,reported,0.012998528000051272,0.009748896000038454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,10520.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,green_area,green_space,E_environment,other,m2,0.004,0.003,,,,,,high,Mean-centred standardized coefficient. Green area (m2) within 300 m radius,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.004,0.003,not harmonised,reported,0.012998528000051272,0.009748896000038454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,10520.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,green_area,green_space,E_environment,other,m2,-0.016,0.003,,,***,,,high,Mean-centred standardized coefficient. Green area (m2) within 300 m radius,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.016,0.003,not harmonised,reported,-0.051928349363321996,0.009736565505622876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.333333333333333,10520.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,green_area,green_space,E_environment,other,m2,-0.017,0.005,,,**,,,high,Mean-centred standardized coefficient. Green area (m2) within 300 m radius,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.017,0.005,not harmonised,reported,-0.0466166500501683,0.01371077942652009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4000000000000004,5308.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,green_area,green_space,E_environment,other,m2,0.02,0.003,,,***,,,high,Mean-centred standardized coefficient. Green area (m2) within 300 m radius,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.02,0.003,not harmonised,reported,0.09203991347920856,0.013805987021881284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.666666666666667,5202.0,m2,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,dist_stream,water_proximity,E_environment,other,m,0.012,0.005,,,*,,,high,Mean-centred standardized coefficient. Distance in metres to nearest natural stream,House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.012,0.005,not harmonised,reported,0.02339292400888753,0.009747051670369804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,dist_stream,water_proximity,E_environment,other,m,0.012,0.003,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest natural stream,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.012,0.003,not harmonised,reported,0.038969255642692,0.009742313910673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,dist_stream,water_proximity,E_environment,other,m,0.058,0.005,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest natural stream,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.058,0.005,not harmonised,reported,0.15723758919029002,0.01355496458536983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.6,5308.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,dist_stream,water_proximity,E_environment,other,m,0.024,0.004,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest natural stream,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.024,0.004,not harmonised,reported,0.08290266722896782,0.01381711120482797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,5202.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,quality_stream,water_quality,E_environment,other,index,0.005,0.003,,,*,,,high,"Mean-centred standardized coefficient. Stream water quality, 5-point Likert (ARPA 2009-2014)",House price (log),log,OLS,10530.0,0.861,se,full sample,other,0.005,0.003,not harmonised,reported,0.016247387934627325,0.009748432760776395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,quality_stream,water_quality,E_environment,other,index,0.008,0.003,,,**,,,high,"Mean-centred standardized coefficient. Stream water quality, 5-point Likert (ARPA 2009-2014)",House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.008,0.003,not harmonised,reported,0.025990469742512547,0.009746426153442206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6666666666666665,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,quality_stream,water_quality,E_environment,other,index,0.011,0.005,,,*,,,high,"Mean-centred standardized coefficient. Stream water quality, 5-point Likert (ARPA 2009-2014)",House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,0.011,0.005,not harmonised,reported,0.0301827851514098,0.013719447796095364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1999999999999997,5308.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,quality_stream,water_quality,E_environment,other,index,0.01,0.004,,,**,,,high,"Mean-centred standardized coefficient. Stream water quality, 5-point Likert (ARPA 2009-2014)",House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.01,0.004,not harmonised,reported,0.03464129328283234,0.013856517313132935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,5202.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,dist_canal,water_proximity,E_environment,other,m,-0.02,0.005,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest canal,House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.02,0.005,not harmonised,reported,-0.038969255642692,0.009742313910673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,dist_canal,water_proximity,E_environment,other,m,-0.074,0.003,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest canal,House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,-0.074,0.003,not harmonised,reported,-0.23382623346487033,0.009479441897224475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.666666666666664,10520.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,dist_canal,water_proximity,E_environment,other,m,-0.071,0.006,,,***,,,high,Mean-centred standardized coefficient. Distance in metres to nearest canal,House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.071,0.006,not harmonised,reported,-0.16031990310826447,0.01354816082605052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.833333333333332,5308.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,dist_canal,water_proximity,E_environment,other,m,-0.003,0.003,,,,,,high,Mean-centred standardized coefficient. Distance in metres to nearest canal,House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,-0.003,0.003,not harmonised,reported,-0.013863506393873152,0.01386350639387315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5202.0,m,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c2,4,2 (Model 2 Full OLS),11,quality_canal,water_quality,E_environment,other,index,-0.001,0.003,,,,,,high,"Mean-centred standardized coefficient. Canal water quality, 5-point Likert",House price (log),log,OLS,10530.0,0.861,se,full sample,other,-0.001,0.003,not harmonised,reported,-0.0032498894040830437,0.009749668212249131,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c3,4,3 (Model 3 Full Spatial-Lag),11,quality_canal,water_quality,E_environment,other,index,0.002,0.003,,,,,,high,"Mean-centred standardized coefficient. Canal water quality, 5-point Likert",House price (log),log,spatial_lag,10530.0,,se,full sample; rho=0.406,other,0.002,0.003,not harmonised,reported,0.006499675836751261,0.009749513755126891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,10520.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c4,4,4 (Model 4 SL Milano),11,quality_canal,water_quality,E_environment,other,index,-0.007,0.005,,,,,,high,"Mean-centred standardized coefficient. Canal water quality, 5-point Likert",House price (log),log,spatial_lag,5318.0,,se,city of Milan only; rho=0.080,other,-0.007,0.005,not harmonised,reported,-0.019212434987184328,0.013723167847988806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,5308.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225,43d34a3cb07ebe23b958a009b21ab082b8ff0225:T4-c5,4,5 (Model 5 SL Province),11,quality_canal,water_quality,E_environment,other,index,0.0,0.004,,,,,,high,"Mean-centred standardized coefficient. Canal water quality, 5-point Likert",House price (log),log,spatial_lag,5212.0,,se,province outside Milan; rho=0.023,other,0.0,0.004,not harmonised,reported,0.0,0.01386483884679505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,5202.0,index,nonstandard_x_transform,2016.0,Italy,Europe,High income,2011.0,2015.0,mixed,listing,journal,True,True,unknown,,2013.0,spatial,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,FlatSize,living_area,A_structural,level,sqft,3.229,,5.414,0.0,*,,,high,"Saleable area excluding flat roof (sq ft). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,3.229,0.5964166974510529,"b (currency per unit, as reported)",|b/t|,0.20033840539319095,0.037003769005022336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.414,701.0,sqft,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,FlatRoof,terrace_balcony,C_exterior,level,sqft,4.056,,3.734,0.0,*,,,high,"Flat roof (private outdoor) area in sq ft. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,4.056,1.0862346009641135,"b (currency per unit, as reported)",|b/t|,0.139649272807372,0.037399376756125334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.734,701.0,sqft,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,AGE,age,A_structural,level,years,-45.506,,-6.948,0.0,*,,,high,"Building age in years. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,-45.506,6.54951065054692,"b (currency per unit, as reported)",|b/t|,-0.25382785187390217,0.036532506026756215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.948,701.0,years,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,Floor,floor_level,A_structural,level,count,187.311,,2.843,0.005,*,,,high,"Floor level of the unit. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,187.311,65.88498065423849,"b (currency per unit, as reported)",|b/t|,0.1067648834883231,0.0375535995386293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.843,701.0,count,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,Orient,sunlight,E_environment,binary,binary,-102.268,,-1.367,0.172,,,,high,"Orientation dummy (direction not defined in table; expected negative). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,-102.268,74.81199707388441,"b (currency per unit, as reported)",|b/t|,-0.05156219726515262,0.03771923720932891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.367,701.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,ViewHw,view,E_environment,binary,binary,-229.866,,-2.19,0.029,**,,,high,"Highway or railway view dummy (negative view). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,-229.866,104.96164383561644,"b (currency per unit, as reported)",|b/t|,-0.08243364197755515,0.03764093240984254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.19,701.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,ViewMount,view,E_environment,binary,binary,390.095,,2.511,0.012,**,,,high,"Mountain view dummy. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,390.095,155.35444046196736,"b (currency per unit, as reported)",|b/t|,0.09441550306443404,0.03760075789105299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.511,701.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,ViewSea,view,E_environment,binary,binary,1368.619,,8.53,0.0,*,,,high,"Sea view dummy (text also cites 1358.619; table value used). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,1368.619,160.44771395076202,"b (currency per unit, as reported)",|b/t|,0.3066518867757855,0.03594981087641096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.53,701.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T3-c1,3,1,11,DUPLEX,dwelling_type,A_structural,binary,binary,588.54,,2.408,0.016,**,,,high,"Duplex (two-storey) unit dummy. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,711.0,0.745,t,"Model 1 (Duplex): South Horizons duplex and typical units, floors 15-19; adj. R2; F=231.95",monetary,588.54,244.41029900332225,"b (currency per unit, as reported)",|b/t|,0.09057507119319323,0.037614232223086894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.408,701.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,FlatSize,living_area,A_structural,level,sqft,2.69,,5.376,0.0,*,,,high,"Saleable area excluding flat roof (sq ft). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,2.69,0.5003720238095237,"b (currency per unit, as reported)",|b/t|,0.20234256256798697,0.03763812547767615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.376,677.0,sqft,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,FlatRoof,terrace_balcony,C_exterior,level,sqft,4.469,,4.442,0.0,*,,,high,"Flat roof (private outdoor) area in sq ft. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,4.469,1.0060783430886988,"b (currency per unit, as reported)",|b/t|,0.16828518078986313,0.03788500242905518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.442,677.0,sqft,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,AGE,age,A_structural,level,years,-56.7,,-2.833,0.005,*,,,high,"Building age in years; quadratic (AgeSq also included). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,-56.7,20.0141193081539,"b (currency per unit, as reported)",|b/t|,-0.10824132054835905,0.03820731399518498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.833,677.0,years,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,AgeSq,age,A_structural,squared,years,1.538,,2.618,0.009,*,,,high,"Age squared. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,other,1.538,0.5874713521772346,not harmonised,|b/t|,0.1001124225243944,0.038240039161342394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.618,677.0,years,nonstandard_x_transform,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,Floor,floor_level,A_structural,level,count,110.119,,4.763,0.0,*,,,high,"Floor level of the unit. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,110.119,23.119672475330674,"b (currency per unit, as reported)",|b/t|,0.18006484725591904,0.037804922791500946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.763,677.0,count,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,Orient,sunlight,E_environment,binary,binary,-101.477,,-0.999,0.318,,,,high,"Orientation dummy (direction not defined in table; expected negative). Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,-101.477,101.57857857857859,"b (currency per unit, as reported)",|b/t|,-0.038366420422884906,0.038404825248133045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.999,677.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,ViewOpen,view,E_environment,binary,binary,596.154,,3.226,0.001,*,,,high,"Open view dummy. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,596.154,184.79665220086795,"b (currency per unit, as reported)",|b/t|,0.12304312818608343,0.0381410812728095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.226,677.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,ViewMount,view,E_environment,binary,binary,338.198,,1.977,0.048,**,,,high,"Mountain view dummy. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,338.198,171.06626201315123,"b (currency per unit, as reported)",|b/t|,0.07576389303166196,0.03832265707216083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.977,677.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,ViewSea,view,E_environment,binary,binary,1040.848,,6.207,0.0,*,,,high,"Sea view dummy. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,1040.848,167.68938295472853,"b (currency per unit, as reported)",|b/t|,0.232043163716567,0.03738410886363251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.207,677.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90,4412b66b7af7dc2e4784f04e96dda07a5d24ef90:T5-c1,5,1,12,ADJOIN,dwelling_type,A_structural,binary,binary,934.079,,2.513,0.012,*,,,high,"Adjoining (combined) flat dummy. Dependent variable is price per sq ft (level); star legend reversed (* 1%, ** 5%).","Price per square foot of saleable area (HK$, index-adjusted)",per_m2,OLS,687.0,0.81,t,Model 2 (Adjoining flats): Chi Fu Fa Yuen top floors and comparable units; adj. R2; F=266.09,monetary,934.079,371.69876641464384,"b (currency per unit, as reported)",|b/t|,0.09613509453337808,0.0382551112349296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.513,677.0,binary,,2016.0,Hong Kong,Asia,High income,1992.0,2011.0,condo_apartment,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,STRDIST,transit_distance,D_location,level,m,-10.56,4.95,-2.16,,,,,high,Straight-line distance to nearest LRT station in metres; MYR per metre,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,-10.56,4.95,"b (currency per unit, as reported)",reported,-0.05443266292245553,0.025200306908544228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.16,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,NETDIST,transit_distance,D_location,level,m,-6.61,3.03,-2.31,,,,,high,Network distance to nearest LRT station in metres; MYR per metre,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,-6.61,3.03,"b (currency per unit, as reported)",reported,-0.05820031929299081,0.025194943416879136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.31,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,TIMESAVINGS,cbd_distance,D_location,other,minutes,2019.25,296.16,6.82,,,,,high,Travel time savings to CBD in minutes (higher = better accessibility); not a distance,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),other,2019.25,296.16,not harmonised,reported,0.16962695047268653,0.02487198687282794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.82,1570.0,minutes,nonstandard_x_transform,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,TIMESAVINGS,cbd_distance,D_location,other,minutes,2046.17,296.15,6.87,,,,,high,Travel time savings to CBD in minutes (higher = better accessibility); not a distance,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),other,2046.17,296.15,not harmonised,reported,0.1708343843777593,0.02486672261684997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.87,1570.0,minutes,nonstandard_x_transform,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,FLRAREA,living_area,A_structural,level,sqft,310.63,13.09,24.07,,,,,high,"Floor area in square feet, mean-centred","SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,310.63,13.09,"b (currency per unit, as reported)",reported,0.5191836580839769,0.02156974067652584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.07,1570.0,sqft,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,FLRAREA,living_area,A_structural,level,sqft,312.65,13.09,23.99,,,,,high,"Floor area in square feet, mean-centred","SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,312.65,13.09,"b (currency per unit, as reported)",reported,0.517921517113771,0.0215890586541797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.99,1570.0,sqft,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,BEDS,bedrooms,A_structural,level,count,31485.34,3922.05,8.01,,,,,high,,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,31485.34,3922.05,"b (currency per unit, as reported)",reported,0.19814595677223037,0.024737322942850234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.01,1570.0,count,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,BEDS,bedrooms,A_structural,level,count,31594.29,3922.04,8.06,,,,,high,,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,31594.29,3922.04,"b (currency per unit, as reported)",reported,0.19933382372879163,0.024731243638807892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.06,1570.0,count,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,TYPTRRD,dwelling_type,A_structural,binary,binary,71160.14,9509.88,7.44,,,,,high,Terraced house dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,71160.14,9509.88,"b (currency per unit, as reported)",reported,0.18454360781635487,0.02480424836241329,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.44,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,TYPTRRD,dwelling_type,A_structural,binary,binary,72143.93,9509.88,7.61,,,,,high,Terraced house dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,72143.93,9509.88,"b (currency per unit, as reported)",reported,0.18861193959123113,0.02478474896073996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.61,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,TYPSEMID,dwelling_type,A_structural,binary,binary,139696.87,22626.95,6.19,,,,,high,Semi-detached house dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,139696.87,22626.95,"b (currency per unit, as reported)",reported,0.1543493995363947,0.024935282639159076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.19,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,TYPSEMID,dwelling_type,A_structural,binary,binary,138713.09,22626.95,6.15,,,,,high,Semi-detached house dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,138713.09,22626.95,"b (currency per unit, as reported)",reported,0.15337552588275943,0.02493910989963568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.15,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,TYPDETCH,dwelling_type,A_structural,binary,binary,173145.42,17052.2,10.16,,,,,high,Detached house dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,173145.42,17052.2,"b (currency per unit, as reported)",reported,0.24837990558460363,0.024446841100846814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.16,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,TYPDETCH,dwelling_type,A_structural,binary,binary,170849.93,17052.2,10.02,,,,,high,Detached house dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,170849.93,17052.2,"b (currency per unit, as reported)",reported,0.2451644122934139,0.024467506216907575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.02,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,TYPCONDO,dwelling_type,A_structural,binary,binary,184622.86,10165.74,18.23,,,,,high,Condominium dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,184622.86,10165.74,"b (currency per unit, as reported)",reported,0.4179683619868986,0.022927502028902827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.23,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,TYPCONDO,dwelling_type,A_structural,binary,binary,184950.79,10165.73,18.25,,,,,high,Condominium dummy,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,184950.79,10165.73,"b (currency per unit, as reported)",reported,0.41834669565498406,0.022923106611232005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.25,1570.0,binary,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,CBD,cbd_distance,D_location,level,m,-17.96,1.52,-11.75,,,,,high,Distance to Kuala Lumpur CBD in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,-17.96,1.52,"b (currency per unit, as reported)",reported,-0.28430597542984853,0.024196253228072215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.75,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,CBD,cbd_distance,D_location,level,m,-18.61,1.55,-11.92,,,,,high,Distance to Kuala Lumpur CBD in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,-18.61,1.55,"b (currency per unit, as reported)",reported,-0.2880802045137492,0.024167802392093058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.92,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,PRIMARYSCH,school_distance,D_location,level,m,26.25,3.94,6.75,,,,,high,Distance to nearest primary school in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,26.25,3.94,"b (currency per unit, as reported)",reported,0.16793525995983422,0.024879297771827288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.75,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,PRIMARYSCH,school_distance,D_location,level,m,26.57,3.93,6.79,,,,,high,Distance to nearest primary school in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,26.57,3.93,"b (currency per unit, as reported)",reported,0.16890212293052065,0.02487512856119597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.79,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,SECONDARYSCH,school_distance,D_location,level,m,-11.11,4.66,-2.4,,,,,high,Distance to nearest secondary school in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,-11.11,4.66,"b (currency per unit, as reported)",reported,-0.06045973033122648,0.025191554304677705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,SECONDARYSCH,school_distance,D_location,level,m,-11.11,4.66,-2.36,,,,,high,Distance to nearest secondary school in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,-11.11,4.66,"b (currency per unit, as reported)",reported,-0.05945566029861103,0.025193076397716542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.36,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,COMMERCIAL,commercial_distance,D_location,level,m,-10.13,3.28,-3.17,,,,,high,Distance to commercial areas in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,-10.13,3.28,"b (currency per unit, as reported)",reported,-0.07974877088267643,0.025157340972453132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.17,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,COMMERCIAL,commercial_distance,D_location,level,m,-9.54,3.28,-2.94,,,,,high,Distance to commercial areas in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,-9.54,3.28,"b (currency per unit, as reported)",reported,-0.0739954956814135,0.025168535946059018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.94,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,PARK,green_space,E_environment,level,m,-1.13,0.31,-3.33,,,,,high,Distance to parks in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,-1.13,0.31,"b (currency per unit, as reported)",reported,-0.08374638872241784,0.02514906568240776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.33,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,PARK,green_space,E_environment,level,m,-1.13,0.31,-3.36,,,,,high,Distance to parks in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,-1.13,0.31,"b (currency per unit, as reported)",reported,-0.08449549802532372,0.025147469650393967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.36,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,HOSPITAL,hospital_distance,D_location,level,m,3.4,1.29,2.64,,,,,high,Distance to hospitals in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,3.4,1.29,"b (currency per unit, as reported)",reported,0.06648019217792078,0.025181890976485145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.64,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,HOSPITAL,hospital_distance,D_location,level,m,3.58,1.28,2.73,,,,,high,Distance to hospitals in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,3.58,1.28,"b (currency per unit, as reported)",reported,0.06873603027357321,0.025178033067242932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.73,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,RECREATION,neighbourhood_amenities,F_neighbourhood,level,m,-4.81,1.7,-2.84,,,,,high,Distance to recreational areas in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,-4.81,1.7,"b (currency per unit, as reported)",reported,-0.07149173117281475,0.025173144779160123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.84,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,RECREATION,neighbourhood_amenities,F_neighbourhood,level,m,-4.65,1.7,-2.76,,,,,high,Distance to recreational areas in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,-4.65,1.7,"b (currency per unit, as reported)",reported,-0.06948774379351565,0.02517671876576654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.76,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,INDUSTRY,hazard_site,E_environment,level,m,28.22,3.72,7.74,,,,,high,Distance to industrial areas in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,28.22,3.72,"b (currency per unit, as reported)",reported,0.19171648959170007,0.02476957229866926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.74,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,INDUSTRY,hazard_site,E_environment,level,m,26.91,3.71,7.23,,,,,high,Distance to industrial areas in metres,"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,26.91,3.71,"b (currency per unit, as reported)",reported,0.17950491341759323,0.024827788854438895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.23,1570.0,m,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c1,4,Model 3 (straight-line distance),9,MALAY,demographics,F_neighbourhood,level,share,3673.45,613.52,5.99,,,,,high,Proportion of Malays in the mukim (census 2000),"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 3 (straight-line distance),monetary,3673.45,613.52,"b (currency per unit, as reported)",reported,0.14947558583101228,0.024954187951754976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.99,1570.0,share,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64,461f8c8aa68c6a2b5157bc03f6d689991ee3ac64:T4-c2,4,Model 4 (network distance),9,MALAY,demographics,F_neighbourhood,level,share,3520.07,605.85,5.83,,,,,high,Proportion of Malays in the mukim (census 2000),"SELLING (house transaction price, MYR)",level,OLS,1580.0,0.782,none,All residential sales within 2 km of Kelana Jaya LRT stations; adjusted R2 78.2%; Model 4 (network distance),monetary,3520.07,605.85,"b (currency per unit, as reported)",reported,0.14556865707439132,0.024968894866962486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.83,1570.0,share,,2013.0,Malaysia,Asia,Upper middle income,2004.0,2005.0,mixed,transaction,journal,False,False,unknown,,2004.5,ols,journal,False,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnHousesize,living_area,A_structural,log,m2,0.82,0.01,,,***,,,high,"ln heated area (m2). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.82,0.01,b (log-log),reported,0.6159149282747173,0.007511157661886797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),82.0,11001.0,m2,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnHousesize,living_area,A_structural,log,m2,0.75,0.02,,,***,,,high,"ln heated area (m2). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.75,0.02,b (log-log),reported,0.3366615364826964,0.008977640972871905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.5,11001.0,m2,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnHousesize,living_area,A_structural,log,m2,0.75,0.02,,,***,,,high,"ln heated area (m2). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.75,0.02,b (log-log),reported,0.3366615364826964,0.008977640972871905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.5,11001.0,m2,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnParcelsize,lot_size,A_structural,log,m2,0.13,0.01,,,***,,,high,"ln parcel size (m2). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.13,0.01,b (log-log),reported,0.1230033007541626,0.009461792365704816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,11001.0,m2,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnParcelsize,lot_size,A_structural,log,m2,0.13,0.01,,,***,,,high,"ln parcel size (m2). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.13,0.01,b (log-log),reported,0.1230033007541626,0.009461792365704816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,11001.0,m2,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnParcelsize,lot_size,A_structural,log,m2,0.1,0.01,,,***,,,high,"ln parcel size (m2). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.1,0.01,b (log-log),reported,0.09491152437764508,0.009491152437764508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,11001.0,m2,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Beds,bedrooms,A_structural,level,count,-0.01,0.008,,,,,,high,"Number of bedrooms. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",semi_elasticity,-1.0,0.8,100*b/1 (per unit count),reported,-0.011916894399039497,0.009533515519231596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,11001.0,count,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Beds,bedrooms,A_structural,level,count,0.001,0.009,,,,,,high,"Number of bedrooms. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",semi_elasticity,0.1,0.8999999999999999,100*b/1 (per unit count),reported,0.0010593541311257313,0.009534187180131581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11111111111111112,11001.0,count,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Beds,bedrooms,A_structural,level,count,0.002,0.008,,,,,,high,"Number of bedrooms. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",semi_elasticity,0.2,0.8,100*b/1 (per unit count),reported,0.002383541361680103,0.009534165446720411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,11001.0,count,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Age,age,A_structural,level,years,-0.007,0.0007,,,***,,,high,"Age of house (years), quadratic. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",semi_elasticity,-0.7000000000000001,0.06999999999999999,100*b/1 (per year),reported,-0.09491152437764508,0.009491152437764508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,11001.0,years,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Age,age,A_structural,level,years,-0.007,0.0007,,,***,,,high,"Age of house (years), quadratic. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",semi_elasticity,-0.7000000000000001,0.06999999999999999,100*b/1 (per year),reported,-0.09491152437764508,0.009491152437764508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,11001.0,years,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Age,age,A_structural,level,years,-0.008,0.0007,,,***,,,high,"Age of house (years), quadratic. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",semi_elasticity,-0.8,0.06999999999999999,100*b/1 (per year),reported,-0.10832106269528104,0.00947809298583709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.428571428571429,11001.0,years,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Agesq,age,A_structural,squared,years,7.5e-05,6.1e-06,,,***,,,high,"Age squared. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",other,7.5e-05,6.1e-06,not harmonised,reported,0.11642647710410146,0.009469353471133587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.295081967213113,11001.0,years,nonstandard_x_transform,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,False,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Agesq,age,A_structural,squared,years,7.3e-05,6e-06,,,***,,,high,"Age squared. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",other,7.3e-05,6e-06,not harmonised,reported,0.11522669648879358,0.009470687382640567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.166666666666666,11001.0,years,nonstandard_x_transform,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,False,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Agesq,age,A_structural,squared,years,7.5e-05,5.9e-06,,,***,,,high,"Age squared. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",other,7.5e-05,5.9e-06,not harmonised,reported,0.1203169283217574,0.009464931694644916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.71186440677966,11001.0,years,nonstandard_x_transform,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,False,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Fireplace,fireplace,B_interior,binary,binary,0.04,0.01,,,***,,,high,"Presence of a fireplace. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.03810906702321704,0.00952726675580426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Fireplace,fireplace,B_interior,binary,binary,0.034,0.01,,,***,,,high,"Presence of a fireplace. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,3.453287879099065,1.034584606728118,100*(exp(b - se^2/2) - 1),reported,0.03239923629285777,0.009529187144958167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4000000000000004,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Fireplace,fireplace,B_interior,binary,binary,0.04,0.01,,,***,,,high,"Presence of a fireplace. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.03810906702321704,0.00952726675580426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Brick,roof_materials,A_structural,binary,binary,-0.013,0.01,,,,,,high,"Masonry exterior dummy. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,-1.2965217952628838,0.9870841350202877,100*(exp(b - se^2/2) - 1),reported,-0.012393498365980955,0.009533460281523812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2999999999999998,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Brick,roof_materials,A_structural,binary,binary,0.01,0.01,,,,,,high,"Masonry exterior dummy. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,0.9999665838355432,1.010050167084168,100*(exp(b - se^2/2) - 1),reported,0.009533759226463817,0.009533759226463817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Brick,roof_materials,A_structural,binary,binary,0.01,0.01,,,,,,high,"Masonry exterior dummy. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,0.9999665838355432,1.010050167084168,100*(exp(b - se^2/2) - 1),reported,0.009533759226463817,0.009533759226463817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Garage,garage,A_structural,binary,binary,0.07,0.01,,,***,,,high,"Presence of a garage. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,7.2454557185766655,1.0725081812542165,100*(exp(b - se^2/2) - 1),reported,0.06659120918162918,0.009513029883089882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.000000000000001,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Garage,garage,A_structural,binary,binary,0.09,0.01,,,***,,,high,"Presence of a garage. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,9.411957635872014,1.0941742837052104,100*(exp(b - se^2/2) - 1),reported,0.08549356685113257,0.009499285205681397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Garage,garage,A_structural,binary,binary,0.1,0.01,,,***,,,high,"Presence of a garage. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,10.511566091118452,1.1051709180756477,100*(exp(b - se^2/2) - 1),reported,0.09491152437764508,0.009491152437764508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Deck,terrace_balcony,C_exterior,binary,binary,0.06,0.01,,,***,,,high,"Presence of a deck. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,6.1783456045305885,1.0618365465453596,100*(exp(b - se^2/2) - 1),reported,0.05711178435255364,0.009518630725425605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Deck,terrace_balcony,C_exterior,binary,binary,0.04,0.01,,,***,,,high,"Presence of a deck. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.03810906702321704,0.00952726675580426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Deck,terrace_balcony,C_exterior,binary,binary,0.04,0.01,,,***,,,high,"Presence of a deck. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,4.075873495467031,1.0408107741923882,100*(exp(b - se^2/2) - 1),reported,0.03810906702321704,0.00952726675580426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Pool,pool,C_exterior,binary,binary,0.1,0.02,,,***,,,high,"Presence of a pool. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,10.494990599397735,2.2103418361512954,100*(exp(b - se^2/2) - 1),reported,0.04761688817195134,0.009523377634390269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Pool,pool,C_exterior,binary,binary,0.1,0.02,,,***,,,high,"Presence of a pool. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,10.494990599397735,2.2103418361512954,100*(exp(b - se^2/2) - 1),reported,0.04761688817195134,0.009523377634390269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Pool,pool,C_exterior,binary,binary,0.1,0.02,,,***,,,high,"Presence of a pool. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,10.494990599397735,2.2103418361512954,100*(exp(b - se^2/2) - 1),reported,0.04761688817195134,0.009523377634390269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnCommonspace,green_space,E_environment,log,share,0.07,0.005,,,***,,,high,"ln percent of neighbourhood (subdivision) area set aside as common/open space by developer. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.07,0.005,b (log-log),reported,0.13230528619793228,0.00945037758556659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.000000000000002,11001.0,share,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnCommonspace,green_space,E_environment,log,share,0.06,0.005,,,***,,,high,"ln percent of neighbourhood (subdivision) area set aside as common/open space by developer. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.06,0.005,b (log-log),reported,0.11366878284651809,0.009472398570543173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,11001.0,share,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnCommonspace,green_space,E_environment,log,share,0.04,0.006,,,***,,,high,"ln percent of neighbourhood (subdivision) area set aside as common/open space by developer. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.04,0.006,b (log-log),reported,0.06343327630675547,0.009514991446013319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.666666666666667,11001.0,share,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnMajorroads,highway_distance,D_location,log,m,0.02,0.005,,,***,,,high,"ln distance to nearest major road (metres; unit per Table 1/2 distances in meters). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.02,0.005,b (log-log),reported,0.03810906702321704,0.00952726675580426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnMajorroads,highway_distance,D_location,log,m,0.008,0.005,,,,,,high,"ln distance to nearest major road (metres; unit per Table 1/2 distances in meters). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.008,0.005,b (log-log),reported,0.015252933425642814,0.009533083391026759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnMajorroads,highway_distance,D_location,log,m,0.002,0.005,,,,,,high,"ln distance to nearest major road (metres; unit per Table 1/2 distances in meters). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.002,0.005,b (log-log),reported,0.00381364927895776,0.0095341231973944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnSchool,school_distance,D_location,log,m,0.03,0.008,,,***,,,high,"ln distance to nearest school (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.03,0.008,b (log-log),reported,0.03573039232750708,0.009528104620668556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.75,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnSchool,school_distance,D_location,log,m,0.04,0.009,,,***,,,high,"ln distance to nearest school (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.04,0.009,b (log-log),reported,0.04233619722551051,0.009525644375739864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.444444444444445,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnSchool,school_distance,D_location,log,m,0.04,0.008,,,***,,,high,"ln distance to nearest school (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.04,0.008,b (log-log),reported,0.04761688817195134,0.009523377634390269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,SFHA,flood_risk,E_environment,binary,binary,0.03,0.01,,,*,,,high,"Property inside FEMA special flood hazard area. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,3.040301251486577,1.030454533953517,100*(exp(b - se^2/2) - 1),reported,0.028590884772914823,0.009530294924304942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,SFHA,flood_risk,E_environment,binary,binary,0.009,0.015,,,,,,high,"Property inside FEMA special flood hazard area. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,0.8927111089013895,1.5135609326608017,100*(exp(b - se^2/2) - 1),reported,0.005720421920320792,0.009534036533867985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,SFHA,flood_risk,E_environment,binary,binary,-0.0004,0.015,,,,,,high,"Property inside FEMA special flood hazard area. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,-0.051236869430737464,1.4994001199840017,100*(exp(b - se^2/2) - 1),reported,-0.0002542451259138462,0.009534192221769234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02666666666666667,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,Bltpostfir,other,other,binary,binary,0.06,0.01,,,***,,,high,"House built after community joined the National Flood Insurance Program (built to flood code); no taxonomy leaf. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,6.1783456045305885,1.0618365465453596,100*(exp(b - se^2/2) - 1),reported,0.05711178435255364,0.009518630725425605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,False,False,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Bltpostfir,other,other,binary,binary,0.04,0.018,,,**,,,high,"House built after community joined the National Flood Insurance Program (built to flood code); no taxonomy leaf. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,4.064217650375057,1.8734593935462986,100*(exp(b - se^2/2) - 1),reported,0.021182340742386306,0.009532053334073837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2222222222222223,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,False,False,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Bltpostfir,other,other,binary,binary,0.06,0.018,,,***,,,high,"House built after community joined the National Flood Insurance Program (built to flood code); no taxonomy leaf. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,6.166454295748602,1.9113057837816472,100*(exp(b - se^2/2) - 1),reported,0.03176460454353401,0.009529381363060202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3333333333333335,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,False,False,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnNonwhite,demographics,F_neighbourhood,log,share,-0.09,0.006,,,***,,,high,"ln percent nonwhite at census block group (2010). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.09,0.006,b (log-log),reported,-0.14157244747296263,0.009438163164864177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.0,11001.0,share,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnNonwhite,demographics,F_neighbourhood,log,share,-0.0861,0.006,,,***,,,high,"ln percent nonwhite at census block group (2010). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.0861,0.006,b (log-log),reported,-0.13555286987999182,0.009446193022995947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.35,11001.0,share,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnNonwhite,demographics,F_neighbourhood,log,share,-0.086,0.006,,,***,,,high,"ln percent nonwhite at census block group (2010). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.086,0.006,b (log-log),reported,-0.13539832121475104,0.009446394503354726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.333333333333332,11001.0,share,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c1,3,1,13,lnIncome,income,F_neighbourhood,log,currency,0.26,0.02,,,***,,,high,"ln median household income at block group (2010 Census, USD). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 1: structural + neighbourhood/location variables; SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.26,0.02,b (log-log),reported,0.1230033007541626,0.009461792365704816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,11001.0,currency,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnIncome,income,F_neighbourhood,log,currency,0.25,0.019,,,***,,,high,"ln median household income at block group (2010 Census, USD). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.25,0.019,b (log-log),reported,0.12447425743690374,0.009460043565204683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.157894736842106,11001.0,currency,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnIncome,income,F_neighbourhood,log,currency,0.27,0.0189,,,***,,,high,"ln median household income at block group (2010 Census, USD). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,0.27,0.0189,b (log-log),reported,0.13495669917306982,0.009446968942114888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.285714285714286,11001.0,currency,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnPark,green_space,E_environment,log,m,-0.09,0.008,,,***,,,high,"ln distance to nearest park (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.09,0.008,b (log-log),reported,-0.10664794742536449,0.009479817548921287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.25,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,True
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnPark,green_space,E_environment,log,m,-0.08,0.008,,,***,,,high,"ln distance to nearest park (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.08,0.008,b (log-log),reported,-0.09491152437764508,0.009491152437764508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,lnMarsh,water_proximity,E_environment,log,m,-0.07,0.005,,,***,,,high,"ln distance to nearest marsh or lake (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.07,0.005,b (log-log),reported,-0.13230528619793228,0.00945037758556659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.000000000000002,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,lnMarsh,water_proximity,E_environment,log,m,-0.04,0.006,,,***,,,high,"ln distance to nearest marsh or lake (metres). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",elasticity,-0.04,0.006,b (log-log),reported,-0.06343327630675547,0.009514991446013319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.666666666666667,11001.0,m,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c2,3,2,13,Waterview,view,E_environment,binary,binary,0.1,0.02,,,***,,,high,"Property has view (no access) of marsh. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 2: + environmental amenity proximity (park, marsh, water view); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,10.494990599397735,2.2103418361512954,100*(exp(b - se^2/2) - 1),reported,0.04761688817195134,0.009523377634390269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Waterview,view,E_environment,binary,binary,0.17,0.02,,,***,,,high,"Property has view (no access) of marsh. Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,18.506781405461826,2.370609702640731,100*(exp(b - se^2/2) - 1),reported,0.08077581957363356,0.009503037596898066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.5,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Wateraccess,waterfront,C_exterior,binary,binary,0.38,0.06,,,***,,,high,"Property has navigable water access to marsh (no dock). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,45.96548446535766,8.773707536605347,100*(exp(b - se^2/2) - 1),reported,0.0602734368465292,0.009516858449451977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.333333333333334,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd,467b5db9559e6802d7f96e2e470749e1440b69fd:T3-c3,3,3,13,Dock,waterfront,C_exterior,binary,binary,0.51,0.03,,,***,,,high,"Presence of a boat dock (water access with dock). Heteroskedasticity-consistent standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1; N=11,011.","lnPrice (sale price, 2011 USD)",log,SAC,11011.0,,se,"Model 3: + marsh accessibility (water access, dock); SARAR (spatial lag + spatial error), inverse-distance weights truncated at 200 m, generalized 2SLS; sale-year dummies included (not extracted)",pct_premium,66.45419824936054,4.995873584837659,100*(exp(b - se^2/2) - 1),reported,0.1599933568151436,0.009411373930302565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,11001.0,binary,,2016.0,United States,North America,High income,2005.0,2011.0,single_family,transaction,journal,True,False,robust,,2008.0,spatial,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Built between 1946 and 1960,age,A_structural,dummy,years,-0.0775,0.0041,,,***,,,medium,"Construction-period dummy (ref. built before 1946), not age; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,-7.458075378481532,0.37942508000262115,100*(exp(b - se^2/2) - 1),reported,-0.09168026454519361,0.004850181737229597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.902439024390244,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Built between 1946 and 1960,age,A_structural,dummy,years,-0.0743,0.0043,,,***,,,medium,"Construction-period dummy (ref. built before 1946), not age; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-7.161544883566783,0.39920904767124843,100*(exp(b - se^2/2) - 1),reported,-0.08386458733482555,0.004853536009956257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.27906976744186,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Built between 1946 and 1960,age,A_structural,dummy,years,-0.0681,0.0043,,,***,,,medium,"Construction-period dummy (ref. built before 1946), not age; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,-6.584158413347363,0.4016918324464311,100*(exp(b - se^2/2) - 1),reported,-0.07690973244460958,0.004856267981084012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.83720930232558,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Built between 1946 and 1960,age,A_structural,dummy,years,0.0075,0.0042,,,*,,,medium,"Construction-period dummy (ref. built before 1946), not age; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,0.751930908503895,0.4231618420867042,100*(exp(b - se^2/2) - 1),reported,0.010973243955174313,0.006145016614897614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7857142857142858,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Built between 1946 and 1960,age,A_structural,dummy,years,0.0074,0.0042,,,*,,,medium,"Construction-period dummy (ref. built before 1946), not age; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,0.741856219155923,0.4231195280182342,100*(exp(b - se^2/2) - 1),reported,0.010826951302511795,0.006145026414939125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.761904761904762,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Built between 1946 and 1960,age,A_structural,dummy,years,0.0089,0.0043,,,**,,,medium,"Construction-period dummy (ref. built before 1946), not age; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,0.8930395152105364,0.43384408078539194,100*(exp(b - se^2/2) - 1),reported,0.012718492337500723,0.0061448895563205735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0697674418604652,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Built between 1961 and 1970,age,A_structural,dummy,years,-0.0651,0.0042,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,-6.30345027558834,0.3935289797528237,100*(exp(b - se^2/2) - 1),reported,-0.07528153474020018,0.004856873209045172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.500000000000002,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Built between 1961 and 1970,age,A_structural,dummy,years,-0.0576,0.0043,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-5.598124457590103,0.40593181765466935,100*(exp(b - se^2/2) - 1),reported,-0.06510622735828155,0.0048603607229272695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.395348837209301,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Built between 1961 and 1970,age,A_structural,dummy,years,-0.0405,0.0043,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,-3.969971350577417,0.4129329407399076,100*(exp(b - se^2/2) - 1),reported,-0.04582695089652822,0.0048655775025943536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.418604651162791,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Built between 1961 and 1970,age,A_structural,dummy,years,0.0538,0.0046,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,5.526236160808673,0.48542582211774954,100*(exp(b - se^2/2) - 1),reported,0.07168937223262097,0.006129574577510343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.695652173913043,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Built between 1961 and 1970,age,A_structural,dummy,years,0.0535,0.0046,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,5.494583038166212,0.4852802162130919,100*(exp(b - se^2/2) - 1),reported,0.07129165479037501,0.006129749757677104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.630434782608695,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Built between 1961 and 1970,age,A_structural,dummy,years,0.0579,0.0047,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,5.959732619201574,0.498016243869284,100*(exp(b - se^2/2) - 1),reported,0.07548991107390324,0.006127851157985238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.319148936170212,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Built between 1971 and 1980,age,A_structural,dummy,years,0.0791,0.0047,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,8.230059383764843,0.5086868975194502,100*(exp(b - se^2/2) - 1),reported,0.08169872619608062,0.004854412302421984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.829787234042552,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Built between 1971 and 1980,age,A_structural,dummy,years,0.082,0.0044,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,8.544530266816475,0.4776005563250016,100*(exp(b - se^2/2) - 1),reported,0.09040037159337876,0.004850751646473982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.636363636363637,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Built between 1971 and 1980,age,A_structural,dummy,years,0.0876,0.0045,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,9.154036222467155,0.49119813635705517,100*(exp(b - se^2/2) - 1),reported,0.09439283827096655,0.00484894717145376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.46666666666667,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Built between 1971 and 1980,age,A_structural,dummy,years,0.0692,0.0053,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,7.163546659450337,0.5679747744447742,100*(exp(b - se^2/2) - 1),reported,0.07998082817095685,0.006125699267428776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.056603773584905,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Built between 1971 and 1980,age,A_structural,dummy,years,0.0693,0.0053,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,7.174263549951876,0.5680315747621872,100*(exp(b - se^2/2) - 1),reported,0.08009566645669902,0.006125642600584484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.075471698113208,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Built between 1971 and 1980,age,A_structural,dummy,years,0.0766,0.0054,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,7.959440535271134,0.5829894788151007,100*(exp(b - se^2/2) - 1),reported,0.08684409842199786,0.006122168818260945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.185185185185185,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Built between 1981 and 1990,age,A_structural,dummy,years,0.2113,0.0056,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,23.526351526075384,0.6917584152329347,100*(exp(b - se^2/2) - 1),reported,0.18075453868746563,0.004790465767391423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.732142857142854,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Built between 1981 and 1990,age,A_structural,dummy,years,0.2064,0.0051,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,22.922881736555432,0.6269148498310401,100*(exp(b - se^2/2) - 1),reported,0.19339831961091733,0.004778737548525574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.470588235294116,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Built between 1981 and 1990,age,A_structural,dummy,years,0.207,0.0052,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,22.996594252879632,0.6395909373259914,100*(exp(b - se^2/2) - 1),reported,0.1903462093301737,0.00478164390587876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.80769230769231,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Built between 1981 and 1990,age,A_structural,dummy,years,0.3006,0.0057,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,35.064702444460515,0.7698813105537297,100*(exp(b - se^2/2) - 1),reported,0.3083015190420392,0.0058460367882223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.73684210526315,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Built between 1981 and 1990,age,A_structural,dummy,years,0.2994,0.0058,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,34.90264444018312,0.782448498426124,100*(exp(b - se^2/2) - 1),reported,0.3023789059555707,0.005857707597001702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.62068965517242,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Built between 1981 and 1990,age,A_structural,dummy,years,0.3011,0.0058,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,35.132173980562385,0.7837797921524958,100*(exp(b - se^2/2) - 1),reported,0.3039376215990775,0.005854660263283459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.91379310344828,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Built between 1991 and 2000,age,A_structural,dummy,years,0.062,0.0057,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,6.3945060800132625,0.6064585364949793,100*(exp(b - se^2/2) - 1),reported,0.05290529012146853,0.004863873446651139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.87719298245614,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Built between 1991 and 2000,age,A_structural,dummy,years,0.0618,0.0058,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,6.373168141956276,0.6169747526514114,100*(exp(b - se^2/2) - 1),reported,0.05182834105995321,0.0048641485137172905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.655172413793105,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Built between 1991 and 2000,age,A_structural,dummy,years,0.0723,0.0059,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,7.495907920576417,0.6342368955285022,100*(exp(b - se^2/2) - 1),reported,0.05958061494819732,0.004862041883739477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.254237288135593,42152.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Built between 1991 and 2000,age,A_structural,dummy,years,0.1975,0.0061,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,21.833039772172835,0.74319536963148,100*(exp(b - se^2/2) - 1),reported,0.19514420881326144,0.006027238854485545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.377049180327866,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Built between 1991 and 2000,age,A_structural,dummy,years,0.1968,0.0061,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,21.747786486463472,0.742675314913125,100*(exp(b - se^2/2) - 1),reported,0.1944787628033787,0.006028051082828303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.26229508196721,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Built between 1991 and 2000,age,A_structural,dummy,years,0.2059,0.0063,,,***,,,medium,"Construction-period dummy (ref. before 1946); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,22.860595289140974,0.7740371109356384,100*(exp(b - se^2/2) - 1),reported,0.1969143983536702,0.006025064155551832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.682539682539684,26479.0,years,year_built_not_age,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Building was renovated before 1990,renovation,A_structural,binary,binary,0.0234,0.0059,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,2.3658111110800295,0.6039687975408125,100*(exp(b - se^2/2) - 1),reported,0.019314067049724593,0.004869786136469022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9661016949152543,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Building was renovated before 1990,renovation,A_structural,binary,binary,0.0179,0.0062,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,1.8041598235244205,0.6311979224133359,100*(exp(b - se^2/2) - 1),reported,0.014060776765536607,0.0048702131813590475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8870967741935485,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Building was renovated before 1990,renovation,A_structural,binary,binary,0.0169,0.0062,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,1.702406548817681,0.6305670399847105,100*(exp(b - se^2/2) - 1),reported,0.013275401042286646,0.004870265471134746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.725806451612903,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Building was renovated before 1990,renovation,A_structural,binary,binary,0.0096,0.0042,,,**,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,0.9637322770116974,0.4240514156804414,100*(exp(b - se^2/2) - 1),reported,0.014045212437716769,0.006144780441501086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2857142857142856,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Building was renovated before 1990,renovation,A_structural,binary,binary,0.0095,0.0042,,,**,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,0.953636408585834,0.4240090126590598,100*(exp(b - se^2/2) - 1),reported,0.013898936553342746,0.006144793002530477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.261904761904762,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Building was renovated before 1990,renovation,A_structural,binary,binary,0.0084,0.0041,,,**,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,0.842690312805483,0.4134585053866362,100*(exp(b - se^2/2) - 1),reported,0.01258955036752259,0.006144899584147933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0487804878048776,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Floor level,floor_level,A_structural,level,count,0.0188,0.0006,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,1.8800000000000001,0.06,100*b/1 (per unit count),reported,0.1508682492520164,0.004814944125064354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.333333333333336,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Floor level,floor_level,A_structural,level,count,0.0194,0.0007,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,1.94,0.06999999999999999,100*b/1 (per unit count),reported,0.13377452582668398,0.0048269158803442664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.714285714285715,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Floor level,floor_level,A_structural,level,count,0.0196,0.0006,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,1.96,0.06,100*b/1 (per unit count),reported,0.15713282037728166,0.004810188378896377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.66666666666667,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Floor level,floor_level,A_structural,level,count,0.0108,0.0008,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,1.08,0.08,100*b/1 (per unit count),reported,0.08267867629844998,0.006124346392477777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.5,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Floor level,floor_level,A_structural,level,count,0.0111,0.0008,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,1.11,0.08,100*b/1 (per unit count),reported,0.08495895150859989,0.006123167676295488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.875,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Floor level,floor_level,A_structural,level,count,0.0113,0.0008,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,1.13,0.08,100*b/1 (per unit count),reported,0.08647839594621536,0.00612236431477631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.124999999999998,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Number of floors in the building,storeys,A_structural,level,count,-0.0058,0.0007,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,-0.58,0.06999999999999999,100*b/1 (per unit count),reported,-0.040324359716418734,0.004866733069222952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.285714285714285,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Number of floors in the building,storeys,A_structural,level,count,-0.0068,0.0007,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,-0.6799999999999999,0.06999999999999999,100*b/1 (per unit count),reported,-0.04726244523784824,0.004865251715660848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.714285714285714,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Number of floors in the building,storeys,A_structural,level,count,-0.0065,0.0007,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,-0.65,0.06999999999999999,100*b/1 (per unit count),reported,-0.04518169188550023,0.004865720664592333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.285714285714285,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Number of floors in the building,storeys,A_structural,level,count,0.0005,0.0006,,,,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,0.05,0.06,100*b/1 (per unit count),reported,0.005121088359874486,0.006145306031849384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Number of floors in the building,storeys,A_structural,level,count,0.0004,0.0006,,,,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,0.04,0.06,100*b/1 (per unit count),reported,0.0040968900277172475,0.006145335041575871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666667,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Number of floors in the building,storeys,A_structural,level,count,0.0019,0.0006,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,0.19,0.06,100*b/1 (per unit count),reported,0.01945670710382889,0.0061442232959459655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.166666666666667,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Number of rooms,bedrooms,A_structural,level,count,0.2703,0.0014,,,***,,,medium,"Total number of rooms (Swiss definition), not bedrooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,27.029999999999998,0.13999999999999999,100*b/1 (per unit count),reported,0.685061593294019,0.0035482287481007277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),193.07142857142856,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Number of rooms,bedrooms,A_structural,level,count,0.2677,0.0015,,,***,,,medium,"Total number of rooms (Swiss definition), not bedrooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,26.77,0.15,100*b/1 (per unit count),reported,0.6560460926577897,0.003676014714182609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),178.46666666666667,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Number of rooms,bedrooms,A_structural,level,count,0.2639,0.0016,,,***,,,medium,"Total number of rooms (Swiss definition), not bedrooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,26.39,0.16,100*b/1 (per unit count),reported,0.6262910454054376,0.0037971416167059497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),164.9375,42152.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Number of rooms,bedrooms,A_structural,level,count,0.275,0.0016,,,***,,,medium,"Total number of rooms (Swiss definition), not bedrooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,27.500000000000004,0.16,100*b/1 (per unit count),reported,0.7261746738946244,0.004225016284477814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),171.875,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Number of rooms,bedrooms,A_structural,level,count,0.2745,0.0016,,,***,,,medium,"Total number of rooms (Swiss definition), not bedrooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,27.450000000000003,0.16,100*b/1 (per unit count),reported,0.7255497005092894,0.004229069292586022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),171.5625,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Number of rooms,bedrooms,A_structural,level,count,0.2728,0.0016,,,***,,,medium,"Total number of rooms (Swiss definition), not bedrooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,27.279999999999998,0.16,100*b/1 (per unit count),reported,0.7234112919096157,0.004242881477475752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),170.49999999999997,26479.0,count,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,18,Surface per room [m2],living_area,A_structural,other,m2,0.0154,0.0002,,,***,,,medium,"Floor area divided by number of rooms (m2 per room), entered together with number of rooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,other,0.0154,0.0002,not harmonised,reported,0.35115914209854604,0.004560508338942157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),77.0,42152.0,m2,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,18,Surface per room [m2],living_area,A_structural,other,m2,0.0151,0.0003,,,***,,,medium,"Floor area divided by number of rooms (m2 per room), entered together with number of rooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,other,0.0151,0.0003,not harmonised,reported,0.23810726779673036,0.004730607969471463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.33333333333334,42152.0,m2,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,18,Surface per room [m2],living_area,A_structural,other,m2,0.0143,0.0003,,,***,,,medium,"Floor area divided by number of rooms (m2 per room), entered together with number of rooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,other,0.0143,0.0003,not harmonised,reported,0.226154591483141,0.004744501919226734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.66666666666667,42152.0,m2,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,18,Surface per room [m2],living_area,A_structural,other,m2,0.0133,0.0003,,,***,,,medium,"Floor area divided by number of rooms (m2 per room), entered together with number of rooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,other,0.0133,0.0003,not harmonised,reported,0.26286433050149655,0.005929270612815712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.333333333333336,26479.0,m2,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,18,Surface per room [m2],living_area,A_structural,other,m2,0.0132,0.0003,,,***,,,medium,"Floor area divided by number of rooms (m2 per room), entered together with number of rooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,other,0.0132,0.0003,not harmonised,reported,0.2610230417608901,0.005932341858202046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.00000000000001,26479.0,m2,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,18,Surface per room [m2],living_area,A_structural,other,m2,0.0129,0.0003,,,***,,,medium,"Floor area divided by number of rooms (m2 per room), entered together with number of rooms; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,other,0.0129,0.0003,not harmonised,reported,0.2554821125949387,0.00594144447895206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),43.00000000000001,26479.0,m2,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Surface per inhabitant [m2],other,other,level,m2,0.0003,0.0001,,,***,,,medium,"Floor area per occupant (m2); occupancy measure; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,0.03,0.01,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.014610524374555015,0.004870174791518339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,42152.0,m2,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Surface per inhabitant [m2],other,other,level,m2,0.0003,0.0001,,,***,,,medium,"Floor area per occupant (m2); occupancy measure; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,0.03,0.01,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.014610524374555015,0.004870174791518339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,42152.0,m2,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Surface per inhabitant [m2],other,other,level,m2,0.0002,0.0001,,,***,,,medium,"Floor area per occupant (m2); occupancy measure; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,0.02,0.01,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.009740927203113392,0.004870463601556696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,42152.0,m2,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Surface per inhabitant [m2],other,other,level,m2,0.0005,0.0001,,,***,,,medium,"Floor area per occupant (m2); occupancy measure; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,0.05,0.01,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.030712438009309874,0.006142487601861975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,26479.0,m2,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Surface per inhabitant [m2],other,other,level,m2,0.0006,0.0001,,,***,,,medium,"Floor area per occupant (m2); occupancy measure; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,0.06,0.01,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.036847280009872724,0.006141213334978788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.999999999999999,26479.0,m2,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Surface per inhabitant [m2],other,other,level,m2,0.0006,0.0001,,,***,,,medium,"Floor area per occupant (m2); occupancy measure; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,0.06,0.01,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.036847280009872724,0.006141213334978788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.999999999999999,26479.0,m2,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Kitchenette,kitchen_quality,B_interior,binary,binary,-0.0755,0.0072,,,***,,,medium,"Dummy: kitchenette instead of full kitchen; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,-7.274430424512457,0.6676414059844791,100*(exp(b - se^2/2) - 1),reported,-0.05100815856670383,0.00486435419444063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.48611111111111,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Kitchenette,kitchen_quality,B_interior,binary,binary,-0.0792,0.0071,,,***,,,medium,"Dummy: kitchenette instead of full kitchen; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-7.616815053545545,0.6559371458072326,100*(exp(b - se^2/2) - 1),reported,-0.054252239087269116,0.004863521433328418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.15492957746479,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Kitchenette,kitchen_quality,B_interior,binary,binary,-0.0791,0.0071,,,***,,,medium,"Dummy: kitchenette instead of full kitchen; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,-7.607576273119577,0.6560027428016084,100*(exp(b - se^2/2) - 1),reported,-0.054183940022133635,0.004863539496297709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.140845070422536,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Kitchenette,kitchen_quality,B_interior,binary,binary,-0.0761,0.0076,,,***,,,medium,"Dummy: kitchenette instead of full kitchen; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,-7.330323381641602,0.7043098824752162,100*(exp(b - se^2/2) - 1),reported,-0.06141855499993812,0.006133784730611429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.013157894736842,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Kitchenette,kitchen_quality,B_interior,binary,binary,-0.078,0.0075,,,***,,,medium,"Dummy: kitchenette instead of full kitchen; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-7.506158771513006,0.6937233199076545,100*(exp(b - se^2/2) - 1),reported,-0.06378188713884317,0.0061328737633503045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.4,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Kitchenette,kitchen_quality,B_interior,binary,binary,-0.0872,0.0076,,,***,,,medium,"Dummy: kitchenette instead of full kitchen; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,-8.353268941177983,0.6965352716952224,100*(exp(b - se^2/2) - 1),reported,-0.07033559869299125,0.006130166858563458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.473684210526315,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,True
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Attic,storage,B_interior,binary,binary,0.0235,0.0073,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,2.37510225372215,0.7473581595450394,100*(exp(b - se^2/2) - 1),reported,0.01567770650933915,0.0048700960646032255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.219178082191781,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Attic,storage,B_interior,binary,binary,0.0228,0.0079,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,2.3029982554575,0.8082189062955869,100*(exp(b - se^2/2) - 1),reported,0.014055806113408979,0.004870213521751357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8860759493670884,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Attic,storage,B_interior,binary,binary,0.0252,0.0077,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,2.5489802960376506,0.7896505571232766,100*(exp(b - se^2/2) - 1),reported,0.015938430503949257,0.00487007598731783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2727272727272725,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Attic,storage,B_interior,binary,binary,0.0316,0.0061,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,3.2085378766285144,0.6295837943449646,100*(exp(b - se^2/2) - 1),reported,0.031818997757287214,0.006142274883526961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.180327868852459,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Attic,storage,B_interior,binary,binary,0.0313,0.0061,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,3.177579959185328,0.6293949475350988,100*(exp(b - se^2/2) - 1),reported,0.031517220167013485,0.006142333642772596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.131147540983607,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Attic,storage,B_interior,binary,binary,0.0345,0.0061,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,3.5082770482023573,0.6314122373094297,100*(exp(b - se^2/2) - 1),reported,0.03473572026633023,0.0061416780760757805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.655737704918033,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Gas heating,heating,A_structural,binary,binary,0.0169,0.0035,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,1.703738350551398,0.3559652645074979,100*(exp(b - se^2/2) - 1),reported,0.023511995625745304,0.0048693482065153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.828571428571428,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Gas heating,heating,A_structural,binary,binary,0.0246,0.0037,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,2.489806103925485,0.3792148783014824,100*(exp(b - se^2/2) - 1),reported,0.03236657078666835,0.004868142760596461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.648648648648648,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Gas heating,heating,A_structural,binary,binary,0.0278,0.0036,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,2.818336320130288,0.3701484093063899,100*(exp(b - se^2/2) - 1),reported,0.03758600955339147,0.004867253035690982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.722222222222222,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Gas heating,heating,A_structural,binary,binary,0.0485,0.0035,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,4.968894245586264,0.36739338013711376,100*(exp(b - se^2/2) - 1),reported,0.08485039695180883,0.006123224522295483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.857142857142858,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Gas heating,heating,A_structural,binary,binary,0.0486,0.0035,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,4.9793916598728005,0.36743012131215563,100*(exp(b - se^2/2) - 1),reported,0.08502408279079719,0.006123133534316671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.885714285714284,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Gas heating,heating,A_structural,binary,binary,0.0513,0.0035,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,5.263219011854536,0.36842352312865917,100*(exp(b - se^2/2) - 1),reported,0.08971062051884364,0.006120607637737871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.657142857142857,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Privately owned building,other,other,binary,binary,0.0627,0.0029,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,6.470290201583562,0.3087651399392759,100*(exp(b - se^2/2) - 1),reported,0.10472867306957231,0.004843909918688352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.620689655172416,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Privately owned building,other,other,binary,binary,0.0659,0.0029,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,6.811540840049668,0.3097547709522174,100*(exp(b - se^2/2) - 1),reported,0.11001054261325763,0.004841131617275373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.724137931034484,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Privately owned building,other,other,binary,binary,0.0588,0.0029,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,6.0558647247613395,0.3075633010027694,100*(exp(b - se^2/2) - 1),reported,0.09827943440709273,0.004847114962254573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.27586206896552,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Privately owned building,other,other,binary,binary,0.1182,0.0032,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,12.546342030853474,0.36015013846271954,100*(exp(b - se^2/2) - 1),reported,0.2213637633176579,0.005992927602508505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.9375,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Privately owned building,other,other,binary,binary,0.1182,0.0032,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,12.546342030853474,0.36015013846271954,100*(exp(b - se^2/2) - 1),reported,0.2213637633176579,0.005992927602508505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.9375,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Privately owned building,other,other,binary,binary,0.1169,0.0032,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,12.400126846675041,0.3596822474777528,100*(exp(b - se^2/2) - 1),reported,0.2190465713253735,0.005996142243295083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.53125,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Publicly owned building,other,other,binary,binary,-0.117,0.0057,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,-11.042925803067149,0.5070635601031445,100*(exp(b - se^2/2) - 1),reported,-0.09948147075316721,0.0048465331905389155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.526315789473685,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Publicly owned building,other,other,binary,binary,-0.1277,0.006,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-11.989846721441854,0.5280704248534493,100*(exp(b - se^2/2) - 1),reported,-0.10311205980636297,0.004844732645561299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.283333333333335,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Publicly owned building,other,other,binary,binary,-0.116,0.0062,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,-10.954189147184568,0.5520946384444331,100*(exp(b - se^2/2) - 1),reported,-0.09075307525195997,0.0048505954013978595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.70967741935484,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Publicly owned building,other,other,binary,binary,-0.2517,0.0058,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,-22.253513059920326,0.45093720895252914,100*(exp(b - se^2/2) - 1),reported,-0.25768242544840203,0.005937854857372794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.39655172413793,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Publicly owned building,other,other,binary,binary,-0.2507,0.0058,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-22.17572768677578,0.4513883717052612,100*(exp(b - se^2/2) - 1),reported,-0.25672625792684023,0.0059394188112312455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.224137931034484,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Publicly owned building,other,other,binary,binary,-0.2549,0.0058,,,***,,,medium,"Owner type dummy (ref. institutional owner); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,-22.501904180375465,0.4494965162216414,100*(exp(b - se^2/2) - 1),reported,-0.2607370684457557,0.005932816779071727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.94827586206897,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Same tenant for at least 5 years,other,other,binary,binary,-0.1615,0.0027,,,***,,,medium,"Tenancy duration >= 5 years (rent-contract / sitting-tenant discount); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,pct_premium,-14.913656993748148,0.22973396349565084,100*(exp(b - se^2/2) - 1),reported,-0.27971068330869137,0.004676277677606605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-59.81481481481482,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Same tenant for at least 5 years,other,other,binary,binary,-0.1613,0.0027,,,***,,,medium,"Tenancy duration >= 5 years (rent-contract / sitting-tenant discount); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-14.896638023306586,0.22977991488333557,100*(exp(b - se^2/2) - 1),reported,-0.27939134693490586,0.004676730543857692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-59.74074074074074,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Same tenant for at least 5 years,other,other,binary,binary,-0.1609,0.0026,,,***,,,medium,"Tenancy duration >= 5 years (rent-contract / sitting-tenant discount); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,pct_premium,-14.862567307922415,0.22135807318842463,100*(exp(b - se^2/2) - 1),reported,-0.28859590057364953,0.004663451469804156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-61.88461538461538,42152.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Same tenant for at least 5 years,other,other,binary,binary,-0.1287,0.0029,,,***,,,medium,"Tenancy duration >= 5 years (rent-contract / sitting-tenant discount); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,pct_premium,-12.076599989371283,0.25497893221497897,100*(exp(b - se^2/2) - 1),reported,-0.2631180766385772,0.0059288455497426085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-44.379310344827594,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Same tenant for at least 5 years,other,other,binary,binary,-0.1273,0.0029,,,***,,,medium,"Tenancy duration >= 5 years (rent-contract / sitting-tenant discount); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,pct_premium,-11.953421024200006,0.25533615271608473,100*(exp(b - se^2/2) - 1),reported,-0.26045102647820356,0.005933291255198665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.896551724137936,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Same tenant for at least 5 years,other,other,binary,binary,-0.127,0.0029,,,***,,,medium,"Tenancy duration >= 5 years (rent-contract / sitting-tenant discount); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,pct_premium,-11.927003088014976,0.2554127650531755,100*(exp(b - se^2/2) - 1),reported,-0.2598787367994263,0.005934238871797923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.793103448275865,26479.0,binary,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,False,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Distance to nearest transportation stop [km],transit_distance,D_location,level,km,-0.1021,0.0226,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,-10.209999999999999,2.26,100*b/1 (X per km->km),reported,-0.02199900786254038,0.0048695159421489964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.517699115044248,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Distance to nearest transportation stop [km],transit_distance,D_location,level,km,-0.1261,0.0218,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,-12.61,2.18,100*b/1 (X per km->km),reported,-0.028162888897105966,0.004868762711791516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.78440366972477,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Distance to nearest transportation stop [km],transit_distance,D_location,level,km,-0.1081,0.0223,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,-10.81,2.23,100*b/1 (X per km->km),reported,-0.023604277854210397,0.004869337614698352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.8475336322869955,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Distance to nearest transportation stop [km],transit_distance,D_location,level,km,-0.1521,0.0223,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,-15.21,2.23,100*b/1 (X per km->km),reported,-0.041878622668869284,0.0061399953025363906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.820627802690583,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Distance to nearest transportation stop [km],transit_distance,D_location,level,km,-0.1523,0.0223,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,-15.229999999999999,2.23,100*b/1 (X per km->km),reported,-0.04193359312313297,0.006139981133590711,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.829596412556053,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,True
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Distance to nearest transportation stop [km],transit_distance,D_location,level,km,-0.0979,0.0219,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,-9.790000000000001,2.19,100*b/1 (X per km->km),reported,-0.027461481784902316,0.006143068959033307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.470319634703197,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Distance to nearest park [km],green_space,E_environment,level,km,0.1,0.0148,,,***,,,medium,"Distance to nearest park (km); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,10.0,1.48,100*b/1 (X per km->km),reported,0.03289229165078397,0.004868059164316028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.756756756756757,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Distance to nearest park [km],green_space,E_environment,level,km,0.1266,0.0143,,,***,,,medium,"Distance to nearest park (km); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,12.659999999999998,1.43,100*b/1 (X per km->km),reported,0.04308094118877341,0.004866172661923065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.853146853146852,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,True
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Distance to nearest park [km],green_space,E_environment,level,km,0.0064,0.0156,,,,,,medium,"Distance to nearest park (km); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,0.64,1.5599999999999998,100*b/1 (X per km->km),reported,0.0019982297281737208,0.004870684962423444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4102564102564103,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Distance to nearest park [km],green_space,E_environment,level,km,0.0465,0.016,,,***,,,medium,"Distance to nearest park (km); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,4.65,1.6,100*b/1 (X per km->km),reported,0.017857182030285742,0.00614440672009832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.90625,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Distance to nearest park [km],green_space,E_environment,level,km,0.0449,0.016,,,***,,,medium,"Distance to nearest park (km); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,4.49,1.6,100*b/1 (X per km->km),reported,0.01724292729683752,0.0061444729788285146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.80625,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Distance to nearest park [km],green_space,E_environment,level,km,0.0026,0.0162,,,,,,medium,"Distance to nearest park (km); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,0.26,1.6199999999999999,100*b/1 (X per km->km),reported,0.0009862961375674553,0.006145383626381837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16049382716049382,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,19,Distance to city centre [km],cbd_distance,D_location,level,km,-0.0117,0.0013,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,semi_elasticity,-1.17,0.13,100*b/1 (X per km->km),reported,-0.0437941945397623,0.004866021615529145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,19,Distance to city centre [km],cbd_distance,D_location,level,km,-0.0106,0.0012,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,-1.06,0.12,100*b/1 (X per km->km),reported,-0.0429847035261324,0.004866192852014988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.833333333333334,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,19,Distance to city centre [km],cbd_distance,D_location,level,km,0.0218,0.0036,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,semi_elasticity,2.18,0.36,100*b/1 (X per km->km),reported,0.029481941281776294,0.004868577459375902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.055555555555555,42152.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,19,Distance to city centre [km],cbd_distance,D_location,level,km,-0.0392,0.0012,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,semi_elasticity,-3.92,0.12,100*b/1 (X per km->km),reported,-0.19682246812858617,0.006025177595773045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.66666666666667,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,19,Distance to city centre [km],cbd_distance,D_location,level,km,-0.039,0.0012,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,semi_elasticity,-3.9,0.12,100*b/1 (X per km->km),reported,-0.19585688771545673,0.006026365775860207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.5,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,19,Distance to city centre [km],cbd_distance,D_location,level,km,-0.0416,0.0018,,,***,,,medium,"parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,semi_elasticity,-4.16,0.18,100*b/1 (X per km->km),reported,-0.14061557393540167,0.006084327718358725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.11111111111111,26479.0,km,,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,True,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,20,% of parks in the district (x100),green_space,E_environment,other,share,0.366,0.0306,,,***,,,medium,"Share of park area in the district; '(x100)' scaling printed in table (coefficient refers to the rescaled variable); absent from -fix models (district FE); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,other,0.366,0.0306,not harmonised,reported,0.05815871928758881,0.004862450301093491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.96078431372549,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,20,% of parks in the district (x100),green_space,E_environment,other,share,0.3778,0.0303,,,***,,,medium,"Share of park area in the district; '(x100)' scaling printed in table (coefficient refers to the rescaled variable); absent from -fix models (district FE); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,other,0.3778,0.0303,not harmonised,reported,0.06061928535126445,0.004861737284656731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.46864686468647,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,20,% of parks in the district (x100),green_space,E_environment,other,share,0.3262,0.0258,,,***,,,medium,"Share of park area in the district; '(x100)' scaling printed in table (coefficient refers to the rescaled variable); absent from -fix models (district FE); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,other,0.3262,0.0258,not harmonised,reported,0.07746516797834173,0.00612692009148135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.643410852713178,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,20,% of parks in the district (x100),green_space,E_environment,other,share,0.3259,0.0259,,,***,,,medium,"Share of park area in the district; '(x100)' scaling printed in table (coefficient refers to the rescaled variable); absent from -fix models (district FE); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,other,0.3259,0.0259,not harmonised,reported,0.07709731137083876,0.006127095319130788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.583011583011585,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,20,% of trees in the district (x100),green_space,E_environment,other,share,-0.1668,0.0217,,,***,,,medium,"Share of tree cover in the district; '(x100)' scaling; absent from -fix models; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,other,-0.1668,0.0217,not harmonised,reported,-0.037413045141168236,0.004867284649660376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.686635944700461,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,20,% of trees in the district (x100),green_space,E_environment,other,share,-0.2104,0.0212,,,***,,,medium,"Share of tree cover in the district; '(x100)' scaling; absent from -fix models; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,other,-0.2104,0.0212,not harmonised,reported,-0.04828296895676217,0.004865013982335352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.924528301886793,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,20,% of trees in the district (x100),green_space,E_environment,other,share,-0.1134,0.0116,,,***,,,medium,"Share of tree cover in the district; '(x100)' scaling; absent from -fix models; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,other,-0.1134,0.0116,not harmonised,reported,-0.05996833112905624,0.006134326641067481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.775862068965518,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,20,% of trees in the district (x100),green_space,E_environment,other,share,-0.1132,0.0117,,,***,,,medium,"Share of tree cover in the district; '(x100)' scaling; absent from -fix models; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,other,-0.1132,0.0117,not harmonised,reported,-0.059353107285632416,0.006134552608143986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.675213675213675,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,20,Population density [per ha] (x100),density,F_neighbourhood,other,per_ha,-0.0258,0.0013,,,***,,,medium,"Inhabitants per hectare, '(x100)' scaling printed (coefficient per 100 inhabitants/ha); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,other,-0.0258,0.0013,not harmonised,reported,-0.0962160780326085,0.004848096955131435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.846153846153847,42152.0,per_ha,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,20,Population density [per ha] (x100),density,F_neighbourhood,other,per_ha,-0.0205,0.0013,,,***,,,medium,"Inhabitants per hectare, '(x100)' scaling printed (coefficient per 100 inhabitants/ha); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,other,-0.0205,0.0013,not harmonised,reported,-0.07658155069694807,0.004856391019806463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.76923076923077,42152.0,per_ha,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,20,Population density [per ha] (x100),density,F_neighbourhood,other,per_ha,-0.0182,0.0014,,,***,,,medium,"Inhabitants per hectare, '(x100)' scaling printed (coefficient per 100 inhabitants/ha); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,other,-0.0182,0.0014,not harmonised,reported,-0.0631924788524804,0.004860959911729262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,42152.0,per_ha,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,20,Population density [per ha] (x100),density,F_neighbourhood,other,per_ha,-0.0298,0.0021,,,***,,,medium,"Inhabitants per hectare, '(x100)' scaling printed (coefficient per 100 inhabitants/ha); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,other,-0.0298,0.0021,not harmonised,reported,-0.08687624641357149,0.006122151592902688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.190476190476192,26479.0,per_ha,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,20,Population density [per ha] (x100),density,F_neighbourhood,other,per_ha,-0.0295,0.0022,,,***,,,medium,"Inhabitants per hectare, '(x100)' scaling printed (coefficient per 100 inhabitants/ha); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,other,-0.0295,0.0022,not harmonised,reported,-0.08212568530916763,0.006124627378988772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.409090909090908,26479.0,per_ha,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,20,Population density [per ha] (x100),density,F_neighbourhood,other,per_ha,-0.023,0.0022,,,***,,,medium,"Inhabitants per hectare, '(x100)' scaling printed (coefficient per 100 inhabitants/ha); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,other,-0.023,0.0022,not harmonised,reported,-0.06411503597293661,0.006132742571324371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.454545454545453,26479.0,per_ha,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G1,2,Geneva Model 1,20,Daytime noise [dB(A)] (x100),noise,E_environment,other,db,-0.2828,0.0318,,,***,,,medium,"Road traffic noise dB(A), '(x100)' scaling: text says one extra dB lowers rent ~0.3% Geneva / 0.4% Zurich, i.e. printed coefficient/100 per dB; sample restricted to >=55 dB(A); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6285,se,Geneva; Model 1: traditional characteristics only,other,-0.2828,0.0318,not harmonised,reported,-0.043274908235725454,0.004866131831315664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.893081761006288,42152.0,db,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,20,Daytime noise [dB(A)] (x100),noise,E_environment,other,db,-0.1854,0.0318,,,***,,,medium,"Road traffic noise dB(A), '(x100)' scaling: text says one extra dB lowers rent ~0.3% Geneva / 0.4% Zurich, i.e. printed coefficient/100 per dB; sample restricted to >=55 dB(A); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,other,-0.1854,0.0318,not harmonised,reported,-0.028385626335454498,0.004868732025175043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.830188679245283,42152.0,db,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,20,Daytime noise [dB(A)] (x100),noise,E_environment,other,db,-0.2784,0.0332,,,***,,,medium,"Road traffic noise dB(A), '(x100)' scaling: text says one extra dB lowers rent ~0.3% Geneva / 0.4% Zurich, i.e. printed coefficient/100 per dB; sample restricted to >=55 dB(A); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,other,-0.2784,0.0332,not harmonised,reported,-0.04080939107006456,0.004866637153470343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.385542168674698,42152.0,db,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z1,2,Zurich Model 1,20,Daytime noise [dB(A)] (x100),noise,E_environment,other,db,-0.3599,0.0413,,,***,,,medium,"Road traffic noise dB(A), '(x100)' scaling: text says one extra dB lowers rent ~0.3% Geneva / 0.4% Zurich, i.e. printed coefficient/100 per dB; sample restricted to >=55 dB(A); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6689,se,Zurich; Model 1: traditional characteristics only,other,-0.3599,0.0413,not harmonised,reported,-0.053476028093025846,0.006136593387724278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.714285714285714,26479.0,db,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,20,Daytime noise [dB(A)] (x100),noise,E_environment,other,db,-0.3591,0.0422,,,***,,,medium,"Road traffic noise dB(A), '(x100)' scaling: text says one extra dB lowers rent ~0.3% Geneva / 0.4% Zurich, i.e. printed coefficient/100 per dB; sample restricted to >=55 dB(A); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,other,-0.3591,0.0422,not harmonised,reported,-0.052222679293505894,0.006137001019732523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.50947867298578,26479.0,db,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,20,Daytime noise [dB(A)] (x100),noise,E_environment,other,db,-0.4062,0.0436,,,***,,,medium,"Road traffic noise dB(A), '(x100)' scaling: text says one extra dB lowers rent ~0.3% Geneva / 0.4% Zurich, i.e. printed coefficient/100 per dB; sample restricted to >=55 dB(A); parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,other,-0.4062,0.0436,not harmonised,reported,-0.05715997121909808,0.006135339106727415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.31651376146789,26479.0,db,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2,2,Geneva Model 2,20,% of foreigners [per ha] (x100),demographics,F_neighbourhood,other,share,-0.2222,0.0118,,,***,,,medium,"Share of foreigners in the hectare, '(x100)' scaling; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6318,se,Geneva; Model 2: + household head nationality + share of foreigners per hectare,other,-0.2222,0.0118,not harmonised,reported,-0.09133430412805545,0.00485033658285803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.830508474576273,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-G2fix,2,Geneva Model 2-fix,20,% of foreigners [per ha] (x100),demographics,F_neighbourhood,other,share,-0.0899,0.0134,,,***,,,medium,"Share of foreigners in the hectare, '(x100)' scaling; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6417,se,Geneva; Model 2 + district fixed effects,other,-0.0899,0.0134,not harmonised,reported,-0.03265984005763065,0.004868096293350954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.708955223880596,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2,2,Zurich Model 2,20,% of foreigners [per ha] (x100),demographics,F_neighbourhood,other,share,-0.0197,0.0107,,,*,,,medium,"Share of foreigners in the hectare, '(x100)' scaling; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6696,se,Zurich; Model 2: + household head nationality + share of foreigners per hectare,other,-0.0197,0.0107,not harmonised,reported,-0.011313679253530464,0.006144993300140912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8411214953271027,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T2-Z2fix,2,Zurich Model 2-fix,20,% of foreigners [per ha] (x100),demographics,F_neighbourhood,other,share,0.0826,0.0118,,,***,,,medium,"Share of foreigners in the hectare, '(x100)' scaling; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6769,se,Zurich; Model 2 + district fixed effects,other,0.0826,0.0118,not harmonised,reported,0.042977958836102635,0.0061397084051575184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.000000000000001,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-G3,3,Geneva Model 3,23,% of low education foreigners (x100),demographics,F_neighbourhood,other,share,-0.393,0.0139,,,***,,,medium,"Share of low-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6447,se,Geneva; Model 3: foreigners split by education; dwelling characteristics as Table 2 (not reprinted),other,-0.393,0.0139,not harmonised,reported,-0.1364234940662189,0.0048251566603573605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.273381294964032,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-G3fix,3,Geneva Model 3-fix,23,% of low education foreigners (x100),demographics,F_neighbourhood,other,share,-0.298,0.0157,,,***,,,medium,"Share of low-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6495,se,Geneva; Model 3 + district fixed effects,other,-0.298,0.0157,not harmonised,reported,-0.09205755631716453,0.004850012195233165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.980891719745223,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-Z3,3,Zurich Model 3,23,% of low education foreigners (x100),demographics,F_neighbourhood,other,share,-0.3219,0.0143,,,***,,,medium,"Share of low-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.696,se,Zurich; Model 3: foreigners split by education,other,-0.3219,0.0143,not harmonised,reported,-0.1370307128807099,0.006087415949655644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.51048951048951,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-Z3fix,3,Zurich Model 3-fix,23,% of low education foreigners (x100),demographics,F_neighbourhood,other,share,-0.2534,0.0159,,,***,,,medium,"Share of low-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6998,se,Zurich; Model 3 + district fixed effects,other,-0.2534,0.0159,not harmonised,reported,-0.09747330842528533,0.006116123141128795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.937106918238994,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-G3,3,Geneva Model 3,23,% of high education foreigners (x100),demographics,F_neighbourhood,other,share,0.4966,0.0241,,,***,,,medium,"Share of high-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6447,se,Geneva; Model 3: foreigners split by education; dwelling characteristics as Table 2 (not reprinted),other,0.4966,0.0241,not harmonised,reported,0.09986290308274938,0.004846347088792308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.605809128630703,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-G3fix,3,Geneva Model 3-fix,23,% of high education foreigners (x100),demographics,F_neighbourhood,other,share,0.4907,0.0245,,,***,,,medium,"Share of high-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,42162.0,0.6495,se,Geneva; Model 3 + district fixed effects,other,0.4907,0.0245,not harmonised,reported,0.0970921568370656,0.004847682581023246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.02857142857143,42152.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-Z3,3,Zurich Model 3,23,% of high education foreigners (x100),demographics,F_neighbourhood,other,share,0.9178,0.0258,,,***,,,medium,"Share of high-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.696,se,Zurich; Model 3: foreigners split by education,other,0.9178,0.0258,not harmonised,reported,0.2135698883048374,0.006003598952129881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.57364341085271,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9,4a0a05883004ee538ec38fa812f7d2e81fdd4eb9:T3-Z3fix,3,Zurich Model 3-fix,23,% of high education foreigners (x100),demographics,F_neighbourhood,other,share,0.937,0.0263,,,***,,,medium,"Share of high-education foreigners in the hectare, '(x100)' scaling; Table 3; parentheses unlabelled (star legend only), consistent with standard errors","ln(net monthly rent, 2000, CHF)",log,OLS,26489.0,0.6998,se,Zurich; Model 3 + district fixed effects,other,0.937,0.0263,not harmonised,reported,0.21387773387423997,0.006003185059650492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.627376425855516,26479.0,share,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2000.0,2000.0,rental,rent,journal,False,True,unknown,,2000.0,ols,journal,False,True,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c1,1,1,9,Distance in 1999 (miles) < 0.2,other,other,dummy,miles (distance band dummy),0.044,0.014,,,***,,,high,shortest distance to a cannabis retailer measured in 1999; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distance in 1999; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,4.487995163550607,1.4629752968438214,100*(exp(b - se^2/2) - 1),reported,0.0075824256425247125,0.0024125899771669543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.142857142857143,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c1,1,1,9,Distance in 1999 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),0.024,0.011,,,**,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distance in 1999; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,2.4228350200930615,1.1267193496796837,100*(exp(b - se^2/2) - 1),reported,0.005263911071477195,0.002412625907760381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.181818181818182,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c1,1,1,9,Distance in 1999 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),0.015,0.008,,,*,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distance in 1999; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,1.5080581517383562,0.8120904516925751,100*(exp(b - se^2/2) - 1),reported,0.004523689964457475,0.002412634647710653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.875,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c2,1,2,9,Distance in 2016 (miles) < 0.2,other,other,dummy,miles (distance band dummy),0.057,0.017,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distance in 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,5.850284568286468,1.7997148776723506,100*(exp(b - se^2/2) - 1),reported,0.008089240149433938,0.0024125803954452097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3529411764705883,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c2,1,2,9,Distance in 2016 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),0.035,0.013,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distance in 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,3.553220263141732,1.3463056214395102,100*(exp(b - se^2/2) - 1),reported,0.006495484252702916,0.002412608436718226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6923076923076925,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c2,1,2,9,Distance in 2016 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),0.027,0.009,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distance in 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,2.7326195210036097,0.9246310224871404,100*(exp(b - se^2/2) - 1),reported,0.0072377884163476874,0.0024125961387825626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c3,1,3,9,Distance in 1999 (miles) < 0.2,other,other,dummy,miles (distance band dummy),-0.036,0.035,,,,,,high,shortest distance to a cannabis retailer measured in 1999; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,-3.595036778815519,3.376241027190931,100*(exp(b - se^2/2) - 1),reported,-0.002481584816554807,0.0024126519049838405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0285714285714285,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c3,1,3,9,Distance in 1999 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),-0.039,0.03,,,,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,-3.8681981310094815,2.8852521274391,100*(exp(b - se^2/2) - 1),reported,-0.0031364417069377234,0.0024126474668751717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c3,1,3,9,Distance in 1999 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),-0.039,0.019,,,**,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,-3.8422871190537555,1.8273263473780967,100*(exp(b - se^2/2) - 1),reported,-0.004952240010815985,0.0024126297488590697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0526315789473686,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c3,1,3,9,Distance in 2016 (miles) < 0.2,other,other,dummy,miles (distance band dummy),0.09,0.041,,,**,,,high,shortest distance to a cannabis retailer measured in 2016; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,9.325501659587609,4.486114563191363,100*(exp(b - se^2/2) - 1),reported,0.005296007192686358,0.0024126254988904527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.195121951219512,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c3,1,3,9,Distance in 2016 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),0.071,0.035,,,**,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,7.292385870675755,3.7575342905392515,100*(exp(b - se^2/2) - 1),reported,0.004894193174939227,0.0024126304383503233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.028571428571428,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c3,1,3,9,Distance in 2016 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),0.063,0.021,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,171804.0,,se,"Transactions 1999-2001; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,6.4792026702287675,2.236556362385741,100*(exp(b - se^2/2) - 1),reported,0.0072377884163476874,0.0024125961387825626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,171794.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c4,1,4,9,Distance in 1999 (miles) < 0.2,other,other,dummy,miles (distance band dummy),0.056,0.016,,,***,,,high,shortest distance to a cannabis retailer measured in 1999; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distance in 1999; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,5.7462319896563585,1.692156293978578,100*(exp(b - se^2/2) - 1),reported,0.006379798781793743,0.0018227996519410697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c4,1,4,9,Distance in 1999 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),0.048,0.011,,,***,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distance in 1999; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,4.910718241989809,1.1540877208569178,100*(exp(b - se^2/2) - 1),reported,0.007953945105599339,0.0018227790866998483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.363636363636364,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c4,1,4,9,Distance in 1999 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),0.032,0.009,,,***,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distance in 1999; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,3.247568919293564,0.9292657547746064,100*(exp(b - se^2/2) - 1),reported,0.006481061208733946,0.0018227984649564221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.555555555555556,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c5,1,5,9,Distance in 2016 (miles) < 0.2,other,other,dummy,miles (distance band dummy),0.067,0.019,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distance in 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,6.910248775869876,2.03166140853174,100*(exp(b - se^2/2) - 1),reported,0.006427765219210812,0.0018227990920150065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5263157894736845,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c5,1,5,9,Distance in 2016 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),0.06,0.015,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distance in 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,6.171709665305558,1.5927548198180392,100*(exp(b - se^2/2) - 1),reported,0.007291153184952353,0.0018227882962380882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c5,1,5,9,Distance in 2016 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),0.046,0.01,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distance in 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,4.702205854521058,1.0470744109569372,100*(exp(b - se^2/2) - 1),reported,0.00838475428723348,0.001822772671137713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c6,1,6,9,Distance in 1999 (miles) < 0.2,other,other,dummy,miles (distance band dummy),-0.03,0.035,,,,,,high,shortest distance to a cannabis retailer measured in 1999; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,-3.0148682343596356,3.396559367419779,100*(exp(b - se^2/2) - 1),reported,-0.001562429591805612,0.0018228345237732146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.857142857142857,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c6,1,6,9,Distance in 1999 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),-0.032,0.03,,,,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,-3.1929147836154192,2.905519746237593,100*(exp(b - se^2/2) - 1),reported,-0.0019443555232846565,0.0018228333030793655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0666666666666667,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c6,1,6,9,Distance in 1999 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),-0.035,0.02,,,*,,,high,shortest distance to a cannabis retailer measured in 1999; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,-3.458768551486413,1.931210832515133,100*(exp(b - se^2/2) - 1),reported,-0.0031899480800431103,0.0018228274743103485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7500000000000002,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c6,1,6,9,Distance in 2016 (miles) < 0.2,other,other,dummy,miles (distance band dummy),0.094,0.042,,,**,,,high,shortest distance to a cannabis retailer measured in 2016; band < 0.2 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,9.759124339367276,4.61395093285213,100*(exp(b - se^2/2) - 1),reported,0.004079648296828873,0.0018228215794341774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.238095238095238,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c6,1,6,9,Distance in 2016 (miles) 0.2-0.4,other,other,dummy,miles (distance band dummy),0.088,0.038,,,**,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.2-0.4 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,9.119999115357547,4.14955486370715,100*(exp(b - se^2/2) - 1),reported,0.004221268544799602,0.0018228205079816463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3157894736842106,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T1-c6,1,6,9,Distance in 2016 (miles) 0.4-1.0,other,other,dummy,miles (distance band dummy),0.076,0.023,,,***,,,high,shortest distance to a cannabis retailer measured in 2016; band 0.4-1.0 miles vs reference > 1 mile. ,log price per m2,log,OLS,300967.0,,se,"Transactions 2012-2016; regressor = distances in 1999 and 2016; properties within 3.1 miles of a retailer in towns that ever had a retailer within 1 mile; town FE, year dummies, house characteristics",pct_premium,7.867722629533769,2.481613920561753,100*(exp(b - se^2/2) - 1),reported,0.006023177388540195,0.0018228036833740063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3043478260869565,300957.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,ols,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c1,"3 (top panel, least square)",1,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.016,0.004,,,***,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,199172.0,,se,"Houses with initial distance to retailer < 0.3 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-1.5880552931783365,0.39365092802211404,100*(exp(b - se^2/2) - 1),reported,-0.008962709241353539,0.0022406773103383847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,199162.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c1,"3 (top panel, least square)",1,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.056,0.009,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,199172.0,,se,"Houses with initial distance to retailer < 0.3 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-5.449915766915748,0.8509852223013565,100*(exp(b - se^2/2) - 1),reported,-0.013941197186165193,0.0022405495477765486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.222222222222223,199162.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c2,"3 (top panel, least square)",2,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.013,0.004,,,***,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,274769.0,,se,"Houses with initial distance to retailer < 0.4 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-1.292376162120601,0.39483365400811504,100*(exp(b - se^2/2) - 1),reported,-0.006200105077629804,0.0019077246392707088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.25,274759.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c2,"3 (top panel, least square)",2,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.034,0.008,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,274769.0,,se,"Houses with initial distance to retailer < 0.4 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-3.345942515576239,0.7732572037100054,100*(exp(b - se^2/2) - 1),reported,-0.008107719065128364,0.0019076986035596148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.25,274759.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c3,"3 (top panel, least square)",3,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.015,0.004,,,***,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,350269.0,,se,"Houses with initial distance to retailer < 0.5 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-1.4895941260930634,0.39404477584122505,100*(exp(b - se^2/2) - 1),reported,-0.006336185714204086,0.001689649523787756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.75,350259.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c3,"3 (top panel, least square)",3,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.028,0.006,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,350269.0,,se,"Houses with initial distance to retailer < 0.5 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-2.7629136031829593,0.5834330200807482,100*(exp(b - se^2/2) - 1),reported,-0.00788494427275839,0.0016896309155910833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.666666666666667,350259.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c4,"3 (top panel, least square)",4,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.016,0.004,,,***,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,422630.0,,se,"Houses with initial distance to retailer < 0.6 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-1.5880552931783365,0.39365092802211404,100*(exp(b - se^2/2) - 1),reported,-0.006152855951383387,0.0015382139878458467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,422620.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3LS-c4,"3 (top panel, least square)",4,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.024,0.006,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,FE_panel,422630.0,,se,"Houses with initial distance to retailer < 0.6 miles; six-digit postcode FE, year dummies, year x DTSC, regional GDP, house characteristics; any variation in distance",pct_premium,-2.373186322670895,0.5857714258547456,100*(exp(b - se^2/2) - 1),reported,-0.006152855951383387,0.0015382139878458467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,422620.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,panel,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c1,"3 (bottom panel, instrumental variable)",1,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.019,0.021,,,,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,122605.0,,se,"Houses with initial distance to retailer < 0.3 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-1.9036963955728625,2.0604766607098925,100*(exp(b - se^2/2) - 1),reported,-0.002584023609070565,0.002856026094235888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9047619047619047,122595.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c1,"3 (bottom panel, instrumental variable)",1,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.068,0.029,,,**,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,122605.0,,se,"Houses with initial distance to retailer < 0.3 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-6.6132300365416725,2.7093553733739197,100*(exp(b - se^2/2) - 1),reported,-0.006696760962406491,0.0028559715869086503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.344827586206897,122595.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c2,"3 (bottom panel, instrumental variable)",2,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.046,0.021,,,**,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,167757.0,,se,"Houses with initial distance to retailer < 0.4 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-4.516860134646528,2.005588120600501,100*(exp(b - se^2/2) - 1),reported,-0.005348166814209996,0.002441554415182824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1904761904761902,167747.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c2,"3 (bottom panel, instrumental variable)",2,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.06,0.021,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,167757.0,,se,"Houses with initial distance to retailer < 0.4 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-5.844310260267571,1.9777055205269223,100*(exp(b - se^2/2) - 1),reported,-0.006975799791195506,0.0024415299269184274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8571428571428568,167747.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c3,"3 (bottom panel, instrumental variable)",3,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.064,0.022,,,***,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,214215.0,,se,"Houses with initial distance to retailer < 0.5 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-6.222197021471009,2.063610998967605,100*(exp(b - se^2/2) - 1),reported,-0.006285414343718878,0.0021606111806533636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.909090909090909,214205.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c3,"3 (bottom panel, instrumental variable)",3,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.058,0.016,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,214215.0,,se,"Houses with initial distance to retailer < 0.5 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-5.647083202642289,1.5098399158988778,100*(exp(b - se^2/2) - 1),reported,-0.00783213001457118,0.0021605875902265324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.625,214205.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c4,"3 (bottom panel, instrumental variable)",4,13,Final distance (miles) < 0.3,other,other,dummy,miles (distance band dummy),-0.067,0.023,,,***,,,high,current (final) distance to nearest cannabis retailer < 0.3 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,261261.0,,se,"Houses with initial distance to retailer < 0.6 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-6.505212508361602,2.150948963074586,100*(exp(b - se^2/2) - 1),reported,-0.005699161090816252,0.001956428434160803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9130434782608696,261251.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51,4a6c4412ae803b09046980401041b08141e48e51:T3IV-c4,"3 (bottom panel, instrumental variable)",4,13,Final distance (miles) > 2.0,other,other,dummy,miles (distance band dummy),-0.047,0.013,,,***,,,high,current (final) distance to nearest cannabis retailer > 2.0 miles vs reference 0.3-2.0 miles; identified from changes in distance over time,log price per m2,log,IV,261261.0,,se,"Houses with initial distance to retailer < 0.6 miles; distance dummies instrumented by policy closures (DTSC, zero-tolerance); excludes closures for other reasons; postcode FE",pct_premium,-4.59932193886099,1.2403136168674822,100*(exp(b - se^2/2) - 1),reported,-0.007073179194012888,0.0019564112664290966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6153846153846154,261251.0,miles,,2022.0,Netherlands,Europe,High income,1999.0,2016.0,mixed,transaction,journal,True,True,clustered,,2007.5,iv,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,House type: rowhouse,dwelling_type,A_structural,binary,binary,0.381,,,,,,,medium,House type dummy (base category not stated); '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,46.37476054097285,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,House type: rowhouse,dwelling_type,A_structural,binary,binary,0.455,,,,,,,medium,House type dummy (base category not stated); '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,57.61733830339912,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,House type: attached,dwelling_type,A_structural,binary,binary,-0.082,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-7.87280413036513,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,House type: attached,dwelling_type,A_structural,binary,binary,-0.01,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,House type: detached,dwelling_type,A_structural,binary,binary,0.01,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,House type: detached,dwelling_type,A_structural,binary,binary,0.018,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,1.8162976389793695,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,House type: detached,dwelling_type,A_structural,binary,binary,0.306,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,35.79823065478924,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,House type: detached,dwelling_type,A_structural,binary,binary,0.264,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,30.212819630089417,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,House type: mixed type,dwelling_type,A_structural,binary,binary,-0.468,,,,*,,,medium,House type dummy (mixed residential/commercial use); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-37.374647634244404,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,House type: mixed type,dwelling_type,A_structural,binary,binary,-0.553,,,,*,,,medium,House type dummy (mixed residential/commercial use); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-42.47784453708362,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,House type: mixed type,dwelling_type,A_structural,binary,binary,-0.414,,,,,,,medium,House type dummy (mixed residential/commercial use); '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-33.89990487340875,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,House type: mixed type,dwelling_type,A_structural,binary,binary,-0.623,,,,*,,,medium,House type dummy (mixed residential/commercial use); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-46.366697738707586,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,House type: other,dwelling_type,A_structural,binary,binary,-0.706,,,,*,,,medium,House type dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-50.63852873461582,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,House type: other,dwelling_type,A_structural,binary,binary,-0.611,,,,*,,,medium,House type dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-45.7192210209676,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,House type: other,dwelling_type,A_structural,binary,binary,0.06,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,6.183654654535964,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,House type: other,dwelling_type,A_structural,binary,binary,0.208,,,,,,,medium,House type dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,23.121316954886773,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Site area,lot_size,A_structural,level,m2,0.009,,,,*,,,medium,Site (lot) area in m2; Gangjin-gun coefficient rounds to 0.000; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,0.8999999999999999,,100*b/1 (X per m2->m2),,,,,,753.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Site area,lot_size,A_structural,level,m2,0.009,,,,*,,,medium,Site (lot) area in m2; Gangjin-gun coefficient rounds to 0.000; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,0.8999999999999999,,100*b/1 (X per m2->m2),,,,,,753.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Site area,lot_size,A_structural,level,m2,0.0,,,,*,,,medium,Site (lot) area in m2; Gangjin-gun coefficient rounds to 0.000; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,897.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Site area,lot_size,A_structural,level,m2,0.0,,,,*,,,medium,Site (lot) area in m2; Gangjin-gun coefficient rounds to 0.000; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,0.0,,100*b/1 (X per m2->m2),,,,,,897.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Floor area,living_area,A_structural,level,m2,-0.001,,,,*,,,medium,Total floor area in m2; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-0.1,,100*b/1 (X per m2->m2),,,,,,753.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Floor area,living_area,A_structural,level,m2,-0.002,,,,*,,,medium,Total floor area in m2; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-0.2,,100*b/1 (X per m2->m2),,,,,,753.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Floor area,living_area,A_structural,level,m2,0.001,,,,*,,,medium,Total floor area in m2; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,0.1,,100*b/1 (X per m2->m2),,,,,,897.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Floor area,living_area,A_structural,level,m2,0.001,,,,*,,,medium,Total floor area in m2; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,0.1,,100*b/1 (X per m2->m2),,,,,,897.0,m2,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Story,storeys,A_structural,level,count,0.084,,,,*,,,medium,Number of floors; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,8.4,,100*b/1 (per unit count),,,,,,753.0,count,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Story,storeys,A_structural,level,count,0.087,,,,*,,,medium,Number of floors; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,8.7,,100*b/1 (per unit count),,,,,,753.0,count,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Story,storeys,A_structural,level,count,0.319,,,,*,,,medium,Number of floors; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,31.900000000000002,,100*b/1 (per unit count),,,,,,897.0,count,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Story,storeys,A_structural,level,count,0.118,,,,,,,medium,Number of floors; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,11.799999999999999,,100*b/1 (per unit count),,,,,,897.0,count,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Road: middle,other,other,binary,binary,0.204,,,,*,,,medium,Adjacent road width category (base: main road); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,22.62981534562105,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Road: middle,other,other,binary,binary,0.204,,,,*,,,medium,Adjacent road width category (base: main road); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,22.62981534562105,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Road: middle,other,other,binary,binary,0.334,,,,,,,medium,Adjacent road width category (base: main road); '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,39.654314357450524,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Road: middle,other,other,binary,binary,0.44,,,,,,,medium,Adjacent road width category (base: main road); '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,55.2707218511336,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Road: narrow,other,other,binary,binary,0.184,,,,*,,,medium,Adjacent road width category; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,20.20158230963014,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Road: narrow,other,other,binary,binary,0.226,,,,*,,,medium,Adjacent road width category; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,25.357566527818687,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Road: narrow,other,other,binary,binary,-0.295,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-25.54684125340906,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Road: narrow,other,other,binary,binary,-0.022,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-2.175976494878995,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Road: narrower,other,other,binary,binary,0.288,,,,*,,,medium,Adjacent road width category; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,33.375730412338456,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Road: narrower,other,other,binary,binary,0.314,,,,*,,,medium,Adjacent road width category; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,36.888973654737555,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Road: narrower,other,other,binary,binary,-0.696,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-50.14243770087834,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Road: narrower,other,other,binary,binary,-0.206,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-18.616692371707934,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Road: narrowest,other,other,binary,binary,0.12,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,12.749685157937574,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Road: narrowest,other,other,binary,binary,0.177,,,,*,,,medium,Adjacent road width category; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,19.363109312713878,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Road: narrowest,other,other,binary,binary,-0.654,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-48.003823554273815,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Road: narrowest,other,other,binary,binary,-0.195,,,,,,,medium,Adjacent road width category; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-17.716534194398157,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Site shape: regular,other,other,binary,binary,0.144,,,,*,,,medium,Regular (vs irregular) site shape; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,15.488410852491352,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Site shape: regular,other,other,binary,binary,0.136,,,,*,,,medium,Regular (vs irregular) site shape; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,14.568189359486183,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Site shape: regular,other,other,binary,binary,0.204,,,,*,,,medium,Regular (vs irregular) site shape; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,22.62981534562105,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Site shape: regular,other,other,binary,binary,0.108,,,,*,,,medium,Regular (vs irregular) site shape; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,11.404774538646766,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Building frame: brick,roof_materials,A_structural,binary,binary,0.246,,,,*,,,medium,Building frame material dummy (base category not stated); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,27.889957354173834,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Building frame: brick,roof_materials,A_structural,binary,binary,0.284,,,,*,,,medium,Building frame material dummy (base category not stated); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,32.84329307527947,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Building frame: brick,roof_materials,A_structural,binary,binary,0.792,,,,*,,,medium,Building frame material dummy (base category not stated); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,120.78076288406328,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Building frame: brick,roof_materials,A_structural,binary,binary,0.734,,,,*,,,medium,Building frame material dummy (base category not stated); * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,108.33975529060255,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Building frame: RC,roof_materials,A_structural,binary,binary,0.375,,,,*,,,medium,Reinforced concrete frame; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,45.49914146182012,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Building frame: RC,roof_materials,A_structural,binary,binary,0.438,,,,*,,,medium,Reinforced concrete frame; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,54.960490741950885,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Building frame: RC,roof_materials,A_structural,binary,binary,1.475,,,,*,,,medium,Reinforced concrete frame; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,337.10357729297584,,100*(exp(b) - 1),,,,,,897.0,binary,implausible_magnitude,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Building frame: RC,roof_materials,A_structural,binary,binary,1.385,,,,*,,,medium,Reinforced concrete frame; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,299.4825904816633,,100*(exp(b) - 1),,,,,,897.0,binary,implausible_magnitude,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Building frame: low quality wood,roof_materials,A_structural,binary,binary,0.197,,,,,,,medium,Building frame material dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,21.774404070590837,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Building frame: low quality wood,roof_materials,A_structural,binary,binary,0.239,,,,,,,medium,Building frame material dummy; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,26.997853658383654,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Building frame: low quality wood,roof_materials,A_structural,binary,binary,0.153,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,16.532497894273778,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Building frame: low quality wood,roof_materials,A_structural,binary,binary,0.237,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,26.744111777528357,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Building frame: high quality wood,roof_materials,A_structural,binary,binary,1.73,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,464.0653908428321,,100*(exp(b) - 1),,,,,,897.0,binary,implausible_magnitude,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Building frame: high quality wood,roof_materials,A_structural,binary,binary,1.786,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,496.55425083136555,,100*(exp(b) - 1),,,,,,897.0,binary,implausible_magnitude,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Building frame: panel,roof_materials,A_structural,binary,binary,0.74,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,109.59355144943643,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Building frame: panel,roof_materials,A_structural,binary,binary,0.862,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,136.7891744567051,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Building frame: steel,roof_materials,A_structural,binary,binary,1.142,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,213.3028159624368,,100*(exp(b) - 1),,,,,,897.0,binary,implausible_magnitude,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Building frame: steel,roof_materials,A_structural,binary,binary,1.304,,,,*,,,medium,Building frame material dummy; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,268.40032478413974,,100*(exp(b) - 1),,,,,,897.0,binary,implausible_magnitude,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,Property age,age,A_structural,level,years,-0.005,,,,*,,,medium,Years since construction; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-0.5,,100*b/1 (per year),,,,,,753.0,years,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Property age,age,A_structural,level,years,-0.005,,,,*,,,medium,Years since construction; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-0.5,,100*b/1 (per year),,,,,,753.0,years,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,Property age,age,A_structural,level,years,-0.01,,,,*,,,medium,Years since construction; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-1.0,,100*b/1 (per year),,,,,,897.0,years,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Property age,age,A_structural,level,years,-0.011,,,,*,,,medium,Years since construction; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-1.0999999999999999,,100*b/1 (per year),,,,,,897.0,years,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c1,A1,1,14,House bearing: south,sunlight,E_environment,binary,binary,-0.063,,,,*,,,medium,1 = house faces south; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.508,none,"Seongdong-gu (Seoul, urban); n=763 benchmark houses in region (80/20 train/test split; estimation sample not stated); baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-6.105652631086677,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,House bearing: south,sunlight,E_environment,binary,binary,-0.038,,,,,,,medium,1 = house faces south; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,-3.728705910880048,,100*(exp(b) - 1),,,,,,753.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c3,A1,3,14,House bearing: south,sunlight,E_environment,binary,binary,0.021,,,,,,,medium,1 = house faces south; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.633,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; baseline OLS without text embeddings; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,House bearing: south,sunlight,E_environment,binary,binary,0.051,,,,,,,medium,1 = house faces south; '+' = not significant at 5%; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",pct_premium,5.232289328320383,,100*(exp(b) - 1),,,,,,897.0,binary,,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Embedding component 1,multimodal_embedding,H_ai_derived,level,index,-0.265,,,,*,,,medium,First PCA component (22% variance) of 768-dim sentence embedding (paraphrase-mpnet-base-v2) of the house's environmental description; text-derived (AI) variable; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-26.5,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,753.0,index,unit_not_harmonisable,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Embedding component 1,multimodal_embedding,H_ai_derived,level,index,-0.393,,,,*,,,medium,First PCA component (22% variance) of 768-dim sentence embedding (paraphrase-mpnet-base-v2) of the house's environmental description; text-derived (AI) variable; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-39.300000000000004,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,897.0,index,unit_not_harmonisable,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c2,A1,2,14,Embedding component 2,multimodal_embedding,H_ai_derived,level,index,0.592,,,,*,,,medium,Second PCA component (15% variance) of sentence embedding of environmental description; text-derived (AI) variable; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,763.0,0.558,none,"Seongdong-gu (Seoul, urban); n=763 in region; OLS with two PCA components of sentence embeddings (paraphrase-mpnet-base-v2) of environmental descriptions; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,59.199999999999996,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,753.0,index,unit_not_harmonisable,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164,4c31e259cf5625db8bd0cbbcae78c68fed2c8164:TA1-c4,A1,4,14,Embedding component 2,multimodal_embedding,H_ai_derived,level,index,-0.09,,,,*,,,medium,Second PCA component (15% variance) of sentence embedding of environmental description; text-derived (AI) variable; * significant at 5% level; no SE reported,log-transformed appraised benchmark house price (million KRW),log,OLS,907.0,0.751,none,"Gangjin-gun (Jeonnam, rural); n=907 in region; OLS with two embedding components; adjusted R2; '*' = significant at 5%, '+' = not significant; no SE/t printed; n_obs recorded is the regional sample size, model may be fit on 80% training subset",semi_elasticity,-9.0,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,897.0,index,unit_not_harmonisable,2026.0,South Korea,Asia,High income,2024.0,2024.0,single_family,appraisal,journal,False,False,unknown,"sentence embeddings (paraphrase-mpnet-base-v2, SBERT);PCA of embeddings;XGBoost;SHAP",2024.0,ols,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,area,living_area,A_structural,level,m2,-3.596185,,,,,,,medium,"apartment area in m2; GWR mean of local coefficients, min=-7.01288, max=-0.424051; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-3.596185,,"b (currency per unit, as reported)",,,,,,2304.0,m2,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,floor,floor_level,A_structural,level,count,54.1917,,,,,,,medium,"floor on which the apartment is located; GWR mean of local coefficients, min=-7.27306, max=221.9165; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,54.1917,,"b (currency per unit, as reported)",,,,,,2304.0,count,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_public,transit_distance,D_location,level,m,1.27809,,,,,,,medium,"distance to nearest public transport station; GWR mean of local coefficients, min=-0.490508, max=4.15467; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,1.27809,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_kinder,school_distance,D_location,level,m,0.558948,,,,,,,medium,"distance to nearest kindergarten; GWR mean of local coefficients, min=-0.91591, max=2.48765; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.558948,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_elem,school_distance,D_location,level,m,-0.014774,,,,,,,medium,"distance to nearest primary school; GWR mean of local coefficients, min=-2.562863, max=3.054714; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-0.014774,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,num_fax,university_distance,D_location,level,count,6.95362,,,,,,,medium,"number of higher-education institutions within 500 m (count, not distance); GWR mean of local coefficients, min=-1404.26, max=206.193; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,6.95362,,"b (currency per unit, as reported)",,,,,,2304.0,count,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_shop,commercial_distance,D_location,level,m,-0.084314,,,,,,,medium,"distance to nearest shopping center; GWR mean of local coefficients, min=-1.533588, max=0.841975; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-0.084314,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_ind,hazard_site,E_environment,level,m,0.209952,,,,,,,medium,"distance to nearest industrial area; GWR mean of local coefficients, min=-1.073735, max=2.349503; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.209952,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_health,hospital_distance,D_location,level,m,0.250067,,,,,,,medium,"distance to nearest health center; GWR mean of local coefficients, min=-1.153004, max=2.759074; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.250067,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_hosp,hospital_distance,D_location,level,m,0.154938,,,,,,,medium,"distance to nearest hospital; GWR mean of local coefficients, min=-0.606382, max=2.718514; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.154938,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,num_kult,neighbourhood_amenities,F_neighbourhood,level,count,7.15274,,,,,,,medium,"number of cultural institutions within 500 m; GWR mean of local coefficients, min=-111.232, max=176.7045; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,7.15274,,"b (currency per unit, as reported)",,,,,,2304.0,count,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,num_sportobj,neighbourhood_amenities,F_neighbourhood,level,count,-11.7921,,,,,,,medium,"number of sports facilities within 500 m; GWR mean of local coefficients, min=-114.2322, max=173.7824; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-11.7921,,"b (currency per unit, as reported)",,,,,,2304.0,count,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,num_sportigr,neighbourhood_amenities,F_neighbourhood,level,count,-3.03338,,,,,,,medium,"number of public sports fields within 500 m; GWR mean of local coefficients, min=-277.8436, max=558.92; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-3.03338,,"b (currency per unit, as reported)",,,,,,2304.0,count,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_zona,green_space,E_environment,level,m,0.079955,,,,,,,medium,"distance to nearest green or recreational area; GWR mean of local coefficients, min=-5.15309, max=3.65268; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.079955,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_forest,green_space,E_environment,level,m,0.075858,,,,,,,medium,"distance to nearest (urban) park-forest; GWR mean of local coefficients, min=-3.542074, max=5.27866; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.075858,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_jarun,green_space,E_environment,level,m,0.972864,,,,,,,medium,"distance to RSC Jarun (recreational sports centre with lake); GWR mean of local coefficients, min=-2.0561, max=8.43639; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.972864,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_bundek,green_space,E_environment,level,m,0.983093,,,,,,,medium,"distance to SRC Bundek (recreational park with lake); GWR mean of local coefficients, min=-14.3909, max=12.36982; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.983093,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_bank,water_proximity,E_environment,level,m,-1.035173,,,,,,,medium,"distance to the Sava river embankment; GWR mean of local coefficients, min=-14.45573, max=17.62028; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-1.035173,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_maksimir,green_space,E_environment,level,m,0.507891,,,,,,,medium,"distance to Maksimir Park; GWR mean of local coefficients, min=-2.89827, max=5.95143; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,0.507891,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520,4d2cf36586abe5a8dcb4cd171a95a31095559520:T2-gwr,2,mean,8,dist_park,green_space,E_environment,level,m,-0.664219,,,,,,,medium,"distance to nearest park in the city centre; GWR mean of local coefficients, min=-4.37027, max=0.91697; no SE/t printed; dep var EUR per m2",asking price per m2 (EUR/m2),per_m2,GWR,2314.0,0.409,none,All apartments; GWR local coefficients summarised as min/mean/max,monetary,-0.664219,,"b (currency per unit, as reported)",,,,,,2304.0,m,,2024.0,Croatia,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,unknown,,2023.0,spatial,journal,False,False,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c1,8,Cluster 2,10,Floor area,living_area,A_structural,level,m2,0.769,0.154,4.992,0.0,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.497; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,252.0,0.804,none,"submarket cluster 2 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.789); only variables significant at 5% are reported",monetary,0.769,0.154,"b (currency per unit, as reported)",reported,0.3055511808933099,0.06120816924946111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.992,242.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c1,8,Cluster 2,10,Number of stories,storeys,A_structural,level,count,12.081,2.002,6.035,0.0,,,,high,"total number of storeys of the apartment building; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.417; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,252.0,0.804,none,"submarket cluster 2 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.789); only variables significant at 5% are reported",monetary,12.081,2.002,"b (currency per unit, as reported)",reported,0.3616813411683892,0.059930628196916184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.035,242.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c2,8,Cluster 3,10,Age,age,A_structural,level,years,1.569,0.752,2.087,0.042,,,,high,"age of the building in years; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.185; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,149.0,0.738,none,"submarket cluster 3 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.692); only variables significant at 5% are reported",monetary,1.569,0.752,"b (currency per unit, as reported)",reported,0.17430713499074166,0.08352042884079618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.087,139.0,years,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c2,8,Cluster 3,10,Floor area,living_area,A_structural,level,m2,0.588,0.194,3.03,0.004,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.482; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,149.0,0.738,none,"submarket cluster 3 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.692); only variables significant at 5% are reported",monetary,0.588,0.194,"b (currency per unit, as reported)",reported,0.2489123897533204,0.08214930354895064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.03,139.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c2,8,Cluster 3,10,Story,floor_level,A_structural,level,count,16.2,5.945,2.725,0.009,,,,high,"storey on which the unit is located (range -2 to 25); unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.456; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,149.0,0.738,none,"submarket cluster 3 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.692); only variables significant at 5% are reported",monetary,16.2,5.945,"b (currency per unit, as reported)",reported,0.22519459541124393,0.08264021849953904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.725,139.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c3,8,Cluster 4,10,Floor area,living_area,A_structural,level,m2,0.606,0.105,5.77,0.0,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.44; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,438.0,0.687,none,"submarket cluster 4 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.675); only variables significant at 5% are reported",monetary,0.606,0.105,"b (currency per unit, as reported)",reported,0.2686503607293311,0.04655985454581128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.77,428.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c3,8,Cluster 4,10,Story,floor_level,A_structural,level,count,6.896,1.713,4.025,0.0,,,,high,"storey on which the unit is located (range -2 to 25); unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.183; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,438.0,0.687,none,"submarket cluster 4 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.675); only variables significant at 5% are reported",monetary,6.896,1.713,"b (currency per unit, as reported)",reported,0.19097490473856946,0.04744718130150794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.025,428.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c3,8,Cluster 4,10,Number of stories,storeys,A_structural,level,count,7.491,1.668,4.492,0.0,,,,high,"total number of storeys of the apartment building; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.193; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,438.0,0.687,none,"submarket cluster 4 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.675); only variables significant at 5% are reported",monetary,7.491,1.668,"b (currency per unit, as reported)",reported,0.21218488782647318,0.04723617271292814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.492,428.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c4,8,Cluster 5,10,Floor area,living_area,A_structural,level,m2,0.379,0.092,4.115,0.0,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.352; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,514.0,0.511,none,"submarket cluster 5 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.488); only variables significant at 5% are reported",monetary,0.379,0.092,"b (currency per unit, as reported)",reported,0.18029297738425296,0.043813603252552354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.115,504.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c4,8,Cluster 5,10,Rooms,bedrooms,A_structural,level,count,13.746,4.15,3.312,0.001,,,,high,"number of rooms (not bedrooms) in the dwelling unit; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.258; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,514.0,0.511,none,"submarket cluster 5 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.488); only variables significant at 5% are reported",monetary,13.746,4.15,"b (currency per unit, as reported)",reported,0.14594850137351228,0.04406657650166434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.312,504.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c4,8,Cluster 5,10,Stove,heating,A_structural,dummy,binary,-32.285,14.263,-2.264,0.025,,,,high,"dummy =1 if heating is a solid-fuel stove (ref: other heating systems; Boilers dummy dropped for collinearity); unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = -0.112; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,514.0,0.511,none,"submarket cluster 5 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.488); only variables significant at 5% are reported",monetary,-32.285,14.263,"b (currency per unit, as reported)",reported,-0.10033764743160596,0.04431874886555034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.264,504.0,binary,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c4,8,Cluster 5,10,Story,floor_level,A_structural,level,count,3.604,1.184,3.043,0.003,,,,high,"storey on which the unit is located (range -2 to 25); unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.194; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,514.0,0.511,none,"submarket cluster 5 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.488); only variables significant at 5% are reported",monetary,3.604,1.184,"b (currency per unit, as reported)",reported,0.1343177185311552,0.04413990093038292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.043,504.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c5,8,Cluster 6,10,Floor area,living_area,A_structural,level,m2,1.445,0.54,2.675,0.02,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.698; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,37.0,0.805,none,"submarket cluster 6 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.674); only variables significant at 5% are reported",monetary,1.445,0.54,"b (currency per unit, as reported)",reported,0.4577123892812812,0.1711074352453388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.675,27.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c6,8,Cluster 7,10,Floor area,living_area,A_structural,level,m2,1.959,0.682,2.874,0.009,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 1.021; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,82.0,0.681,none,"submarket cluster 7 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.553); only variables significant at 5% are reported",monetary,1.959,0.682,"b (currency per unit, as reported)",reported,0.32080233566259053,0.11162224622915469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.874,72.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c6,8,Cluster 7,10,Number of stories,storeys,A_structural,level,count,25.875,9.241,2.8,0.011,,,,high,"total number of storeys of the apartment building; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.627; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,82.0,0.681,none,"submarket cluster 7 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.553); only variables significant at 5% are reported",monetary,25.875,9.241,"b (currency per unit, as reported)",reported,0.3133630367250916,0.11191537025896131,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,72.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c7,8,Cluster 8,10,Central,heating,A_structural,dummy,binary,224.649,64.84,3.465,0.004,,,,high,"dummy =1 if common central heating for the whole block; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.821; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,50.0,0.805,none,"submarket cluster 8 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.699); only variables significant at 5% are reported",monetary,224.649,64.84,"b (currency per unit, as reported)",reported,0.48048028632362655,0.13866674929974795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.465,40.0,binary,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c7,8,Cluster 8,10,Bathrooms,bathrooms,A_structural,level,count,110.295,50.079,2.202,0.046,,,,high,"number of bathrooms; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.403; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,50.0,0.805,none,"submarket cluster 8 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.699); only variables significant at 5% are reported",monetary,110.295,50.079,"b (currency per unit, as reported)",reported,0.32880762606497854,0.14932226433468598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.202,40.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c7,8,Cluster 8,10,Story,floor_level,A_structural,level,count,38.114,15.913,2.395,0.032,,,,high,"storey on which the unit is located (range -2 to 25); unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.85; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,50.0,0.805,none,"submarket cluster 8 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.699); only variables significant at 5% are reported",monetary,38.114,15.913,"b (currency per unit, as reported)",reported,0.3541410816792559,0.14786683994958494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.395,40.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c8,8,Cluster 9,10,Floor area,living_area,A_structural,level,m2,1.587,0.201,7.889,0.0,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.914; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,206.0,0.651,none,"submarket cluster 9 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.621); only variables significant at 5% are reported",monetary,1.587,0.201,"b (currency per unit, as reported)",reported,0.49092290237398306,0.0622287872194173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.889,196.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c8,8,Cluster 9,10,Rooms,bedrooms,A_structural,level,count,-25.211,9.581,-2.632,0.01,,,,high,"number of rooms (not bedrooms) in the dwelling unit; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = -0.269; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,206.0,0.651,none,"submarket cluster 9 (neighbourhood cluster from cluster analysis); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.621); only variables significant at 5% are reported",monetary,-25.211,9.581,"b (currency per unit, as reported)",reported,-0.18476321631988737,0.07019879039509398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.632,196.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c9,8,Overall,10,Floor area,living_area,A_structural,level,m2,0.777,0.063,12.401,0.0,,,,high,"floor area of the dwelling in m2; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.449; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,1733.0,0.751,none,"overall sample with 8 cluster dummies cls1-cls8 (not extracted); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.746); only variables significant at 5% are reported",monetary,0.777,0.063,"b (currency per unit, as reported)",reported,0.2862526188790424,0.023083027084835287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.401,1723.0,m2,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c9,8,Overall,10,Central,heating,A_structural,dummy,binary,29.481,7.078,4.165,0.0,,,,high,"dummy =1 if common central heating for the whole block; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.085; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,1733.0,0.751,none,"overall sample with 8 cluster dummies cls1-cls8 (not extracted); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.746); only variables significant at 5% are reported",monetary,29.481,7.078,"b (currency per unit, as reported)",reported,0.09983827143820609,0.02397077345455128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.165,1723.0,binary,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c9,8,Overall,10,Bathrooms,bathrooms,A_structural,level,count,11.4,4.343,2.625,0.009,,,,high,"number of bathrooms; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.072; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,1733.0,0.751,none,"overall sample with 8 cluster dummies cls1-cls8 (not extracted); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.746); only variables significant at 5% are reported",monetary,11.4,4.343,"b (currency per unit, as reported)",reported,0.06311316870356465,0.024043111887072244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.625,1723.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c9,8,Overall,10,Story,floor_level,A_structural,level,count,5.938,0.943,6.297,0.0,,,,high,"storey on which the unit is located (range -2 to 25); unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.149; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,1733.0,0.751,none,"overall sample with 8 cluster dummies cls1-cls8 (not extracted); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.746); only variables significant at 5% are reported",monetary,5.938,0.943,"b (currency per unit, as reported)",reported,0.1499858810228535,0.02381862490437566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.297,1723.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5,4d32787a617d281fb36974fdcd341ff661cf2fe5:T8-c9,8,Overall,10,Number of stories,storeys,A_structural,level,count,4.493,0.871,5.159,0.0,,,,high,"total number of storeys of the apartment building; unstandardized B with Std. Error, t and Sig. columns; printed standardized Beta = 0.129; dep var in thousands of TL (p printed as 0.000 means <0.0005)","asking price of dwelling unit (,000 TL)",level,OLS,1733.0,0.751,none,"overall sample with 8 cluster dummies cls1-cls8 (not extracted); n_obs = cluster frequency from Table 3 (regression may use fewer complete cases, not reported); r2 = R Square (adjusted R2 = 0.746); only variables significant at 5% are reported",monetary,4.493,0.871,"b (currency per unit, as reported)",reported,0.12333724585113087,0.023907200203747023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.159,1723.0,count,,2015.0,Turkey,Asia,Upper middle income,2011.0,2011.0,condo_apartment,listing,journal,False,True,OLS,,2011.0,ols,journal,False,True,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T2-c1,2,1,3,RentType (x2),other,other,dummy,binary,-70.768,184.13,-0.384,0.701,,,,high,Rent type: whole apartment vs shared room (coding direction not stated),monthly asking rent (CNY),level,OLS,11128.0,,se,All listings; six regressors incl. district code and community ID,monetary,-70.768,184.13,"b (currency per unit, as reported)",reported,-0.0036417909266518323,0.009483830538155814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.384,11118.0,binary,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,False,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T4-c1,4,1,4,RentType (x2),other,other,dummy,binary,-68.823,184.037,-0.374,0.708,,,,high,Rent type: whole apartment vs shared room (coding direction not stated),monthly asking rent (CNY),level,OLS,11128.0,0.464,se,All listings; five regressors (community ID dropped),monetary,-68.823,184.037,"b (currency per unit, as reported)",reported,-0.0035469538303707438,0.009483833771044769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.374,11118.0,binary,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,False,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T2-c1,2,1,3,Area (x3),living_area,A_structural,level,m2,41.938,0.445,94.142,0.0,**,,,high,Size of the dwelling (or room for shared rentals) in square metres,monthly asking rent (CNY),level,OLS,11128.0,,se,All listings; six regressors incl. district code and community ID,monetary,41.938,0.445,"b (currency per unit, as reported)",reported,0.66600562195703,0.007074479211797389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),94.142,11118.0,m2,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,True,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T4-c1,4,1,4,Area (x3),living_area,A_structural,level,m2,41.947,0.445,94.328,0.0,**,,,high,Size of the dwelling (or room for shared rentals) in square metres,monthly asking rent (CNY),level,OLS,11128.0,0.464,se,All listings; five regressors (community ID dropped),monetary,41.947,0.445,"b (currency per unit, as reported)",reported,0.6667368489586256,0.007068281411231296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),94.328,11118.0,m2,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,True,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T2-c1,2,1,3,BuldFloor (x4),storeys,A_structural,level,count,35.954,3.518,10.221,0.0,**,,,high,Highest floor of the building where the dwelling is located (building height in storeys),monthly asking rent (CNY),level,OLS,11128.0,,se,All listings; six regressors incl. district code and community ID,monetary,35.954,3.518,"b (currency per unit, as reported)",reported,0.09648264129791198,0.009439647910959003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.221,11118.0,count,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,True,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T4-c1,4,1,4,BuldFloor (x5),storeys,A_structural,level,count,35.757,3.471,10.3,0.0,**,,,high,Highest floor of the building where the dwelling is located (building height in storeys),monthly asking rent (CNY),level,OLS,11128.0,0.464,se,All listings; five regressors (community ID dropped),monetary,35.757,3.471,"b (currency per unit, as reported)",reported,0.0972213516065064,0.00943896617538897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.3,11118.0,count,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,True,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T2-c1,2,1,3,ElevatorFlag (x5),accessibility,B_interior,dummy,binary,129.228,81.439,1.587,0.113,,,,high,1 if the building has at least one elevator,monthly asking rent (CNY),level,OLS,11128.0,,se,All listings; six regressors incl. district code and community ID,monetary,129.228,81.439,"b (currency per unit, as reported)",reported,0.015049234411246294,0.009482819414773972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.587,11118.0,binary,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,True,False
4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1,4e466a17fb2cf5a4adc290cec91b0f1aaedbabd1:T4-c1,4,1,4,ElevatorFlag (x4),accessibility,B_interior,dummy,binary,125.881,80.864,1.557,0.12,,,,high,1 if the building has at least one elevator,monthly asking rent (CNY),level,OLS,11128.0,0.464,se,All listings; five regressors (community ID dropped),monetary,125.881,80.864,"b (currency per unit, as reported)",reported,0.01476481244369167,0.009482859629859775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.557,11118.0,binary,,2024.0,China,Asia,Upper middle income,2020.0,2020.0,rental,rent,conference,False,False,OLS,,2020.0,ols,other,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,PD,density,F_neighbourhood,other,ordinal (valuer perception),0.044,0.001,,,***,,,medium,"City-level population density rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.044,0.001,not harmonised,reported,0.056894099445115,0.0012930477146617045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Pdev,other,other,other,ordinal (valuer perception),-0.004,0.001,,,***,,,medium,"City population development rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.004,0.001,not harmonised,reported,-0.005180512750046948,0.001295128187511737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,PGR,other,other,other,ordinal (valuer perception),0.039,0.001,,,***,,,medium,"City population growth rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.039,0.001,not harmonised,reported,0.05044636548186226,0.001293496550816981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,P (logs),other,other,log,count (population),0.035,0.0,,,***,,,medium,Log of total city population (numeric); averaged beta across 4 urban-level models,log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",elasticity,0.035,,b (log-log),,,,,,596160.0,count,,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Eco,other,other,other,ordinal (valuer perception),0.035,0.0,,,***,,,medium,"Economic activity in the area. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.035,,not harmonised,,,,,,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,UENV,other,other,other,ordinal (valuer perception),0.158,0.004,,,***,,,medium,"Urban environment quality. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.158,0.004,not harmonised,reported,0.05109143618516417,0.0012934540806370679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.5,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Inc,income,F_neighbourhood,other,ordinal (valuer perception),0.054,0.001,,,***,,,medium,"Income level of the city (valuer perception, not monetary). Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.054,0.001,not harmonised,reported,0.06976744186046512,0.0012919896640826874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Resarea,other,other,other,ordinal,0.092,0.001,,,***,,,medium,"Residence typology of area (first vs secondary homes). Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.092,0.001,not harmonised,reported,0.11831645158027078,0.0012860483867420738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Con,other,other,other,unknown,0.001,0.0,,,***,,,medium,"Undefined abbreviation in PDF. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.001,,not harmonised,,,,,,596160.0,unknown,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,URR,other,other,other,unknown,-0.001,0.0,,,***,,,medium,"Undefined abbreviation in PDF. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.001,,not harmonised,,,,,,596160.0,unknown,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qroad,public_services,F_neighbourhood,other,ordinal (valuer perception),0.02,0.003,,,***,,,medium,"Quality of roads. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.02,0.003,not harmonised,reported,0.008633981949135849,0.0012950972923703773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.666666666666667,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qw-pipe,public_services,F_neighbourhood,other,ordinal (valuer perception),-0.634,0.001,,,***,,,medium,"Quality of water pipes/supply. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.634,0.001,not harmonised,reported,-0.6345980732102483,0.0010009433331391931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-634.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qlig,public_services,F_neighbourhood,other,ordinal (valuer perception),-0.009,0.002,,,***,,,medium,"Quality of street lighting. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.009,0.002,not harmonised,reported,-0.005828056070411943,0.0012951235712026538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qsh,commercial_distance,D_location,other,ordinal (valuer perception),0.006,0.001,,,***,,,medium,"Quality of shops (city level). Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.006,0.001,not harmonised,reported,0.007770638784808231,0.0012951064641347051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qschool,school_quality,F_neighbourhood,other,ordinal (valuer perception),0.012,0.002,,,***,,,medium,"Quality of schools. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.012,0.002,not harmonised,reported,0.007770638784808231,0.0012951064641347051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qchurch,neighbourhood_amenities,F_neighbourhood,other,ordinal (valuer perception),0.049,0.002,,,***,,,medium,"Quality/presence of churches. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.049,0.002,not harmonised,reported,0.031715104064512376,0.0012944940434494847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.5,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qleis,neighbourhood_amenities,F_neighbourhood,other,ordinal (valuer perception),0.08,0.001,,,***,,,medium,"Quality of leisure amenities. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.08,0.001,not harmonised,reported,0.10305992873717572,0.0012882491092146965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),80.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qhealth,hospital_distance,D_location,other,ordinal (valuer perception),-0.113,0.001,,,***,,,medium,"Quality of health facilities (rating, not distance). Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.113,0.001,not harmonised,reported,-0.14480885335387084,0.0012814942774678835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-113.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Bus,transit_distance,D_location,other,ordinal (accessibility rating),0.007,0.0,,,***,,,medium,"Bus accessibility rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.007,,not harmonised,,,,,,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Train,transit_distance,D_location,other,ordinal (accessibility rating),-0.002,0.001,,,***,,,medium,"Train accessibility rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.002,0.001,not harmonised,reported,-0.0025902824441253374,0.0012951412220626687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Und-Tram,transit_distance,D_location,other,ordinal (accessibility rating),0.029,0.0,,,***,,,medium,"Underground/tram accessibility rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.029,,not harmonised,,,,,,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,TB,dwelling_type,A_structural,other,categorical,0.101,0.001,,,***,,,medium,"Type of building (categorical). Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.101,0.001,not harmonised,reported,0.12970470895708575,0.0012842050391790667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),101.0,596160.0,categorical,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,dwel,other,other,level,count,-0.001,0.0,,,***,,,medium,"Number of dwellings (in building), definition in Table S1; averaged beta",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",semi_elasticity,-0.1,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,596160.0,count,unit_not_harmonisable,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,lift,accessibility,B_interior,level,count,0.058,0.001,,,***,,,medium,Number of lifts (text: 'number of lifts'); averaged beta,log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",semi_elasticity,5.800000000000001,0.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0749073971896988,0.0012915068480982552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),58.0,596160.0,count,unit_not_harmonisable,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,age,age,A_structural,level,years,-0.008,0.0,,,***,,,medium,"Age of the dwelling (years, assumed level); averaged beta",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",semi_elasticity,-0.8,,100*b/1 (per year),,,,,,596160.0,years,,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Qshop_n,commercial_distance,D_location,other,ordinal (valuer perception),0.118,0.0,,,***,,,medium,"Quality of shops in the neighbourhood. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.118,,not harmonised,,,,,,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Inc_t,income,F_neighbourhood,other,ordinal (valuer perception),0.096,0.001,,,***,,,medium,"Neighbourhood income level (valuer perception, not monetary). Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.096,0.001,not harmonised,reported,0.12338393969512715,0.0012852493718242411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),96.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,PD_t,density,F_neighbourhood,other,ordinal (valuer perception),-0.02,0.001,,,***,,,medium,"Neighbourhood population density rating. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,-0.02,0.001,not harmonised,reported,-0.02589422579124829,0.0012947112895624144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Ori,sunlight,E_environment,other,categorical,0.003,0.0,,,***,,,medium,"Orientation of the dwelling. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.003,,not harmonised,,,,,,596160.0,categorical,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,Vi,view,E_environment,other,categorical,0.019,0.001,,,***,,,medium,"View. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.019,0.001,not harmonised,reported,0.024600318635475977,0.0012947536123934726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,596160.0,categorical,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,ConQ,condition,A_structural,other,ordinal,0.033,0.001,,,***,,,medium,"Quality of construction. Categorical valuer-perception variable (definition in supplementary Table S1, unit unknown); averaged beta across 4 urban-level models",log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",other,0.033,0.001,not harmonised,reported,0.042700820906469775,0.0012939642698930234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.0,596160.0,ordinal,nonstandard_x_transform,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,m2 (log),living_area,A_structural,log,m2,0.811,0.001,,,***,,,medium,Log of dwelling surface in m2; averaged beta,log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",elasticity,0.811,0.001,b (log-log),reported,0.7242569867126145,0.0008930419071672189,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),811.0,596160.0,m2,,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36,4e94c698b9b6217c36e2e8842148142f5bccfb36:T2-c1,2,"1 (b, unstandardized)",9,m2_O (log),other,other,log,m2,0.034,0.0,,,***,,,medium,Log of additional/other surface area (m2); averaged beta,log of asking price,log,OLS,596170.0,0.768,se,"Average of four urban-level HI model betas, Alicante province, 2010 (4 quarters); Adj R2 0.768; SE column is an average of model SEs",elasticity,0.034,,b (log-log),,,,,,596160.0,m2,,2019.0,Spain,Europe,High income,2010.0,2010.0,unknown,listing,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,Terrace,dwelling_type,A_structural,binary,binary,-0.063,0.015,,0.0,,,,high,terrace house vs semi-detached (reference),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-6.116215151012794,1.4084152105336998,100*(exp(b - se^2/2) - 1),reported,-0.2921743548953888,0.06956532259414018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.2,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,offroad,parking,A_structural,binary,binary,0.011,0.02,,0.585,,,,high,off-road parking vs on-road,"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,1.0858530520096998,2.022121444889439,100*(exp(b - se^2/2) - 1),reported,0.03997463571617019,0.07268115584758215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5499999999999999,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,FVista2,view,E_environment,binary,binary,0.045,0.039,,0.253,,,,high,front vista = field (ref: houses),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,4.523265813505684,4.079508653643996,100*(exp(b - se^2/2) - 1),reported,0.08363589914737059,0.07248444592772117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1538461538461537,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,FVista3,view,E_environment,binary,binary,-0.132,0.075,,0.079,,,,high,front vista = countryside (ref: houses),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-12.612025121927662,6.572557463095299,100*(exp(b - se^2/2) - 1),reported,-0.12698478802203933,0.07215044773979506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7600000000000002,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,RVista2,view,E_environment,binary,binary,0.069,0.051,,0.175,,,,high,rear vista = field (ref: houses),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,7.004371202586368,5.464324666656566,100*(exp(b - se^2/2) - 1),reported,0.09793886745740699,0.0723895976859095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3529411764705885,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,RVista3,view,E_environment,binary,binary,-0.004,0.036,,0.91,,,,high,rear vista = countryside (ref: houses),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-0.4637214764395381,3.585628761638369,100*(exp(b - se^2/2) - 1),reported,-0.00808188012866489,0.07273692115798401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11111111111111112,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,RVista4,view,E_environment,binary,binary,0.041,0.038,,0.281,,,,high,rear vista = sea (ref: houses),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,4.110015981035087,3.959038001072822,100*(exp(b - se^2/2) - 1),reported,0.07824128285879837,0.07251631094230093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0789473684210527,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,fWF1,hazard_site,E_environment,binary,binary,-0.024,0.065,,0.714,,,,high,partial/full view of 1 wind turbine from front of house (ref: no view),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-2.5774516923348645,6.345857113426411,100*(exp(b - se^2/2) - 1),reported,-0.026847905137068282,0.07271307641289326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36923076923076925,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,fWF3,hazard_site,E_environment,binary,binary,-0.007,0.032,,0.813,,,,high,view of up to 2 turbines from front of house (ref: no view),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-0.7483855446057674,3.1776782173863523,100*(exp(b - se^2/2) - 1),reported,-0.015909707257829475,0.07273009032150617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21875,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,FscreenV,hazard_site,E_environment,binary,binary,0.1,0.046,,0.033,,,,high,screened view of wind farm from front,"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,10.400226557051106,5.083786223147979,100*(exp(b - se^2/2) - 1),reported,0.15618823854259092,0.07184658972959182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.173913043478261,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,FsideV,hazard_site,E_environment,binary,binary,0.16,0.04,,0.0,,,,high,side view of wind farm from front,"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,17.257243771837594,4.694043483967241,100*(exp(b - se^2/2) - 1),reported,0.27937211830783126,0.06984302957695782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,RfacingV,hazard_site,E_environment,binary,binary,-0.042,0.035,,0.233,,,,high,facing view of wind farm from rear of house,"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-4.171734734183696,3.356044232003696,100*(exp(b - se^2/2) - 1),reported,-0.08695652173913043,0.07246376811594203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,newH,age,A_structural,dummy,binary,-0.015,0.026,,0.56,,,,high,new-build dummy (not age in years),"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,-1.5220971967298325,2.5612910429679627,100*(exp(b - se^2/2) - 1),reported,-0.04192807621385372,0.07267533210401311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.576923076923077,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,Bed3&4,bedrooms,A_structural,dummy,binary,0.055,0.043,,0.204,,,,high,dummy for 3- or 4-bedroom house vs 2-bedroom; VIF 13.9,"ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",pct_premium,5.556429425088161,4.543124643104625,100*(exp(b - se^2/2) - 1),reported,0.09263855096638261,0.07242650348280823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2790697674418605,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T5-c1,5,1,13,DistMetre,hazard_site,E_environment,level,m,0.0,,,0.559,,,,low,"Distance to wind farm in metres; B and Std. Error both printed as .000 (rounded to 3 dp), standardized beta .019; unusable magnitude","ln(house price, GBP)",log,OLS,199.0,,se,"All independent variables (model with multicollinearity, VIF>10 for Bed3&4 and location dummies)",semi_elasticity,0.0,,100*b/0.001 (X per m->km),,,,,,189.0,m,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,False,False,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,Terrace,dwelling_type,A_structural,binary,binary,-0.073,0.017,,0.0,,,,high,terrace vs semi-detached,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,-7.0533487589464645,1.5803214110438477,100*(exp(b - se^2/2) - 1),reported,-0.2981455052239388,0.06943114505215012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.294117647058823,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,offroad,parking,A_structural,binary,binary,0.013,0.02,,0.523,,,,high,off-road parking vs on-road,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,1.288227064668379,2.0261697347196184,100*(exp(b - se^2/2) - 1),reported,0.047227784683194385,0.07265813028183753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6499999999999999,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,FVista2,view,E_environment,binary,binary,0.04,0.038,,0.295,,,,high,front vista = field,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,4.005958002714705,3.955080941931075,100*(exp(b - se^2/2) - 1),reported,0.07634421963945381,0.07252700865748113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0526315789473684,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,FVista3,view,E_environment,binary,binary,-0.038,0.08,,0.636,,,,high,front vista = countryside,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,-4.036281668288777,7.701703527129596,100*(exp(b - se^2/2) - 1),reported,-0.03453056110202922,0.07269591810953521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.475,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,RVista2,view,E_environment,binary,binary,0.024,0.055,,0.663,,,,high,rear vista = field,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,2.2742249806421366,5.633596748398419,100*(exp(b - se^2/2) - 1),reported,0.031724807044826664,0.0727026828110611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4363636363636364,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,RVista3,view,E_environment,binary,binary,-0.011,0.035,,0.749,,,,high,rear vista = countryside,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,-1.1545335157181036,3.4617109757137907,100*(exp(b - se^2/2) - 1),reported,-0.02285495036730397,0.07272029662323992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3142857142857142,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,RVista4,view,E_environment,binary,binary,0.022,0.036,,0.551,,,,high,rear vista = sea,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,2.158158507387764,3.680077624093577,100*(exp(b - se^2/2) - 1),reported,0.04440793990992342,0.07266753803442014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6111111111111112,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,fWF1,hazard_site,E_environment,binary,binary,-0.026,0.061,,0.675,,,,high,partial/full view of 1 wind turbine from front (ref: no view),"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,-2.747597555968395,5.9434440466133704,100*(exp(b - se^2/2) - 1),reported,-0.030988744671963955,0.07270436249960775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4262295081967213,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,FscreenV,hazard_site,E_environment,binary,binary,0.1,0.041,,0.016,,,,high,screened view of wind farm from front,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,10.424241217826967,4.531200764110156,100*(exp(b - se^2/2) - 1),reported,0.17468508101305913,0.07162088321535426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4390243902439024,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,FsideV,hazard_site,E_environment,binary,binary,0.261,0.063,,0.0,,,,high,side view of wind farm from front,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,29.565388730766195,8.178834292232247,100*(exp(b - se^2/2) - 1),reported,0.28853221902855336,0.06964570804137495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.142857142857143,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,FfacingV,hazard_site,E_environment,binary,binary,0.013,0.031,,0.685,,,,high,facing view of wind farm from front,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,1.2598197012957169,3.1405630888154086,100*(exp(b - se^2/2) - 1),reported,0.030489394647215295,0.07270547954335956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4193548387096774,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,RfacingV,hazard_site,E_environment,binary,binary,-0.074,0.036,,0.041,,,,high,facing view of wind farm from rear,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,-7.192989048195065,3.343218097828634,100*(exp(b - se^2/2) - 1),reported,-0.14787583642422142,0.07193959609826987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.055555555555556,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,newH,age,A_structural,dummy,binary,-0.021,0.024,,0.394,,,,high,new-build dummy (not age in years),"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,-2.1063009886065798,2.350125514966703,100*(exp(b - se^2/2) - 1),reported,-0.06351836059412146,0.07259241210756738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.875,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,Bed3,bedrooms,A_structural,dummy,binary,0.012,0.028,,0.684,,,,high,dummy for 3-bedroom house vs 2-bedroom,"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",pct_premium,1.1675634278220715,2.8338024088250178,100*(exp(b - se^2/2) - 1),reported,0.031158847642487792,0.07270397783247151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42857142857142855,189.0,binary,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,DistMetre,hazard_site,E_environment,level,m,7.426e-05,,,0.707,,,,medium,"Distance to wind farm in metres; Std. Error printed as .000 (rounded), standardized beta .012","ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",semi_elasticity,7.425999999999999,19.75587327636193,100*b/0.001 (X per m->km),|b/z(p)| (two-sided normal),0.027331630295740716,0.07271212288702238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37588821795517746,189.0,m,,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,True,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,trsviewint,hazard_site,E_environment,other,binary,0.086,0.037,,0.021,,,,high,Interaction terrace x RfacingV (facing view of wind farm from rear),"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",other,0.086,0.037,not harmonised,reported,0.16670392401301265,0.07172145568001709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.324324324324324,189.0,binary,nonstandard_x_transform,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,False,True,False
4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566,4f4475636117754066fcc0578aecfcf69e646566:T6-c1,6,1,14,sidrfint,hazard_site,E_environment,other,binary,-0.113,0.07,,0.108,,,,high,Interaction FsideV x FfacingV (side and front-facing views of wind farm),"ln(house price, GBP)",log,OLS,199.0,0.975,se,"Model after removing medlocation and adding interactions (R2 0.975 from Table 7, adj. R2 0.971)",other,-0.113,0.07,not harmonised,reported,-0.11662078050879163,0.07224296137712756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.614285714285714,189.0,binary,nonstandard_x_transform,2008.0,United Kingdom,Europe,High income,2000.0,2007.0,single_family,transaction,journal,True,True,OLS,,2003.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Living area,living_area,A_structural,level,1000 sqft,0.212,0.029,,,***,,,high,Living area in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.2281949008342461,0.031215340208458196,100*b/92.903 (X per 1000sqft->m2),reported,0.1745791066765188,0.023881104215184176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.310344827586206,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Living area,living_area,A_structural,level,1000 sqft,0.214,0.029,,,***,,,high,Living area in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.23034768291758803,0.031215340208458196,100*b/92.903 (X per 1000sqft->m2),reported,0.17617519266207188,0.023874208351402266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.379310344827585,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Living area,living_area,A_structural,level,1000 sqft,0.215,0.029,,,***,,,high,Living area in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.231424073959259,0.031215340208458196,100*b/92.903 (X per 1000sqft->m2),reported,0.17697271571485787,0.023870738398748273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.413793103448276,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Living area,living_area,A_structural,level,1000 sqft,0.214,0.029,,,***,,,high,Living area in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.23034768291758803,0.031215340208458196,100*b/92.903 (X per 1000sqft->m2),reported,0.17617519266207188,0.023874208351402266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.379310344827585,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Lot size,lot_size,A_structural,level,1000 sqft,0.006,0.001,,,***,,,high,Lot size in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.006458346250025833,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.1440046082211958,0.024000768036865967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Lot size,lot_size,A_structural,level,1000 sqft,0.004,0.002,,,**,,,high,Lot size in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.004305564166683889,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.04845015831115092,0.024225079155575462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Lot size,lot_size,A_structural,level,1000 sqft,0.004,0.002,,,**,,,high,Lot size in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.004305564166683889,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.04845015831115092,0.024225079155575462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Lot size,lot_size,A_structural,level,1000 sqft,0.004,0.002,,,**,,,high,Lot size in 1000 ft2,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.004305564166683889,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.04845015831115092,0.024225079155575462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1700.0,1000sqft,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Baths,bathrooms,A_structural,level,count,0.069,0.014,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,6.9,1.4000000000000001,100*b/1 (per unit count),reported,0.11869045576035737,0.02408212145862323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.928571428571429,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Baths,bathrooms,A_structural,level,count,0.07,0.014,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,7.000000000000001,1.4000000000000001,100*b/1 (per unit count),reported,0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Baths,bathrooms,A_structural,level,count,0.068,0.014,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,6.800000000000001,1.4000000000000001,100*b/1 (per unit count),reported,0.11699401969383899,0.02408700405461391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.857142857142858,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Baths,bathrooms,A_structural,level,count,0.068,0.014,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,6.800000000000001,1.4000000000000001,100*b/1 (per unit count),reported,0.11699401969383899,0.02408700405461391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.857142857142858,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Bedrooms,bedrooms,A_structural,level,count,0.01,0.012,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,1.0,1.2,100*b/1 (per unit count),reported,0.020207175225248876,0.024248610270298645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Bedrooms,bedrooms,A_structural,level,count,0.008,0.012,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.8,1.2,100*b/1 (per unit count),reported,0.01616692848321183,0.024250392724817743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Bedrooms,bedrooms,A_structural,level,count,0.01,0.012,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,1.0,1.2,100*b/1 (per unit count),reported,0.020207175225248876,0.024248610270298645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Bedrooms,bedrooms,A_structural,level,count,0.008,0.012,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,0.8,1.2,100*b/1 (per unit count),reported,0.01616692848321183,0.024250392724817743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Age,age,A_structural,level,years,-0.002,0.0004,,,***,,,high,"Home age at sale, years","ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-0.2,0.04,100*b/1 (per year),reported,-0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1700.0,years,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Age,age,A_structural,level,years,-0.002,0.0004,,,***,,,high,"Home age at sale, years","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-0.2,0.04,100*b/1 (per year),reported,-0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1700.0,years,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Age,age,A_structural,level,years,-0.002,0.0004,,,***,,,high,"Home age at sale, years","ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-0.2,0.04,100*b/1 (per year),reported,-0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1700.0,years,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Age,age,A_structural,level,years,-0.002,0.0004,,,***,,,high,"Home age at sale, years","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-0.2,0.04,100*b/1 (per year),reported,-0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1700.0,years,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Stories,storeys,A_structural,level,count,0.082,0.029,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,8.200000000000001,2.9000000000000004,100*b/1 (per unit count),reported,0.06841833891124921,0.024196729614953987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8275862068965516,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Stories,storeys,A_structural,level,count,0.084,0.029,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,8.4,2.9000000000000004,100*b/1 (per unit count),reported,0.07007898069176266,0.024193933810251395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.896551724137931,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Stories,storeys,A_structural,level,count,0.081,0.029,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,8.1,2.9000000000000004,100*b/1 (per unit count),reported,0.06758780383837522,0.024198102608801005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.793103448275862,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Stories,storeys,A_structural,level,count,0.083,0.029,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,8.3,2.9000000000000004,100*b/1 (per unit count),reported,0.06924873176008074,0.02419534001255833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8620689655172415,1700.0,count,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Air conditioning,air_conditioning,A_structural,binary,binary,0.105,0.015,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,11.058566244049217,1.6660659155335578,100*(exp(b - se^2/2) - 1),reported,0.16737983495058364,0.02391140499294052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Air conditioning,air_conditioning,A_structural,binary,binary,0.106,0.015,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,11.169680358090783,1.6677328147597963,100*(exp(b - se^2/2) - 1),reported,0.1689286466552302,0.023904997168192957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.066666666666666,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Air conditioning,air_conditioning,A_structural,binary,binary,0.105,0.015,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,11.058566244049217,1.6660659155335578,100*(exp(b - se^2/2) - 1),reported,0.16737983495058364,0.02391140499294052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Air conditioning,air_conditioning,A_structural,binary,binary,0.105,0.015,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,11.058566244049217,1.6660659155335578,100*(exp(b - se^2/2) - 1),reported,0.16737983495058364,0.02391140499294052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Fireplace,fireplace,B_interior,binary,binary,0.092,0.016,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,9.622449636462814,1.754183715329307,100*(exp(b - se^2/2) - 1),reported,0.13812132380685582,0.024021099792496666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Fireplace,fireplace,B_interior,binary,binary,0.089,0.016,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,9.294075095645503,1.7489290501221284,100*(exp(b - se^2/2) - 1),reported,0.1336992098208472,0.024035813001500626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5625,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Fireplace,fireplace,B_interior,binary,binary,0.091,0.016,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,9.512881979785327,1.7524304084135447,100*(exp(b - se^2/2) - 1),reported,0.13664818991007824,0.024026055368804967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6875,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Fireplace,fireplace,B_interior,binary,binary,0.092,0.016,,,***,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,9.622449636462814,1.754183715329307,100*(exp(b - se^2/2) - 1),reported,0.13812132380685582,0.024021099792496666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Pool,pool,C_exterior,binary,binary,0.014,0.052,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,1.2728324241768618,5.273311986480159,100*(exp(b - se^2/2) - 1),reported,0.0065296660838064235,0.024253045454138138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2692307692307693,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Pool,pool,C_exterior,binary,binary,0.007,0.052,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,0.5663980022891035,5.236527697787612,100*(exp(b - se^2/2) - 1),reported,0.003264885243598369,0.024253433238159306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13461538461538464,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Pool,pool,C_exterior,binary,binary,0.013,0.052,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,1.1716102112943272,5.268041310271007,100*(exp(b - se^2/2) - 1),reported,0.006063279169595053,0.02425311667838021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Pool,pool,C_exterior,binary,binary,0.011,0.052,,,,,,high,,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,0.9694691992640836,5.257515756712541,100*(exp(b - se^2/2) - 1),reported,0.005130493775170161,0.0242532433008044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21153846153846154,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Basement area,basement,A_structural,level,1000 sqft,0.125,0.037,,,***,,,high,"Basement area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,12.5,3.6999999999999997,100*b (unit as reported: 1000sqft; no harmonised unit defined for this characteristic),reported,0.08166403222747984,0.024172553539334028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3783783783783785,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Basement area,basement,A_structural,level,1000 sqft,0.131,0.037,,,***,,,high,"Basement area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,13.100000000000001,3.6999999999999997,100*b (unit as reported: 1000sqft; no harmonised unit defined for this characteristic),reported,0.08555586555157388,0.024164633781742238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.540540540540541,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Basement area,basement,A_structural,level,1000 sqft,0.124,0.036,,,***,,,high,"Basement area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,12.4,3.5999999999999996,100*b (unit as reported: 1000sqft; no harmonised unit defined for this characteristic),reported,0.08325005514722408,0.02416937084919409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4444444444444446,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Basement area,basement,A_structural,level,1000 sqft,0.127,0.036,,,***,,,high,"Basement area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,12.7,3.5999999999999996,100*b (unit as reported: 1000sqft; no harmonised unit defined for this characteristic),reported,0.08524970344327654,0.02416527026738547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.527777777777778,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Garage parking area,garage,A_structural,level,1000 sqft,0.329,0.049,,,***,,,high,"Garage area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,32.9,4.9,100*b (unit as reported: 1000sqft; count expected),reported,0.16072814933966323,0.023938235008034946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.714285714285714,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Garage parking area,garage,A_structural,level,1000 sqft,0.323,0.049,,,***,,,high,"Garage area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,32.300000000000004,4.9,100*b (unit as reported: 1000sqft; count expected),reported,0.15787065322821844,0.023949417981989796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.591836734693877,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Garage parking area,garage,A_structural,level,1000 sqft,0.325,0.049,,,***,,,high,"Garage area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,32.5,4.9,100*b (unit as reported: 1000sqft; count expected),reported,0.15882359639988483,0.023945711457213407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.63265306122449,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Garage parking area,garage,A_structural,level,1000 sqft,0.324,0.049,,,***,,,high,"Garage area in 1000 ft2 (continuous, not a dummy)","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,32.4,4.9,100*b (unit as reported: 1000sqft; count expected),reported,0.15834718012407561,0.023947567364443534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.612244897959184,1700.0,1000sqft,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Distance to highway,highway_distance,D_location,level,km,0.041,0.014,,,***,,,high,Linear term of a quadratic in distance to highway (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,4.1000000000000005,1.4000000000000001,100*b/1 (X per km->km),reported,0.07084979578508309,0.024192613194906416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.928571428571429,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Distance to highway,highway_distance,D_location,level,km,0.043,0.014,,,***,,,high,Linear term of a quadratic in distance to highway (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,4.3,1.4000000000000001,100*b/1 (X per km->km),reported,0.07428725185038029,0.024186547114077306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.071428571428571,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Distance to highway,highway_distance,D_location,level,km,0.044,0.014,,,***,,,high,Linear term of a quadratic in distance to highway (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,4.3999999999999995,1.4000000000000001,100*b/1 (X per km->km),reported,0.07600499513704667,0.024183407543605758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.142857142857143,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Distance to highway,highway_distance,D_location,level,km,0.043,0.014,,,***,,,high,Linear term of a quadratic in distance to highway (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,4.3,1.4000000000000001,100*b/1 (X per km->km),reported,0.07428725185038029,0.024186547114077306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.071428571428571,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Square Distance to highway,highway_distance,D_location,squared,km,-0.007,0.002,,,***,,,high,Quadratic term of distance to highway (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",other,-0.007,0.002,not harmonised,reported,-0.0845832672526816,0.024166647786480457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Square Distance to highway,highway_distance,D_location,squared,km,-0.007,0.002,,,***,,,high,Quadratic term of distance to highway (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",other,-0.007,0.002,not harmonised,reported,-0.0845832672526816,0.024166647786480457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Square Distance to highway,highway_distance,D_location,squared,km,-0.007,0.002,,,***,,,high,Quadratic term of distance to highway (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",other,-0.007,0.002,not harmonised,reported,-0.0845832672526816,0.024166647786480457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Square Distance to highway,highway_distance,D_location,squared,km,-0.007,0.002,,,***,,,high,Quadratic term of distance to highway (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",other,-0.007,0.002,not harmonised,reported,-0.0845832672526816,0.024166647786480457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Distance to Railway,other,other,level,km,-0.036,0.011,,,***,,,high,"Distance to railway (rail line, nuisance proxy; not a transit station); linear term of quadratic","ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-3.5999999999999996,1.0999999999999999,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.0791264213176739,0.024177517624844803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2727272727272725,1700.0,km,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Distance to Railway,other,other,level,km,-0.034,0.011,,,***,,,high,"Distance to railway (rail line, nuisance proxy; not a transit station); linear term of quadratic","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-3.4000000000000004,1.0999999999999999,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.0747557935236577,0.024185697904712786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0909090909090913,1700.0,km,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Distance to Railway,other,other,level,km,-0.032,0.011,,,***,,,high,"Distance to railway (rail line, nuisance proxy; not a transit station); linear term of quadratic","ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-3.2,1.0999999999999999,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.07038085338176359,0.024193418349981233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.909090909090909,1700.0,km,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Distance to Railway,other,other,level,km,-0.03,0.011,,,***,,,high,"Distance to railway (rail line, nuisance proxy; not a transit station); linear term of quadratic","ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-3.0,1.0999999999999999,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.06600184807761962,0.024200677628460527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7272727272727275,1700.0,km,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Square Distance to Railway,other,other,squared,km,0.003,0.001,,,***,,,high,Distance to railway squared (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",other,0.003,0.001,not harmonised,reported,0.07256884707522898,0.024189615691742994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Square Distance to Railway,other,other,squared,km,0.003,0.001,,,***,,,high,Distance to railway squared (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",other,0.003,0.001,not harmonised,reported,0.07256884707522898,0.024189615691742994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Square Distance to Railway,other,other,squared,km,0.003,0.001,,,***,,,high,Distance to railway squared (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",other,0.003,0.001,not harmonised,reported,0.07256884707522898,0.024189615691742994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Square Distance to Railway,other,other,squared,km,0.003,0.001,,,***,,,high,Distance to railway squared (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",other,0.003,0.001,not harmonised,reported,0.07256884707522898,0.024189615691742994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Distance to Airport,airport_distance,D_location,level,km,-0.03,0.006,,,***,,,high,Linear term of a quadratic in distance to airport (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-3.0,0.6,100*b/1 (X per km->km),reported,-0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Distance to Airport,airport_distance,D_location,level,km,-0.03,0.006,,,***,,,high,Linear term of a quadratic in distance to airport (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-3.0,0.6,100*b/1 (X per km->km),reported,-0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Distance to Airport,airport_distance,D_location,level,km,-0.028,0.006,,,***,,,high,Linear term of a quadratic in distance to airport (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-2.8000000000000003,0.6,100*b/1 (X per km->km),reported,-0.11246521862381834,0.024099689705103927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.666666666666667,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Distance to Airport,airport_distance,D_location,level,km,-0.028,0.006,,,***,,,high,Linear term of a quadratic in distance to airport (km),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-2.8000000000000003,0.6,100*b/1 (X per km->km),reported,-0.11246521862381834,0.024099689705103927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.666666666666667,1700.0,km,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Square Distance to Airport,airport_distance,D_location,squared,km,0.001,0.0002,,,***,,,high,Quadratic term of distance to airport (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",other,0.001,0.0002,not harmonised,reported,0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Square Distance to Airport,airport_distance,D_location,squared,km,0.001,0.0002,,,***,,,high,Quadratic term of distance to airport (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",other,0.001,0.0002,not harmonised,reported,0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Square Distance to Airport,airport_distance,D_location,squared,km,0.001,0.0002,,,***,,,high,Quadratic term of distance to airport (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",other,0.001,0.0002,not harmonised,reported,0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Square Distance to Airport,airport_distance,D_location,squared,km,0.001,0.0002,,,***,,,high,Quadratic term of distance to airport (km^2),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",other,0.001,0.0002,not harmonised,reported,0.12038585308576921,0.02407717061715384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,1700.0,km,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Water,water_proximity,E_environment,level,share,0.151,0.322,,,,,,high,Share of land covered by water within 1 km buffer of parcel (share 0-1 per Table 1 means given in %); not a distance,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,15.1,32.2,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.011372829465651471,0.024251993959866054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.468944099378882,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Water,water_proximity,E_environment,level,share,0.132,0.321,,,,,,high,Share of land covered by water within 1 km buffer of parcel (share 0-1 per Table 1 means given in %); not a distance,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,13.200000000000001,32.1,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.009972931583871098,0.024252356351686537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.411214953271028,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Water,water_proximity,E_environment,level,share,0.059,0.323,,,,,,high,Share of land covered by water within 1 km buffer of parcel (share 0-1 per Table 1 means given in %); not a distance,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,5.8999999999999995,32.300000000000004,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.004430173824514699,0.024253324496919453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18266253869969037,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Water,water_proximity,E_environment,level,share,-0.009,0.326,,,,,,high,Share of land covered by water within 1 km buffer of parcel (share 0-1 per Table 1 means given in %); not a distance,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,-0.8999999999999999,32.6,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.0006695767288379406,0.024253557066796518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02760736196319018,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Trees/Forest,green_space,E_environment,level,share,1.664,0.206,,,***,,,high,Share of land covered by trees/forest within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,166.4,20.599999999999998,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.19225743555614627,0.0238011007960133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.077669902912621,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Trees/Forest,green_space,E_environment,level,share,1.514,0.211,,,***,,,high,Share of land covered by trees/forest within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,151.4,21.099999999999998,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.17145103798405031,0.023894431317460114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.175355450236967,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Trees/Forest,green_space,E_environment,level,share,1.437,0.225,,,***,,,high,Share of land covered by trees/forest within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,143.70000000000002,22.5,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.15307389018492193,0.023967728108286315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.386666666666667,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Trees/Forest,green_space,E_environment,level,share,1.429,0.22,,,***,,,high,Share of land covered by trees/forest within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,142.9,22.0,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.15561866086252185,0.02395808634692428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.495454545454546,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Grass/herbaceous plants,green_space,E_environment,level,share,0.517,0.194,,,***,,,high,Share of land covered by grass/herbaceous plants within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,51.7,19.400000000000002,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0644999063275031,0.02420305962772844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6649484536082473,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Grass/herbaceous plants,green_space,E_environment,level,share,0.461,0.194,,,**,,,high,Share of land covered by grass/herbaceous plants within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,46.1,19.400000000000002,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05753798520232174,0.024213382059111534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3762886597938144,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Grass/herbaceous plants,green_space,E_environment,level,share,0.44,0.196,,,**,,,high,Share of land covered by grass/herbaceous plants within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,44.0,19.6,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05436624955632184,0.02421769298417973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2448979591836733,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Grass/herbaceous plants,green_space,E_environment,level,share,0.454,0.195,,,**,,,high,Share of land covered by grass/herbaceous plants within 1 km buffer of parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,45.4,19.5,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05637745889039398,0.024214987849398295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3282051282051284,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c1,3,Parcel,10,Ash,landscaping,C_exterior,binary,binary,0.005,0.015,,,,,,high,Ash_parcel: 1 if ash trees on the sales parcel,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7558,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Parcel; year-of-sale and zip-code fixed effects; adjusted R2",pct_premium,0.489946331041069,1.5075187812891013,100*(exp(b - se^2/2) - 1),reported,0.008084256647467437,0.02425276994240231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33333333333333337,1700.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c2,3,Buffer 0.1 km,10,Ash,green_space,E_environment,level,share,0.165,0.056,,,***,,,high,Ash_buffer0.1: share of properties within 0.1 km buffer that have ash trees (one percentage point = 0.01),"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,16.5,5.6000000000000005,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.07127961839845642,0.024191870486748843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9464285714285716,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c3,3,Buffer 0.5 km,10,Ash,green_space,E_environment,level,share,0.757,0.095,,,**,,,high,Ash_buffer0.5: share of properties within 0.5 km buffer that have ash trees,"ln(sale price), 2005 USD",log,OLS,1710.0,0.7567,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 0.5 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,75.7,9.5,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.18975143436605107,0.023812927694550658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.968421052631579,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T3-c4,3,Buffer 1 km,10,Ash,green_space,E_environment,level,share,0.38,0.129,,,***,,,high,Ash_buffer1.0: share of properties within 1 km buffer that have ash trees,"ln(sale price), 2005 USD",log,OLS,1710.0,0.757,se,"Model 1, home sales 2008-2011 (pre-EAB), ash variable measured at Buffer 1 km; year-of-sale and zip-code fixed effects; adjusted R2",semi_elasticity,38.0,12.9,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.07126295934536608,0.02419189935671638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.945736434108527,1700.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T4-c1,4,Parcel,11,EAB,other,other,level,count,0.0006,0.054,,,,,,high,"EAB_parcel: number of EAB-infested ash trees on the parcel (forest pest infestation, variable of interest)","ln(sale price), 2005 USD",log,OLS,2792.0,0.7737,se,"Model 2, home sales 2012-2015 (post-EAB), EAB variable at Parcel; same structural/location controls as Model 1 but only the EAB coefficient is printed; year and zip-code FE; adjusted R2",semi_elasticity,0.06,5.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0002106584663576866,0.018959261972191794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01111111111111111,2782.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T4-c2,4,Buffer 0.1 km,11,EAB,other,other,level,count,0.0055,0.006,,,,,,high,"EAB_buffer0.1: number of EAB-infested ash trees within 0.1 km (forest pest infestation, variable of interest)","ln(sale price), 2005 USD",log,OLS,2792.0,0.7738,se,"Model 2, home sales 2012-2015 (post-EAB), EAB variable at Buffer 0.1 km; same structural/location controls as Model 1 but only the EAB coefficient is printed; year and zip-code FE; adjusted R2",semi_elasticity,0.5499999999999999,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01737669982124928,0.018956399804999212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9166666666666666,2782.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T4-c3,4,Buffer 0.5 km,11,EAB,other,other,level,count,-0.0005,0.0005,,,,,,high,"EAB_buffer0.5: number within 0.5 km (forest pest infestation, variable of interest)","ln(sale price), 2005 USD",log,OLS,2792.0,0.7738,se,"Model 2, home sales 2012-2015 (post-EAB), EAB variable at Buffer 0.5 km; same structural/location controls as Model 1 but only the EAB coefficient is printed; year and zip-code FE; adjusted R2",semi_elasticity,-0.05,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.01895585582337043,0.01895585582337043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,2782.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T4-c4,4,Buffer 1 km,11,EAB,other,other,level,count,-0.0006,0.0002,,,***,,,high,"EAB_buffer1.0: number within 1 km (forest pest infestation, variable of interest)","ln(sale price), 2005 USD",log,OLS,2792.0,0.7742,se,"Model 2, home sales 2012-2015 (post-EAB), EAB variable at Buffer 1 km; same structural/location controls as Model 1 but only the EAB coefficient is printed; year and zip-code FE; adjusted R2",semi_elasticity,-0.06,0.02,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.05678600763255731,0.018928669210852442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,2782.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c1,5,Parcel,12,Ash,landscaping,C_exterior,binary,binary,0.007,0.016,,,,,,high,Ash_parcel: 1 if ash trees on the sales parcel,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7647,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",pct_premium,0.6895666372711551,1.6112392916269578,100*(exp(b - se^2/2) - 1),reported,0.006527530819625936,0.01492007044485928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4375,4492.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c2,5,Buffer 0.1 km,12,Ash,green_space,E_environment,level,share,0.183,0.049,,,***,,,high,Ash_buffer0.1: share of properties within 0.1 km buffer that have ash trees (one percentage point = 0.01),"ln(sale price), 2005 USD",log,OLS,4502.0,0.7657,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,18.3,4.9,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05563677203439021,0.014897277757842185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7346938775510203,4492.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c3,5,Buffer 0.5 km,12,Ash,green_space,E_environment,level,share,0.298,0.075,,,***,,,high,Ash_buffer0.5: share of properties within 0.5 km buffer that have ash trees,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,29.799999999999997,7.5,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05917977220016274,0.014894237969839616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.973333333333333,4492.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c4,5,Buffer 1 km,12,Ash,green_space,E_environment,level,share,0.393,0.101,,,***,,,high,Ash_buffer1.0: share of properties within 1 km buffer that have ash trees,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7657,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,39.300000000000004,10.100000000000001,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.057958965442412136,0.01489530664041635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.891089108910891,4492.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c1,5,Parcel,12,Ash*Post,landscaping,C_exterior,other,binary,-0.002,0.02,,,,,,high,Interaction: Ash_parcel x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7647,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.002,0.02,not harmonised,reported,-0.0014920371709404973,0.01492037170940497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1,4492.0,binary,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c2,5,Buffer 0.1 km,12,Ash*Post,green_space,E_environment,other,share,-0.066,0.056,,,,,,high,Interaction: Ash_buffer0.1 x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7657,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.066,0.056,not harmonised,reported,-0.017582025198809807,0.014918081986868925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1785714285714286,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c3,5,Buffer 0.5 km,12,Ash*Post,green_space,E_environment,other,share,-0.102,0.077,,,,,,high,Interaction: Ash_buffer0.5 x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.102,0.077,not harmonised,reported,-0.0197608109118968,0.014917474904078958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3246753246753247,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c4,5,Buffer 1 km,12,Ash*Post,green_space,E_environment,other,share,-0.204,0.098,,,**,,,high,Interaction: Ash_buffer1.0 x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7657,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.204,0.098,not harmonised,reported,-0.031043797972556235,0.014913197065247609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.081632653061224,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c1,5,Parcel,12,Ash*Outbreak,landscaping,C_exterior,other,binary,-0.021,0.028,,,,,,high,Interaction: Ash_parcel x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7647,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.021,0.028,not harmonised,reported,-0.011189590664846087,0.014919454219794783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,4492.0,binary,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c2,5,Buffer 0.1 km,12,Ash*Outbreak,green_space,E_environment,other,share,-0.066,0.048,,,,,,high,Interaction: Ash_buffer0.1 x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7657,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.066,0.048,not harmonised,reported,-0.020511217936265026,0.014917249408192746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.375,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c3,5,Buffer 0.5 km,12,Ash*Outbreak,green_space,E_environment,other,share,-0.093,0.051,,,*,,,high,Interaction: Ash_buffer0.5 x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.093,0.051,not harmonised,reported,-0.027197702072945848,0.014914868878712238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.823529411764706,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T5-c4,5,Buffer 1 km,12,Ash*Outbreak,green_space,E_environment,other,share,-0.095,0.054,,,*,,,high,Interaction: Ash_buffer1.0 x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7657,se,"Model 3 difference-in-differences, home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.095,0.054,not harmonised,reported,-0.02623979323137891,0.01491525088941538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7592592592592593,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c1,6,Parcel,12,Ash,landscaping,C_exterior,binary,binary,0.007,0.016,,,,,,high,Ash_parcel: 1 if ash trees on the sales parcel,"ln(sale price), 2005 USD",log,OLS,4502.0,0.765,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",pct_premium,0.6895666372711551,1.6112392916269578,100*(exp(b - se^2/2) - 1),reported,0.006527530819625936,0.01492007044485928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4375,4492.0,binary,,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,True,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c2,6,Buffer 0.1 km,12,Ash,green_space,E_environment,level,share,0.182,0.049,,,***,,,high,Ash_buffer0.1: share of properties within 0.1 km buffer that have ash trees (one percentage point = 0.01),"ln(sale price), 2005 USD",log,OLS,4502.0,0.7659,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,18.2,4.9,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05533367937909536,0.014897529063602599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.714285714285714,4492.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c3,6,Buffer 0.5 km,12,Ash,green_space,E_environment,level,share,0.301,0.075,,,***,,,high,Ash_buffer0.5: share of properties within 0.5 km buffer that have ash trees,"ln(sale price), 2005 USD",log,OLS,4502.0,0.766,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,30.099999999999998,7.5,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.05977342368670394,0.01489371022093952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.013333333333334,4492.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c4,6,Buffer 1 km,12,Ash,green_space,E_environment,level,share,0.393,0.101,,,***,,,high,Ash_buffer1.0: share of properties within 1 km buffer that have ash trees,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,39.300000000000004,10.100000000000001,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.057958965442412136,0.01489530664041635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.891089108910891,4492.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c1,6,Parcel,12,Ash*Post,landscaping,C_exterior,other,binary,-0.005,0.02,,,,,,high,Interaction: Ash_parcel x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival; SE printed with a stray leading minus sign '−(...)',"ln(sale price), 2005 USD",log,OLS,4502.0,0.765,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.005,0.02,not harmonised,reported,-0.0037300711299640883,0.014920284519856353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,4492.0,binary,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c2,6,Buffer 0.1 km,12,Ash*Post,green_space,E_environment,other,share,-0.07,0.056,,,,,,high,Interaction: Ash_buffer0.1 x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival; SE printed with a stray leading minus sign '−(...)',"ln(sale price), 2005 USD",log,OLS,4502.0,0.7659,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.07,0.056,not harmonised,reported,-0.01864724254429462,0.014917794035435696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c3,6,Buffer 0.5 km,12,Ash*Post,green_space,E_environment,other,share,-0.104,0.077,,,,,,high,Interaction: Ash_buffer0.5 x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival; SE printed with a stray leading minus sign '−(...)',"ln(sale price), 2005 USD",log,OLS,4502.0,0.766,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.104,0.077,not harmonised,reported,-0.020148122013384676,0.014917359567602116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3506493506493507,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c4,6,Buffer 1 km,12,Ash*Post,green_space,E_environment,other,share,-0.21,0.098,,,**,,,high,Interaction: Ash_buffer1.0 x post (sale after 2011) dummy; change in ash capitalisation after EAB arrival; SE printed with a stray leading minus sign '−(...)',"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.21,0.098,not harmonised,reported,-0.03195593176800253,0.014912768158401181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.142857142857143,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c1,6,Parcel,12,Ash*Outbreak,landscaping,C_exterior,other,binary,0.001,0.029,,,,,,high,Interaction: Ash_parcel x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.765,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,0.001,0.029,not harmonised,reported,0.0005144960807707392,0.014920386342351438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.034482758620689655,4492.0,binary,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c2,6,Buffer 0.1 km,12,Ash*Outbreak,green_space,E_environment,other,share,-0.011,0.054,,,,,,high,Interaction: Ash_buffer0.1 x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7659,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.011,0.054,not harmonised,reported,-0.003039324322928219,0.014920319403465802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2037037037037037,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c3,6,Buffer 0.5 km,12,Ash*Outbreak,green_space,E_environment,other,share,-0.033,0.058,,,,,,high,Interaction: Ash_buffer0.5 x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.766,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.033,0.058,not harmonised,reported,-0.008488880580747416,0.014919850717677275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5689655172413793,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c4,6,Buffer 1 km,12,Ash*Outbreak,green_space,E_environment,other,share,-0.044,0.06,,,,,,high,Interaction: Ash_buffer1.0 x outbreak (EAB-infested tree within 1 km) dummy,"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",other,-0.044,0.06,not harmonised,reported,-0.010940963198192597,0.014919495270262633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7333333333333333,4492.0,share,nonstandard_x_transform,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,True,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c1,6,Parcel,12,EAB_Buffer3,other,other,level,count,-0.001,0.0002,,,***,,,high,EAB_buffer1.0: number of EAB-infested ash trees within 1 km buffer (severity of outbreak),"ln(sale price), 2005 USD",log,OLS,4502.0,0.765,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Parcel; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,-0.1,0.02,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.07439520743655768,0.014879041487311537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,4492.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c2,6,Buffer 0.1 km,12,EAB_Buffer3,other,other,level,count,-0.001,0.0003,,,**,,,high,EAB_buffer1.0: number of EAB-infested ash trees within 1 km buffer (severity of outbreak),"ln(sale price), 2005 USD",log,OLS,4502.0,0.7659,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,-0.1,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.04967323147320024,0.01490196944196007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,4492.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c3,6,Buffer 0.5 km,12,EAB_Buffer3,other,other,level,count,-0.001,0.0003,,,**,,,high,EAB_buffer1.0: number of EAB-infested ash trees within 1 km buffer (severity of outbreak),"ln(sale price), 2005 USD",log,OLS,4502.0,0.766,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 0.5 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,-0.1,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.04967323147320024,0.01490196944196007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,4492.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a,51b11fac87d973139852a88a8c12a9cdbf87b57a:T6-c4,6,Buffer 1 km,12,EAB_Buffer3,other,other,level,count,-0.001,0.0003,,,*,,,high,EAB_buffer1.0: number of EAB-infested ash trees within 1 km buffer (severity of outbreak),"ln(sale price), 2005 USD",log,OLS,4502.0,0.7658,se,"Model 4 (Model 3 plus EAB severity within 1 km), home sales 2008-2015, ash variable at Buffer 1 km; only ash/EAB coefficients printed (controls as Model 1); year and zip-code FE; adjusted R2; 'Sum 1'/'Sum 2' linear combinations not extracted",semi_elasticity,-0.1,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.04967323147320024,0.01490196944196007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,4492.0,count,unit_not_harmonisable,2019.0,United States,North America,High income,2008.0,2015.0,single_family,transaction,journal,True,True,unknown,,2011.5,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.023,0.001,,,***,,,high,building transfer area,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,0.6957468691390888,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.3061771744685008,0.013312051063847863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.0,5114.0,pyeong,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.022,0.001,,,***,,,high,building transfer area,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,0.6654970052634762,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.29403987791369596,0.01336544899607709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,5114.0,pyeong,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.022,0.001,,,***,,,high,building transfer area,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,0.6654970052634762,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.29403987791369596,0.01336544899607709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,5114.0,pyeong,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.018,0.001,,,***,,,high,building transfer area,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,0.544497549761026,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.2017384562323821,0.011207692012910116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,7637.0,pyeong,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.017,0.001,,,***,,,high,building transfer area,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,0.5142476858854136,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.19095097814228348,0.011232410478957853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,7637.0,pyeong,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.017,0.001,,,***,,,high,building transfer area,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,0.5142476858854136,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.19095097814228348,0.011232410478957853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,7637.0,pyeong,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,AGE,age,A_structural,level,years,-0.013,0.001,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-1.3,0.1,100*b/1 (per year),reported,-0.17885580773948248,0.013758139056883268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,5114.0,years,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,AGE,age,A_structural,level,years,-0.013,0.001,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-1.3,0.1,100*b/1 (per year),reported,-0.17885580773948248,0.013758139056883268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,5114.0,years,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,AGE,age,A_structural,level,years,-0.013,0.001,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-1.3,0.1,100*b/1 (per year),reported,-0.17885580773948248,0.013758139056883268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,5114.0,years,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,AGE,age,A_structural,level,years,-0.004,0.001,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-0.4,0.1,100*b/1 (per year),reported,-0.045723991428284164,0.011430997857071041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,7637.0,years,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,AGE,age,A_structural,level,years,-0.004,0.001,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-0.4,0.1,100*b/1 (per year),reported,-0.045723991428284164,0.011430997857071041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,7637.0,years,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,AGE,age,A_structural,level,years,-0.004,0.001,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-0.4,0.1,100*b/1 (per year),reported,-0.045723991428284164,0.011430997857071041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,7637.0,years,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,AGES,age,A_structural,squared,years squared,0.001,0.001,,,***,,,medium,square of house age,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",other,0.001,0.001,not harmonised,reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,years sqd,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,AGES,age,A_structural,squared,years squared,0.001,0.001,,,***,,,medium,square of house age,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",other,0.001,0.001,not harmonised,reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,years sqd,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,AGES,age,A_structural,squared,years squared,0.001,0.001,,,***,,,medium,square of house age,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",other,0.001,0.001,not harmonised,reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,years sqd,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,AGES,age,A_structural,squared,years squared,0.001,0.001,,,***,,,medium,square of house age,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",other,0.001,0.001,not harmonised,reported,0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,7637.0,years sqd,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,AGES,age,A_structural,squared,years squared,0.001,0.001,,,***,,,medium,square of house age,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",other,0.001,0.001,not harmonised,reported,0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,7637.0,years sqd,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,AGES,age,A_structural,squared,years squared,0.001,0.001,,,***,,,medium,square of house age,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",other,0.001,0.001,not harmonised,reported,0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,7637.0,years sqd,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,FLOOR,floor_level,A_structural,dummy,binary (first floor = 1 vs other floors),0.124,0.021,,,***,,,high,residence on 1st floor,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,13.176628901704458,2.377233329098268,100*(exp(b - se^2/2) - 1),reported,0.08228990988664409,0.013936194416286499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.904761904761904,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,FLOOR,floor_level,A_structural,dummy,binary (first floor = 1 vs other floors),0.126,0.02,,,***,,,high,residence on 1st floor,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,13.405533453349939,2.26856433656605,100*(exp(b - se^2/2) - 1),reported,0.0877569262984512,0.013929670841023997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.3,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,FLOOR,floor_level,A_structural,dummy,binary (first floor = 1 vs other floors),0.129,0.02,,,***,,,high,residence on 1st floor,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,13.746260889318407,2.275380248331463,100*(exp(b - se^2/2) - 1),reported,0.08982971086721732,0.013927086956157722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.45,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,FLOOR,floor_level,A_structural,dummy,binary (first floor = 1 vs other floors),0.221,0.013,,,***,,,high,residence on 1st floor,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,24.721803618715743,1.6215204597332842,100*(exp(b - se^2/2) - 1),reported,0.19095097814228348,0.011232410478957853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,True
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,FLOOR,floor_level,A_structural,dummy,binary (first floor = 1 vs other floors),0.219,0.012,,,***,,,high,residence on 1st floor,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,24.474165206213485,1.4937975320250374,100*(exp(b - se^2/2) - 1),reported,0.2044240580388083,0.011201318248701825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.25,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,FLOOR,floor_level,A_structural,dummy,binary (first floor = 1 vs other floors),0.221,0.012,,,***,,,high,residence on 1st floor,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,24.723362651004898,1.4967881166768777,100*(exp(b - se^2/2) - 1),reported,0.2062119022244369,0.011197026365127793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.416666666666668,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,True
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,ROOM,bedrooms,A_structural,level,count,0.085,0.007,,,***,,,high,number of rooms,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.5,0.7000000000000001,100*b/1 (per unit count),reported,0.16740491472871646,0.013786287095306061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.142857142857144,5114.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,ROOM,bedrooms,A_structural,level,count,0.085,0.007,,,***,,,high,number of rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.5,0.7000000000000001,100*b/1 (per unit count),reported,0.16740491472871646,0.013786287095306061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.142857142857144,5114.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,ROOM,bedrooms,A_structural,level,count,0.086,0.007,,,***,,,high,number of rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.6,0.7000000000000001,100*b/1 (per unit count),reported,0.16931824120930172,0.013781717307733863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.285714285714285,5114.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,ROOM,bedrooms,A_structural,level,count,0.082,0.005,,,***,,,high,number of rooms,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.200000000000001,0.5,100*b/1 (per unit count),reported,0.1844448534689467,0.011246637406643094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.4,7637.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,ROOM,bedrooms,A_structural,level,count,0.078,0.005,,,***,,,high,number of rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,7.8,0.5,100*b/1 (per unit count),reported,0.17573229038961174,0.011264890409590497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.6,7637.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,ROOM,bedrooms,A_structural,level,count,0.081,0.005,,,***,,,high,number of rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.1,0.5,100*b/1 (per unit count),reported,0.1822707002918571,0.011251277795793648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.2,7637.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,LIVROOM,other,other,level,count (living rooms),0.167,0.012,,,***,,,high,number of living rooms,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,16.7,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.19102188255351824,0.013726123297258794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.916666666666668,5114.0,count,unit_not_harmonisable,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,LIVROOM,other,other,level,count (living rooms),0.166,0.011,,,***,,,high,number of living rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,16.6,1.0999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.2064782028208246,0.013682290548367896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.090909090909092,5114.0,count,unit_not_harmonisable,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,LIVROOM,other,other,level,count (living rooms),0.173,0.011,,,***,,,high,number of living rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,17.299999999999997,1.0999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.21479118560416807,0.013657243015293922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.727272727272727,5114.0,count,unit_not_harmonisable,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,LIVROOM,other,other,level,count (living rooms),0.097,0.008,,,***,,,high,number of living rooms,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,9.700000000000001,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.13742947826801252,0.011334389960248456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.125,7637.0,count,unit_not_harmonisable,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,LIVROOM,other,other,level,count (living rooms),0.088,0.008,,,***,,,high,number of living rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.799999999999999,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.1248871622698648,0.011353378388169527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,7637.0,count,unit_not_harmonisable,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,LIVROOM,other,other,level,count (living rooms),0.092,0.008,,,***,,,high,number of living rooms,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,9.2,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.13046928793197843,0.01134515547234595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.5,7637.0,count,unit_not_harmonisable,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,BATH,bathrooms,A_structural,level,count,0.087,0.01,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.7,1.0,100*b/1 (per unit count),reported,0.12076707405522506,0.013881272879910927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.7,5114.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,BATH,bathrooms,A_structural,level,count,0.085,0.01,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.5,1.0,100*b/1 (per unit count),reported,0.11802994419706878,0.013885875787890446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.5,5114.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,BATH,bathrooms,A_structural,level,count,0.083,0.01,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,8.3,1.0,100*b/1 (per unit count),reported,0.11529012142031876,0.0138903760747372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.3,5114.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,BATH,bathrooms,A_structural,level,count,0.018,0.007,,,**,,,high,,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,1.7999999999999998,0.7000000000000001,100*b/1 (per unit count),reported,0.02941203955374915,0.01143801538201356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.571428571428571,7637.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,BATH,bathrooms,A_structural,level,count,0.017,0.007,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,1.7000000000000002,0.7000000000000001,100*b/1 (per unit count),reported,0.027779335358189095,0.011438549853371978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.428571428571429,7637.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,BATH,bathrooms,A_structural,level,count,0.019,0.007,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,1.9,0.7000000000000001,100*b/1 (per unit count),reported,0.031044508366704086,0.011437450450890983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.714285714285714,7637.0,count,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,TYPE,dwelling_type,A_structural,binary,binary (multistoried building = 1 vs apartment house),0.068,0.012,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,7.028824495088415,1.2844383701745292,100*(exp(b - se^2/2) - 1),reported,0.07899290530704707,0.013939924465949483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.666666666666667,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,TYPE,dwelling_type,A_structural,binary,binary (multistoried building = 1 vs apartment house),0.073,0.012,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,7.565308710427221,1.2908766442976443,100*(exp(b - se^2/2) - 1),reported,0.08476089744982494,0.013933298210930128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.083333333333333,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,TYPE,dwelling_type,A_structural,binary,binary (multistoried building = 1 vs apartment house),0.069,0.012,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,7.135906851838358,1.2857234509780155,100*(exp(b - se^2/2) - 1),reported,0.08014715740629483,0.01393863607065997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,TYPE,dwelling_type,A_structural,binary,binary (multistoried building = 1 vs apartment house),0.272,0.008,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,31.254499919910383,1.0500696010488866,100*(exp(b - se^2/2) - 1),reported,0.3625854566188909,0.010664278135849733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.0,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,TYPE,dwelling_type,A_structural,binary,binary (multistoried building = 1 vs apartment house),0.255,0.008,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,29.042032676173136,1.032369296698312,100*(exp(b - se^2/2) - 1),reported,0.3426623977331172,0.010750192870058578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.875,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,TYPE,dwelling_type,A_structural,binary,binary (multistoried building = 1 vs apartment house),0.289,0.008,,,***,,,high,,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,33.504900627675525,1.0680733828228068,100*(exp(b - se^2/2) - 1),reported,0.38202355665162496,0.010575046550910032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.125,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,True,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,TREAT,other,other,dummy,binary (located in neighbouring area of luxury housing project),0.021,0.024,,,,,,high,DiD treatment-group indicator,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,2.0927982034712356,2.4509329239300683,100*(exp(b - se^2/2) - 1),reported,0.012234752382650023,0.013982574151600025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.875,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,TREAT,other,other,dummy,binary (located in neighbouring area of luxury housing project),0.012,0.023,,,,,,high,DiD treatment-group indicator,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,1.180463114496666,2.327766264391979,100*(exp(b - se^2/2) - 1),reported,0.0072956079854143195,0.013983248638710779,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5217391304347826,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,TREAT,other,other,dummy,binary (located in neighbouring area of luxury housing project),0.003,0.029,,,,,,high,DiD treatment-group indicator,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,0.258282977255897,2.9087130630597935,100*(exp(b - se^2/2) - 1),reported,0.0014465799473803401,0.013983606158009955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.10344827586206896,5114.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,TREAT,other,other,dummy,binary (located in neighbouring area of luxury housing project),0.05,0.021,,,**,,,high,DiD treatment-group indicator,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,5.103931665392669,2.2076693023896508,100*(exp(b - se^2/2) - 1),reported,0.02723505057004078,0.011438721239417126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.380952380952381,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,TREAT,other,other,dummy,binary (located in neighbouring area of luxury housing project),0.014,0.02,,,,,,high,DiD treatment-group indicator,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,1.3895659527321857,2.0281969178769845,100*(exp(b - se^2/2) - 1),reported,0.00800981919141459,0.011442598844877987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,TREAT,other,other,dummy,binary (located in neighbouring area of luxury housing project),0.027,0.026,,,,,,high,DiD treatment-group indicator,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",pct_premium,2.702061112484766,2.6711562871850725,100*(exp(b - se^2/2) - 1),reported,0.011882241097452818,0.011442158093843453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0384615384615385,7637.0,binary,,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,DD,other,other,other,binary interaction (TIME x TREAT),0.146,0.033,,,***,,,high,DiD interaction: spillover of luxury housing construction (during) / completion (after) on neighbouring prices,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",other,0.146,0.033,not harmonised,reported,0.061748868783201634,0.013956936094833245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.424242424242424,5114.0,binary interaction,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,DD,other,other,other,binary interaction (TIME x TREAT),0.132,0.032,,,***,,,high,DiD interaction: spillover of luxury housing construction (during) / completion (after) on neighbouring prices,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",other,0.132,0.032,not harmonised,reported,0.057586712233174006,0.01396041508683006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.125,5114.0,binary interaction,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,DD,other,other,other,binary interaction (TIME x TREAT),0.13,0.032,,,***,,,high,DiD interaction: spillover of luxury housing construction (during) / completion (after) on neighbouring prices,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",other,0.13,0.032,not harmonised,reported,0.056717014568855084,0.013961111278487405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0625,5114.0,binary interaction,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,DD,other,other,other,binary interaction (TIME x TREAT),0.05,0.026,,,*,,,high,DiD interaction: spillover of luxury housing construction (during) / completion (after) on neighbouring prices,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",other,0.05,0.026,not harmonised,reported,0.022000377494515836,0.011440196297148233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9230769230769234,7637.0,binary interaction,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,DD,other,other,other,binary interaction (TIME x TREAT),0.053,0.024,,,**,,,high,DiD interaction: spillover of luxury housing construction (during) / completion (after) on neighbouring prices,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",other,0.053,0.024,not harmonised,reported,0.025261818690056403,0.011439314123799128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.208333333333333,7637.0,binary interaction,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,DD,other,other,other,binary interaction (TIME x TREAT),0.058,0.025,,,**,,,high,DiD interaction: spillover of luxury housing construction (during) / completion (after) on neighbouring prices,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",other,0.058,0.025,not harmonised,reported,0.02653833075949315,0.011438935672195326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.32,7637.0,binary interaction,nonstandard_x_transform,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,MRT,transit_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest MRT station; coefficient rounded to 0.001,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,MRT,transit_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest MRT station; coefficient rounded to 0.001,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,MRT,transit_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest MRT station; coefficient rounded to 0.001,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,MRT,transit_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest MRT station; coefficient rounded to 0.001,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,MRT,transit_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest MRT station; coefficient rounded to 0.001,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,MRT,transit_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest MRT station; coefficient rounded to 0.001,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,HISC,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest senior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,HISC,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest senior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,HISC,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest senior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,HISC,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest senior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,HISC,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest senior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,HISC,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest senior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,JUNI,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest junior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,JUNI,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest junior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,JUNI,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest junior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,JUNI,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest junior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,JUNI,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest junior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,JUNI,school_distance,D_location,level,m,0.001,0.001,,,,,,medium,distance to nearest junior high school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c1,7,1,12,PRIM,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest primary school; rounded,"ln(PRICE), total price in ten thousand NT$",log,OLS,5124.0,0.797,se,"During construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c2,7,2,12,PRIM,school_distance,D_location,level,m,-0.001,0.001,,,,,,low,distance to nearest primary school; rounded; printed as 'v0.001' in PDF (typo; sign unclear),"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,5124.0,0.807,se,"During construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,False,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c3,7,3,12,PRIM,school_distance,D_location,level,m,-0.001,0.001,,,,,,medium,distance to nearest primary school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,5124.0,0.813,se,"During construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.01398225379936557,0.01398225379936557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5114.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c4,7,4,12,PRIM,school_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest primary school; rounded,"ln(PRICE), total price in ten thousand NT$",log,OLS,7647.0,0.719,se,"After construction sample (PSM-matched); OLS; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,ols,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c5,7,5,12,PRIM,school_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest primary school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_lag,7647.0,0.746,se,"After construction sample (PSM-matched); spatial_lag; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97,5242a8699dbfd95d3c698daaa18f7bab00d59c97:T7-c6,7,6,12,PRIM,school_distance,D_location,level,m,-0.001,0.001,,,***,,,medium,distance to nearest primary school; rounded,"ln(PRICE), total price in ten thousand NT$",log,spatial_error,7647.0,0.757,se,"After construction sample (PSM-matched); spatial_error; location fixed effects; DD specification with TIME, TREAT, DD",semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.011442216819293473,0.011442216819293473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7637.0,m,implausible_magnitude,2020.0,Taiwan,Asia,High income,2008.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2011.0,spatial,journal,False,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Rooms,bedrooms,A_structural,level,count,0.059,0.031,,,*,,,high,Median number of rooms in block group White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,semi_elasticity,5.8999999999999995,3.1,100*b/1 (per unit count),reported,0.07683157956966678,0.04036913502813001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9032258064516128,610.0,count,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Rooms,bedrooms,A_structural,level,count,0.049,,,,,,,medium,Median number of rooms in block group GWR median of local coefficients; range 0.031 to 0.104; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,semi_elasticity,4.9,,100*b/1 (per unit count),,,,,,610.0,count,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,House Age,age,A_structural,level,years,-0.014,0.003,,,***,,,high,Median house age White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,semi_elasticity,-1.4000000000000001,0.3,100*b/1 (per year),reported,-0.18566267696174743,0.03978485934894588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.666666666666667,610.0,years,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,House Age,age,A_structural,level,years,-0.015,,,,,,,medium,Median house age GWR median of local coefficients; range -0.017 to -0.013; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,semi_elasticity,-1.5,,100*b/1 (per year),,,,,,610.0,years,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,House Age-squared,age,A_structural,squared,years,0.0,0.0,,,***,,,high,White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,other,0.0,,not harmonised,,,,,,610.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,House Age-squared,age,A_structural,squared,years,0.0,,,,,,,medium,GWR median of local coefficients; range 0.0 to 0.0; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,other,0.0,,not harmonised,,,,,,610.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Income),income,F_neighbourhood,log,currency,0.42,0.067,,,***,,,high,Log median household income White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.42,0.067,b (log-log),reported,0.2460101948235074,0.039244483459940464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.26865671641791,610.0,currency,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Income),income,F_neighbourhood,log,currency,0.496,,,,,,,medium,Log median household income GWR median of local coefficients; range 0.266 to 0.525; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.496,,b (log-log),,,,,,610.0,currency,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Median Age,demographics,F_neighbourhood,level,years,0.007,0.003,,,**,,,high,Median age of residents White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,semi_elasticity,0.7000000000000001,0.3,100*b (unit as reported: years; no harmonised unit defined for this characteristic),reported,0.09405510171827214,0.04030932930783092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3333333333333335,610.0,years,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,False,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Median Age,demographics,F_neighbourhood,level,years,0.0,,,,,,,medium,Median age of residents GWR median of local coefficients; range -0.002 to 0.014; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,semi_elasticity,0.0,,100*b (unit as reported: years; no harmonised unit defined for this characteristic),,,,,,610.0,years,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Poverty,other,other,level,share,-0.001,0.002,,,,,,high,Poverty rate (%) White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,semi_elasticity,-0.1,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02024026108485969,0.04048052216971938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,610.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Poverty,other,other,level,share,-0.001,,,,,,,medium,Poverty rate (%) GWR median of local coefficients; range -0.002 to 0.001; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,semi_elasticity,-0.1,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,610.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Pop den),density,F_neighbourhood,log,per_km2,0.0,0.021,,,,,,high,Log population density White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.0,0.021,b (log-log),reported,0.0,0.0404888165089458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,610.0,per_km2,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Pop den),density,F_neighbourhood,log,per_km2,0.014,,,,,,,medium,Log population density GWR median of local coefficients; range -0.031 to 0.036; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.014,,b (log-log),,,,,,610.0,per_km2,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Vacant,vacancy,F_neighbourhood,level,share,-0.009,0.004,,,**,,,high,Percentage of vacant houses White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,semi_elasticity,-0.8999999999999999,0.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.09072414698666884,0.040321843105186154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,610.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,False,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Vacant,vacancy,F_neighbourhood,level,share,-0.008,,,,,,,medium,Percentage of vacant houses GWR median of local coefficients; range -0.012 to -0.006; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,semi_elasticity,-0.8,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,610.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Recreation,other,other,level,share,0.019,0.006,,,***,,,high,Percentage of houses used for recreational purposes White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,semi_elasticity,1.9,0.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12717354490507077,0.040160066812127616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1666666666666665,610.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Recreation,other,other,level,share,0.022,,,,,,,medium,Percentage of houses used for recreational purposes GWR median of local coefficients; range 0.009 to 0.033; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,semi_elasticity,2.1999999999999997,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,610.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Road),highway_distance,D_location,log,km,0.004,0.014,,,,,,high,Log distance to nearest road White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.004,0.014,b (log-log),reported,0.01156745931220217,0.0404861075927076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2857142857142857,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Road),highway_distance,D_location,log,km,0.004,,,,,,,medium,Log distance to nearest road GWR median of local coefficients; range -0.004 to 0.028; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.004,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Rail),transit_distance,D_location,log,km,0.013,0.013,,,,,,high,"Log distance to nearest railroad (line, not station) White SE.",ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.013,0.013,b (log-log),reported,0.04045566970313674,0.04045566970313674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Rail),transit_distance,D_location,log,km,0.011,,,,,,,medium,"Log distance to nearest railroad (line, not station) GWR median of local coefficients; range -0.021 to 0.028; no SE.",ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.011,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(School),school_distance,D_location,log,km,0.0,0.022,,,,,,high,White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.0,0.022,b (log-log),reported,0.0,0.0404888165089458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(School),school_distance,D_location,log,km,0.011,,,,,,,medium,GWR median of local coefficients; range -0.021 to 0.016; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.011,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Shopping centers),commercial_distance,D_location,log,km,-0.036,0.021,,,*,,,high,White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.036,0.021,b (log-log),reported,-0.06924280582381005,0.040391636730555874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.714285714285714,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Shopping centers),commercial_distance,D_location,log,km,-0.029,,,,,,,medium,GWR median of local coefficients; range -0.051 to -0.013; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,-0.029,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Hospital),hospital_distance,D_location,log,km,-0.086,0.025,,,***,,,high,White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.086,0.025,b (log-log),reported,-0.1379498873346141,0.04010171143448086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4399999999999995,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Hospital),hospital_distance,D_location,log,km,-0.137,,,,,,,medium,GWR median of local coefficients; range -0.157 to -0.032; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,-0.137,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Airport),airport_distance,D_location,log,km,0.006,0.026,,,,,,high,White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.006,0.026,b (log-log),reported,0.009343165209253849,0.04048704924010001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23076923076923078,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Airport),airport_distance,D_location,log,km,0.052,,,,,,,medium,GWR median of local coefficients; range 0.0 to 0.076; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.052,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Park),green_space,E_environment,log,km,-0.01,0.021,,,,,,high,Log distance to nearest public park White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.01,0.021,b (log-log),reported,-0.019276806230402954,0.04048129308384621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.47619047619047616,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Park),green_space,E_environment,log,km,0.01,,,,,,,medium,Log distance to nearest public park GWR median of local coefficients; range -0.036 to 0.023; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.01,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Bay),water_proximity,E_environment,log,km,-0.059,0.009,,,***,,,high,Log distance to nearest bay White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.059,0.009,b (log-log),reported,-0.2565435632351774,0.03913376388333214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.555555555555555,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Bay),water_proximity,E_environment,log,km,-0.054,,,,,,,medium,Log distance to nearest bay GWR median of local coefficients; range -0.064 to -0.047; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,-0.054,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(River),water_proximity,E_environment,log,km,-0.004,0.012,,,,,,high,Log distance to nearest river White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.004,0.012,b (log-log),reported,-0.013495043168856575,0.040485129506569725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(River),water_proximity,E_environment,log,km,-0.025,,,,,,,medium,Log distance to nearest river GWR median of local coefficients; range -0.032 to -0.005; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,-0.025,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Stream),water_proximity,E_environment,log,km,-0.03,0.017,,,*,,,high,Log distance to nearest stream White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.03,0.017,b (log-log),reported,-0.07126916173107088,0.04038585831427351,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.764705882352941,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Stream),water_proximity,E_environment,log,km,-0.055,,,,,,,medium,Log distance to nearest stream GWR median of local coefficients; range -0.078 to -0.033; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,-0.055,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Bayou),water_proximity,E_environment,log,km,0.038,0.018,,,**,,,high,Log distance to nearest bayou White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,0.038,0.018,b (log-log),reported,0.08516583673303921,0.040341712136702776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.111111111111111,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Bayou),water_proximity,E_environment,log,km,0.048,,,,,,,medium,Log distance to nearest bayou GWR median of local coefficients; range 0.032 to 0.073; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,0.048,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-OLS,2,Global Model (OLS),9,Ln(Water),water_proximity,E_environment,log,km,-0.029,0.011,,,***,,,high,Log distance to nearest other water body White SE.,ln(housing value),log,OLS,620.0,0.518,se,all 620 block groups; White SE,elasticity,-0.029,0.011,b (log-log),reported,-0.10614026876872888,0.040260101946759226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6363636363636367,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,ols,journal,True,True,False
527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd,527c17fa11ad1ccaff05d13eaeb2dfa2ff940bdd:T2-GWR,2,Local Model (GWR) - median of local coefficients,9,Ln(Water),water_proximity,E_environment,log,km,-0.015,,,,,,,medium,Log distance to nearest other water body GWR median of local coefficients; range -0.04 to -0.012; no SE.,ln(housing value),log,GWR,620.0,0.572,none,median of local GWR coefficients; min-max in notes,elasticity,-0.015,,b (log-log),,,,,,610.0,km,,2021.0,United States,North America,High income,2010.0,2010.0,mixed,appraisal,journal,False,False,robust,,2010.0,spatial,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,BTS_Prox,hazard_site,E_environment,dummy,binary,0.014,0.007,,,*,,,high,Dummy = 1 if apartment within 200 m of a base transceiver station (cell phone tower),ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,pct_premium,1.4073613830602127,0.7098689212569446,100*(exp(b - se^2/2) - 1),reported,0.04689360772322401,0.02344680386161201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Ln Area,living_area,A_structural,log,m2,0.913,0.017,,,***,,,high,Natural log of usable area in m2,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,elasticity,0.913,0.017,b (log-log),reported,0.7834373335469489,0.014587551665167722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),53.705882352941174,1815.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Rooms,bedrooms,A_structural,level,count,0.008,0.006,,,,,,high,Number of rooms (not bedrooms),ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,semi_elasticity,0.8,0.6,100*b/1 (per unit count),reported,0.03128151887348551,0.023461139155114136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,1815.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Floor2,floor_level,A_structural,dummy,binary,0.014,0.009,,,,,,high,Dummy for 'other floors' (above 2nd); base = ground floor,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,pct_premium,1.405738878258167,0.9126886130446429,100*(exp(b - se^2/2) - 1),reported,0.03648865911843895,0.023456995147567897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5555555555555558,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Floor3,floor_level,A_structural,dummy,binary,0.076,0.01,,,***,,,high,Dummy for first or second floor; base = ground floor,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,pct_premium,7.890862737725679,1.078962574157284,100*(exp(b - se^2/2) - 1),reported,0.17561941651496862,0.02310781796249587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",0.0,0.004,,,,,,high,Road noise in interval brackets 1-4,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,semi_elasticity,0.0,0.4,100*b (unit as reported: ordinal; noise unit not dB),reported,0.0,0.02347262634065101,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,1815.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,School,school_distance,D_location,level,100 m,-0.006,0.001,,,***,,,high,Linear distance to nearest elementary school in 100 m units,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,semi_elasticity,-5.999999999999999,1.0,100*b/0.1 (X per 100m->km),reported,-0.13945947983338056,0.023243246638896764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Bus,transit_distance,D_location,level,100 m,0.01,0.003,,,***,,,high,Linear distance to nearest public transport stop in 100 m units,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,semi_elasticity,10.0,2.9999999999999996,100*b/0.1 (X per 100m->km),reported,0.07800368966178357,0.02340110689853507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3333333333333335,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c1,3,Mod. 1,14,Trans_C,other,other,dummy,binary,-0.097,0.013,,,***,,,high,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C',ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS proximity dummy,pct_premium,-9.252067910924843,1.1798228079732545,100*(exp(b - se^2/2) - 1),reported,-0.17251595328361702,0.02312069476996929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.461538461538462,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,BTS_Dist,hazard_site,E_environment,level,100 m,0.006,0.004,,,,,,high,Linear distance to nearest base transceiver station in 100 m units (Table 2 label is a copy-paste error),ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,semi_elasticity,5.999999999999999,4.0,100*b/0.1 (X per 100m->km),reported,0.03518713605785183,0.023458090705234552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Ln Area,living_area,A_structural,log,m2,0.919,0.017,,,***,,,high,Natural log of usable area in m2,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,elasticity,0.919,0.017,b (log-log),reported,0.7854138594468192,0.014528874440256719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.05882352941176,1815.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Rooms,bedrooms,A_structural,level,count,0.006,0.006,,,,,,high,Number of rooms (not bedrooms),ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,semi_elasticity,0.6,0.6,100*b/1 (per unit count),reported,0.023466162723196606,0.023466162723196606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1815.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Floor2,floor_level,A_structural,dummy,binary,0.015,0.009,,,,,,high,Dummy for 'other floors' (above 2nd); base = ground floor,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,pct_premium,1.507195336911038,0.9136017581541469,100*(exp(b - se^2/2) - 1),reported,0.039091141700047634,0.02345468502002858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Floor3,floor_level,A_structural,dummy,binary,0.077,0.01,,,***,,,high,Dummy for first or second floor; base = ground floor,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,pct_premium,7.998807563881072,1.0800420763926004,100*(exp(b - se^2/2) - 1),reported,0.1778575604636294,0.02309838447579603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.7,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.001,0.004,,,,,,high,Road noise in interval brackets 1-4,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,semi_elasticity,-0.1,0.4,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.0058680555520181955,0.023472222208072782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,1815.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,School,school_distance,D_location,level,100 m,-0.005,0.001,,,***,,,high,Linear distance to nearest elementary school in 100 m units,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,semi_elasticity,-5.0,1.0,100*b/0.1 (X per 100m->km),reported,-0.11656310103003922,0.023312620206007845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Bus,transit_distance,D_location,level,100 m,0.009,0.003,,,**,,,high,Linear distance to nearest public transport stop in 100 m units,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,semi_elasticity,8.999999999999998,2.9999999999999996,100*b/0.1 (X per 100m->km),reported,0.07024393586862704,0.02341464528954235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c2,3,Mod. 2,14,Trans_C,other,other,dummy,binary,-0.097,0.013,,,***,,,high,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C',ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; BTS distance,pct_premium,-9.252067910924843,1.1798228079732545,100*(exp(b - se^2/2) - 1),reported,-0.17251595328361702,0.02312069476996929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.461538461538462,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,BTS_Num,hazard_site,E_environment,level,count,0.006,0.004,,,,,,high,Number of base transceiver stations within 200 m,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,semi_elasticity,0.6,0.4,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03518713605785183,0.023458090705234552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,1815.0,count,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Ln Area,living_area,A_structural,log,m2,0.912,0.017,,,***,,,high,Natural log of usable area in m2,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,elasticity,0.912,0.017,b (log-log),reported,0.7831055819834641,0.014597362822060188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),53.64705882352941,1815.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Rooms,bedrooms,A_structural,level,count,0.008,0.006,,,,,,high,Number of rooms (not bedrooms),ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,semi_elasticity,0.8,0.6,100*b/1 (per unit count),reported,0.03128151887348551,0.023461139155114136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,1815.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Floor2,floor_level,A_structural,dummy,binary,0.014,0.009,,,,,,high,Dummy for 'other floors' (above 2nd); base = ground floor,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,pct_premium,1.405738878258167,0.9126886130446429,100*(exp(b - se^2/2) - 1),reported,0.03648865911843895,0.023456995147567897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5555555555555558,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Floor3,floor_level,A_structural,dummy,binary,0.076,0.01,,,***,,,high,Dummy for first or second floor; base = ground floor,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,pct_premium,7.890862737725679,1.078962574157284,100*(exp(b - se^2/2) - 1),reported,0.17561941651496862,0.02310781796249587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.0,0.004,,,,,,high,Road noise in interval brackets 1-4,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,semi_elasticity,-0.0,0.4,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.0,0.02347262634065101,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,1815.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,School,school_distance,D_location,level,100 m,-0.006,0.001,,,***,,,high,Linear distance to nearest elementary school in 100 m units,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,semi_elasticity,-5.999999999999999,1.0,100*b/0.1 (X per 100m->km),reported,-0.13945947983338056,0.023243246638896764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Bus,transit_distance,D_location,level,100 m,0.011,0.003,,,***,,,high,Linear distance to nearest public transport stop in 100 m units,ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,semi_elasticity,10.999999999999998,2.9999999999999996,100*b/0.1 (X per 100m->km),reported,0.08574929257125442,0.023386170701251208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6666666666666665,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c3,3,Mod. 3,14,Trans_C,other,other,dummy,binary,-0.097,0.013,,,***,,,high,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C',ln transaction price (PLN),log,OLS,1825.0,0.883,se,Full sample; number of BTS,pct_premium,-9.252067910924843,1.1798228079732545,100*(exp(b - se^2/2) - 1),reported,-0.17251595328361702,0.02312069476996929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.461538461538462,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,BTS_Prox,hazard_site,E_environment,dummy,binary,-0.001,0.008,,,,,,high,Dummy = 1 if apartment within 200 m of a base transceiver station (cell phone tower),ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,pct_premium,-0.10314676711368209,0.7992003998667,100*(exp(b - se^2/2) - 1),reported,-0.004354540053299681,0.03483632042639745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.125,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Ln Area,living_area,A_structural,log,m2,0.882,0.023,,,***,,,high,Natural log of usable area in m2,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,elasticity,0.882,0.023,b (log-log),reported,0.8005554908529765,0.02087616359367173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.34782608695652,824.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Rooms,bedrooms,A_structural,level,count,0.011,0.008,,,,,,high,Number of rooms (not bedrooms),ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,semi_elasticity,1.0999999999999999,0.8,100*b/1 (per unit count),reported,0.047845536638266684,0.034796753918739404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.375,824.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Floor2,floor_level,A_structural,dummy,binary,-0.007,0.013,,,,,,high,Dummy for 'other floors' (above 2nd); base = ground floor,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,pct_premium,-0.7059464087071254,1.2909317758132055,100*(exp(b - se^2/2) - 1),reported,-0.018754897186581333,0.03483052334650818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5384615384615385,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Floor3,floor_level,A_structural,dummy,binary,0.037,0.013,,,**,,,high,Dummy for first or second floor; base = ground floor,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,pct_premium,3.760533948248579,1.3490009270896042,100*(exp(b - se^2/2) - 1),reported,0.09866666666666667,0.034666666666666665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8461538461538463,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.006,0.005,,,,,,high,Road noise in interval brackets 1-4,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,semi_elasticity,-0.6,0.5,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.041767500913794615,0.03480625076149551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,824.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,School,school_distance,D_location,level,100 m,-0.006,0.002,,,***,,,high,Linear distance to nearest elementary school in 100 m units,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,semi_elasticity,-5.999999999999999,2.0,100*b/0.1 (X per 100m->km),reported,-0.10394383930128556,0.03464794643376185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Bus,transit_distance,D_location,level,100 m,0.013,0.004,,,***,,,high,Linear distance to nearest public transport stop in 100 m units,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,semi_elasticity,13.0,4.0,100*b/0.1 (X per 100m->km),reported,0.11250036404577593,0.034615496629469514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c4,3,Mod. 4,14,Trans_C,other,other,dummy,binary,-0.039,0.018,,,*,,,high,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C',ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS proximity dummy,pct_premium,-3.840508184910363,1.73115127646346,100*(exp(b - se^2/2) - 1),reported,-0.0752653158093795,0.03473783806586746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.166666666666667,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,BTS_Dist,hazard_site,E_environment,level,100 m,0.008,0.005,,,,,,high,Linear distance to nearest base transceiver station in 100 m units (Table 2 label is a copy-paste error),ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,semi_elasticity,8.0,5.0,100*b/0.1 (X per 100m->km),reported,0.055652258075312155,0.03478266129707009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Ln Area,living_area,A_structural,log,m2,0.886,0.023,,,***,,,high,Natural log of usable area in m2,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,elasticity,0.886,0.023,b (log-log),reported,0.8018536268449228,0.02081561333796075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.52173913043478,824.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Rooms,bedrooms,A_structural,level,count,0.01,0.008,,,,,,high,Number of rooms (not bedrooms),ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,semi_elasticity,1.0,0.8,100*b/1 (per unit count),reported,0.04350458540743126,0.03480366832594501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,824.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Floor2,floor_level,A_structural,dummy,binary,-0.007,0.013,,,,,,high,Dummy for 'other floors' (above 2nd); base = ground floor,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,pct_premium,-0.7059464087071254,1.2909317758132055,100*(exp(b - se^2/2) - 1),reported,-0.018754897186581333,0.03483052334650818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5384615384615385,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Floor3,floor_level,A_structural,dummy,binary,0.037,0.013,,,**,,,high,Dummy for first or second floor; base = ground floor,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,pct_premium,3.760533948248579,1.3490009270896042,100*(exp(b - se^2/2) - 1),reported,0.09866666666666667,0.034666666666666665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8461538461538463,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.006,0.005,,,,,,high,Road noise in interval brackets 1-4,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,semi_elasticity,-0.6,0.5,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.041767500913794615,0.03480625076149551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,824.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,School,school_distance,D_location,level,100 m,-0.006,0.002,,,**,,,high,Linear distance to nearest elementary school in 100 m units,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,semi_elasticity,-5.999999999999999,2.0,100*b/0.1 (X per 100m->km),reported,-0.10394383930128556,0.03464794643376185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Bus,transit_distance,D_location,level,100 m,0.011,0.004,,,**,,,high,Linear distance to nearest public transport stop in 100 m units,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,semi_elasticity,10.999999999999998,4.0,100*b/0.1 (X per 100m->km),reported,0.09536417273569421,0.0346778809947979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c5,3,Mod. 5,14,Trans_C,other,other,dummy,binary,-0.039,0.018,,,*,,,high,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C',ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; BTS distance,pct_premium,-3.840508184910363,1.73115127646346,100*(exp(b - se^2/2) - 1),reported,-0.0752653158093795,0.03473783806586746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.166666666666667,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,BTS_Num,hazard_site,E_environment,level,count,0.003,0.005,,,,,,high,Number of base transceiver stations within 200 m,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,semi_elasticity,0.3,0.5,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.020897425955551597,0.034829043259252665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,824.0,count,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Ln Area,living_area,A_structural,log,m2,0.88,0.023,,,***,,,high,Natural log of usable area in m2,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,elasticity,0.88,0.023,b (log-log),reported,0.7999021667095436,0.020906533902635797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.26086956521739,824.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Rooms,bedrooms,A_structural,level,count,0.011,0.008,,,,,,high,Number of rooms (not bedrooms),ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,semi_elasticity,1.0999999999999999,0.8,100*b/1 (per unit count),reported,0.047845536638266684,0.034796753918739404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.375,824.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Floor2,floor_level,A_structural,dummy,binary,-0.007,0.013,,,,,,high,Dummy for 'other floors' (above 2nd); base = ground floor,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,pct_premium,-0.7059464087071254,1.2909317758132055,100*(exp(b - se^2/2) - 1),reported,-0.018754897186581333,0.03483052334650818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5384615384615385,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Floor3,floor_level,A_structural,dummy,binary,0.037,0.013,,,**,,,high,Dummy for first or second floor; base = ground floor,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,pct_premium,3.760533948248579,1.3490009270896042,100*(exp(b - se^2/2) - 1),reported,0.09866666666666667,0.034666666666666665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8461538461538463,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.006,0.005,,,,,,high,Road noise in interval brackets 1-4,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,semi_elasticity,-0.6,0.5,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.041767500913794615,0.03480625076149551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,824.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,School,school_distance,D_location,level,100 m,-0.006,0.002,,,**,,,high,Linear distance to nearest elementary school in 100 m units,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,semi_elasticity,-5.999999999999999,2.0,100*b/0.1 (X per 100m->km),reported,-0.10394383930128556,0.03464794643376185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Bus,transit_distance,D_location,level,100 m,0.013,0.004,,,***,,,high,Linear distance to nearest public transport stop in 100 m units,ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,semi_elasticity,13.0,4.0,100*b/0.1 (X per 100m->km),reported,0.11250036404577593,0.034615496629469514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,824.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T3-c6,3,Mod. 6,14,Trans_C,other,other,dummy,binary,-0.04,0.018,,,*,,,high,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C',ln transaction price (PLN),log,OLS,834.0,0.875,se,CEM-matched sample; number of BTS,pct_premium,-3.9366196130021236,1.7294209904741815,100*(exp(b - se^2/2) - 1),reported,-0.07718384161783276,0.03473272872802474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2222222222222223,824.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,ols,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Treatment (BTS_5G*after),hazard_site,E_environment,dummy,binary,-0.021,0.005,,,,,,medium,DiD interaction: within 200 m of a 5G BTS x after May 2020; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,pct_premium,-2.079327559109634,0.4896094822847298,100*(exp(b - se^2/2) - 1),reported,-0.09810942031813139,0.02335938579003128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.2,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,BTS_Prox,hazard_site,E_environment,dummy,binary,0.026,0.005,,,,,,medium,Dummy = 1 if apartment within 200 m of a base transceiver station (cell phone tower); Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,pct_premium,2.6328119291768814,0.513170474236721,100*(exp(b - se^2/2) - 1),reported,0.12115847973716586,0.023299707641762667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.199999999999999,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Ln Area,living_area,A_structural,log,m2,0.919,0.019,,,*,,,medium,Natural log of usable area in m2; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,elasticity,0.919,0.019,b (log-log),reported,0.7504163910550959,0.015514593503859437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.36842105263158,1815.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Rooms,bedrooms,A_structural,level,count,0.007,0.009,,,,,,medium,Number of rooms (not bedrooms); Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,semi_elasticity,0.7000000000000001,0.8999999999999999,100*b/1 (per unit count),reported,0.01825344547675407,0.023468715612969517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7777777777777779,1815.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Floor2,floor_level,A_structural,dummy,binary,0.015,0.004,,,,,,medium,Dummy for 'other floors' (above 2nd); base = ground floor; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,pct_premium,1.510494374368565,0.40604522584628755,100*(exp(b - se^2/2) - 1),reported,0.08768332190933387,0.023382219175822366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.75,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Floor3,floor_level,A_structural,dummy,binary,0.077,0.007,,,,,,medium,Dummy for first or second floor; base = ground floor; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,pct_premium,8.001561568587379,0.7560294534748203,100*(exp(b - se^2/2) - 1),reported,0.25,0.022727272727272728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.001,0.003,,,,,,medium,Road noise in interval brackets 1-4; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,semi_elasticity,-0.1,0.3,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.00782396929905507,0.023471907897165214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,1815.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,School,school_distance,D_location,level,100 m,-0.006,0.0,,,*,,,medium,Linear distance to nearest elementary school in 100 m units; Table 4 stars inconsistent with printed SEs; DiD specification; SE printed as (0.000),ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,semi_elasticity,-5.999999999999999,,100*b/0.1 (X per 100m->km),,,,,,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Bus,transit_distance,D_location,level,100 m,0.012,0.006,,,,,,medium,Linear distance to nearest public transport stop in 100 m units; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,semi_elasticity,11.999999999999998,5.999999999999999,100*b/0.1 (X per 100m->km),reported,0.04689360772322401,0.02344680386161201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c1,4,Mod. 7,16,Trans_C,other,other,dummy,binary,-0.098,0.006,,,*,,,medium,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C'; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences (5G introduction) with BTS proximity dummy; quarterly FE,pct_premium,-9.336741577947038,0.5439893422523525,100*(exp(b - se^2/2) - 1),reported,-0.35797907471441714,0.021917086207005134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.333333333333332,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Treatment (BTS_5G*after),hazard_site,E_environment,dummy,binary,-0.022,0.004,,,,,,medium,DiD interaction: within 200 m of a 5G BTS x after May 2020; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,pct_premium,-2.1767590839366746,0.39129609402048404,100*(exp(b - se^2/2) - 1),reported,-0.12803687993289598,0.023279432715071994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,BTS_Dist,hazard_site,E_environment,level,100 m,0.01,0.011,,,,,,medium,Linear distance to nearest base transceiver station in 100 m units (Table 2 label is a copy-paste error); Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,semi_elasticity,10.0,10.999999999999998,100*b/0.1 (X per 100m->km),reported,0.021333894659190798,0.023467284125109875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9090909090909092,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Ln Area,living_area,A_structural,log,m2,0.916,0.015,,,*,,,medium,Natural log of usable area in m2; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,elasticity,0.916,0.015,b (log-log),reported,0.8201385147566828,0.013430215853002449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),61.06666666666667,1815.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Rooms,bedrooms,A_structural,level,count,0.007,0.008,,,,,,medium,Number of rooms (not bedrooms); Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,semi_elasticity,0.7000000000000001,0.8,100*b/1 (per unit count),reported,0.02053421751014348,0.023467677154449692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.875,1815.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Floor2,floor_level,A_structural,dummy,binary,0.015,0.005,,,,,,medium,Dummy for 'other floors' (above 2nd); base = ground floor; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,pct_premium,1.5100375781716746,0.5075565323078595,100*(exp(b - se^2/2) - 1),reported,0.07024393586862705,0.02341464528954235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Floor3,floor_level,A_structural,dummy,binary,0.077,0.005,,,*,,,medium,Dummy for first or second floor; base = ground floor; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,pct_premium,8.00285759510233,0.5400210381963002,100*(exp(b - se^2/2) - 1),reported,0.3399500518250425,0.022074678689937823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.4,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.001,0.003,,,,,,medium,Road noise in interval brackets 1-4; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,semi_elasticity,-0.1,0.3,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.00782396929905507,0.023471907897165214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,1815.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,School,school_distance,D_location,level,100 m,-0.005,0.001,,,,,,medium,Linear distance to nearest elementary school in 100 m units; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,semi_elasticity,-5.0,1.0,100*b/0.1 (X per 100m->km),reported,-0.11656310103003922,0.023312620206007845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Bus,transit_distance,D_location,level,100 m,0.007,0.006,,,,,,medium,Linear distance to nearest public transport stop in 100 m units; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,semi_elasticity,7.0,5.999999999999999,100*b/0.1 (X per 100m->km),reported,0.0273744682761948,0.02346382995102411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1666666666666667,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c2,4,Mod. 8,16,Trans_C,other,other,dummy,binary,-0.095,0.003,,,*,,,medium,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C'; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with BTS distance; quarterly FE,pct_premium,-9.063115770076635,0.27281188034046944,100*(exp(b - se^2/2) - 1),reported,-0.5965529266727023,0.018838513473874812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.666666666666668,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,False,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Treatment (BTS_5G*after),hazard_site,E_environment,dummy,binary,-0.022,0.005,,,,,,medium,DiD interaction: within 200 m of a 5G BTS x after May 2020; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,pct_premium,-2.1771992875303403,0.489120117525605,100*(exp(b - se^2/2) - 1),reported,-0.10273309938750282,0.023348431678977915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.3999999999999995,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,BTS_Num,hazard_site,E_environment,level,count,0.014,0.012,,,,,,medium,Number of base transceiver stations within 200 m; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,semi_elasticity,1.4000000000000001,1.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0273744682761948,0.02346382995102411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1666666666666667,1815.0,count,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Ln Area,living_area,A_structural,log,m2,0.915,0.02,,,*,,,medium,Natural log of usable area in m2; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,elasticity,0.915,0.02,b (log-log),reported,0.7318302350759097,0.015996289291276715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.75,1815.0,m2,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Rooms,bedrooms,A_structural,level,count,0.007,0.009,,,,,,medium,Number of rooms (not bedrooms); Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,semi_elasticity,0.7000000000000001,0.8999999999999999,100*b/1 (per unit count),reported,0.01825344547675407,0.023468715612969517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7777777777777779,1815.0,count,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Floor2,floor_level,A_structural,dummy,binary,0.014,0.005,,,,,,medium,Dummy for 'other floors' (above 2nd); base = ground floor; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,pct_premium,1.4085782786981804,0.5070492294692461,100*(exp(b - se^2/2) - 1),reported,0.06558186400955365,0.023422094289126304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Floor3,floor_level,A_structural,dummy,binary,0.077,0.006,,,,,,medium,Dummy for first or second floor; base = ground floor; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,pct_premium,8.002263581019097,0.6480252458355603,100*(exp(b - se^2/2) - 1),reported,0.2884299752006152,0.02247506300264534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.833333333333332,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Noise,noise,E_environment,level,"ordinal (4 brackets of 5 dB: <55, 55-60, 60-65, 65-70 dB)",-0.002,0.003,,,,,,medium,Road noise in interval brackets 1-4; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,semi_elasticity,-0.2,0.3,100*b (unit as reported: ordinal; noise unit not dB),reported,-0.01564650197497446,0.023469752962461687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,1815.0,ordinal,unit_not_harmonisable,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,School,school_distance,D_location,level,100 m,-0.006,0.001,,,,,,medium,Linear distance to nearest elementary school in 100 m units; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,semi_elasticity,-5.999999999999999,1.0,100*b/0.1 (X per 100m->km),reported,-0.13945947983338056,0.023243246638896764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Bus,transit_distance,D_location,level,100 m,0.013,0.005,,,,,,medium,Linear distance to nearest public transport stop in 100 m units; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,semi_elasticity,13.0,5.0,100*b/0.1 (X per 100m->km),reported,0.0609154934940191,0.023429035959238117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5999999999999996,1815.0,100m,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,True,True,False
52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9,52cbefc12720084fe56091430bf37e9fbeba96b9:T4-c3,4,Mod. 9,16,Trans_C,other,other,dummy,binary,-0.098,0.005,,,*,,,medium,Dummy = 1 if buyer or seller is a corporate (non-household) party; printed name 'Trans_C'; Table 4 stars inconsistent with printed SEs; DiD specification,ln transaction price (PLN),log,other,1825.0,,se,Difference-in-differences with number of BTS; quarterly FE,pct_premium,-9.336242928654437,0.45332445187696047,100*(exp(b - se^2/2) - 1),reported,-0.41795320274084863,0.02132414299698207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.6,1815.0,binary,,2024.0,Poland,Europe,High income,2016.0,2021.0,condo_apartment,transaction,journal,True,False,unknown,,2018.5,other,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,bathrooms,bathrooms,A_structural,level,count,0.1798,0.0024,,,**,,,high,,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,17.98,0.24,100*b/1 (per unit count),reported,0.31204036883855796,0.004165166213640372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),74.91666666666667,52029.0,count,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,bedrooms,bedrooms,A_structural,level,count,0.0814,0.0021,,,**,,,high,,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,8.14,0.21,100*b/1 (per unit count),reported,0.16753303783095905,0.004322105398587395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.761904761904766,52029.0,count,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,building_type,dwelling_type,A_structural,dummy,binary,0.0874,0.0057,,,**,,,high,"1 = house, 0 = apartment","ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,9.131539711091087,0.622059881633916,100*(exp(b - se^2/2) - 1),reported,0.06707100172830727,0.004374195764889604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.333333333333334,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,seller_type,other,other,dummy,binary,-0.0662,0.0044,,,**,,,high,"1 = seller is the owner, 0 = real estate agent; printed name 'seller_type'","ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,-6.406540305061858,0.41181520900965707,100*(exp(b - se^2/2) - 1),reported,-0.06581727042045378,0.004374561780211431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.045454545454543,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,total_surface,living_area,A_structural,level,m2,0.0008,,,,**,,,high,Total surface area of the property in m2; SE printed as 0.0000 (rounded to 4 decimals); not usable as printed,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,0.08,,100*b/1 (X per m2->m2),,,,,,52029.0,m2,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,quality_municipality,public_services,F_neighbourhood,level,index,0.0273,0.0003,,,**,,,high,ICVU 2019 Urban Quality of Life Index of the municipality,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,2.73,0.03,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.3705499236219095,0.004071977182658346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),91.00000000000001,52029.0,index,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,parking_lots,parking,A_structural,level,count,0.0159,0.0013,,,**,,,high,Number of parking lots,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,1.59,0.13,100*b/1 (per unit count),reported,0.053543603414987824,0.004377778895565042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.230769230769232,52029.0,count,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,swimpool,pool,C_exterior,dummy,binary,0.0002,0.0034,,,,,,high,,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,0.019423886192537765,0.34006800680045335,100*(exp(b - se^2/2) - 1),reported,0.000257886331806303,0.004384067640707151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05882352941176471,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,terrace,terrace_balcony,C_exterior,dummy,binary,0.0582,0.0032,,,**,,,high,,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,5.992153309300052,0.33917662716964564,100*(exp(b - se^2/2) - 1),reported,0.07948296846051685,0.004370197578585119,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.1875,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,storage_units,storage,B_interior,level,count,0.0304,0.0016,,,**,,,high,Number of storage units,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,3.04,0.16,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.08300980654380179,0.004368937186515883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,52029.0,count,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,new_house,age,A_structural,dummy,binary,0.0753,0.0073,,,**,,,high,1 = new construction home,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,7.817883618171129,0.7870915221868603,100*(exp(b - se^2/2) - 1),reported,0.045175790264248776,0.004379591884847491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.315068493150685,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,simce_4b,school_quality,F_neighbourhood,level,score,0.0082,0.0001,,,**,,,high,Average SIMCE 4th-grade score of schools within 2 km,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,0.8200000000000001,0.01,100*b (unit as reported: score; no harmonised unit defined for this characteristic),reported,0.3382975314993629,0.004125579652431256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),82.0,52029.0,score,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,nearby_schools_3km,school_distance,D_location,level,count,-0.0008,,,,**,,,high,"Number of schools within 3 km (count, not distance); SE printed as 0.0000 (rounded to 4 decimals); not usable as printed","ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,-0.08,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,52029.0,count,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_subway,transit_distance,D_location,level,km,0.0045,0.0011,,,**,,,high,Min geodesic distance to subway,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,0.44999999999999996,0.11,100*b/1 (X per km->km),reported,0.01793193902015316,0.004383362871592996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.09090909090909,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_park,green_space,E_environment,level,km,-0.0019,0.0017,,,,,,high,Min geodesic distance to park,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,-0.19,0.16999999999999998,100*b/1 (X per km->km),reported,-0.00489978164955706,0.004384015160130001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1176470588235294,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_hospital,hospital_distance,D_location,level,km,0.0038,0.001,,,**,,,high,Min geodesic distance to hospital or health clinic,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,0.38,0.1,100*b/1 (X per km->km),reported,0.01665714625742714,0.004383459541428195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_police,public_services,F_neighbourhood,level,km,0.0263,0.002,,,**,,,high,Min geodesic distance to police station,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,2.63,0.2,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.05755492655363119,0.004376800498374995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.15,52029.0,km,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_university,university_distance,D_location,level,km,-0.0176,0.0013,,,**,,,high,,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,-1.76,0.13,100*b/1 (X per km->km),reported,-0.05924926197025383,0.0043763659409846575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.53846153846154,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_mall,commercial_distance,D_location,level,km,-0.0039,0.0014,,,**,,,high,Min geodesic distance to mall,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,-0.38999999999999996,0.13999999999999999,100*b/1 (X per km->km),reported,-0.012211849588395786,0.0043837408778856675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7857142857142856,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_penitentiary,hazard_site,E_environment,level,km,-0.005,0.0006,,,**,,,high,Min geodesic distance to penitentiary,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,-0.5,0.06,100*b/1 (X per km->km),reported,-0.03650954123366662,0.004381144948039994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.333333333333334,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,True
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_industry,hazard_site,E_environment,level,km,0.0096,0.0009,,,**,,,high,Min geodesic distance to industry,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,0.96,0.09,100*b/1 (X per km->km),reported,0.04671234199911687,0.004379282062417207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.666666666666666,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,dist_dump,hazard_site,E_environment,level,km,0.0019,0.0005,,,**,,,high,Min geodesic distance to dump,"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,semi_elasticity,0.19,0.05,100*b/1 (X per km->km),reported,0.01665714625742714,0.004383459541428195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_bathroom,other,other,dummy,binary,-0.0506,0.0035,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of bathroom; printed name 'photo_bathroom',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,-4.934696473460232,0.3327306003115748,100*(exp(b - se^2/2) - 1),reported,-0.0632541704651751,0.004375288470911322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.457142857142857,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_bedroom,other,other,dummy,binary,-0.0015,0.0038,,,,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of bedroom; printed name 'photo_bedroom',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,-0.15060847143825162,0.3794304272863301,100*(exp(b - se^2/2) - 1),reported,-0.0017305504822777264,0.00438406122177024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39473684210526316,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_livingroom,other,other,dummy,binary,0.0492,0.0039,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of livingroom; printed name 'photo_livingroom',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,5.042242732304758,0.4096678621682336,100*(exp(b - se^2/2) - 1),reported,0.05522230793399104,0.004377378067938314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.615384615384617,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_diningroom,other,other,dummy,binary,0.0488,0.0032,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of diningroom; printed name 'photo_diningroom',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,5.000495163377194,0.3360033048553238,100*(exp(b - se^2/2) - 1),reported,0.06670811191413083,0.0043743024205987425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.25,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_interior,other,other,dummy,binary,0.0017,0.003,,,,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of interior; printed name 'photo_interior',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,0.16969381728175748,0.3005104337457545,100*(exp(b - se^2/2) - 1),reported,0.0024842974127640854,0.004384054257818975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5666666666666667,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_exterior,other,other,dummy,binary,-0.0011,0.0031,,,,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of exterior; printed name 'photo_exterior',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,-0.11041949276470175,0.30965918748125054,100*(exp(b - se^2/2) - 1),reported,-0.0015556350741739197,0.004384062481762865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3548387096774194,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_balcony,other,other,dummy,binary,0.0249,0.003,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of balcony; printed name 'photo_balcony',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,2.5207980692487864,0.30756377824163433,100*(exp(b - se^2/2) - 1),reported,0.03636369656494279,0.004381168260836481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.299999999999999,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_laundryroom,other,other,dummy,binary,-0.0326,0.0039,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of laundryroom; printed name 'photo_laundryroom',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,-3.2081708620322447,0.3774910044462467,100*(exp(b - se^2/2) - 1),reported,-0.036621727853441095,0.004381126951792033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.35897435897436,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_floorplans,other,other,dummy,binary,0.0445,0.0054,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of floorplans; printed name 'photo_floorplans',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,4.54897333590194,0.5645726874236461,100*(exp(b - se^2/2) - 1),reported,0.03610441142265269,0.004381209476007292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.24074074074074,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-OLS,8,OLS,20,photo_swimpool,other,other,dummy,binary,-0.0092,0.0034,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of swimpool; printed name 'photo_swimpool',"ln(listing price, UF)",log,OLS,52039.0,0.8654,se,Full filtered dataset (assumed); SE in separate column,pct_premium,-0.9163536534694705,0.3368863447756492,100*(exp(b - se^2/2) - 1),reported,-0.011861937049290729,0.004383759344303095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7058823529411766,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,ols,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,bathrooms,bathrooms,A_structural,level,count,0.1712,0.0023,,,**,,,high,,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,17.119999999999997,0.22999999999999998,100*b/1 (per unit count),reported,0.310226973864136,0.004167768924576593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),74.43478260869566,52029.0,count,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,bedrooms,bedrooms,A_structural,level,count,0.0752,0.0021,,,**,,,high,,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,7.5200000000000005,0.21,100*b/1 (per unit count),reported,0.15509179405278947,0.00433102084455928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.80952380952381,52029.0,count,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,building_type,dwelling_type,A_structural,dummy,binary,0.0957,0.0055,,,**,,,high,"1 = house, 0 = apartment","ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,10.041224194295717,0.6052358871921919,100*(exp(b - se^2/2) - 1),reported,0.0760617963260054,0.00437136760494284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.4,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,seller_type,other,other,dummy,binary,-0.0649,0.0043,,,**,,,high,"1 = seller is the owner, 0 = real estate agent; printed name 'seller_type'","ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,-6.284748920577698,0.4029793051679709,100*(exp(b - se^2/2) - 1),reported,-0.06602445698419512,0.004374501772450524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.093023255813954,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,total_surface,living_area,A_structural,level,m2,0.0007,,,,**,,,high,Total surface area of the property in m2; SE printed as 0.0000 (rounded to 4 decimals); not usable as printed,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,0.06999999999999999,,100*b/1 (X per m2->m2),,,,,,52029.0,m2,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,quality_municipality,public_services,F_neighbourhood,level,index,0.0178,0.0003,,,**,,,high,ICVU 2019 Urban Quality of Life Index of the municipality,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,1.78,0.03,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2517438603955162,0.004242874051609824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.333333333333336,52029.0,index,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,parking_lots,parking,A_structural,level,count,0.0139,0.0012,,,**,,,high,Number of parking lots,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,1.39,0.12,100*b/1 (per unit count),reported,0.05071676583760077,0.004378425827706541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.583333333333334,52029.0,count,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,swimpool,pool,C_exterior,dummy,binary,0.0021,0.0033,,,,,,high,,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,0.20967501126512023,0.3306937281596225,100*(exp(b - se^2/2) - 1),reported,0.0027898504615379797,0.004384050725273968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6363636363636364,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,terrace,terrace_balcony,C_exterior,dummy,binary,0.0465,0.0031,,,**,,,high,,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,4.759304537185738,0.32475540451124546,100*(exp(b - se^2/2) - 1),reported,0.0656192841941052,0.00437461894627368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,storage_units,storage,B_interior,level,count,0.0289,0.0015,,,**,,,high,Number of storage units,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,2.8899999999999997,0.15,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.08416665993236783,0.004368511761195563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.266666666666666,52029.0,count,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,new_house,age,A_structural,dummy,binary,0.0668,0.007,,,**,,,high,1 = new construction home,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,6.905544828411325,0.7483571483244144,100*(exp(b - se^2/2) - 1),reported,0.04179996743742738,0.00438023610871245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.542857142857143,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,simce_4b,school_quality,F_neighbourhood,level,score,0.0036,0.0001,,,**,,,high,Average SIMCE 4th-grade score of schools within 2 km,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,0.36,0.01,100*b (unit as reported: score; no harmonised unit defined for this characteristic),reported,0.15589675259079142,0.004330465349744206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.0,52029.0,score,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,nearby_schools_3km,school_distance,D_location,level,count,0.0014,,,,**,,,high,"Number of schools within 3 km (count, not distance); SE printed as 0.0000 (rounded to 4 decimals); not usable as printed","ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,0.13999999999999999,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,52029.0,count,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_subway,transit_distance,D_location,level,km,0.0054,0.001,,,**,,,high,Min geodesic distance to subway,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,0.54,0.1,100*b/1 (X per km->km),reported,0.023667334718271392,0.00438283976264285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.4,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_park,green_space,E_environment,level,km,0.0087,0.0016,,,**,,,high,Min geodesic distance to park,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,0.8699999999999999,0.16,100*b/1 (X per km->km),reported,0.02383159818561065,0.004382822654824948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.437499999999999,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_hospital,hospital_distance,D_location,level,km,-0.0084,0.0009,,,**,,,high,Min geodesic distance to hospital or health clinic,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,-0.84,0.09,100*b/1 (X per km->km),reported,-0.040883754895596695,0.004380402310242504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.333333333333332,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_police,public_services,F_neighbourhood,level,km,0.0194,0.0019,,,**,,,high,Min geodesic distance to police station,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,1.94,0.19,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.0447188584721774,0.004379682015316343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.210526315789474,52029.0,km,unit_not_harmonisable,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_university,university_distance,D_location,level,km,-0.0121,0.0011,,,**,,,high,,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,-1.21,0.11,100*b/1 (X per km->km),reported,-0.04816876690828824,0.004378978809844386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.999999999999998,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_mall,commercial_distance,D_location,level,km,0.0052,0.0013,,,**,,,high,Min geodesic distance to mall,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,0.52,0.13,100*b/1 (X per km->km),reported,0.01753357538357703,0.0043833938458942585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_penitentiary,hazard_site,E_environment,level,km,-0.0035,0.0006,,,**,,,high,Min geodesic distance to penitentiary,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,-0.35000000000000003,0.06,100*b/1 (X per km->km),reported,-0.02556537004553264,0.004382634864948452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.833333333333334,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_industry,hazard_site,E_environment,level,km,-0.0006,0.0009,,,,,,high,Min geodesic distance to industry,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,-0.06,0.09,100*b/1 (X per km->km),reported,-0.0029226993744796174,0.0043840490617194266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,dist_dump,hazard_site,E_environment,level,km,-0.0045,0.0005,,,**,,,high,Min geodesic distance to dump,"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),semi_elasticity,-0.44999999999999996,0.05,100*b/1 (X per km->km),reported,-0.03942593239325762,0.0043806591548064015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,52029.0,km,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,True,True,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_bathroom,other,other,dummy,binary,-0.045,0.0034,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of bathroom; printed name 'photo_bathroom',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,-4.400804381637591,0.325039143823254,100*(exp(b - se^2/2) - 1),reported,-0.057926993239936324,0.004376706155906299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.23529411764706,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_bedroom,other,other,dummy,binary,-0.0015,0.0037,,,,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of bedroom; printed name 'photo_bedroom',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,-0.15057102790940746,0.369445416041953,100*(exp(b - se^2/2) - 1),reported,-0.001777321971132225,0.004384060862126155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4054054054054054,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_livingroom,other,other,dummy,binary,0.0438,0.0037,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of livingroom; printed name 'photo_livingroom',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,4.476622786641449,0.3865661503468164,100*(exp(b - se^2/2) - 1),reported,0.05182813376404893,0.0043781756832644075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.837837837837837,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_diningroom,other,other,dummy,binary,0.0441,0.003,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of diningroom; printed name 'photo_diningroom',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,4.508215546873751,0.3135260575047056,100*(exp(b - se^2/2) - 1),reported,0.06431238180098503,0.004374991959250682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.7,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_interior,other,other,dummy,binary,0.0036,0.0029,,,,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of interior; printed name 'photo_interior',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,0.36022676265952125,0.291045881457071,100*(exp(b - se^2/2) - 1),reported,0.005442210450751945,0.004384002863105733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2413793103448276,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_exterior,other,other,dummy,binary,-0.0004,0.003,,,,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of exterior; printed name 'photo_exterior',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,-0.040441820090464375,0.29988002399680036,100*(exp(b - se^2/2) - 1),reported,-0.0005845422716658348,0.004384067037493761,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13333333333333333,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_balcony,other,other,dummy,binary,0.0209,0.0029,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of balcony; printed name 'photo_balcony',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,2.111564073800798,0.2961247810161084,100*(exp(b - se^2/2) - 1),reported,0.03157976426754848,0.0043818811663105545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.206896551724138,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_laundryroom,other,other,dummy,binary,-0.0249,0.0037,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of laundryroom; printed name 'photo_laundryroom',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,-2.4599228744882407,0.3609007557216117,100*(exp(b - se^2/2) - 1),reported,-0.02949075882084024,0.004382160949281483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.729729729729729,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_floorplans,other,other,dummy,binary,0.0422,0.0052,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of floorplans; printed name 'photo_floorplans',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,4.308897584750637,0.5424136008230126,100*(exp(b - se^2/2) - 1),reported,0.035555899659198906,0.004381295692602708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.115384615384617,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d,53d1b6d48e64b056e88ebabd287c3f06ec3b559d:T8-SAR,8,SAR,20,photo_swimpool,other,other,dummy,binary,-0.0115,0.0032,,,**,,,high,AI-derived (CNN DenseNet201) indicator: listing includes at least one photo of swimpool; printed name 'photo_swimpool',"ln(listing price, UF)",log,spatial_lag,52039.0,0.8774,se,Full filtered dataset (assumed); KNN spatial weights; rho = 0.4271 (0.0080),pct_premium,-1.1439189196395638,0.3163410791193322,100*(exp(b - se^2/2) - 1),reported,-0.01575328852771583,0.004383523764233971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5937499999999996,52029.0,binary,,2025.0,Chile,South America,High income,2020.0,2020.0,mixed,listing,journal,False,False,unknown,LightGBM (DART) AVM;SHAP / TreeSHAP;CNN (DenseNet201) image classification into 16 photo-feature indicators;Extended Isolation Forest outlier removal,2020.0,spatial,journal,False,False,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T5,5,1,12,C,energy_efficiency,A_structural,binary,binary,-0.12,0.89,-0.98,0.33,,,,low,EPC level dummy (ref: B); printed SE inconsistent with printed t (t implies SE ~0.12) - do not use SE,log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,503.0,0.04,se,listings with EPC level B-G,pct_premium,-40.31264517491589,78.93591886782701,100*(exp(b - se^2/2) - 1),reported,-0.04409405189311698,0.04499393050318059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.98,493.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,False,False,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T5,5,1,12,D,energy_efficiency,A_structural,binary,binary,-0.2,0.85,-1.6,0.1,,,,low,EPC level dummy (ref: B); printed SE inconsistent with printed t (t implies SE ~0.12) - do not use SE,log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,503.0,0.04,se,listings with EPC level B-G,pct_premium,-42.95045014097973,69.59211401162845,100*(exp(b - se^2/2) - 1),reported,-0.07187400748401394,0.04492125467750871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,493.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,False,False,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T5,5,1,12,E,energy_efficiency,A_structural,binary,binary,-0.27,0.86,-2.17,0.03,*,,,low,EPC level dummy (ref: B); printed SE inconsistent with printed t (t implies SE ~0.12) - do not use SE,log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,503.0,0.04,se,listings with EPC level B-G,pct_premium,-47.26021069255913,65.65063651296937,100*(exp(b - se^2/2) - 1),reported,-0.09726845786170626,0.04482417412981855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.17,493.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,False,False,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T5,5,1,12,F,energy_efficiency,A_structural,binary,binary,-0.4,0.87,-3.23,0.0,**,,,low,EPC level dummy (ref: B); printed SE inconsistent with printed t (t implies SE ~0.12) - do not use SE,log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,503.0,0.04,se,listings with EPC level B-G,pct_premium,-54.088290843241424,58.317844005100625,100*(exp(b - se^2/2) - 1),reported,-0.1439566479206426,0.04456862164725777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.23,493.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,False,False,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T5,5,1,12,G,energy_efficiency,A_structural,binary,binary,-0.27,0.82,-2.32,0.02,*,,,low,EPC level dummy (ref: B); printed SE inconsistent with printed t (t implies SE ~0.12) - do not use SE,log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,503.0,0.04,se,listings with EPC level B-G,pct_premium,-45.458046965589325,62.59711853562195,100*(exp(b - se^2/2) - 1),reported,-0.10392179423315007,0.044793876824633656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.32,493.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,False,False,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,to be partially renovated,condition,A_structural,binary,binary,0.07,0.04,1.81,0.07,.,,,high,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,7.165051782032883,4.290032725016866,100*(exp(b - se^2/2) - 1),reported,0.07606142519023683,0.04202288684543471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.81,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,good,condition,A_structural,binary,binary,0.17,0.04,4.67,3.73e-06,***,,,high,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,18.435698663573596,4.741219405281462,100*(exp(b - se^2/2) - 1),reported,0.1931122810927704,0.041351666186888736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.67,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,refurbished,condition,A_structural,binary,binary,0.28,0.04,7.25,1.45e-12,***,,,high,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,32.20717317762225,5.292519249349748,100*(exp(b - se^2/2) - 1),reported,0.2922146122842764,0.040305463763348474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.25,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,council housing,condition,A_structural,binary,binary,-0.08,0.05,-1.5,0.13,,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,-7.803682816210166,4.615581731933179,100*(exp(b - se^2/2) - 1),reported,-0.06309151753013043,0.042061011686753615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,medium-level,condition,A_structural,binary,binary,0.11,0.02,4.46,1.01e-05,***,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,11.605483716885256,2.2325561409177426,100*(exp(b - se^2/2) - 1),reported,0.18473150155107676,0.04141961918185577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.46,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,distinguished,condition,A_structural,binary,binary,0.25,0.03,7.17,2.59e-12,***,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,28.344773523830646,3.852076250063224,100*(exp(b - se^2/2) - 1),reported,0.28926134860420927,0.04034328432415749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.17,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T6,6,1,15,classy,condition,A_structural,binary,binary,0.46,0.07,6.2,1.15e-09,***,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,573.0,0.77,se,"all listings (Model 1, without EPC)",pct_premium,58.019775405307136,11.088517894961374,100*(exp(b - se^2/2) - 1),reported,0.2528107489944396,0.04077592725716768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.2,563.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,to be partially renovated,condition,A_structural,binary,binary,0.08,0.04,1.76,0.08,.,,,high,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,8.242078458025826,4.3331482706998345,100*(exp(b - se^2/2) - 1),reported,0.08117431843183974,0.046121771836272585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.76,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,good,condition,A_structural,binary,binary,0.16,0.04,3.98,8.09e-05,***,,,high,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,17.257243771837594,4.694043483967241,100*(exp(b - se^2/2) - 1),reported,0.1811261982373892,0.04550909503451989,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.98,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,refurbished,condition,A_structural,binary,binary,0.26,0.05,5.78,1.48e-08,***,,,high,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,29.5309936862024,6.484650433328859,100*(exp(b - se^2/2) - 1),reported,0.2583839557306619,0.044703106527796174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.78,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,council housing,condition,A_structural,binary,binary,-0.06,0.06,-0.95,0.34,,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,-5.992911783264187,5.650587201505492,100*(exp(b - se^2/2) - 1),reported,-0.0439183405999402,0.046229832210463374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.95,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,medium-level,condition,A_structural,binary,binary,0.1,0.03,3.74,0.0,***,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,10.467370304428613,3.3155127542269427,100*(exp(b - se^2/2) - 1),reported,0.17053151960129123,0.04559666299499766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.74,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,distinguished,condition,A_structural,binary,binary,0.24,0.04,6.05,3.21e-09,***,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,27.02325576924174,5.084996601285619,100*(exp(b - se^2/2) - 1),reported,0.2695947533059062,0.04456111624891012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.05,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,classy,condition,A_structural,binary,binary,0.49,0.08,5.85,9.77e-09,***,,,high,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,62.71011566030722,13.058529759643031,100*(exp(b - se^2/2) - 1),reported,0.26130070827104634,0.044666787738640405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.85,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,C,energy_efficiency,A_structural,binary,binary,-0.03,0.07,-0.41,0.68,,,,high,EPC level dummy (ref: B),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,-3.1929147836154192,6.793118734839558,100*(exp(b - se^2/2) - 1),reported,-0.018969123619803577,0.046266155170252635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.41,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,D,energy_efficiency,A_structural,binary,binary,-0.1,0.067,-1.42,0.15,,,,high,EPC level dummy (ref: B),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,-9.719121206703974,6.062410700840929,100*(exp(b - se^2/2) - 1),reported,-0.06556836132085773,0.0461749023386322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.42,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,E,energy_efficiency,A_structural,binary,binary,-0.06,0.07,-0.92,0.36,,,,high,EPC level dummy (ref: B),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,-6.053996535909767,6.592351735089741,100*(exp(b - se^2/2) - 1),reported,-0.04253399555447309,0.04623260386355771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.92,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,F,energy_efficiency,A_structural,binary,binary,-0.14,0.07,-2.01,0.04,*,,,high,EPC level dummy (ref: B),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,-13.276908524602792,6.085507647791641,100*(exp(b - se^2/2) - 1),reported,-0.09261196887921094,0.04607560640756764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.01,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,True
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T7,7,1,17,G,energy_efficiency,A_structural,binary,binary,-0.1,0.07,-1.45,0.15,,,,high,EPC level dummy (ref: B),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.79,se,listings with EPC level (Model 2),pct_premium,-9.737672021134525,6.333861926251717,100*(exp(b - se^2/2) - 1),reported,-0.0669474637102453,0.04617066462775539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.45,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,to be completely renovated,condition,A_structural,binary,binary,0.21,0.13,1.63,0.1,,,,medium,apartment condition dummy; reference category unclear in Table 8,log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,22.32974004037336,16.03781477943766,100*(exp(b - se^2/2) - 1),reported,0.07521375148565196,0.046143405819418386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.63,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,to be partially renovated,condition,A_structural,binary,binary,0.07,0.04,1.75,0.08,.,,,medium,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,7.165051782032883,4.290032725016866,100*(exp(b - se^2/2) - 1),reported,0.08071611411802512,0.046123493781728635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,good,condition,A_structural,binary,binary,0.16,0.04,3.9,0.0,***,,,medium,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,17.257243771837594,4.694043483967241,100*(exp(b - se^2/2) - 1),reported,0.17760144738413128,0.04553883266259776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,refurbished,condition,A_structural,binary,binary,0.26,0.04,5.66,2.72e-08,***,,,medium,apartment condition dummy (ref: to be completely renovated),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,29.58929575034177,5.187720346663087,100*(exp(b - se^2/2) - 1),reported,0.2533673604735399,0.04476455132041341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.66,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,medium-level,condition,A_structural,binary,binary,0.1,0.03,3.77,0.0,***,,,medium,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,10.467370304428613,3.3155127542269427,100*(exp(b - se^2/2) - 1),reported,0.17185917381878166,0.045585987750339965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.77,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,council housing,condition,A_structural,binary,binary,-0.05,0.06,-0.91,0.36,,,,medium,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,-5.048124839589862,5.7073765470042845,100*(exp(b - se^2/2) - 1),reported,-0.042072492365109654,0.046233508093527095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.91,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,classy,condition,A_structural,binary,binary,0.49,0.08,5.91,6.82e-09,***,,,medium,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,62.71011566030722,13.058529759643031,100*(exp(b - se^2/2) - 1),reported,0.26379510100141085,0.044635380880103354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.91,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,distinguished,condition,A_structural,binary,binary,0.24,0.04,6.08,2.61e-09,***,,,medium,building quality level dummy (ref: economical),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,27.02325576924174,5.084996601285619,100*(exp(b - se^2/2) - 1),reported,0.27083375308016094,0.04454502517765805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.08,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,DE,energy_efficiency,A_structural,binary,binary,-0.06,0.03,-1.77,0.08,.,,,medium,EPC cluster dummy (ref: B-C),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,-5.86591651165066,2.825293600752746,100*(exp(b - se^2/2) - 1),reported,-0.08163247128437198,0.04612004027365649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.77,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a,53f863bd54970307af93c0ba10a947b3b65b890a:T8,8,1,18,FG,energy_efficiency,A_structural,binary,binary,-0.09,0.03,-2.65,0.01,**,,,medium,EPC cluster dummy (ref: B-C),log listing price per m2 (text says 'log listing price'; intercepts 7.95-8.04 and absence of size regressor imply price per m2; intercepts of Tables 6-7 misprinted as 785.08 / 79620.42),log,OLS,477.0,0.78,se,"listings with EPC level (Model 2, EPC grouped)",pct_premium,-8.647999124049011,2.741793555813685,100*(exp(b - se^2/2) - 1),reported,-0.12171564243992926,0.04593043110940727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.65,467.0,binary,,2014.0,Italy,Europe,High income,2012.0,2012.0,condo_apartment,listing,journal,False,True,OLS,,2012.0,ols,journal,True,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,log (1 + Area),living_area,A_structural,log,m2,-0.319,,,,,-0.321,-0.316,high,"Total floor area in m2 entered as log(1+Area); dependent variable is log price PER square metre, so the negative coefficient is a size discount on unit price, not on total price",log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",other,-0.319,0.0012755336322503893,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.2960257204867171,0.0011836701018556764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-250.09140639999978,651192.0,m2,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Rooms (3 Rooms),bedrooms,A_structural,dummy,count,0.066,,,,,0.065,0.068,high,"Number of rooms (all rooms, not bedrooms) category cat2 = 3 rooms; base category <= 2 rooms",log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.066,0.0007653201793502337,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.10626257240971389,0.0012321953178002943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),86.23841599999993,651192.0,count,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Rooms (4 Rooms),bedrooms,A_structural,dummy,count,0.059,,,,,0.057,0.062,high,Number of rooms category 4 rooms; base <= 2 rooms,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.059,0.0012755336322503876,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.05722598616095928,0.001237180847237268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),46.25515040000002,651192.0,count,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Rooms (5 Rooms),bedrooms,A_structural,dummy,count,0.064,,,,,0.061,0.067,high,Number of rooms category 5 rooms; base <= 2 rooms,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.064,0.0015306403587004673,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.05174520116021154,0.001237551457263969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.812565333333296,651192.0,count,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Rooms (> 5 Rooms),bedrooms,A_structural,dummy,count,0.109,,,,,0.106,0.113,high,Number of rooms category > 5 rooms; base <= 2 rooms,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.109,0.001785747085150545,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.07542462667075624,0.0012356817176685413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),61.038878857142805,651192.0,count,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Property type (Terraced),dwelling_type,A_structural,dummy,binary,-0.052,,,,,-0.054,-0.05,high,Base category semi-detached,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,-0.052,0.0010204269058003098,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.0630235257593995,0.0012367481035248107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-50.95906400000004,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Property type (Detached),dwelling_type,A_structural,dummy,binary,0.141,,,,,0.138,0.144,high,Base category semi-detached,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.141,0.0015306403587004602,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.11341748782890068,0.0012312154911583947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.11830800000034,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Property type (Flats),dwelling_type,A_structural,dummy,binary,-0.25,,,,,-0.252,-0.247,high,Base category semi-detached,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,-0.25,0.0012755336322503893,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.23601923224821844,0.0012042018743620732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-195.99639999999982,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Fireplace,fireplace,B_interior,binary,binary,0.064,,,,,0.062,0.065,high,1 if fireplace present,log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.064,0.0007653201793502337,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.10307723623998102,0.001232610764470448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),83.62513066666659,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Ventilation (Extract-only),other,other,dummy,binary,0.029,,,,,0.022,0.036,high,Mechanical ventilation type (EPC); base category natural ventilation; printed name 'Ventilation (Extract-only)',log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.029,0.0035714941703010867,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.010061704058522938,0.0012391488754589467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.11985085714286,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,False,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Ventilation (Both supply and extract),other,other,dummy,binary,0.055,,,,,0.043,0.065,high,Mechanical ventilation type (EPC); base natural ventilation; printed name 'Ventilation (Both supply and extract)',log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.055,0.005612347981901709,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.012143155276370362,0.0012391202365319325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.799819999999999,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,False,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,Wind turbine,other,other,binary,binary,0.006,,,,,-0.019,0.031,high,"1 if the property has a wind turbine (EPC micro-generation feature on the dwelling, not proximity to a wind farm); printed name 'Wind turbine'; CI includes zero",log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",monetary,0.006,0.012755336322503884,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.0005829143329955926,0.0012392113940944676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47039136,651192.0,binary,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,False,False
549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12,549587ac03dcb9d550c7fe68da027277dd876b12:T2-c1,2,1,17,log (1 + CO2 emission),energy_efficiency,A_structural,log,kg/m2 per year,-0.031,,,,,-0.035,-0.028,high,"EPC CO2 emissions per year in kg/m2, entered as log(1+x); main effect at trend = 0 (an interaction trend x CO2 = 0.076 (0.070, 0.081) is also reported but not extracted)",log(price per square metre),per_m2,other,651202.0,0.796,none,"Full sample, 983 MSOAs x 106 months; Bayesian posterior mean with 95% credible interval; R2 from Table 3 (D&C-STBD)",other,-0.031,0.0017857470851505442,not harmonised,(ci_hi-ci_lo)/(2*1.96),-0.02150734231831854,0.0012389249630410412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.359681142857138,651192.0,kg/m2 per year,,2024.0,United Kingdom,Europe,High income,2011.0,2019.0,mixed,transaction,journal,False,False,unknown,,2015.0,other,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Floor area,living_area,A_structural,level,m2,-0.00179,2.94e-05,,,***,,,high,"Floor area in m2 (text says area squared also included but not printed); stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,-0.00179,2.94e-05,"b (currency per unit, as reported)",reported,-0.10488484018301499,0.0017226895538439333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-60.8843537414966,333259.0,m2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Age2,age,A_structural,dummy,binary,0.0431,0.00245,,,***,,,high,"Construction-decade dummy 1980-1989 (base Age1 = 1950-1979); construction period dummy, not age in years; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0431,0.00245,"b (currency per unit, as reported)",reported,0.030459213675678408,0.001731440220543204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.591836734693878,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Age3,age,A_structural,dummy,binary,0.0492,0.00269,,,***,,,high,"Construction-decade dummy 1990-1999 (base 1950-1979); stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0492,0.00269,"b (currency per unit, as reported)",reported,0.031666788248482146,0.0017313752111466863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.28996282527881,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Age4,age,A_structural,dummy,binary,0.0631,0.00306,,,***,,,high,"Construction-decade dummy 2000-2009 (base 1950-1979); stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0631,0.00306,"b (currency per unit, as reported)",reported,0.035697688598309386,0.0017311398908213425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.62091503267974,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Age2 (second entry; presumably Age5),age,A_structural,dummy,binary,0.0336,0.00445,,,***,,,medium,"Printed as 'Age2' a second time; by table order this is Age5 (2010-2019) construction-decade dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0336,0.00445,"b (currency per unit, as reported)",reported,0.013078296484052577,0.001732095814108154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.550561797752809,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Elevator,accessibility,B_interior,binary,binary,-0.021,0.00132,,,***,,,medium,"Defined as dummy (1 = apartment with elevator) in Table 1, though descriptive stats (mean 1.94, max 8) suggest a count; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,-0.021,0.00132,"b (currency per unit, as reported)",reported,-0.02754796779349986,0.0017315865470199907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.90909090909091,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Dec1,condition,A_structural,binary,binary,0.0214,0.00112,,,***,,,high,"Refined decoration (finish) dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0214,0.00112,"b (currency per unit, as reported)",reported,0.03308011831516672,0.0017312959118218096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.107142857142858,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Dec2,condition,A_structural,binary,binary,-0.00185,0.00113,,,,,,high,"Simple decoration dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,-0.00185,0.00113,"b (currency per unit, as reported)",reported,-0.002835963226122431,0.0017322369975774847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6371681415929205,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Dec3,condition,A_structural,binary,binary,-0.0157,0.00185,,,***,,,high,"Roughcast (unfinished) dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,-0.0157,0.00185,"b (currency per unit, as reported)",reported,-0.014699076768240634,0.00173205681663982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.486486486486486,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Floor1,floor_level,A_structural,dummy,binary,-0.0324,0.00853,,,***,,,high,"Top floor dummy (floor-level category); stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,-0.0324,0.00853,"b (currency per unit, as reported)",reported,-0.006579541568113782,0.0017322064683953876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.798358733880422,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Floor2,floor_level,A_structural,dummy,binary,0.00618,0.00849,,,,,,high,"High floor dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.00618,0.00849,"b (currency per unit, as reported)",reported,0.0012609256989800281,0.0017322425864628541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7279151943462898,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Floor3,floor_level,A_structural,dummy,binary,0.011,0.00849,,,,,,high,"Middle floor dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.011,0.00849,"b (currency per unit, as reported)",reported,0.0022443622624398426,0.0017322396007376601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2956419316843346,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Floor4,floor_level,A_structural,dummy,binary,0.00149,0.00852,,,,,,high,"Low floor dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.00149,0.00852,"b (currency per unit, as reported)",reported,0.0003029393646995645,0.0017322438840538855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17488262910798122,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Floor5,floor_level,A_structural,dummy,binary,0.0131,0.00861,,,,,,high,"Ground floor dummy; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0131,0.00861,"b (currency per unit, as reported)",reported,0.002635576899945498,0.0017322379472160866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.521486643437863,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Bedroom number,bedrooms,A_structural,level,count,0.0207,0.000884,,,***,,,high,"; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.0207,0.000884,"b (currency per unit, as reported)",reported,0.040529397785483756,0.001730820659051577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.41628959276018,333259.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T8-c1,8,1,18,Orientation,sunlight,E_environment,binary,binary,0.022,0.000616,,,***,,,high,"1 = southern orientation; stars: * p<0.1, ** p<0.05, *** p<0.01","ln(housing unit price per m2, yuan/m2) of resale apartment (log of price per m2)",per_m2,OLS,333269.0,0.925,se,"Housing-level first-stage hedonic (Eq. 1): Lianjia resale apartment transactions, Beijing, 2013Q4-2019Q4, with community and quarter fixed effects; dependent variable is log of price per m2; adjusted R2; SE clustered at community level",monetary,0.022,0.000616,"b (currency per unit, as reported)",reported,0.06174780241261956,0.001728938467553348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.71428571428571,333259.0,binary,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,PM2.5,air_pollution,E_environment,level,ug/m3,-0.00074,5.64e-05,,,***,,,high,"Quarterly mean PM2.5 concentration, kriged to community; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",semi_elasticity,-0.074,0.00564,100*b/1 (per ug/m3),reported,-0.07147212642992243,0.00544733504141571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.120567375886525,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000104,1.83e-05,,,***,,,high,"Quarterly mean PM2.5 concentration, kriged to community; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",semi_elasticity,-0.0104,0.00183,100*b/1 (per ug/m3),reported,-0.031021968367700405,0.005458673280085745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.683060109289617,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000852,4.89e-05,,,***,,,high,"Quarterly mean PM2.5 concentration, kriged to community; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",semi_elasticity,-0.0852,0.00489,100*b/1 (per ug/m3),reported,-0.09472609793992734,0.005436744353594422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.42331288343558,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,School,school_distance,D_location,log,m,0.00109,0.00152,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00109,0.00152,b (log-log),reported,0.003916298223133659,0.005461259907489139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7171052631578947,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,School,school_distance,D_location,log,m,-0.0005,0.000621,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.0005,0.000621,b (log-log),reported,-0.004397140895118323,0.005461248991736957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8051529790660226,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,School,school_distance,D_location,log,m,-0.000619,0.000639,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.000619,0.000639,b (log-log),reported,-0.005290294993490464,0.005461225364847184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9687010954616587,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Hospital,hospital_distance,D_location,log,m,0.00259,0.00193,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00259,0.00193,b (log-log),reported,0.007328700399422827,0.0054611551238942305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3419689119170983,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Hospital,hospital_distance,D_location,log,m,9.56e-06,0.00149,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,9.56e-06,0.00149,b (log-log),reported,3.504029870338062e-05,0.005461301785359533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.006416107382550336,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Hospital,hospital_distance,D_location,log,m,-9.38e-05,0.00153,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-9.38e-05,0.00153,b (log-log),reported,-0.00033481704514238154,0.005461301482599613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06130718954248367,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Park,green_space,E_environment,log,m,-0.00214,0.00207,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.00214,0.00207,b (log-log),reported,-0.005645893504989894,0.005461214745480877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.033816425120773,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Park,green_space,E_environment,log,m,-0.000844,0.00128,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.000844,0.00128,b (log-log),reported,-0.003601022518821679,0.005461266379255627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6593749999999999,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Park,green_space,E_environment,log,m,-0.000768,0.00132,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.000768,0.00132,b (log-log),reported,-0.003177468636598186,0.0054612742191531335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5818181818181818,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Shopping mall,commercial_distance,D_location,log,m,0.00255,0.00236,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00255,0.00236,b (log-log),reported,0.0059008801267764555,0.005461206705565661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0805084745762712,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Shopping mall,commercial_distance,D_location,log,m,-0.000897,0.00117,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.000897,0.00117,b (log-log),reported,-0.004186961337463291,0.00546125391843038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7666666666666666,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Shopping mall,commercial_distance,D_location,log,m,-0.000798,0.0012,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.000798,0.0012,b (log-log),reported,-0.0036317417387406498,0.00546126577254233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.665,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Farmer’s market,commercial_distance,D_location,log,m,0.00031,0.00185,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00031,0.00185,b (log-log),reported,0.0009151366732845805,0.005461299501859594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16756756756756755,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Farmer’s market,commercial_distance,D_location,log,m,0.000366,0.000826,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.000366,0.000826,b (log-log),reported,0.0024198917702067052,0.005461285798335352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4430992736077482,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Farmer’s market,commercial_distance,D_location,log,m,0.000478,0.00085,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.000478,0.00085,b (log-log),reported,0.003071164639739549,0.0054612760330096585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5623529411764706,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,0.0113,0.00195,,,***,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.0113,0.00195,b (log-log),reported,0.03163170702576583,0.005458568911525961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.794871794871795,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.00154,0.000812,,,*,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.00154,0.000812,b (log-log),reported,-0.010357085780438022,0.005461008866049139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8965517241379308,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.00136,0.000834,,,,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.00136,0.000834,b (log-log),reported,-0.008905366799678157,0.005461085228626163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.630695443645084,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.00384,0.00185,,,**,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00384,0.00185,b (log-log),reported,0.011335163005747976,0.005460950927248374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.075675675675676,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.000859,0.000733,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.000859,0.000733,b (log-log),reported,0.006399948376672974,0.005461189941910699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1718963165075034,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.000895,0.000753,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.000895,0.000753,b (log-log),reported,0.006491051962491488,0.005461186734923006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1885790172642763,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,Population,density,F_neighbourhood,log,per_km2,0.19,0.00186,,,***,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.19,0.00186,b (log-log),reported,0.48718994730158693,0.004769333168320799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),102.1505376344086,33528.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,Population,density,F_neighbourhood,log,per_km2,-0.00397,0.00439,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.00397,0.00439,b (log-log),reported,-0.004938747991329998,0.005461235184367429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9043280182232346,33528.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,Population,density,F_neighbourhood,log,per_km2,-0.0041,0.00452,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.0041,0.00452,b (log-log),reported,-0.004953774909646831,0.00546123477843992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9070796460176993,33528.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,D_Bus stop,transit_distance,D_location,log,m,0.00146,0.00195,,,,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00146,0.00195,b (log-log),reported,0.004088940489759693,0.005461256133583152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7487179487179487,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,D_Bus stop,transit_distance,D_location,log,m,-0.00187,0.00108,,,*,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.00187,0.00108,b (log-log),reported,-0.009455720162574455,0.0054610576340002204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7314814814814814,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,D_Bus stop,transit_distance,D_location,log,m,-0.002,0.00111,,,*,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.002,0.00111,b (log-log),reported,-0.009839707029090443,0.005461037401145197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8018018018018016,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,D_Metro station,transit_distance,D_location,log,m,0.00111,0.00239,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,0.00111,0.00239,b (log-log),reported,0.002536412337111685,0.005461284221348583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46443514644351463,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c2,3,2,14,D_Metro station,transit_distance,D_location,log,m,-0.000214,0.00229,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.000214,0.00229,b (log-log),reported,-0.0005103573932661442,0.00546130107747416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09344978165938865,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c3,3,3,14,D_Metro station,transit_distance,D_location,log,m,-0.00012,0.00235,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.83,se,"(4) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.00012,0.00235,b (log-log),reported,-0.0002788749741112926,0.005461301576346147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05106382978723404,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T3-c1,3,1,14,D_CBD,cbd_distance,D_location,log,m,-0.0162,0.00241,,,***,,,high,"log distance (m) to nearest urban centre/subcentre (time-invariant; dropped in FE models); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.458,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); main models; year FE in all, community FE in FE/FE2sls (PM2.5 instrumented by thermal inversions); adjusted R2; SE clustered at community level",elasticity,-0.0162,0.00241,b (log-log),reported,-0.03668611296855124,0.005457625447790648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.721991701244813,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,AQI,air_pollution,E_environment,level,index,-0.000415,6.61e-05,,,***,,,high,"Quarterly mean air quality index (composite of 6 pollutants); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",semi_elasticity,-0.0415,0.0066099999999999996,100*b (unit as reported: index; pollution unit not ug/m3),reported,-0.03426791410644061,0.005458094270929456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.2783661119515894,33528.0,index,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,AQI,air_pollution,E_environment,level,index,-0.000184,2.21e-05,,,***,,,high,"Quarterly mean air quality index (composite of 6 pollutants); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",semi_elasticity,-0.0184,0.0022099999999999997,100*b (unit as reported: index; pollution unit not ug/m3),reported,-0.045422730671928174,0.005455664933965286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.32579185520362,33528.0,index,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,AQI,air_pollution,E_environment,level,index,-0.00117,7.79e-05,,,***,,,high,"Quarterly mean air quality index (composite of 6 pollutants); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",semi_elasticity,-0.117,0.00779,100*b (unit as reported: index; pollution unit not ug/m3),reported,-0.08175013822804353,0.005443022023901359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.019255455712454,33528.0,index,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,School,school_distance,D_location,log,m,0.00111,0.00152,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.00111,0.00152,b (log-log),reported,0.0039881557734834094,0.005461258356481786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7302631578947368,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,School,school_distance,D_location,log,m,-0.000475,0.000621,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000475,0.000621,b (log-log),reported,-0.004177287787766464,0.0054612541393746815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7648953301127214,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,School,school_distance,D_location,log,m,-0.000552,0.000659,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000552,0.000659,b (log-log),reported,-0.004574517518548244,0.005461244646237849,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8376327769347496,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Hospital,hospital_distance,D_location,log,m,0.00262,0.00193,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.00262,0.00193,b (log-log),reported,0.007413584182175502,0.005461151706717068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3575129533678756,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Hospital,hospital_distance,D_location,log,m,2.64e-05,0.00149,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,2.64e-05,0.00149,b (log-log),reported,9.676400439396894e-05,0.005461301763144459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01771812080536913,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Hospital,hospital_distance,D_location,log,m,-0.000113,0.00158,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000113,0.00158,b (log-log),reported,-0.0003905867437029412,0.0054613013721296205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07151898734177214,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Park,green_space,E_environment,log,m,-0.00224,0.00208,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00224,0.00208,b (log-log),reported,-0.005881300207484963,0.005461207335521752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0769230769230769,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Park,green_space,E_environment,log,m,-0.000873,0.00128,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000873,0.00128,b (log-log),reported,-0.003724752647110178,0.00546126390412489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6820312499999999,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Park,green_space,E_environment,log,m,-0.00077,0.00136,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00077,0.00136,b (log-log),reported,-0.00309204579031033,0.005461275681587077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5661764705882352,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Shopping mall,commercial_distance,D_location,log,m,0.00244,0.00237,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.00244,0.00237,b (log-log),reported,0.005622517187372376,0.005461215464783825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0295358649789028,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Shopping mall,commercial_distance,D_location,log,m,-0.000918,0.00117,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000918,0.00117,b (log-log),reported,-0.004284982064478862,0.005461251650806393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7846153846153846,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Shopping mall,commercial_distance,D_location,log,m,-0.000873,0.00124,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000873,0.00124,b (log-log),reported,-0.0038449042097999124,0.00546126142056345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7040322580645161,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Farmer’s market,commercial_distance,D_location,log,m,0.000272,0.00185,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.000272,0.00185,b (log-log),reported,0.0008029587068407511,0.005461300028144814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.14702702702702702,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Farmer’s market,commercial_distance,D_location,log,m,0.000334,0.000825,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.000334,0.000825,b (log-log),reported,0.002210994350222139,0.005461288439919953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.40484848484848485,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Farmer’s market,commercial_distance,D_location,log,m,0.000444,0.000877,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.000444,0.000877,b (log-log),reported,0.0027648902232234487,0.00546128091388956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5062713797035348,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,0.0112,0.00196,,,***,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.0112,0.00196,b (log-log),reported,0.031192253355195897,0.005458644337159281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.714285714285714,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.00157,0.000811,,,*,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00157,0.000811,b (log-log),reported,-0.010571842971123485,0.005460996592089902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.935881627620222,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.00145,0.00086,,,*,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00145,0.00086,b (log-log),reported,-0.009207618492940729,0.005461070278571743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6860465116279069,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.00372,0.00185,,,**,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.00372,0.00185,b (log-log),reported,0.010980982563663902,0.005460972511499521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0108108108108107,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.000822,0.000732,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.000822,0.000732,b (log-log),reported,0.0061326579937760846,0.005461199089348046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1229508196721312,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.000985,0.000776,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.000985,0.000776,b (log-log),reported,0.006932027078096067,0.0054611705711700995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2693298969072164,33528.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,Population,density,F_neighbourhood,log,per_km2,0.191,0.00187,,,***,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.191,0.00187,b (log-log),reported,0.4871481157974418,0.004769460610163435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),102.13903743315508,33528.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,Population,density,F_neighbourhood,log,per_km2,-0.00392,0.00439,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00392,0.00439,b (log-log),reported,-0.0048765486236308974,0.005461236851464194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8929384965831435,33528.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,Population,density,F_neighbourhood,log,per_km2,-0.00387,0.00466,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00387,0.00466,b (log-log),reported,-0.004535412134262931,0.005461245619034949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8304721030042919,33528.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,D_Bus stop,transit_distance,D_location,log,m,0.00143,0.00196,,,,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.00143,0.00196,b (log-log),reported,0.0039844895734211135,0.00546125843629747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.729591836734694,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,D_Bus stop,transit_distance,D_location,log,m,-0.00182,0.00108,,,*,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00182,0.00108,b (log-log),reported,-0.009202915127184982,0.005461070515032846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6851851851851851,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,D_Bus stop,transit_distance,D_location,log,m,-0.00196,0.00114,,,*,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00196,0.00114,b (log-log),reported,-0.009389192695490184,0.005461061057581025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.719298245614035,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,D_Metro station,transit_distance,D_location,log,m,0.00106,0.0024,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,0.00106,0.0024,b (log-log),reported,0.0024120679398584766,0.005461285901566361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4416666666666667,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c2,9,2,19,D_Metro station,transit_distance,D_location,log,m,-0.00022,0.00229,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,33538.0,0.839,se,"(2) FE; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.00022,0.00229,b (log-log),reported,-0.0005246664751734333,0.005461301037032556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09606986899563319,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c3,9,3,19,D_Metro station,transit_distance,D_location,log,m,-0.000283,0.00242,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,0.819,se,"(3) FE2sls; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.000283,0.00242,b (log-log),reported,-0.0006386562359524883,0.005461300674929405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11694214876033059,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T9-c1,9,1,19,D_CBD,cbd_distance,D_location,log,m,-0.0162,0.00241,,,***,,,high,"log distance (m) to nearest urban centre/subcentre (time-invariant; dropped in FE models); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,33538.0,0.455,se,"(1) OLS; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); robustness with AQI instead of PM2.5; SE clustered at community level",elasticity,-0.0162,0.00241,b (log-log),reported,-0.03668611296855124,0.005457625447790648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.721991701244813,33528.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000717,8.53e-05,,,***,,,high,"Quarterly mean PM2.5 concentration, kriged to community; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",semi_elasticity,-0.0717,0.00853,100*b/1 (per ug/m3),reported,-0.06704865502671037,0.007976639154502644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.405627198124266,15646.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,PM2.5,air_pollution,E_environment,level,ug/m3,-9.75e-05,2.57e-05,,,***,,,high,"Quarterly mean PM2.5 concentration, kriged to community; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",semi_elasticity,-0.00975,0.0025700000000000002,100*b/1 (per ug/m3),reported,-0.030315879204702665,0.007990954826265216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7937743190661477,15646.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,True
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000832,7.29e-05,,,***,,,high,"Quarterly mean PM2.5 concentration, kriged to community; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",semi_elasticity,-0.0832,0.00729,100*b/1 (per ug/m3),reported,-0.09086441834661471,0.007961557809456987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.412894375857338,15646.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,AQI,air_pollution,E_environment,level,index,-0.00117,0.000114,,,***,,,high,"Quarterly mean air quality index (composite of 6 pollutants); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",semi_elasticity,-0.117,0.0114,100*b (unit as reported: index; pollution unit not ug/m3),reported,-0.08177534066619331,0.007967853705936783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.263157894736842,15646.0,index,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,School,school_distance,D_location,log,m,-0.00165,0.0023,,,,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00165,0.0023,b (log-log),reported,-0.005735183298240244,0.00799449793088034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.717391304347826,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,School,school_distance,D_location,log,m,-0.0018,0.000885,,,**,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.0018,0.000885,b (log-log),reported,-0.01625811406335697,0.007993572747817177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0338983050847457,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,School,school_distance,D_location,log,m,-0.00175,0.000911,,,*,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00175,0.000911,b (log-log),reported,-0.015355600353716314,0.007993686812706036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.920965971459934,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,School,school_distance,D_location,log,m,-0.00172,0.000939,,,*,,,high,"log distance (m) to nearest school; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00172,0.000939,b (log-log),reported,-0.014642479674435256,0.007993772333892271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8317358892438764,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Hospital,hospital_distance,D_location,log,m,0.00256,0.00286,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00256,0.00286,b (log-log),reported,0.007155848703235944,0.007994424723146406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8951048951048951,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Hospital,hospital_distance,D_location,log,m,-0.000151,0.00229,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000151,0.00229,b (log-log),reported,-0.0005271567133709142,0.007994628302115189,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06593886462882097,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Hospital,hospital_distance,D_location,log,m,-7.01e-05,0.00236,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-7.01e-05,0.00236,b (log-log),reported,-0.00023746758730767894,0.007994629187533844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02970338983050847,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Hospital,hospital_distance,D_location,log,m,2.55e-05,0.00243,,,,,,high,"log distance (m) to nearest hospital; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,2.55e-05,0.00243,b (log-log),reported,8.389425897641962e-05,0.007994629384811753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.010493827160493827,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Park,green_space,E_environment,log,m,-0.00305,0.00289,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00305,0.00289,b (log-log),reported,-0.008436938358996116,0.007994344871311072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.055363321799308,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Park,green_space,E_environment,log,m,-0.000518,0.0017,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000518,0.0017,b (log-log),reported,-0.002436003381565243,0.007994605692395584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3047058823529412,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Park,green_space,E_environment,log,m,-0.0003,0.00175,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.0003,0.00175,b (log-log),reported,-0.0013705066122570123,0.007994621904832572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1714285714285714,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Park,green_space,E_environment,log,m,-0.000374,0.0018,,,,,,high,"log distance (m) to nearest city park; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000374,0.0018,b (log-log),reported,-0.0016611040418595218,0.00799461838328112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.20777777777777778,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Shopping mall,commercial_distance,D_location,log,m,-0.0026,0.00355,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.0026,0.00355,b (log-log),reported,-0.005855121175363372,0.007994492374053835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.732394366197183,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Shopping mall,commercial_distance,D_location,log,m,-0.00185,0.00163,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00185,0.00163,b (log-log),reported,-0.009073285649692292,0.007994300329188343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1349693251533743,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Shopping mall,commercial_distance,D_location,log,m,-0.00171,0.00168,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00171,0.00168,b (log-log),reported,-0.008137121248528383,0.007994364735396307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0178571428571428,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Shopping mall,commercial_distance,D_location,log,m,-0.00181,0.00173,,,,,,high,"log distance (m) to nearest shopping mall; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00181,0.00173,b (log-log),reported,-0.008364030681994376,0.007994349767873077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.046242774566474,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Farmer’s market,commercial_distance,D_location,log,m,-0.00509,0.00267,,,*,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00509,0.00267,b (log-log),reported,-0.015238928281631025,0.00799370108289879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9063670411985019,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Farmer’s market,commercial_distance,D_location,log,m,-0.000803,0.00115,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000803,0.00115,b (log-log),reported,-0.005582249908023022,0.007994504849597106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6982608695652174,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Farmer’s market,commercial_distance,D_location,log,m,-0.000657,0.00119,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000657,0.00119,b (log-log),reported,-0.004413798622465208,0.007994551538407302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5521008403361345,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Farmer’s market,commercial_distance,D_location,log,m,-0.00075,0.00123,,,,,,high,"log distance (m) to nearest farmers' market; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00075,0.00123,b (log-log),reported,-0.004874716112660408,0.00799453442476307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6097560975609756,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,0.00914,0.00299,,,***,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00914,0.00299,b (log-log),reported,0.024431137885561973,0.007992243137618194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05685618729097,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.0017,0.00119,,,,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.0017,0.00119,b (log-log),reported,-0.011420154380654662,0.007994108066458264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4285714285714284,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.00172,0.00123,,,,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00172,0.00123,b (log-log),reported,-0.011178783235822307,0.007994129872128743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3983739837398375,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Leisure facilities,neighbourhood_amenities,F_neighbourhood,log,count,-0.00174,0.00127,,,,,,high,"log number of sport/leisure facilities within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00174,0.00127,b (log-log),reported,-0.010952614798423282,0.007994149881607798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3700787401574803,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.0141,0.0028,,,***,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.0141,0.0028,b (log-log),reported,0.04022608431789729,0.007988158587951235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.035714285714286,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.00156,0.00107,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00156,0.00107,b (log-log),reported,0.011654929718846196,0.00799408640972143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4579439252336448,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.00134,0.0011,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00134,0.0011,b (log-log),reported,0.009738450376370287,0.007994250308960683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2181818181818183,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Basic services,neighbourhood_amenities,F_neighbourhood,log,count,0.00149,0.00113,,,,,,high,"log number of basic services (restaurant, post office...) within 1 km; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00149,0.00113,b (log-log),reported,0.010541005326751276,0.00799418524780466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3185840707964602,15646.0,count,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,Population,density,F_neighbourhood,log,per_km2,0.233,0.00326,,,***,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.233,0.00326,b (log-log),reported,0.4961171560387903,0.006941381668182215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.47239263803682,15646.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,True
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,Population,density,F_neighbourhood,log,per_km2,0.00111,0.00624,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00111,0.00624,b (log-log),reported,0.0014221201401960679,0.007994621328669786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1778846153846154,15646.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,Population,density,F_neighbourhood,log,per_km2,2.2e-05,0.00642,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,2.2e-05,0.00642,b (log-log),reported,2.7395926326916754e-05,0.007994629409945709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0034267912772585666,15646.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,Population,density,F_neighbourhood,log,per_km2,0.00131,0.00662,,,,,,high,"log population density (people/km2); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.00131,0.00662,b (log-log),reported,0.0015820168316015312,0.00799461940855125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19788519637462235,15646.0,per_km2,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,D_Bus stop,transit_distance,D_location,log,m,-0.00194,0.00301,,,,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00194,0.00301,b (log-log),reported,-0.005152616336971844,0.007994523285714046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6445182724252492,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,D_Bus stop,transit_distance,D_location,log,m,-0.00205,0.00154,,,,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00205,0.00154,b (log-log),reported,-0.010641598893450385,0.007994176729713948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3311688311688314,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,D_Bus stop,transit_distance,D_location,log,m,-0.00243,0.00158,,,,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00243,0.00158,b (log-log),reported,-0.012294608325596678,0.007994025166437347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5379746835443038,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,D_Bus stop,transit_distance,D_location,log,m,-0.00232,0.00163,,,,,,high,"log distance (m) to nearest bus stop; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00232,0.00163,b (log-log),reported,-0.011378122454847633,0.007994111897155879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4233128834355828,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,D_Metro station,transit_distance,D_location,log,m,0.0333,0.00361,,,***,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,0.0333,0.00361,b (log-log),reported,0.07354575925546761,0.007972978706073214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.22437673130194,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c2,10,2,20,D_Metro station,transit_distance,D_location,log,m,-0.000692,0.00311,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,FE_panel,15656.0,0.852,se,"(2) FE PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000692,0.00311,b (log-log),reported,-0.0017788664953911845,0.00799461676396905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.222508038585209,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,panel,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c3,10,3,20,D_Metro station,transit_distance,D_location,log,m,-0.000271,0.00319,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.843,se,"(3) FE2sls PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.000271,0.00319,b (log-log),reported,-0.0006791674204479646,0.007994627569110728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08495297805642632,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c4,10,4,20,D_Metro station,transit_distance,D_location,log,m,-0.00028,0.00329,,,,,,high,"log distance (m) to nearest metro station; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,15656.0,0.833,se,"(4) FE2sls AQI; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.00028,0.00329,b (log-log),reported,-0.0006803938351017584,0.00799462756244566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0851063829787234,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T10-c1,10,1,20,D_CBD,cbd_distance,D_location,log,m,-0.0287,0.00362,,,***,,,high,"log distance (m) to nearest urban centre/subcentre (time-invariant; dropped in FE models); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,OLS,15656.0,0.45,se,"(1) OLS PM2.5; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); subsample of communities within 3 km of an air monitoring station; column 4 uses AQI; parentheses = standard errors (clustering not stated in this table note)",elasticity,-0.0287,0.00362,b (log-log),reported,-0.063255901142112,0.007978618889701932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.928176795580111,15646.0,m,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T4-c1,4,1,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.0141,0.00039,,,***,,,high,"Quarterly mean PM2.5; IV quantile regression (magnitude much larger than Table 3; printed as is); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,quantile,33538.0,,se,"IV quantile regression, quantile Q25; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); other variables controlled but not reported",semi_elasticity,-1.41,0.039,100*b/1 (per ug/m3),reported,-0.19370729260341393,0.005357861284775279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-36.15384615384615,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,quantile,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T4-c2,4,2,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.0151,0.00033,,,***,,,high,"Quarterly mean PM2.5; IV quantile regression (magnitude much larger than Table 3; printed as is); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,quantile,33538.0,,se,"IV quantile regression, quantile Q50; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); other variables controlled but not reported",semi_elasticity,-1.51,0.033,100*b/1 (per ug/m3),reported,-0.24244059810367466,0.00529837068703395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-45.75757575757576,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,quantile,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T4-c3,4,3,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.0168,0.00028,,,***,,,high,"Quarterly mean PM2.5; IV quantile regression (magnitude much larger than Table 3; printed as is); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,quantile,33538.0,,se,"IV quantile regression, quantile Q75; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); other variables controlled but not reported",semi_elasticity,-1.68,0.027999999999999997,100*b/1 (per ug/m3),reported,-0.3113869966259735,0.005189783277099558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-60.0,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,quantile,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T4-c4,4,4,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.0189,0.00031,,,***,,,high,"Quarterly mean PM2.5; IV quantile regression (magnitude much larger than Table 3; printed as is); stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,quantile,33538.0,,se,"IV quantile regression, quantile Q90; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); other variables controlled but not reported",semi_elasticity,-1.8900000000000001,0.031,100*b/1 (per ug/m3),reported,-0.3159117205383836,0.005181620813063435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-60.96774193548387,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,quantile,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T5-c1,5,1,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.00111,0.000176,,,***,,,high,"Quarterly mean PM2.5; communities within Ring2; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,333269.0,,se,"FE2sls stratified by ring road (Ring2); AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); N per ring not printed",semi_elasticity,-0.11100000000000002,0.0176,100*b/1 (per ug/m3),reported,-0.010924295812211325,0.001732140597251525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.3068181818181825,333259.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T5-c2,5,2,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000867,9.32e-05,,,***,,,high,"Quarterly mean PM2.5; communities within Ring3; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,333269.0,,se,"FE2sls stratified by ring road (Ring3); AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); N per ring not printed",semi_elasticity,-0.0867,0.00932,100*b/1 (per ug/m3),reported,-0.016112237765354708,0.001732019100035823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.302575107296137,333259.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T5-c3,5,3,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.00104,0.000106,,,***,,,high,"Quarterly mean PM2.5; communities within Ring4; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,333269.0,,se,"FE2sls stratified by ring road (Ring4); AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); N per ring not printed",semi_elasticity,-0.104,0.0106,100*b/1 (per ug/m3),reported,-0.01699314708974906,0.0017319938379936545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.81132075471698,333259.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T5-c4,5,4,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000774,0.000124,,,***,,,high,"Quarterly mean PM2.5; communities within Ring5; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,333269.0,,se,"FE2sls stratified by ring road (Ring5); AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); N per ring not printed",semi_elasticity,-0.0774,0.012400000000000001,100*b/1 (per ug/m3),reported,-0.010811923062967776,0.0017321427129302384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.241935483870967,333259.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T5-c5,5,5,15,PM2.5,air_pollution,E_environment,level,ug/m3,-0.000622,8.09e-05,,,***,,,high,"Quarterly mean PM2.5; communities within Ring6; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,333269.0,,se,"FE2sls stratified by ring road (Ring6); AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5); N per ring not printed",semi_elasticity,-0.062200000000000005,0.00809,100*b/1 (per ug/m3),reported,-0.013317184166885305,0.0017320903522524457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.688504326328801,333259.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 (2013 baseline),air_pollution,E_environment,level,ug/m3,-0.00232,4.1e-05,,,***,,,high,"Base-year 2013 PM2.5 coefficient; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",semi_elasticity,-0.232,0.0041,100*b/1 (per ug/m3),reported,-0.2952529419867577,0.005217832164421149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-56.58536585365854,33528.0,ug/m3,,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,True,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 x Year2014,air_pollution,E_environment,other,ug/m3,-0.00329,3.9e-05,,,***,,,high,"Interaction PM2.5 x year 2014 dummy: change in PM2.5 effect relative to 2013; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",other,-0.00329,3.9e-05,not harmonised,reported,-0.4184376334735245,0.004960202949990107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-84.35897435897436,33528.0,ug/m3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 x Year2015,air_pollution,E_environment,other,ug/m3,-0.00341,4.1e-05,,,***,,,high,"Interaction PM2.5 x year 2015 dummy: change in PM2.5 effect relative to 2013; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",other,-0.00341,4.1e-05,not harmonised,reported,-0.413557701209974,0.00497239464797916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-83.17073170731707,33528.0,ug/m3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 x Year2016,air_pollution,E_environment,other,ug/m3,-0.00217,5e-05,,,***,,,high,"Interaction PM2.5 x year 2016 dummy: change in PM2.5 effect relative to 2013; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",other,-0.00217,5e-05,not harmonised,reported,-0.23063074391402758,0.005314072440415382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.4,33528.0,ug/m3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 x Year2017,air_pollution,E_environment,other,ug/m3,0.00078,4.8e-05,,,***,,,high,"Interaction PM2.5 x year 2017 dummy: change in PM2.5 effect relative to 2013; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",other,0.00078,4.8e-05,not harmonised,reported,0.08839872791609862,0.005439921717913762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.25,33528.0,ug/m3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 x Year2018,air_pollution,E_environment,other,ug/m3,0.000134,6e-05,,,**,,,high,"Interaction PM2.5 x year 2018 dummy: change in PM2.5 effect relative to 2013; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",other,0.000134,6e-05,not harmonised,reported,0.012196000195629627,0.005460895609983415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2333333333333334,33528.0,ug/m3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46,54da27518406c538ad01d44349b80f8a974bfe46:T6-c1,6,1,16,PM2.5 x Year2019,air_pollution,E_environment,other,ug/m3,0.00014,7.1e-05,,,**,,,high,"Interaction PM2.5 x year 2019 dummy: change in PM2.5 effect relative to 2013; stars: * p<0.1, ** p<0.05, *** p<0.01","ln community price index (lnCP, community x quarter)",log,IV,33538.0,,se,"Dynamic-effects model (Eq. 6) with PM2.5 x year interactions, 2013 base; AGGREGATE second stage: unit of analysis = community x quarter panel (N=33,538), dependent variable is the community-quarter log price index (lnCP) from the first-stage hedonic; accessibility/neighbourhood variables enter in logs (Eqs. 2-5)",other,0.00014,7.1e-05,not harmonised,reported,0.010768139737964871,0.005460985152825042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9718309859154926,33528.0,ug/m3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2013.0,2019.0,condo_apartment,transaction,journal,True,True,clustered,,2016.0,iv,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Area,living_area,A_structural,level,m2,0.01,,59.16,,***,,,high,Gross floor area; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,1.0,0.016903313049357677,100*b/1 (X per m2->m2),|b/t|,0.8592970466477441,0.014524966981875323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.16,1240.0,m2,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Area,living_area,A_structural,log,m2,1.008,,70.7,,***,,,high,Gross floor area; printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,1.008,0.014257425742574256,b (log-log),|b/t|,0.8951167255988607,0.012660774053732119,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),70.7,1240.0,m2,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Age,age,A_structural,level,years,-0.011,,-7.65,,***,,,high,2015 minus year of occupation permit; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,-1.0999999999999999,0.1437908496732026,100*b/1 (per year),|b/t|,-0.21229349921710175,0.027750784211385847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.65,1240.0,years,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Age,age,A_structural,log,years,-0.153,,-11.26,,***,,,high,2015 minus year of occupation permit; printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,-0.153,0.013587921847246892,b (log-log),|b/t|,-0.3045705275000428,0.027048892317943413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.26,1240.0,years,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Local environment,other,other,level,index,0.068,,8.36,,***,,,high,"Environment quality of the residential complex, 5-point Likert; printed significance letter 'a'",ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,6.800000000000001,0.813397129186603,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.23098774037907915,0.027630112485535787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.36,1240.0,index,unit_not_harmonisable,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Local environment,other,other,log,index,0.177,,7.94,,***,,,high,"Environment quality of the residential complex, 5-point Likert; printed significance letter 'a'",ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,0.177,0.02229219143576826,b (log-log),|b/t|,0.2199586414646777,0.027702599680689884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.94,1240.0,index,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,School,school_quality,F_neighbourhood,binary,binary,0.089,,4.0,,***,,,high,Property within attendance zone of a provincial-level demonstration elementary school (xuequfang); printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,pct_premium,9.281011818982176,2.4321044603260846,100*(exp(b - se^2/2) - 1),|b/t|,0.11286652959662007,0.028216632399155017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,School,school_quality,F_neighbourhood,binary,binary,0.108,,5.45,,***,,,high,Property within attendance zone of a provincial-level demonstration elementary school (xuequfang); printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,pct_premium,11.382902680564634,2.207654247738321,100*(exp(b - se^2/2) - 1),|b/t|,0.15294860311998618,0.028063963875226826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.45,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Kindergarten,school_distance,D_location,binary,binary,0.009,,0.47,,,,,high,At least one kindergarten within 500 m; printed significance letter 'c',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,pct_premium,0.8855640337954274,1.9322054459499598,100*(exp(b - se^2/2) - 1),|b/t|,0.013345914402707128,0.02839556255895134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Kindergarten,school_distance,D_location,binary,binary,-0.011,,-0.68,,,,,high,At least one kindergarten within 500 m; printed significance letter 'c',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,pct_premium,-1.1069120516266184,1.599950450960155,100*(exp(b - se^2/2) - 1),|b/t|,-0.019307102859467042,0.02839279832274565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.68,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Middle school,school_distance,D_location,binary,binary,-0.001,,-0.06,,,,,high,"At least one middle school within 1,000 m; printed significance letter 'c'",ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,pct_premium,-0.11382406010709278,1.665000833055625,100*(exp(b - se^2/2) - 1),|b/t|,-0.0017038830293643983,0.028398050489406645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Middle school,school_distance,D_location,binary,binary,0.032,,1.91,,,,,high,"At least one middle school within 1,000 m; printed significance letter 'c'",ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,pct_premium,3.23726047082884,1.7298722601970569,100*(exp(b - se^2/2) - 1),|b/t|,0.05416074279338552,0.02835640983946886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.91,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Xiamen University,university_distance,D_location,binary,binary,0.206,,3.45,,***,,,high,"Xiamen University within 1,000 m; printed significance letter 'a'",ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,pct_premium,22.656471746470007,7.336903189073456,100*(exp(b - se^2/2) - 1),|b/t|,0.09750656152300757,0.028262771455944222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.45,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Xiamen University,university_distance,D_location,binary,binary,0.144,,3.04,,***,,,high,"Xiamen University within 1,000 m; printed significance letter 'a'",ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,pct_premium,15.358918942849996,5.470503671960116,100*(exp(b - se^2/2) - 1),|b/t|,0.08601028062986361,0.02829285547034987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.04,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,BRT,transit_distance,D_location,binary,binary,0.077,,4.72,,***,,,high,BRT station within 800 m; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,pct_premium,7.989836895757829,1.7619330483523354,100*(exp(b - se^2/2) - 1),|b/t|,0.13285087626533462,0.028146372090113267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.72,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,BRT,transit_distance,D_location,binary,binary,0.1,,6.85,,***,,,high,BRT station within 800 m; printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,pct_premium,10.505315878773814,1.6133882015702887,100*(exp(b - se^2/2) - 1),|b/t|,0.1909476749063951,0.02787557297903578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.85,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Distance to hospital,hospital_distance,D_location,level,km,0.047,,4.41,,***,,,high,Euclidean distance to nearest Class 2A/3A hospital; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,4.7,1.0657596371882085,100*b/1 (X per km->km),|b/t|,0.12426489342378419,0.02817798036820503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.41,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Distance to hospital,hospital_distance,D_location,log,km,0.018,,2.13,,**,,,high,Euclidean distance to nearest Class 2A/3A hospital; printed significance letter 'b',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,0.018,0.008450704225352112,b (log-log),|b/t|,0.06037758173944477,0.028346282506781582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.13,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Distance to cultural/sports center,neighbourhood_amenities,F_neighbourhood,level,km,-0.057,,-5.04,,***,,,high,Euclidean distance to nearest cultural/sports centre; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,-5.7,1.130952380952381,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.14168254417395856,0.028111615907531466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.04,1240.0,km,unit_not_harmonisable,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Distance to cultural/sports center,neighbourhood_amenities,F_neighbourhood,log,km,-0.049,,-5.25,,***,,,high,Euclidean distance to nearest cultural/sports centre; printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,-0.049,0.009333333333333334,b (log-log),|b/t|,-0.1474601299221955,0.028087643794703906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.25,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Distance to city center,cbd_distance,D_location,level,km,-0.021,,-6.04,,***,,,high,Euclidean distance to Zhongshan Road; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,-2.1,0.34768211920529807,100*b/1 (X per km->km),|b/t|,-0.169055638028726,0.027989344044491063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.04,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Distance to city center,cbd_distance,D_location,log,km,-0.077,,-6.36,,***,,,high,Euclidean distance to Zhongshan Road; printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,-0.077,0.01210691823899371,b (log-log),|b/t|,-0.17773619203308624,0.027945942143567017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.36,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c1,2,1,10,Distance to lake,water_proximity,E_environment,level,km,-0.072,,-6.06,,***,,,high,Euclidean distance to nearest lake; printed significance letter 'a',ln(price),log,OLS,1250.0,0.801,t,Semi-log model; adjusted R2,semi_elasticity,-7.199999999999999,1.188118811881188,100*b/1 (X per km->km),|b/t|,-0.16959934904241158,0.027986691261124026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.06,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T2-c2,2,2,10,Distance to lake,water_proximity,E_environment,log,km,-0.051,,-5.95,,***,,,high,Euclidean distance to nearest lake; printed significance letter 'a',ln(price),log,OLS,1250.0,0.85,t,Double-log model (continuous regressors in logs); adjusted R2,elasticity,-0.051,0.00857142857142857,b (log-log),|b/t|,-0.16660703429748522,0.028001182234871462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.95,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Area,living_area,A_structural,level,m2,0.055,,63.53,,***,,,high,Gross floor area; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.055,0.0008657327246969935,not harmonised,|b/t|,0.8746291657538846,0.013767183468501249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),63.53,1240.0,m2,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Area,living_area,A_structural,other,m2,1.429,,59.11,,***,,,high,Gross floor area; regressor Box-Cox transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,1.429,0.024175266452376925,not harmonised,|b/t|,0.8591068758581271,0.014534036133617437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.11,1240.0,m2,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Area,living_area,A_structural,other,m2,1.035,,70.56,,***,,,high,Gross floor area; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,1.035,0.014668367346938774,not harmonised,|b/t|,0.8947635709755326,0.01268088961133124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),70.56,1240.0,m2,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Area,living_area,A_structural,other,m2,0.833,,70.52,,***,,,high,Gross floor area; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.833,0.011812251843448668,not harmonised,|b/t|,0.8946623602425103,0.012686647195724758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),70.52,1240.0,m2,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Age,age,A_structural,level,years,-0.074,,-9.3,,***,,,high,2015 minus year of occupation permit; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,-0.074,0.007956989247311827,not harmonised,|b/t|,-0.2553471167549282,0.02745667922096002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.3,1240.0,years,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Age,age,A_structural,other,years,-3.044,,-10.17,,***,,,high,2015 minus year of occupation permit; regressor Box-Cox transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,-3.044,0.2993117010816126,not harmonised,|b/t|,-0.2774684509497001,0.027283033525044253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.17,1240.0,years,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Age,age,A_structural,other,years,-0.169,,-11.32,,***,,,high,2015 minus year of occupation permit; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,-0.169,0.014929328621908128,not harmonised,|b/t|,-0.3060418197888222,0.027035496447775812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.32,1240.0,years,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Age,age,A_structural,other,years,-0.151,,-11.2,,***,,,high,2015 minus year of occupation permit; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,-0.151,0.013482142857142857,not harmonised,|b/t|,-0.30309705182720215,0.02706223677028591,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.2,1240.0,years,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Local environment,other,other,level,index,0.434,,9.36,,***,,,high,"Environment quality of the residential complex, 5-point Likert; printed significance letter 'a'","Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.434,0.04636752136752137,not harmonised,|b/t|,0.2568861303174977,0.027445099392895054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.36,1240.0,index,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,False,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Local environment,other,other,other,index,16.629,,8.05,,***,,,high,"Environment quality of the residential complex, 5-point Likert; regressor Box-Cox transformed; printed significance letter 'a'",price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,16.629,2.065714285714286,not harmonised,|b/t|,0.22285556850415833,0.02768392155331159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.05,1240.0,index,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,False,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Local environment,other,other,other,index,0.203,,8.0,,***,,,high,"Environment quality of the residential complex, 5-point Likert; regressor Box-Cox transformed; printed significance letter 'a'","Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,0.203,0.025375,not harmonised,|b/t|,0.2215395102486845,0.027692438781085564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,1240.0,index,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,False,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Local environment,other,other,other,index,0.195,,8.01,,***,,,high,"Environment quality of the residential complex, 5-point Likert; regressor Box-Cox transformed; printed significance letter 'a'","Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.195,0.024344569288389514,not harmonised,|b/t|,0.22180281901478247,0.02769073895315637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.01,1240.0,index,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,False,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,School,school_quality,F_neighbourhood,binary,binary,0.616,,5.14,,***,,,high,Property within attendance zone of a provincial-level demonstration elementary school (xuequfang); dummy not transformed; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.616,0.1198443579766537,not harmonised,|b/t|,0.14443561910665564,0.028100315001294875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.14,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,School,school_quality,F_neighbourhood,binary,binary,36.359,,5.9,,***,,,high,Property within attendance zone of a provincial-level demonstration elementary school (xuequfang); dummy not transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",monetary,36.359,6.162542372881356,"b (currency per unit, as reported)",|b/t|,0.16524536277361582,0.028007688605697592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,School,school_quality,F_neighbourhood,binary,binary,0.128,,5.48,,,,,medium,"Property within attendance zone of a provincial-level demonstration elementary school (xuequfang); dummy not transformed; printed significance letter 'c' (inconsistent with t-value, as printed)","Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,0.128,0.02335766423357664,not harmonised,|b/t|,0.15377066772114126,0.028060340825025776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.48,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,School,school_quality,F_neighbourhood,binary,binary,0.129,,5.46,,,,,medium,"Property within attendance zone of a provincial-level demonstration elementary school (xuequfang); dummy not transformed; printed significance letter 'c' (inconsistent with t-value, as printed)","Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.129,0.023626373626373626,not harmonised,|b/t|,0.1532226600225747,0.028062758245892806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.46,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Kindergarten,school_distance,D_location,binary,binary,-0.005,,-0.05,,,,,high,At least one kindergarten within 500 m; dummy not transformed; printed significance letter 'c',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,-0.005,0.09999999999999999,not harmonised,|b/t|,-0.0014199031542643974,0.028398063085287945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Kindergarten,school_distance,D_location,binary,binary,-4.621,,-0.88,,,,,high,At least one kindergarten within 500 m; dummy not transformed; printed significance letter 'c',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",monetary,-4.621,5.251136363636364,"b (currency per unit, as reported)",|b/t|,-0.024982520930819676,0.028389228330476905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.88,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Kindergarten,school_distance,D_location,binary,binary,-0.013,,-0.7,,,,,high,At least one kindergarten within 500 m; dummy not transformed; printed significance letter 'c',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,-0.013,0.018571428571428572,not harmonised,|b/t|,-0.019874737723005027,0.028392482461435754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Kindergarten,school_distance,D_location,binary,binary,-0.013,,-0.67,,,,,high,At least one kindergarten within 500 m; dummy not transformed; printed significance letter 'c',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,-0.013,0.019402985074626865,not harmonised,|b/t|,-0.019023278392056357,0.02839295282396471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.67,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Middle school,school_distance,D_location,binary,binary,0.029,,0.28,,,,,high,"At least one middle school within 1,000 m; dummy not transformed; printed significance letter 'c'; coefficient printed '0.029a' with t-value letter 'c'","Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.029,0.10357142857142856,not harmonised,|b/t|,0.007951214322463004,0.02839719400879644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.28,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Middle school,school_distance,D_location,binary,binary,4.011,,0.76,,,,,high,"At least one middle school within 1,000 m; dummy not transformed; printed significance letter 'c'",price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",monetary,4.011,5.277631578947369,"b (currency per unit, as reported)",|b/t|,0.021577524811343406,0.028391480014925533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.76,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Middle school,school_distance,D_location,binary,binary,0.036,,1.84,,***,,,medium,"At least one middle school within 1,000 m; dummy not transformed; printed significance letter 'a' (inconsistent with t-value, as printed)","Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,0.036,0.019565217391304346,not harmonised,|b/t|,0.052181301415933044,0.028359402943441876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.84,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Middle school,school_distance,D_location,binary,binary,0.033,,1.7,,***,,,medium,"At least one middle school within 1,000 m; dummy not transformed; printed significance letter 'a' (inconsistent with t-value, as printed)","Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.033,0.019411764705882354,not harmonised,|b/t|,0.04822059606467893,0.02836505650863467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Xiamen University,university_distance,D_location,binary,binary,0.99,,3.09,,**,,,high,"Xiamen University within 1,000 m; dummy not transformed; printed significance letter 'b'","Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.99,0.32038834951456313,not harmonised,|b/t|,0.08741420057390477,0.02828938529899831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.09,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Xiamen University,university_distance,D_location,binary,binary,42.604,,2.53,,**,,,high,"Xiamen University within 1,000 m; dummy not transformed; printed significance letter 'b'",price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",monetary,42.604,16.839525691699606,"b (currency per unit, as reported)",|b/t|,0.07166244875338765,0.028325078558651247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.53,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Xiamen University,university_distance,D_location,binary,binary,0.17,,3.04,,***,,,high,"Xiamen University within 1,000 m; dummy not transformed; printed significance letter 'a'","Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,0.17,0.05592105263157895,not harmonised,|b/t|,0.08601028062986361,0.02829285547034987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.04,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Xiamen University,university_distance,D_location,binary,binary,0.172,,3.03,,***,,,high,"Xiamen University within 1,000 m; dummy not transformed; printed significance letter 'a'","Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.172,0.05676567656765676,not harmonised,|b/t|,0.08572943487480054,0.02829354286297048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.03,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,BRT,transit_distance,D_location,binary,binary,0.429,,4.93,,***,,,high,BRT station within 800 m; dummy not transformed; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.429,0.08701825557809331,not harmonised,|b/t|,0.13865036229133812,0.028123805738608145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.93,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,BRT,transit_distance,D_location,binary,binary,19.596,,4.38,,***,,,high,BRT station within 800 m; dummy not transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",monetary,19.596,4.473972602739726,"b (currency per unit, as reported)",|b/t|,0.123432476676261,0.028180930748004797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.38,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,BRT,transit_distance,D_location,binary,binary,0.117,,6.81,,***,,,high,BRT station within 800 m; dummy not transformed; printed significance letter 'a',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,0.117,0.017180616740088108,not harmonised,|b/t|,0.1898729642834959,0.02788149255264257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.81,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,BRT,transit_distance,D_location,binary,binary,0.116,,6.72,,***,,,high,BRT station within 800 m; dummy not transformed; printed significance letter 'a',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.116,0.017261904761904763,not harmonised,|b/t|,0.18745237336218276,0.027894698416991482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.72,1240.0,binary,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Distance to hospital,hospital_distance,D_location,level,km,0.252,,4.47,,***,,,high,Euclidean distance to nearest Class 2A/3A hospital; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,0.252,0.0563758389261745,not harmonised,|b/t|,0.12592893841956446,0.02817202201779965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.47,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Distance to hospital,hospital_distance,D_location,other,km,11.195,,4.06,,***,,,high,Euclidean distance to nearest Class 2A/3A hospital; regressor Box-Cox transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,11.195,2.7573891625615765,not harmonised,|b/t|,0.11453747937405197,0.028211201816268957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.06,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Distance to hospital,hospital_distance,D_location,other,km,0.022,,2.16,,**,,,high,Euclidean distance to nearest Class 2A/3A hospital; regressor Box-Cox transformed; printed significance letter 'b',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,0.022,0.010185185185185184,not harmonised,|b/t|,0.06122480460796693,0.028344816948132835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.16,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Distance to hospital,hospital_distance,D_location,other,km,0.023,,2.21,,**,,,high,Euclidean distance to nearest Class 2A/3A hospital; regressor Box-Cox transformed; printed significance letter 'b',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,0.023,0.010407239819004524,not harmonised,|b/t|,0.06263654779997849,0.028342329321257236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.21,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Distance to cultural/sports center,neighbourhood_amenities,F_neighbourhood,level,km,-0.294,,-4.71,,***,,,high,Euclidean distance to nearest cultural/sports centre; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,-0.294,0.062420382165605096,not harmonised,|b/t|,-0.13257436469923928,0.0281474235030232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.71,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Distance to cultural/sports center,neighbourhood_amenities,F_neighbourhood,other,km,-12.988,,-4.16,,***,,,high,Euclidean distance to nearest cultural/sports centre; regressor Box-Cox transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,-12.988,3.1221153846153844,not harmonised,|b/t|,-0.11732022998932011,0.028201978362817334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.16,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Distance to cultural/sports center,neighbourhood_amenities,F_neighbourhood,other,km,-0.059,,-5.31,,***,,,high,Euclidean distance to nearest cultural/sports centre; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,-0.059,0.011111111111111112,not harmonised,|b/t|,-0.1491081268345281,0.02808062652251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.31,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Distance to cultural/sports center,neighbourhood_amenities,F_neighbourhood,other,km,-0.062,,-5.39,,***,,,high,Euclidean distance to nearest cultural/sports centre; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,-0.062,0.011502782931354361,not harmonised,|b/t|,-0.15130352256193427,0.028071154464180757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.39,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Distance to city center,cbd_distance,D_location,level,km,-0.134,,-7.26,,***,,,high,Euclidean distance to Zhongshan Road; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,-0.134,0.018457300275482095,not harmonised,|b/t|,-0.20192331600711347,0.027813128926599654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.26,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Distance to city center,cbd_distance,D_location,other,km,-5.477,,-7.12,,***,,,high,Euclidean distance to Zhongshan Road; regressor Box-Cox transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,-5.477,0.7692415730337079,not harmonised,|b/t|,-0.1981838590430955,0.027834811663356107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.12,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Distance to city center,cbd_distance,D_location,other,km,-0.088,,-6.37,,***,,,high,Euclidean distance to Zhongshan Road; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,-0.088,0.013814756671899528,not harmonised,|b/t|,-0.17800680311607345,0.027944553079446384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.37,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Distance to city center,cbd_distance,D_location,other,km,-0.084,,-6.28,,***,,,high,Euclidean distance to Zhongshan Road; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,-0.084,0.01337579617834395,not harmonised,|b/t|,-0.17556985609652567,0.027956983454860775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.28,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c1,3,1,12,Distance to lake,water_proximity,E_environment,level,km,-0.345,,-5.42,,***,,,high,Euclidean distance to nearest lake; printed significance letter 'a',"Box-Cox(price), lambda = 0.317",boxcox,other,1250.0,0.826,t,Simple left-hand-side Box-Cox model; adjusted R2; ML Box-Cox,other,-0.345,0.06365313653136531,not harmonised,|b/t|,-0.15212622088507446,0.02806756842898053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.42,1240.0,km,,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c2,3,2,12,Distance to lake,water_proximity,E_environment,other,km,-10.734,,-3.32,,***,,,high,Euclidean distance to nearest lake; regressor Box-Cox transformed; printed significance letter 'a',price (untransformed),level,other,1250.0,0.806,t,"Simple right-hand-side Box-Cox model (regressors transformed, lambda = 1.128); adjusted R2",other,-10.734,3.233132530120482,not harmonised,|b/t|,-0.09386540119330657,0.028272711202803183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.32,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c3,3,3,12,Distance to lake,water_proximity,E_environment,other,km,-0.061,,-5.99,,***,,,high,Euclidean distance to nearest lake; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), common lambda = 0.030",boxcox,other,1250.0,0.851,t,Simple both-side Box-Cox model (same lambda both sides); adjusted R2,other,-0.061,0.01018363939899833,not harmonised,|b/t|,-0.16769568602230916,0.02799594090522691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.99,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955,58a9ab2513035056fdc6d56cafc04ae9d61a0955:T3-c4,3,4,12,Distance to lake,water_proximity,E_environment,other,km,-0.063,,-5.99,,***,,,high,Euclidean distance to nearest lake; regressor Box-Cox transformed; printed significance letter 'a',"Box-Cox(price), LHS lambda = 0.032",boxcox,other,1250.0,0.851,t,Separate both-side Box-Cox model (RHS lambda = 0.080); adjusted R2,other,-0.063,0.010517529215358931,not harmonised,|b/t|,-0.16769568602230916,0.02799594090522691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.99,1240.0,km,nonstandard_x_transform,2018.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,listing,journal,False,False,unknown,,2014.0,other,journal,False,True,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c2,5,2,21,CondAverage,image_condition,H_ai_derived,level,"index (1-5 condition scale, room average)",0.087,,,,***,,,high,"Computer-vision average condition across rooms, continuous 1-5",ln(sales price per m2),log,OLS,12084.0,0.6952,none,Average condition (continuous); year x quarter FE + district FE; R2 adjusted,semi_elasticity,8.7,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,12074.0,index,unit_not_harmonisable,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c4,5,4,21,CondAverage,image_condition,H_ai_derived,level,"index (1-5 condition scale, room average)",0.082,,,,***,,,high,"Computer-vision average condition across rooms, continuous 1-5",ln(sales price per m2),log,OLS,12084.0,0.771,none,Average condition (continuous); year x quarter FE + postal code FE; R2 adjusted,semi_elasticity,8.200000000000001,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,12074.0,index,unit_not_harmonisable,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.062,,,,**,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-6.011711320891111,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.057,,,,**,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-5.540593063347665,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.05,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-4.877057549928598,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.041,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-4.017087005220111,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.011,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-1.0939721224631271,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.014,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-1.3902455737138109,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.027,,,,***,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,2.736780276348938,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.025,,,,***,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,2.531512052442886,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.099,,,,***,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-9.42572919764515,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.087,,,,***,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-8.332290436684776,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.043,,,,***,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-4.208860993296937,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.031,,,,***,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-3.0524426923974057,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.004,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-0.39920106560085156,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.003,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.136,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,14.568189359486183,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.145,,,,*,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,15.603957026802151,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.045,,,,**,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-4.4002518166900035,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.039,,,,**,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-3.8249290853633267,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.011,,,,***,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-1.0939721224631271,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.01,,,,***,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.021,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,-0.015,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-1.4888060396937353,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.172,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-15.80208268315001,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.181,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-16.556464130421045,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.087,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-8.332290436684776,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.091,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-8.698228910073425,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.036,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,-3.535970651687692,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.037,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,-3.6323864650946502,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c1,5,1,21,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.079,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.6981,none,Room-specific condition dummies; year x quarter FE + city district FE; R2 is adjusted,pct_premium,8.22043220703148,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T5-c3,5,3,21,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.078,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.772,none,Room-specific condition dummies; year x quarter FE + postal code FE; R2 adjusted,pct_premium,8.112265867008306,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.048,,,,*,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-4.6866212922495265,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBath = 1,image_condition,H_ai_derived,dummy,binary,0.051,,,,,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,5.232289328320383,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.054,,,,*,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-5.256789349820168,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.188,,,,***,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-17.13852927673194,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.068,,,,***,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-6.5739526422786465,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.03,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.01,,,,,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.034,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-3.342849536249337,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.047,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-4.591260240962891,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.042,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-4.1130219427515495,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.02,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-1.9801326693244747,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBath = 3,image_condition,H_ai_derived,dummy,binary,0.011,,,,**,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,1.106072244471945,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.01,,,,*,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.021,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-2.078103543054044,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.014,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-1.3902455737138109,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.024,,,,**,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,2.4290317890621527,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.017,,,,,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,1.7145322325240686,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.03,,,,**,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.007,,,,,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,0.7024557266848586,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.023,,,,***,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,2.326653954721758,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.053,,,,**,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-5.161998751770181,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.016,,,,,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-1.5872679944714863,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.157,,,,,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-14.529594118231493,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.083,,,,***,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.031,,,,***,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-3.0524426923974057,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBed = 2,image_condition,H_ai_derived,dummy,binary,0.012,,,,,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,1.2072288866077807,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBed = 2,image_condition,H_ai_derived,dummy,binary,0.011,,,,,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,1.106072244471945,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.047,,,,,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-4.591260240962891,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.038,,,,***,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-3.728705910880048,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.017,,,,***,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-1.6856315365090357,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBed = 3,image_condition,H_ai_derived,dummy,binary,0.0,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,0.0,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBed = 3,image_condition,H_ai_derived,dummy,binary,0.01,,,,*,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBed = 3,image_condition,H_ai_derived,dummy,binary,0.003,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,0.3004504503377081,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.002,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.009,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,0.9040621773867885,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.067,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,6.9295478174600245,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.126,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,13.428216828302508,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.11,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,11.627807045887128,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.146,,,,*,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,15.719618805079616,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.022,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-2.175976494878995,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.015,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-1.4888060396937353,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,0.001,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,0.10005001667083846,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,0.001,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,0.10005001667083846,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.036,,,,**,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-3.535970651687692,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.015,,,,***,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-1.4888060396937353,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.007,,,,,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-0.6975557066764893,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.009,,,,,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.009,,,,,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.01,,,,***,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,-0.057,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-5.540593063347665,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.02,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.034,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,3.4584606728117917,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.017,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,1.7145322325240686,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,-0.013,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-1.2915864979712421,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.079,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-7.596007555491324,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.17,,,,,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-15.63351834036163,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.184,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-16.806419617332825,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.186,,,,,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-16.972640501806737,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.148,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-13.75688850579545,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.059,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-5.729323084290028,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.025,,,,,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-2.4690087971667385,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.089,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-8.515442642554804,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.055,,,,*,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-5.351485204651619,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.077,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-7.411014639350466,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.021,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-2.078103543054044,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.009,,,,,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.04,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,-3.9210560847676823,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.033,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,-3.246144041096799,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.031,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,-3.0524426923974057,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c1,6,1 Cheap homes,22,CondKit = 5,image_condition,H_ai_derived,dummy,binary,-0.006,,,,,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.714,none,Lowest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c2,6,2 Expensive homes,22,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.042,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.283,none,Highest transaction-price quartile; postal code FE; R2 adjusted,pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c3,6,3 Few pictures,22,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.083,,,,**,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.861,none,Quartile with fewest listing images; postal code FE; R2 adjusted,pct_premium,8.654180854823812,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c4,6,4 Many pictures,22,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.061,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.694,none,Quartile with most listing images; postal code FE; R2 adjusted,pct_premium,6.289891418719518,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T6-c5,6,5 Full,22,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.076,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.756,none,Full sample with postal code FE + broker FE; R2 adjusted,pct_premium,7.896257415728392,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.034,,,,,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-3.342849536249337,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBath = 1,image_condition,H_ai_derived,dummy,binary,0.01,,,,,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.05,,,,,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-4.877057549928598,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.218,,,,***,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-19.587455833344038,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBath = 1,image_condition,H_ai_derived,dummy,binary,-0.062,,,,**,,,high,Computer-vision bathroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-6.011711320891111,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.039,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-3.8249290853633267,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.019,,,,,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,-1.8820637757194003,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.043,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-4.208860993296937,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.051,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-4.972132946757302,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBath = 2,image_condition,H_ai_derived,dummy,binary,-0.049,,,,***,,,high,Computer-vision bathroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-4.781887030149512,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.007,,,,,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-0.6975557066764893,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBath = 3,image_condition,H_ai_derived,dummy,binary,0.01,,,,**,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.01,,,,*,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.022,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-2.175976494878995,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBath = 3,image_condition,H_ai_derived,dummy,binary,-0.012,,,,***,,,high,Computer-vision bathroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-1.1928287138069482,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.013,,,,,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.021,,,,*,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.036,,,,**,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,3.6655846490923683,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.004,,,,,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,0.4008010677341911,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBath = 5,image_condition,H_ai_derived,dummy,binary,0.026,,,,***,,,high,Computer-vision bathroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,2.634094847344204,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.0504,,,,*,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-4.915099118087774,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.016,,,,,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-1.5872679944714863,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.222,,,,,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-19.908463566534085,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBed = 1,image_condition,H_ai_derived,dummy,binary,-0.095,,,,***,,,high,Computer-vision bedroom condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-9.062706553176858,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBed = 2,image_condition,H_ai_derived,dummy,binary,0.035,,,,***,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,3.5619708799623284,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBed = 2,image_condition,H_ai_derived,dummy,binary,0.022,,,,,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,2.2243784470438177,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBed = 2,image_condition,H_ai_derived,dummy,binary,0.01,,,,,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.07,,,,*,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBed = 2,image_condition,H_ai_derived,dummy,binary,-0.041,,,,***,,,high,Computer-vision bedroom condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-4.017087005220111,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.023,,,,***,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-2.273751622672293,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBed = 3,image_condition,H_ai_derived,dummy,binary,0.002,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,0.2002001334000303,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBed = 3,image_condition,H_ai_derived,dummy,binary,0.009,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,0.9040621773867885,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.003,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBed = 3,image_condition,H_ai_derived,dummy,binary,-0.003,,,,,,,high,Computer-vision bedroom condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.035,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,3.5619708799623284,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.042,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.119,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,12.636991828835287,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.132,,,,,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,14.11083192672351,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondBed = 5,image_condition,H_ai_derived,dummy,binary,0.146,,,,*,,,high,Computer-vision bedroom condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,15.719618805079616,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.03,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,0.008,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,0.8032085504273478,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,0.013,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,0.01,,,,,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondLiv = 2,image_condition,H_ai_derived,dummy,binary,-0.044,,,,**,,,high,Computer-vision living room condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-4.3046042526953325,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.013,,,,***,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-1.2915864979712421,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.008,,,,,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,-0.7968085162939342,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.012,,,,**,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-1.1928287138069482,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.004,,,,,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-0.39920106560085156,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondLiv = 3,image_condition,H_ai_derived,dummy,binary,-0.01,,,,***,,,high,Computer-vision living room condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.013,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.005,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,0.501252085940096,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.033,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,3.3550539241305577,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.051,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,5.232289328320383,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondLiv = 5,image_condition,H_ai_derived,dummy,binary,0.018,,,,,,,high,Computer-vision living room condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,1.8162976389793695,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.083,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.157,,,,,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,-14.529594118231493,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.191,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-17.386741184880616,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.419,,,,,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-34.2295804732183,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondKit = 1,image_condition,H_ai_derived,dummy,binary,-0.168,,,,***,,,high,Computer-vision kitchen condition level 1 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-15.464616531534126,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.059,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-5.729323084290028,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.024,,,,,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,-2.3714290242090708,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.085,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-8.148771559854262,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.077,,,,**,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-7.411014639350466,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondKit = 2,image_condition,H_ai_derived,dummy,binary,-0.083,,,,***,,,high,Computer-vision kitchen condition level 2 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-7.964885277987532,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.022,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-2.175976494878995,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.012,,,,**,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,-1.1928287138069482,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.037,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,-3.6323864650946502,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.034,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,-3.342849536249337,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondKit = 3,image_condition,H_ai_derived,dummy,binary,-0.034,,,,***,,,high,Computer-vision kitchen condition level 3 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,-3.342849536249337,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c1,7,1 Cheap homes,31,CondKit = 5,image_condition,H_ai_derived,dummy,binary,-0.029,,,,,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.608,none,Appendix: lowest price quartile; district FE; R2 adjusted,pct_premium,-2.858353553339521,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c2,7,2 Expensive homes,31,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.052,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3926.0,0.141,none,Appendix: highest price quartile; district FE; R2 adjusted,pct_premium,5.337574251336474,,100*(exp(b) - 1),,,,,,3916.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c3,7,3 Few pictures,31,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.089,,,,,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3110.0,0.825,none,Appendix: fewest images quartile; district FE; R2 adjusted,pct_premium,9.308065632633022,,100*(exp(b) - 1),,,,,,3100.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c4,7,4 Many pictures,31,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.066,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,3105.0,0.592,none,Appendix: most images quartile; district FE; R2 adjusted,pct_premium,6.822671716599338,,100*(exp(b) - 1),,,,,,3095.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4,5bcd886348a12f52aa055354a9647ba5c4fa64c4:T7-c5,7,5 Full,31,CondKit = 5,image_condition,H_ai_derived,dummy,binary,0.081,,,,***,,,high,Computer-vision kitchen condition level 5 dummy; reference level 4,ln(sales price per m2),log,OLS,15702.0,0.707,none,Appendix: full sample; district FE + broker FE; R2 adjusted,pct_premium,8.437089656676044,,100*(exp(b) - 1),,,,,,15692.0,binary,,2026.0,Norway,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,CNN image classification (room type and 1-5 condition per room);XGBoost AVM with SHAP (not extracted),2022.5,ols,journal,False,False,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,air distance,transit_distance,D_location,level,m,-53.84791,19.80832,-2.72,0.007,,-92.74359,-14.95222,high,"air (Euclidean) distance to nearest metro station in metres, within 500 m radius; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,-53.84791,19.80832,"b (currency per unit, as reported)",reported,-0.10568400590500093,0.03885441393566211,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.72,655.0,m,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,age,age,A_structural,level,years,-3856.095,241.4267,-15.97,0.0,,-4330.161,-3382.029,high,"age of building in years; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,-3856.095,241.4267,"b (currency per unit, as reported)",reported,-0.5293884484264596,0.033148932274668724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.97,655.0,years,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,size,living_area,A_structural,level,m2,612.0502,158.6117,3.86,0.0,,,923.5007,medium,"floor area in m2; printed lower CI bound (8,559.484) is a typesetting error duplicating the 'rooms' value, so ci_lo not recorded; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,612.0502,158.6117,"b (currency per unit, as reported)",reported,0.14913598448017432,0.03863626540937159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.86,655.0,m2,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,rooms,bedrooms,A_structural,level,count,18641.78,5134.587,3.63,0.0,,8559.484,28724.07,high,"number of rooms in the housing unit; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,18641.78,5134.587,"b (currency per unit, as reported)",reported,0.14043032353146126,0.03868603954034746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.63,655.0,count,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,baths,bathrooms,A_structural,level,count,23672.19,6665.66,3.55,0.0,,10583.47,36760.9,high,"number of bathrooms; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,23672.19,6665.66,"b (currency per unit, as reported)",reported,0.13739450883796692,0.03870267854590618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.55,655.0,count,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,alaturka,other,other,dummy,binary,14625.76,4799.835,3.05,0.002,,5200.786,24050.73,high,"1 if the unit has an alaturka (squat) toilet; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,14625.76,4799.835,"b (currency per unit, as reported)",reported,0.11833600293766482,0.038798689487758956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,655.0,binary,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,False,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,corner,other,other,dummy,binary,-25102.66,10815.1,-2.32,0.021,,-46339.24,-3866.091,high,"1 if building located at a corner (dual facade); Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,-25102.66,10815.1,"b (currency per unit, as reported)",reported,-0.09027972754647733,0.03891367566658505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.32,655.0,binary,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,False,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,south,sunlight,E_environment,dummy,binary,-22210.63,10416.01,-2.13,0.033,,-42663.55,-1757.709,high,"1 if unit has a south facade; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,-22210.63,10416.01,"b (currency per unit, as reported)",reported,-0.08293924050945367,0.03893861056781863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.13,655.0,binary,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,west,sunlight,E_environment,dummy,binary,-20810.5,11364.87,-1.83,0.068,,-43126.59,1505.596,high,"1 if unit has a west facade; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,-20810.5,11364.87,"b (currency per unit, as reported)",reported,-0.07132192109154634,0.03897372737242969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.83,655.0,binary,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,north,sunlight,E_environment,dummy,binary,-19716.2,14065.37,-1.4,0.161,,-47335.01,7902.614,high,"1 if unit has a north facade; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,-19716.2,14065.37,"b (currency per unit, as reported)",reported,-0.05462086478577961,0.039014903418414015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,655.0,binary,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697,5bed224cb10ce261eda55857ae903598b9f96697:T2-c1,2,1,6,elevator,accessibility,B_interior,dummy,binary,22008.68,4815.631,4.57,0.0,,12552.69,31464.67,high,"1 if building has an elevator; Stata-style output: Coef., Std. Err., t, P>|t|, 95% CI; dependent variable in Turkish lira levels","price_new (selling price of housing unit, Turkish lira)",level,OLS,665.0,0.5733,se,"Apartments within 500 m of 5 Esenler-Kirazli metro stations (control case), 2017 survey",monetary,22008.68,4815.631,"b (currency per unit, as reported)",reported,0.17578419646621732,0.03846481323111976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.57,655.0,binary,,2019.0,Turkey,Asia,Upper middle income,2017.0,2017.0,condo_apartment,listing,journal,False,False,OLS,,2017.0,ols,journal,False,True,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Area,living_area,A_structural,level,m2,0.008,,,,***,,,high,"floor area; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.8,,100*b/1 (X per m2->m2),,,,,,1692.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Area,living_area,A_structural,level,m2,0.008,,,,***,,,high,"floor area; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.8,,100*b/1 (X per m2->m2),,,,,,1692.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Area,living_area,A_structural,level,m2,0.007,,,,***,,,high,"floor area; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,1023.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Area,living_area,A_structural,level,m2,0.008,,,,***,,,high,"floor area; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.8,,100*b/1 (X per m2->m2),,,,,,1023.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Area,living_area,A_structural,level,m2,0.007,,,,***,,,high,"floor area; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,1075.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Area,living_area,A_structural,level,m2,0.006,,,,***,,,high,"floor area; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,1075.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Area,living_area,A_structural,level,m2,0.004,,,,***,,,high,"floor area; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,283.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Area,living_area,A_structural,level,m2,0.004,,,,***,,,high,"floor area; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,0.4,,100*b/1 (X per m2->m2),,,,,,283.0,m2,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Well-furnished,condition,A_structural,dummy,binary,0.044,,,,**,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,4.498235488844382,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Well-furnished,condition,A_structural,dummy,binary,0.047,,,,***,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,4.812200907965569,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Well-furnished,condition,A_structural,dummy,binary,0.037,,,,**,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,3.7693020838157176,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Well-furnished,condition,A_structural,dummy,binary,0.043,,,,***,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,4.393789485061261,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Well-furnished,condition,A_structural,dummy,binary,0.131,,,,***,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,13.99677813119904,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Well-furnished,condition,A_structural,dummy,binary,0.117,,,,***,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,12.41194296905368,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Well-furnished,condition,A_structural,dummy,binary,0.121,,,,*,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,12.862491236734396,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Well-furnished,condition,A_structural,dummy,binary,0.104,,,,*,,,high,"interior: well-furnished vs simply furnished (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,10.960045491558246,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Other,condition,A_structural,dummy,binary,-0.017,,,,,,,high,"interior: other vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-1.6856315365090357,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Other,condition,A_structural,dummy,binary,0.018,,,,,,,high,"interior: other vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,1.8162976389793695,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Other,condition,A_structural,dummy,binary,0.016,,,,,,,high,"interior: other vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,1.6128685406094911,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Other,condition,A_structural,dummy,binary,0.021,,,,,,,high,"interior: other vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Other,condition,A_structural,dummy,binary,0.034,,,,*,,,high,"interior: other vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,3.4584606728117917,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Other,condition,A_structural,dummy,binary,0.038,,,,**,,,high,"interior: other vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,3.873123287849767,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Other,condition,A_structural,dummy,binary,0.005,,,,,,,high,"interior: other vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,0.501252085940096,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Other,condition,A_structural,dummy,binary,0.003,,,,,,,high,"interior: other vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,0.3004504503377081,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Rough housing,condition,A_structural,dummy,binary,0.023,,,,,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,2.326653954721758,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Rough housing,condition,A_structural,dummy,binary,0.065,,,,*,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,6.715902438419263,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Rough housing,condition,A_structural,dummy,binary,-0.013,,,,,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-1.2915864979712421,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Rough housing,condition,A_structural,dummy,binary,-0.016,,,,,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,-1.5872679944714863,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Rough housing,condition,A_structural,dummy,binary,-0.135,,,,,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-12.628408831196559,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Rough housing,condition,A_structural,dummy,binary,-0.125,,,,,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-11.750309741540455,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Rough housing,condition,A_structural,dummy,binary,-0.81,,,,***,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-55.51419337770589,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Rough housing,condition,A_structural,dummy,binary,-0.76,,,,***,,,high,"interior: rough (unfinished) housing vs simply furnished; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,-53.23335729900908,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.08,,,,***,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,8.328706767495863,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.104,,,,***,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,10.960045491558246,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.162,,,,***,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,17.586024132099976,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.188,,,,***,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,20.683351534960526,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.047,,,,**,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,4.812200907965569,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.041,,,,,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,4.185210554547947,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.071,,,,,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,7.358122586835747,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,High-rise buildings,storeys,A_structural,dummy,binary (building >10 floors),0.068,,,,,,,high,"total floors >10 vs medium-high building 7-9 floors (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,7.036530847877431,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),0.042,,,,*,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),0.049,,,,**,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,5.022035074002806,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),0.023,,,,,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,2.326653954721758,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),0.01,,,,,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),-0.007,,,,,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-0.6975557066764893,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),-0.006,,,,,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),0.097,,,,*,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,10.186037362101064,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Multistory building,storeys,A_structural,dummy,binary (building 4-6 floors),0.083,,,,*,,,high,"total floors 4-6 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,8.654180854823812,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),-0.45,,,,***,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-36.23718483782267,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),-0.308,,,,***,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-26.508468199093127,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),-0.024,,,,,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-2.3714290242090708,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),-0.075,,,,,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,-7.225651367144714,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),0.041,,,,,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,4.185210554547947,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),0.059,,,,,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,6.077524074015894,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),0.168,,,,,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,18.293661064781077,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Low-rise building,storeys,A_structural,dummy,binary (building 1-3 floors),0.133,,,,,,,high,"total floors 1-3 vs 7-9 (reference); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,14.224999833089424,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Bedroom,bedrooms,A_structural,level,count,0.097,,,,***,,,high,"stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,9.700000000000001,,100*b/1 (per unit count),,,,,,1692.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Bedroom,bedrooms,A_structural,level,count,0.091,,,,***,,,high,"stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,9.1,,100*b/1 (per unit count),,,,,,1692.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Bedroom,bedrooms,A_structural,level,count,0.094,,,,***,,,high,"stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,9.4,,100*b/1 (per unit count),,,,,,1023.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Bedroom,bedrooms,A_structural,level,count,0.065,,,,***,,,high,"stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,6.5,,100*b/1 (per unit count),,,,,,1023.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Bedroom,bedrooms,A_structural,level,count,0.073,,,,***,,,high,"stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,7.3,,100*b/1 (per unit count),,,,,,1075.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Bedroom,bedrooms,A_structural,level,count,0.084,,,,***,,,high,"stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,8.4,,100*b/1 (per unit count),,,,,,1075.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Bedroom,bedrooms,A_structural,level,count,0.092,,,,***,,,high,"stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,9.2,,100*b/1 (per unit count),,,,,,283.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Bedroom,bedrooms,A_structural,level,count,0.085,,,,***,,,high,"stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,8.5,,100*b/1 (per unit count),,,,,,283.0,count,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Livingroom,other,other,level,count,0.156,,,,***,,,high,"number of living rooms; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,15.6,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1692.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Livingroom,other,other,level,count,0.146,,,,***,,,high,"number of living rooms; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,14.6,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1692.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Livingroom,other,other,level,count,0.131,,,,***,,,high,"number of living rooms; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,13.100000000000001,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1023.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Livingroom,other,other,level,count,0.137,,,,***,,,high,"number of living rooms; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,13.700000000000001,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1023.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Livingroom,other,other,level,count,0.179,,,,***,,,high,"number of living rooms; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,17.9,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1075.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Livingroom,other,other,level,count,0.175,,,,***,,,high,"number of living rooms; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,17.5,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1075.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Livingroom,other,other,level,count,0.151,,,,***,,,high,"number of living rooms; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,15.1,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,283.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Livingroom,other,other,level,count,0.162,,,,***,,,high,"number of living rooms; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,16.2,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,283.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Building Age,age,A_structural,level,years,-0.006,,,,***,,,high,"age of apartment building; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.6,,100*b/1 (per year),,,,,,1692.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Building Age,age,A_structural,level,years,-0.007,,,,***,,,high,"age of apartment building; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.7000000000000001,,100*b/1 (per year),,,,,,1692.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Building Age,age,A_structural,level,years,-0.007,,,,***,,,high,"age of apartment building; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.7000000000000001,,100*b/1 (per year),,,,,,1023.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Building Age,age,A_structural,level,years,-0.006,,,,***,,,high,"age of apartment building; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.6,,100*b/1 (per year),,,,,,1023.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Building Age,age,A_structural,level,years,-0.008,,,,***,,,high,"age of apartment building; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.8,,100*b/1 (per year),,,,,,1075.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Building Age,age,A_structural,level,years,-0.008,,,,***,,,high,"age of apartment building; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.8,,100*b/1 (per year),,,,,,1075.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Building Age,age,A_structural,level,years,-0.0004,,,,,,,high,"age of apartment building; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.04,,100*b/1 (per year),,,,,,283.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Building Age,age,A_structural,level,years,-0.001,,,,,,,high,"age of apartment building; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,-0.1,,100*b/1 (per year),,,,,,283.0,years,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.016,,,,***,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,1.6,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.01,,,,*,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,1.0,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.021,,,,***,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,2.1,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.019,,,,***,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,1.9,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.009,,,,,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.8999999999999999,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.005,,,,,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.5,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.044,,,,***,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,4.3999999999999995,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Culture,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.041,,,,***,,,high,"number of libraries and culture centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,4.1000000000000005,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.024,,,,,,,high,"number of sports centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,2.4,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.025,,,,,,,high,"number of sports centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,2.5,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.056,,,,**,,,high,"number of sports centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,5.6000000000000005,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.063,,,,***,,,high,"number of sports centres within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,6.3,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,0.007,,,,,,,high,"number of sports centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.7000000000000001,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,-0.0002,,,,,,,high,"number of sports centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.02,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,-0.034,,,,,,,high,"number of sports centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-3.4000000000000004,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Sports,neighbourhood_amenities,F_neighbourhood,level,count within 1 km,-0.018,,,,,,,high,"number of sports centres within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,-1.7999999999999998,,100*b (unit as reported: count within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,-0.017,,,,***,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-1.7000000000000002,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,-0.011,,,,**,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-1.0999999999999999,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,-0.01,,,,,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-1.0,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,-0.012,,,,,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-1.2,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,-0.011,,,,**,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-1.0999999999999999,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,-0.011,,,,*,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-1.0999999999999999,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,0.009,,,,,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.8999999999999999,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Hospital,hospital_distance,D_location,level,count within 1 km,0.007,,,,,,,high,"number of hospitals within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,0.7000000000000001,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,-0.008,,,,***,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.8,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,-0.006,,,,***,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.6,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,0.007,,,,*,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.7000000000000001,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,0.001,,,,,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.1,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,-0.002,,,,,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.2,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,-0.002,,,,,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.2,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,0.0002,,,,,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.02,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Shopping,commercial_distance,D_location,level,count within 1 km,0.001,,,,,,,high,"number of shopping facilities within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,0.1,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Park,green_space,E_environment,level,count within 1 km,0.01,,,,,,,high,"number of district-level parks within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,1.0,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Park,green_space,E_environment,level,count within 1 km,-0.003,,,,,,,high,"number of district-level parks within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.3,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Park,green_space,E_environment,level,count within 1 km,-0.054,,,,***,,,high,"number of district-level parks within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-5.4,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Park,green_space,E_environment,level,count within 1 km,-0.056,,,,***,,,high,"number of district-level parks within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-5.6000000000000005,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Park,green_space,E_environment,level,count within 1 km,-0.015,,,,,,,high,"number of district-level parks within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-1.5,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Park,green_space,E_environment,level,count within 1 km,-0.016,,,,,,,high,"number of district-level parks within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-1.6,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Park,green_space,E_environment,level,count within 1 km,0.001,,,,,,,high,"number of district-level parks within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.1,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Park,green_space,E_environment,level,count within 1 km,-0.001,,,,,,,high,"number of district-level parks within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,-0.1,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.046,,,,***,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-4.6,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.079,,,,***,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-7.9,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.109,,,,***,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-10.9,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.039,,,,***,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-3.9,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.039,,,,***,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-3.9,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.081,,,,***,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-8.1,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Blue-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.053,,,,*,,,high,"number of blue-collar jobs within 1 km; cell blank in inner-district owner-occupied OLS column; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,-5.3,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.029,,,,**,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,2.9000000000000004,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.011,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,1.0999999999999999,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.083,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,8.3,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.169,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,16.900000000000002,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.002,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.2,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-0.004,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,-0.4,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.258,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,25.8,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Pink-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.198,,,,,,,high,"number of pink-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,19.8,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.051,,,,,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,5.1,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.107,,,,**,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,10.7,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1692.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.011,,,,,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,1.0999999999999999,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.355,,,,,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,35.5,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1023.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.159,,,,***,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,15.9,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",0.163,,,,***,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,16.3,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,1075.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-1.601,,,,**,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-160.1,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,White-collar jobs,employment_access,D_location,level,"10,000 jobs within 1 km",-1.315,,,,**,,,high,"number of white-collar jobs within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,-131.5,,100*b (unit as reported: 10000 jobs within 1 km; no harmonised unit defined for this characteristic),,,,,,283.0,10000 jobs within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,0.005,,,,***,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.5,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,0.003,,,,,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.3,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1692.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,0.009,,,,***,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,0.8999999999999999,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,0.008,,,,**,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.8,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1023.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,-0.001,,,,,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.1,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,0.0002,,,,,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",semi_elasticity,0.02,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1075.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,-0.004,,,,,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",semi_elasticity,-0.4,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Bus stops,transit_distance,D_location,level,count within 1 km,-0.006,,,,,,,high,"number of bus stops within 1 km (count, not distance); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",semi_elasticity,-0.6,,"100*b (unit as reported: count within 1 km; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,283.0,count within 1 km,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),-0.066,,,,***,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-6.386913570838115,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),-0.097,,,,***,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-9.24439938667273,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),0.071,,,,**,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,7.358122586835747,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),0.075,,,,*,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,7.788415088463152,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),-0.026,,,,,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-2.566491039125063,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),-0.029,,,,,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-2.858353553339521,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),0.288,,,,***,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,33.375730412338456,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,BRT,transit_distance,D_location,dummy,binary (BRT station within 1 km),0.286,,,,***,,,high,"= 1 if BRT station within 1 km; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,33.109245525229156,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),0.138,,,,***,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,14.797555027417864,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),0.09,,,,***,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),0.09,,,,***,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),0.156,,,,***,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,16.88262030898691,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),0.06,,,,**,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,6.183654654535964,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),0.062,,,,**,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,6.396234472803375,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),-0.103,,,,**,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-9.78730265184835,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Metro (ref: No),transit_distance,D_location,dummy,binary (planned metro station within 1 km),-0.089,,,,**,,,high,"= 1 if planned metro station within 1 km (metro not yet operating in 2016-2017); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,-8.515442642554804,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,River,water_proximity,E_environment,dummy,binary (river or lake within 300 m),-0.013,,,,,,,high,"= 1 if river or lake within 300 m; not in outer-district rental models; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-1.2915864979712421,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,River,water_proximity,E_environment,dummy,binary (river or lake within 300 m),0.024,,,,,,,high,"= 1 if river or lake within 300 m; not in outer-district rental models; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,2.4290317890621527,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,River,water_proximity,E_environment,dummy,binary (river or lake within 300 m),-0.08,,,,,,,high,"= 1 if river or lake within 300 m; not in outer-district rental models; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-7.688365361336425,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,River,water_proximity,E_environment,dummy,binary (river or lake within 300 m),-0.117,,,,,,,high,"= 1 if river or lake within 300 m; not in outer-district rental models; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,-11.041480683658866,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,River,water_proximity,E_environment,dummy,binary (river or lake within 300 m),-0.017,,,,,,,high,"= 1 if river or lake within 300 m; not in outer-district rental models; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-1.6856315365090357,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,River,water_proximity,E_environment,dummy,binary (river or lake within 300 m),-0.024,,,,,,,high,"= 1 if river or lake within 300 m; not in outer-district rental models; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-2.3714290242090708,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.19,,,,***,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,20.92495976572515,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.135,,,,**,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,14.453678435131456,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.054,,,,***,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,5.548460215508011,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.07,,,,***,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,7.250818125421654,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.163,,,,**,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,17.7036689688467,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.148,,,,**,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,15.951289636297394,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.141,,,,***,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,15.142464798474409,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Seascape,view,E_environment,dummy,binary (sea within 300 m),0.125,,,,***,,,high,"= 1 if sea within 300 m (first-line sea view housing); stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,13.314845306682631,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,School district,school_quality,F_neighbourhood,dummy,binary,0.096,,,,***,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,10.075906399397883,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,School district,school_quality,F_neighbourhood,dummy,binary,0.102,,,,***,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,10.73834717279334,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,School district,school_quality,F_neighbourhood,dummy,binary,-0.048,,,,,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-4.6866212922495265,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,School district,school_quality,F_neighbourhood,dummy,binary,-0.053,,,,*,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,-5.161998751770181,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,School district,school_quality,F_neighbourhood,dummy,binary,-0.0002,,,,,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-0.019998000133325533,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,School district,school_quality,F_neighbourhood,dummy,binary,-0.001,,,,,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-0.09995001666249781,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,School district,school_quality,F_neighbourhood,dummy,binary,-0.005,,,,,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-0.498752080731768,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,School district,school_quality,F_neighbourhood,dummy,binary,0.017,,,,,,,high,"= 1 if in one of 21 high-quality primary school districts; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,1.7145322325240686,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c1,2,"OLS Owner-occupied housing, inner districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),0.02,,,,,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1702.0,0.79,none,"Owner-occupied housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c2,2,"SEM Owner-occupied housing, inner districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),0.028,,,,,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1702.0,,none,"Owner-occupied housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,2.839568442142504,,100*(exp(b) - 1),,,,,,1692.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c3,2,"OLS Owner-occupied housing, outer districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),-0.039,,,,*,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed","ln total housing price (1,000 yuan)",log,OLS,1033.0,0.837,none,"Owner-occupied housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-3.8249290853633267,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c4,2,"SEM Owner-occupied housing, outer districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),-0.037,,,,*,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed","ln total housing price (1,000 yuan)",log,spatial_error,1033.0,,none,"Owner-occupied housing, outer districts; SEM; year 2017 dummy included; stars only",pct_premium,-3.6323864650946502,,100*(exp(b) - 1),,,,,,1023.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c5,2,"OLS Rental housing, inner districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),-0.03,,,,,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,1085.0,0.78,none,"Rental housing, inner districts; OLS; year 2017 dummy included; stars only",pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c6,2,"SEM Rental housing, inner districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),-0.047,,,,,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_error,1085.0,,none,"Rental housing, inner districts; SEM; year 2017 dummy included; stars only",pct_premium,-4.591260240962891,,100*(exp(b) - 1),,,,,,1075.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c7,2,"OLS Rental housing, outer districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),-0.035,,,,,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed",ln total rental price (yuan/month),log,OLS,293.0,0.747,none,"Rental housing, outer districts; OLS; year 2017 dummy included; stars only",pct_premium,-3.4394583742433538,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,ols,journal,False,False,False
5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5,5cf2ca5773ec64d2d8be794899e97064def914d5:T2-c8,2,"SLM Rental housing, outer districts",12,Urban villages,other,other,dummy,binary (urban village within 100 m),-0.031,,,,,,,high,"= 1 if an urban village within 100 m; stars only, no SE printed",ln total rental price (yuan/month),log,spatial_lag,293.0,,none,"Rental housing, outer districts; SLM; year 2017 dummy included; stars only",pct_premium,-3.0524426923974057,,100*(exp(b) - 1),,,,,,283.0,binary,,2022.0,China,Asia,Upper middle income,2016.0,2017.0,condo_apartment,listing,journal,True,False,unknown,,2016.5,spatial,journal,False,False,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Bedrooms,bedrooms,A_structural,level,count,0.0427,0.003,,,***,,,high,,ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",semi_elasticity,4.2700000000000005,0.3,100*b/1 (per unit count),reported,0.1505807168675525,0.010579441466104391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.233333333333334,8732.0,count,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,House,dwelling_type,A_structural,binary,binary,0.1685,0.0154,,,***,,,high,House (vs apartment),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",pct_premium,18.33878923883139,1.8226334693410822,100*(exp(b - se^2/2) - 1),reported,0.11629616855779301,0.010628848639703337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.941558441558442,8732.0,binary,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Floor,floor_level,A_structural,level,count,0.0155,0.0009,,,***,,,high,,ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",semi_elasticity,1.55,0.09,100*b/1 (per unit count),reported,0.18125035407861786,0.010524214107790716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.22222222222222,8732.0,count,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Area,living_area,A_structural,level,m2,0.0064,0.0001,,,***,,,high,,ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",semi_elasticity,0.64,0.01,100*b/1 (X per m2->m2),reported,0.5650677197537494,0.008829183121152336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),64.0,8732.0,m2,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Age2,age,A_structural,dummy,binary,-0.1034,0.0063,,,***,,,high,Building age class 2 (class bounds not printed),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",pct_premium,-9.82517005199094,0.5681127027571758,100*(exp(b - se^2/2) - 1),reported,-0.17299180159095368,0.010540119439294083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.412698412698415,8732.0,binary,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Age3,age,A_structural,dummy,binary,-0.0729,0.0068,,,***,,,high,Building age class 3 (class bounds not printed),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",pct_premium,-7.032769951510565,0.6321917804347289,100*(exp(b - se^2/2) - 1),reported,-0.11397833153010199,0.01063172365438537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.72058823529412,8732.0,binary,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Garage,garage,A_structural,binary,binary,0.1126,0.0059,,,***,,,high,Off-street parking inside property,ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",pct_premium,11.91646905030639,0.6603186601430716,100*(exp(b - se^2/2) - 1),reported,0.2001039776244937,0.010485021918157308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.084745762711865,8732.0,binary,,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,True,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Educational Index,education_level,F_neighbourhood,level,index,0.416,0.0225,,,***,,,high,Neighbourhood education level indicator,ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",semi_elasticity,41.6,2.25,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.19409541553743534,0.010497949157673788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.488888888888887,8732.0,index,unit_not_harmonisable,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Entropy Index,other,other,level,index,0.2312,0.0234,,,***,,,high,Land-use mixture (entropy) indicator,ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",semi_elasticity,23.119999999999997,2.34,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.10514798082844559,0.010642139928138526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.880341880341879,8732.0,index,unit_not_harmonisable,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,False,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,2MAccess,transit_distance,D_location,other,index,0.0916,0.0133,,,***,,,high,Impedance function of walking distance to the two closest subway lines (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,0.0916,0.0133,not harmonised,reported,0.0735039332554959,0.01067251432639842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.887218045112783,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,1Maccess,transit_distance,D_location,other,index,0.0652,0.0084,,,***,,,high,Impedance function of walking distance to closest subway line (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,0.0652,0.0084,not harmonised,reported,0.08277865497053741,0.01066473468945574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.761904761904762,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Network1,highway_distance,D_location,other,index,-0.0732,0.0069,,,***,,,high,Impedance of distance to nearest motorway node (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,-0.0732,0.0069,not harmonised,reported,-0.11280393678724465,0.010633157975846831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.608695652173914,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Network2,highway_distance,D_location,other,index,0.0458,0.0064,,,***,,,high,Impedance of distance to nearest ring/radial road node (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,0.0458,0.0064,not harmonised,reported,0.07635875230223553,0.010670218662321122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.15625,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Network3,highway_distance,D_location,other,index,-0.038,0.006,,,***,,,high,Impedance of distance to nearest urban distribution road node (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,-0.038,0.006,not harmonised,reported,-0.06762079685157434,0.010676967923932789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.333333333333333,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Sintra,transit_distance,D_location,other,index,-0.0614,0.0134,,,***,,,high,Impedance of walking time to Sintra commuter-rail stations (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,-0.0614,0.0134,not harmonised,reported,-0.04897621503510915,0.010688620219388642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.582089552238806,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109,5cf7ffdc50779ba179e5d6c37bf66eba2f4f8109:T3-res,3,Residential model (Martinez and Viegas 2009),7,Cascais,transit_distance,D_location,other,index,0.1517,0.0259,,,***,,,high,Impedance of walking time to Cascais commuter-rail stations (higher = closer),ln(asking price),log,spatial_lag,8742.0,0.795,se,"residential properties, Lisbon; pseudo R2",other,0.1517,0.0259,not harmonised,reported,0.06255722861750855,0.010680502446891702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.857142857142858,8732.0,index,nonstandard_x_transform,2012.0,Portugal,Europe,High income,2007.0,2007.0,mixed,listing,journal,False,False,unknown,,2007.0,spatial,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c1,4,1,12,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.0457,0.0037,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.1683,se,"All transactions within 3 km of a station opened 2010-2018; no controls, no FE",pct_premium,-4.467802144076549,0.35347155157140653,100*(exp(b - se^2/2) - 1),reported,-0.013529487005321044,0.0010953851623564084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.35135135135135,833272.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c1,4,1,12,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0105,0.0053,,,*,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.1683,se,"All transactions within 3 km of a station opened 2010-2018; no controls, no FE",other,0.0105,0.0053,not harmonised,reported,0.0021702962115569533,0.0010954828496430335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9811320754716981,833272.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c2,4,2,12,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0122,0.0022,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.7155,se,Region (3-digit postal code) FE + quarter FE; no structural controls,pct_premium,1.227227386477936,0.2227004391846622,100*(exp(b - se^2/2) - 1),reported,0.0060748525593663305,0.0010954652156234365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.545454545454546,833272.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c2,4,2,12,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0236,0.0031,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.7155,se,Region (3-digit postal code) FE + quarter FE; no structural controls,other,0.0236,0.0031,not harmonised,reported,0.008339534547527276,0.0010954473346328204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.612903225806452,833272.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c3,4,3,12,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.018,0.0021,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.742,se,Region + quarter FE; age and area linear,pct_premium,1.8160731342905967,0.21381422504185665,100*(exp(b - se^2/2) - 1),reported,0.009389461186873168,0.0010954371384685364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.571428571428571,833272.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c3,4,3,12,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0247,0.003,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.742,se,Region + quarter FE; age and area linear,other,0.0247,0.003,not harmonised,reported,0.009019129852235826,0.0010954408727411936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.233333333333333,833272.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c3,4,3,12,Age (centuries),age,A_structural,other,centuries (years/100),-1.1762,0.0046,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.742,se,Region + quarter FE; age and area linear,other,-1.1762,0.0046,not harmonised,reported,-0.2697289156009385,0.0010548826830167632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-255.69565217391303,833272.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c3,4,3,12,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),16.9128,0.121,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.742,se,Region + quarter FE; age and area linear,other,16.9128,0.121,not harmonised,reported,0.15135759828600967,0.0010828644217756472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),139.77520661157027,833272.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c4,4,4,12,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0228,0.0021,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7466,se,"Region + quarter + frame-type FE; age, area and squares",pct_premium,2.305965085425732,0.21484300040768764,100*(exp(b - se^2/2) - 1),reported,0.011893007759743597,0.0010954086094500683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.857142857142858,833271.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c4,4,4,12,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0226,0.0029,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7466,se,"Region + quarter + frame-type FE; age, area and squares",other,0.0226,0.0029,not harmonised,reported,0.008536925303102603,0.0010954461672122808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.793103448275862,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c4,4,4,12,Age (centuries),age,A_structural,other,centuries (years/100),-2.5029,0.0125,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7466,se,"Region + quarter + frame-type FE; age, area and squares",other,-2.5029,0.0125,not harmonised,reported,-0.2142574236724696,0.0010700458651587642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-200.23199999999997,833271.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c4,4,4,12,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),10.248,0.3469,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7466,se,"Region + quarter + frame-type FE; age, area and squares",other,10.248,0.3469,not harmonised,reported,0.03234553787427123,0.0010949128696901532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.541654655520322,833271.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c4,4,4,12,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1766,0.0354,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7466,se,"Region + quarter + frame-type FE; age, area and squares",other,4.1766,0.0354,not harmonised,reported,0.1281825642675406,0.001086448971668567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),117.98305084745762,833271.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c4,4,4,12,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),127.9,12.3798,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 1.279 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7466,se,"Region + quarter + frame-type FE; age, area and squares",other,127.9,12.3798,not harmonised,reported,0.011317121246933112,0.0010954159312961888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.331346225302509,833271.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c5,4,5,12,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0139,0.0021,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7524,se,"Full specification: region + quarter + frame FE (region x quarter per eq. 2), age, area and squares; baseline model (identical to T5 c1, T7 c2, T8 c2, T9 c1, not re-extracted)",pct_premium,1.3994818301882317,0.21293938137421356,100*(exp(b - se^2/2) - 1),reported,0.007250883959285077,0.0010954572888128534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.619047619047619,833271.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c5,4,5,12,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0371,0.003,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7524,se,"Full specification: region + quarter + frame FE (region x quarter per eq. 2), age, area and squares; baseline model (identical to T5 c1, T7 c2, T8 c2, T9 c1, not re-extracted)",other,0.0371,0.003,not harmonised,reported,0.013546268224803641,0.001095385570738839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.366666666666667,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c5,4,5,12,Age (centuries),age,A_structural,other,centuries (years/100),-2.5031,0.0125,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7524,se,"Full specification: region + quarter + frame FE (region x quarter per eq. 2), age, area and squares; baseline model (identical to T5 c1, T7 c2, T8 c2, T9 c1, not re-extracted)",other,-2.5031,0.0125,not harmonised,reported,-0.214273758367852,0.0010700419398338662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-200.248,833271.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c5,4,5,12,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),10.7196,0.3442,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7524,se,"Full specification: region + quarter + frame FE (region x quarter per eq. 2), age, area and squares; baseline model (identical to T5 c1, T7 c2, T8 c2, T9 c1, not re-extracted)",other,10.7196,0.3442,not harmonised,reported,0.034097455409689024,0.001094849075713176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.14352120859965,833271.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c5,4,5,12,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1717,0.0352,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7524,se,"Full specification: region + quarter + frame FE (region x quarter per eq. 2), age, area and squares; baseline model (identical to T5 c1, T7 c2, T8 c2, T9 c1, not re-extracted)",other,4.1717,0.0352,not harmonised,reported,0.12875009086534095,0.00108636843456145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),118.51420454545455,833271.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T4-c5,4,5,12,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),114.3,12.2858,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 1.143 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7524,se,"Full specification: region + quarter + frame FE (region x quarter per eq. 2), age, area and squares; baseline model (identical to T5 c1, T7 c2, T8 c2, T9 c1, not re-extracted)",other,114.3,12.2858,not harmonised,reported,0.010191241686827526,0.0010954291961157096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.303423464487457,833271.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c2,5,2,13,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0164,0.0023,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,725519.0,0.7542,se,41 stations (4 stations re-opened for a second time removed); full specification,pct_premium,1.653252944929573,0.23380310018172012,100*(exp(b - se^2/2) - 1),reported,0.008371039532865573,0.0011739872515604158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.130434782608696,725509.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c2,5,2,13,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0388,0.0032,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,725519.0,0.7542,se,41 stations (4 stations re-opened for a second time removed); full specification,other,0.0388,0.0032,not harmonised,reported,0.014233652125374072,0.0011739094536390986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.125,725509.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c2,5,2,13,Age (centuries),age,A_structural,other,centuries (years/100),-2.5244,0.0135,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,725519.0,0.7542,se,41 stations (4 stations re-opened for a second time removed); full specification,other,-2.5244,0.0135,not harmonised,reported,-0.2144281835576982,0.0011467202020396632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-186.9925925925926,725509.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c2,5,2,13,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),13.814,0.3653,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,725519.0,0.7542,se,41 stations (4 stations re-opened for a second time removed); full specification,other,13.814,0.3653,not harmonised,reported,0.044352774393651005,0.0011728730625452956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.8154941144265,725509.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c2,5,2,13,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.2409,0.0381,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,725519.0,0.7542,se,41 stations (4 stations re-opened for a second time removed); full specification,other,4.2409,0.0381,not harmonised,reported,0.12957900446113574,0.001164130271869007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),111.30971128608923,725509.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c2,5,2,13,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),32.12,12.8276,,,*,,,high,Squared area in hectares ((m2/10000)^2); printed as 3.212 x 10,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,725519.0,0.7542,se,41 stations (4 stations re-opened for a second time removed); full specification,other,32.12,12.8276,not harmonised,reported,0.002939725969222787,0.0011740233139104056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5039758021765564,725509.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c3,5,3,13,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.0178,0.0058,,,**,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,107762.0,0.7396,se,Only the 4 stations opened for a second time (junction stations); full specification,pct_premium,-1.7659038904621949,0.5697673408413955,100*(exp(b - se^2/2) - 1),reported,-0.009348896652378948,0.0030462696957189827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0689655172413794,107752.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c3,5,3,13,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),-0.0005,0.0083,,,,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,107762.0,0.7396,se,Only the 4 stations opened for a second time (junction stations); full specification,other,-0.0005,0.0083,not harmonised,reported,-0.00018351823963723977,0.0030464027779781803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.060240963855421686,107752.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c3,5,3,13,Age (centuries),age,A_structural,other,centuries (years/100),-2.5746,0.0314,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,107762.0,0.7396,se,Only the 4 stations opened for a second time (junction stations); full specification,other,-2.5746,0.0314,not harmonised,reported,-0.24233987259981923,0.0029555938785187303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-81.99363057324842,107752.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c3,5,3,13,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),-17.5511,1.1811,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,107762.0,0.7396,se,Only the 4 stations opened for a second time (junction stations); full specification,other,-17.5511,1.1811,not harmonised,reported,-0.0452231127732082,0.003043286090127468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.859961053255441,107752.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c3,5,3,13,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.122,0.0908,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,107762.0,0.7396,se,Only the 4 stations opened for a second time (junction stations); full specification,other,4.122,0.0908,not harmonised,reported,0.13699211702553937,0.003017681762716879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.39647577092511,107752.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T5-c3,5,3,13,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),990.3,48.9493,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 9.903 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,107762.0,0.7396,se,Only the 4 stations opened for a second time (junction stations); full specification,other,990.3,48.9493,not harmonised,reported,0.06151546999209713,0.003040633338669252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.23113711534179,107752.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c1,6,1,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.1403,0.0392,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,80423.0,0.641,se,Station cluster: North (region without prior BRT line); full specification,pct_premium,-13.157002146526342,3.406862070817948,100*(exp(b - se^2/2) - 1),reported,-0.012620422164878794,0.0035261621444280023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5790816326530615,80413.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c1,6,1,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.1138,0.0415,,,**,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,80423.0,0.641,se,Station cluster: North (region without prior BRT line); full specification,other,0.1138,0.0415,not harmonised,reported,0.009669649409941266,0.0035262781240119733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.742168674698795,80413.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c1,6,1,15,Age (centuries),age,A_structural,other,centuries (years/100),-3.2597,0.0485,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,80423.0,0.641,se,Station cluster: North (region without prior BRT line); full specification,other,-3.2597,0.0485,not harmonised,reported,-0.23062413512579766,0.0034313803581928356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-67.21030927835051,80413.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c1,6,1,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),24.7873,1.0445,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,80423.0,0.641,se,Station cluster: North (region without prior BRT line); full specification,other,24.7873,1.0445,not harmonised,reported,0.0833954114611601,0.0035141587535222362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.731258975586403,80413.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c1,6,1,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),6.8146,0.1437,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,80423.0,0.641,se,Station cluster: North (region without prior BRT line); full specification,other,6.8146,0.1437,not harmonised,reported,0.16494188333797763,0.0034781423173285866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.422407794015314,80413.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c1,6,1,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),-219.0,29.6552,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as -2.19 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,80423.0,0.641,se,Station cluster: North (region without prior BRT line); full specification,other,-219.0,29.6552,not harmonised,reported,-0.0260335207042961,0.003525247777123478,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.3848768512773475,80413.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c2,6,2,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.1193,0.0094,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,55045.0,0.5066,se,Station cluster: South (region without prior BRT line); full specification,pct_premium,-11.24977123481492,0.8342890084669331,100*(exp(b - se^2/2) - 1),reported,-0.05402048715767053,0.004256434025834895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.691489361702127,55035.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c2,6,2,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0688,0.0133,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,55045.0,0.5066,se,Station cluster: South (region without prior BRT line); full specification,other,0.0688,0.0133,not harmonised,reported,0.0220450838864248,0.0042616223210675845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.172932330827068,55035.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c2,6,2,15,Age (centuries),age,A_structural,other,centuries (years/100),-2.3733,0.0724,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,55045.0,0.5066,se,Station cluster: South (region without prior BRT line); full specification,other,-2.3733,0.0724,not harmonised,reported,-0.13838711822599786,0.00422164385436407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.78038674033149,55035.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c2,6,2,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),-1.5277,2.8774,,,,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,55045.0,0.5066,se,Station cluster: South (region without prior BRT line); full specification,other,-1.5277,2.8774,not harmonised,reported,-0.002263170334260207,0.004262647325914983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5309307013275874,55035.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c2,6,2,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.7623,0.2565,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,55045.0,0.5066,se,Station cluster: South (region without prior BRT line); full specification,other,4.7623,0.2565,not harmonised,reported,0.07889582592763551,0.004249370965801925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.566471734892787,55035.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c2,6,2,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),262.0,161.2154,,,,,,high,Squared area in hectares ((m2/10000)^2); printed as 2.62 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,55045.0,0.5066,se,Station cluster: South (region without prior BRT line); full specification,other,262.0,161.2154,not harmonised,reported,0.006927313783248891,0.00426255596370986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6251549169620274,55035.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c3,6,3,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.0323,0.0101,,,**,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,88720.0,0.7257,se,Station cluster: West (region without prior BRT line); full specification,pct_premium,-3.183330891843561,0.9778982344198422,100*(exp(b - se^2/2) - 1),reported,-0.010736675454727533,0.0033572886096826025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.198019801980198,88710.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c3,6,3,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0337,0.0124,,,**,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,88720.0,0.7257,se,Station cluster: West (region without prior BRT line); full specification,other,0.0337,0.0124,not harmonised,reported,0.009124390146651538,0.0033573423685008628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.717741935483871,88710.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c3,6,3,15,Age (centuries),age,A_structural,other,centuries (years/100),-3.1817,0.0397,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,88720.0,0.7257,se,Station cluster: West (region without prior BRT line); full specification,other,-3.1817,0.0397,not harmonised,reported,-0.2598382850553574,0.003242159825469933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-80.14357682619648,88710.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c3,6,3,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),-30.7669,1.3572,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,88720.0,0.7257,se,Station cluster: West (region without prior BRT line); full specification,other,-30.7669,1.3572,not harmonised,reported,-0.07589257429105165,0.003347799155190002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.66939286766873,88710.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c3,6,3,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),9.3918,0.1322,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,88720.0,0.7257,se,Station cluster: West (region without prior BRT line); full specification,other,9.3918,0.1322,not harmonised,reported,0.23201469370167432,0.0032658641056412343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.04236006051437,88710.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c3,6,3,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),1473.0,56.9606,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 14.73 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,88720.0,0.7257,se,Station cluster: West (region without prior BRT line); full specification,other,1473.0,56.9606,not harmonised,reported,0.08649899885651369,0.0033448980816472057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.859980407509752,88710.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c4,6,4,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.0819,0.0075,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,71263.0,0.7436,se,Station cluster: Sayyad (region without prior BRT line); full specification,pct_premium,-7.866182250191034,0.6910230678740487,100*(exp(b - se^2/2) - 1),reported,-0.04087501784773232,0.0037431335025395903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.92,71253.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c4,6,4,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.001,0.0122,,,,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,71263.0,0.7436,se,Station cluster: Sayyad (region without prior BRT line); full specification,other,0.001,0.0122,not harmonised,reported,0.0003070708362140066,0.0037462642018108805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.08196721311475409,71253.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c4,6,4,15,Age (centuries),age,A_structural,other,centuries (years/100),-2.7542,0.042,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,71263.0,0.7436,se,Station cluster: Sayyad (region without prior BRT line); full specification,other,-2.7542,0.042,not harmonised,reported,-0.23857209608370616,0.003638090202423811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-65.57619047619048,71253.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c4,6,4,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),23.5485,1.0859,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,71263.0,0.7436,se,Station cluster: Sayyad (region without prior BRT line); full specification,other,23.5485,1.0859,not harmonised,reported,0.08097358658340288,0.003733962573875924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.68569849894097,71253.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c4,6,4,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),5.1907,0.1278,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,71263.0,0.7436,se,Station cluster: Sayyad (region without prior BRT line); full specification,other,5.1907,0.1278,not harmonised,reported,0.15042618336261324,0.0037036365487780015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.61580594679186,71253.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c4,6,4,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),-161.0,36.8792,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as -1.61 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,71263.0,0.7436,se,Station cluster: Sayyad (region without prior BRT line); full specification,other,-161.0,36.8792,not harmonised,reported,-0.016352521667377926,0.0037457634600966704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.365604459966594,71253.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c5,6,5,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0569,0.0062,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,284352.0,0.7222,se,Station cluster: Azadi-Enghelab (region with pre-existing BRT line); full specification,pct_premium,5.8529604973039495,0.6563009690666892,100*(exp(b - se^2/2) - 1),reported,0.0172082124148905,0.0018750600522376292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.17741935483871,284342.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c5,6,5,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0175,0.0067,,,**,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,284352.0,0.7222,se,Station cluster: Azadi-Enghelab (region with pre-existing BRT line); full specification,other,0.0175,0.0067,not harmonised,reported,0.004898211449285688,0.001875315240583663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.611940298507463,284342.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c5,6,5,15,Age (centuries),age,A_structural,other,centuries (years/100),-2.6335,0.0195,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,284352.0,0.7222,se,Station cluster: Azadi-Enghelab (region with pre-existing BRT line); full specification,other,-2.6335,0.0195,not harmonised,reported,-0.2455149785043562,0.0018179388953236929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-135.05128205128207,284342.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c5,6,5,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),17.7836,0.7445,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,284352.0,0.7222,se,Station cluster: Azadi-Enghelab (region with pre-existing BRT line); full specification,other,17.7836,0.7445,not harmonised,reported,0.044750632004592006,0.0018734590030937918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.886635325721958,284342.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c5,6,5,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1099,0.0532,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,284352.0,0.7222,se,Station cluster: Azadi-Enghelab (region with pre-existing BRT line); full specification,other,4.1099,0.0532,not harmonised,reported,0.14337998069478197,0.0018559612090227014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),77.25375939849624,284342.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c5,6,5,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),-143.0,34.197,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as -1.43 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,284352.0,0.7222,se,Station cluster: Azadi-Enghelab (region with pre-existing BRT line); full specification,other,-143.0,34.197,not harmonised,reported,-0.00784177123507662,0.0018752800764050017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.181653361405971,284342.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c6,6,6,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0516,0.0052,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,87913.0,0.7417,se,Station cluster: Valiasr (region with pre-existing BRT line); full specification,pct_premium,5.294024062689662,0.5475363277670956,100*(exp(b - se^2/2) - 1),reported,0.03345040658157967,0.003370971205895625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.923076923076923,87903.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c6,6,6,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0128,0.0088,,,,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,87913.0,0.7417,se,Station cluster: Valiasr (region with pre-existing BRT line); full specification,other,0.0128,0.0088,not harmonised,reported,0.004905917295537149,0.0033728181406817892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4545454545454546,87903.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c6,6,6,15,Age (centuries),age,A_structural,other,centuries (years/100),-2.6704,0.0408,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,87913.0,0.7417,se,Station cluster: Valiasr (region with pre-existing BRT line); full specification,other,-2.6704,0.0408,not harmonised,reported,-0.21556671998137714,0.0032935598319503404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-65.45098039215685,87903.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c6,6,6,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),7.4146,1.279,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,87913.0,0.7417,se,Station cluster: Valiasr (region with pre-existing BRT line); full specification,other,7.4146,1.279,not harmonised,reported,0.019549350323576048,0.0033722141536770382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.79718530101642,87903.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c6,6,6,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1474,0.1043,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,87913.0,0.7417,se,Station cluster: Valiasr (region with pre-existing BRT line); full specification,other,4.1474,0.1043,not harmonised,reported,0.13292861095814038,0.003342926682483976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.764141898370085,87903.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c6,6,6,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),-43.2,47.4865,,,,,,high,Squared area in hectares ((m2/10000)^2); printed as -4.32 x 10,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,87913.0,0.7417,se,Station cluster: Valiasr (region with pre-existing BRT line); full specification,other,-43.2,47.4865,not harmonised,reported,-0.0030683838820640737,0.0033728428522137874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9097322396891749,87903.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c7,6,7,15,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0071,0.0029,,,*,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,81984.0,0.7136,se,Station cluster: Navvab (region with pre-existing BRT line); full specification,pct_premium,0.7121029805031487,0.2920663267797811,100*(exp(b - se^2/2) - 1),reported,0.008550793325637357,0.0034925775555420184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4482758620689657,81974.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c7,6,7,15,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),-0.0137,0.0064,,,*,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,81984.0,0.7136,se,Station cluster: Navvab (region with pre-existing BRT line); full specification,other,-0.0137,0.0064,not harmonised,reported,-0.007476363205418048,0.0034926076288084307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.140625,81974.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c7,6,7,15,Age (centuries),age,A_structural,other,centuries (years/100),-2.6744,0.0364,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,81984.0,0.7136,se,Station cluster: Navvab (region with pre-existing BRT line); full specification,other,-2.6744,0.0364,not harmonised,reported,-0.2485640484264917,0.003383088304937295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-73.47252747252746,81974.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c7,6,7,15,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),-8.8337,1.6689,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,81984.0,0.7136,se,Station cluster: Navvab (region with pre-existing BRT line); full specification,other,-8.8337,1.6689,not harmonised,reported,-0.01848417465906186,0.0034921085262696644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.293127209539217,81974.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c7,6,7,15,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),3.9768,0.1124,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,81984.0,0.7136,se,Station cluster: Navvab (region with pre-existing BRT line); full specification,other,3.9768,0.1124,not harmonised,reported,0.12264178141963973,0.0034663388230656575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.380782918149464,81974.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T6-c7,6,7,15,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),28.1,97.8459,,,,,,high,Squared area in hectares ((m2/10000)^2); printed as 2.81 x 10,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,81984.0,0.7136,se,Station cluster: Navvab (region with pre-existing BRT line); full specification,other,28.1,97.8459,not harmonised,reported,0.0010030565204281864,0.003492703487265632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.28718627964993937,81974.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c1,7,1,18,T=1 * M1=1,transit_distance,D_location,other,binary x binary (post x within 200m),0.0484,0.0062,,,***,,,high,DiD term with treatment radius 200m; M=1 dummy itself not reported in Table 7,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7522,se,Treatment group radius 200m (control 3 km); full specification,other,0.0484,0.0062,not harmonised,reported,0.008551546430491327,0.0010954460303521946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.806451612903226,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c1,7,1,18,Age (centuries),age,A_structural,other,centuries (years/100),-2.4984,0.0125,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7522,se,Treatment group radius 200m (control 3 km); full specification,other,-2.4984,0.0125,not harmonised,reported,-0.21388984552330614,0.001070134113449138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-199.872,833271.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c1,7,1,18,Area (Hectar2),living_area,A_structural,other,hectares (m2/10000),10.8345,0.3444,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000; header printed 'Hectar2' (typo for Hectares),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7522,se,Treatment group radius 200m (control 3 km); full specification,other,10.8345,0.3444,not harmonised,reported,0.03444251415505744,0.0010948361138032933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.45905923344948,833271.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c1,7,1,18,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1609,0.0352,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7522,se,Treatment group radius 200m (control 3 km); full specification,other,4.1609,0.0352,not harmonised,reported,0.12842227746114462,0.0010864149983494656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),118.20738636363635,833271.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c1,7,1,18,Area2 (Hectar2),living_area,A_structural,squared,hectares squared ((m2/10000)^2),110.7304,12.2888,,,***,,,high,Squared area in hectares ((m2/10000)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7522,se,Treatment group radius 200m (control 3 km); full specification,other,110.7304,12.2888,not harmonised,reported,0.009870589743677372,0.0010954327198502171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.010676388255973,833271.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c3,7,3,18,T=1 * M3=1,transit_distance,D_location,other,binary x binary (post x within 600m),0.0271,0.0022,,,***,,,high,DiD term with treatment radius 600m; M=1 dummy itself not reported in Table 7,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 600m (control 3 km); full specification,other,0.0271,0.0022,not harmonised,reported,0.013493168309831369,0.0010953863572556832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.318181818181817,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c3,7,3,18,Age (centuries),age,A_structural,other,centuries (years/100),-2.5052,0.0125,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 600m (control 3 km); full specification,other,-2.5052,0.0125,not harmonised,reported,-0.21444526181357618,0.0010700007075960811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-200.41599999999997,833271.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c3,7,3,18,Area (Hectar2),living_area,A_structural,other,hectares (m2/10000),10.678,0.3442,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000; header printed 'Hectar2' (typo for Hectares),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 600m (control 3 km); full specification,other,10.678,0.3442,not harmonised,reported,0.03396528493925129,0.001094854006002088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.022661243463105,833271.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c3,7,3,18,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1743,0.0352,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 600m (control 3 km); full specification,other,4.1743,0.0352,not harmonised,reported,0.12882900260818267,0.0010863572076295499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),118.58806818181816,833271.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c3,7,3,18,Area2 (Hectar2),living_area,A_structural,squared,hectares squared ((m2/10000)^2),116.0682,12.2818,,,***,,,high,Squared area in hectares ((m2/10000)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 600m (control 3 km); full specification,other,116.0682,12.2818,not harmonised,reported,0.01035225168299135,0.001095427384246186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.450422576495303,833271.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c4,7,4,18,T=1 * M4=1,transit_distance,D_location,other,binary x binary (post x within 800m),0.0136,0.0019,,,***,,,high,DiD term with treatment radius 800m; M=1 dummy itself not reported in Table 7,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,Treatment group radius 800m (control 3 km); full specification,other,0.0136,0.0019,not harmonised,reported,0.00784113303536023,0.0010954524093517968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.157894736842105,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c4,7,4,18,Age (centuries),age,A_structural,other,centuries (years/100),-2.5119,0.0125,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,Treatment group radius 800m (control 3 km); full specification,other,-2.5119,0.0125,not harmonised,reported,-0.2149923068344469,0.001069868957932476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-200.95199999999997,833271.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c4,7,4,18,Area (Hectar2),living_area,A_structural,other,hectares (m2/10000),10.6476,0.3441,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000; header printed 'Hectar2' (typo for Hectares),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,Treatment group radius 800m (control 3 km); full specification,other,10.6476,0.3441,not harmonised,reported,0.03387852909390648,0.001094857231790565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.943330427201396,833271.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c4,7,4,18,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1881,0.0351,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,Treatment group radius 800m (control 3 km); full specification,other,4.1881,0.0351,not harmonised,reported,0.1296098511059052,0.0010862457376417164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),119.31908831908834,833271.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c4,7,4,18,Area2 (Hectar2),living_area,A_structural,squared,hectares squared ((m2/10000)^2),116.9601,12.2776,,,***,,,high,Squared area in hectares ((m2/10000)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,Treatment group radius 800m (control 3 km); full specification,other,116.9601,12.2776,not harmonised,reported,0.010435360796777947,0.0010954264378922464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.526299928324754,833271.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c5,7,5,18,T=1 * M5=1,transit_distance,D_location,other,binary x binary (post x within 1000m),0.0111,0.0019,,,***,,,high,DiD term with treatment radius 1000m; M=1 dummy itself not reported in Table 7,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 1000m (control 3 km); full specification,other,0.0111,0.0019,not harmonised,reported,0.006399813969726195,0.0010954636524756552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.842105263157895,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c5,7,5,18,Age (centuries),age,A_structural,other,centuries (years/100),-2.5083,0.0125,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 1000m (control 3 km); full specification,other,-2.5083,0.0125,not harmonised,reported,-0.21469839731581059,0.001069939786487913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-200.66400000000002,833271.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c5,7,5,18,Area (Hectar2),living_area,A_structural,other,hectares (m2/10000),10.8049,0.3441,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000; header printed 'Hectar2' (typo for Hectares),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 1000m (control 3 km); full specification,other,10.8049,0.3441,not harmonised,reported,0.0343784389409092,0.0010948385306265543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.40046498111014,833271.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c5,7,5,18,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1844,0.0352,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 1000m (control 3 km); full specification,other,4.1844,0.0352,not harmonised,reported,0.12913552114787813,0.001086313532263959,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),118.875,833271.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T7-c5,7,5,18,Area2 (Hectar2),living_area,A_structural,squared,hectares squared ((m2/10000)^2),113.1338,12.2798,,,***,,,high,Squared area in hectares ((m2/10000)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7526,se,Treatment group radius 1000m (control 3 km); full specification,other,113.1338,12.2798,not harmonised,reported,0.010092199553973726,0.001095430296541675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.213000211729833,833271.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c1,8,1,19,T=1 * M1=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0413,0.003,,,***,,,high,DiD term with control ring 400 m-2km; M=1 dummy itself not reported in Table 8,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,628782.0,0.7538,se,Control group outer radius 2km (treatment 400 m); full specification,other,0.0413,0.003,not harmonised,reported,0.017358682451957333,0.0012609212434835835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.766666666666667,628772.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c1,8,1,19,Age (centuries),age,A_structural,other,centuries (years/100),-2.5158,0.0143,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,628782.0,0.7538,se,Control group outer radius 2km (treatment 400 m); full specification,other,-2.5158,0.0143,not harmonised,reported,-0.21660034025343944,0.0012311729333111473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-175.93006993006992,628772.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c1,8,1,19,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),7.7151,0.3964,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,628782.0,0.7538,se,Control group outer radius 2km (treatment 400 m); full specification,other,7.7151,0.3964,not harmonised,reported,0.024537512589007285,0.001260731551150664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.462916246215944,628772.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c1,8,1,19,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.1837,0.0402,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,628782.0,0.7538,se,Control group outer radius 2km (treatment 400 m); full specification,other,4.1837,0.0402,not harmonised,reported,0.13013054015553305,0.0012503878658250897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),104.07213930348259,628772.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c1,8,1,19,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),221.0,14.2462,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 2.21 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,628782.0,0.7538,se,Control group outer radius 2km (treatment 400 m); full specification,other,221.0,14.2462,not harmonised,reported,0.019559761129957404,0.0012608699955185482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.512908705479356,628772.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c3,8,3,19,T=1 * M3=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0349,0.003,,,***,,,high,DiD term with control ring 400 m-4km; M=1 dummy itself not reported in Table 8,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,977048.0,0.7478,se,Control group outer radius 4km (treatment 400 m); full specification,other,0.0349,0.003,not harmonised,reported,0.011768425669018492,0.001011612521692134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.633333333333333,977038.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c3,8,3,19,Age (centuries),age,A_structural,other,centuries (years/100),-2.4259,0.0117,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,977048.0,0.7478,se,Control group outer radius 4km (treatment 400 m); full specification,other,-2.4259,0.0117,not harmonised,reported,-0.20529616965560465,0.0009901336349274802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-207.34188034188034,977038.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c3,8,3,19,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),12.3332,0.3236,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,977048.0,0.7478,se,Control group outer radius 4km (treatment 400 m); full specification,other,12.3332,0.3236,not harmonised,reported,0.038529106781583035,0.001010931384759857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.11248454882571,977038.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c3,8,3,19,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.0274,0.0333,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,977048.0,0.7478,se,Control group outer radius 4km (treatment 400 m); full specification,other,4.0274,0.0333,not harmonised,reported,0.12145013355093966,0.001004193635409021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),120.94294294294293,977038.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c3,8,3,19,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),68.4,11.6416,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 6.84 x 10,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,977048.0,0.7478,se,Control group outer radius 4km (treatment 400 m); full specification,other,68.4,11.6416,not harmonised,reported,0.005944016809011964,0.0010116647088273928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.87548103353491,977038.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c4,8,4,19,T=1 * M4=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0342,0.003,,,***,,,high,DiD term with control ring 400 m-5km; M=1 dummy itself not reported in Table 8,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1077506.0,0.7481,se,Control group outer radius 5km (treatment 400 m); full specification,other,0.0342,0.003,not harmonised,reported,0.010981731694853647,0.0009633097977941795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.4,1077496.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c4,8,4,19,Age (centuries),age,A_structural,other,centuries (years/100),-2.3996,0.0112,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1077506.0,0.7481,se,Control group outer radius 5km (treatment 400 m); full specification,other,-2.3996,0.0112,not harmonised,reported,-0.20214071709970427,0.0009434805932308249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-214.25,1077496.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c4,8,4,19,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),12.2424,0.3108,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1077506.0,0.7481,se,Control group outer radius 5km (treatment 400 m); full specification,other,12.2424,0.3108,not harmonised,reported,0.03791973204389008,0.0009626750244430043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.389961389961385,1077496.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c4,8,4,19,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.0404,0.0321,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1077506.0,0.7481,se,Control group outer radius 5km (treatment 400 m); full specification,other,4.0404,0.0321,not harmonised,reported,0.12037655245379293,0.0009563625714698427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),125.86915887850469,1077496.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c4,8,4,19,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),90.0,11.2525,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 9 x 10,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1077506.0,0.7481,se,Control group outer radius 5km (treatment 400 m); full specification,other,90.0,11.2525,not harmonised,reported,0.007705002123631437,0.0009633392932906972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.998222617196179,1077496.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c5,8,5,19,T=1 * M5=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0307,0.003,,,***,,,high,DiD term with control ring 400 m-6km; M=1 dummy itself not reported in Table 8,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1169264.0,0.7478,se,Control group outer radius 6km (treatment 400 m); full specification,other,0.0307,0.003,not harmonised,reported,0.009463313797645208,0.0009247537913008346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.233333333333334,1169254.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c5,8,5,19,Age (centuries),age,A_structural,other,centuries (years/100),-2.3846,0.0108,,,***,,,high,Age of building expressed in centuries (years/100); level of age/100,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1169264.0,0.7478,se,Control group outer radius 6km (treatment 400 m); full specification,other,-2.3846,0.0108,not harmonised,reported,-0.20006321692462745,0.0009060986089012735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-220.79629629629628,1169254.0,hundred_years,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c5,8,5,19,Area (Hectares),living_area,A_structural,other,hectares (m2/10000),12.4509,0.3007,,,***,,,high,Surface area of dwelling expressed in hectares (m2/10000); level of area/10000,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1169264.0,0.7478,se,Control group outer radius 6km (treatment 400 m); full specification,other,12.4509,0.3007,not harmonised,reported,0.0382643828253353,0.0009241179284692933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.40638510143,1169254.0,ha,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c5,8,5,19,Age2 (centuries),age,A_structural,squared,centuries squared ((years/100)^2),4.0831,0.0312,,,***,,,high,Squared age in centuries ((years/100)^2),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1169264.0,0.7478,se,Control group outer radius 6km (treatment 400 m); full specification,other,4.0831,0.0312,not harmonised,reported,0.12014989800556701,0.0009180957649270629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),130.86858974358975,1169254.0,centuries sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T8-c5,8,5,19,Area2 (Hectares),living_area,A_structural,squared,hectares squared ((m2/10000)^2),102.8,10.9528,,,***,,,high,Squared area in hectares ((m2/10000)^2); printed as 1.028 x 10^2,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,1169264.0,0.7478,se,Control group outer radius 6km (treatment 400 m); full specification,other,102.8,10.9528,not harmonised,reported,0.008679549129099693,0.0009247603667432209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.38572785041268,1169254.0,hectares sqd ^2),nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T9-c2,9,2,20,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 150 m),0.046,0.0096,,,***,,,high,"DiD term, band 0-150 m from metro station","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,"Stepwise treatment: three distance bands (<150 m, 150-400/500 m, 400 m-1 km) vs control; full specification (age/area controls not reported in table)",other,0.046,0.0096,not harmonised,reported,0.005249131849494723,0.0010954709946771595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.791666666666667,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T9-c2,9,2,20,T=1 * M=2,transit_distance,D_location,other,binary x binary (post x 150-400/500 m band),0.0362,0.0032,,,***,,,high,"DiD term, second band (text: 150-400 m; table comment: 150-500 m)","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,"Stepwise treatment: three distance bands (<150 m, 150-400/500 m, 400 m-1 km) vs control; full specification (age/area controls not reported in table)",other,0.0362,0.0032,not harmonised,reported,0.012391734842113334,0.0010954019749934438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.3125,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T9-c2,9,2,20,T=1 * M=3,transit_distance,D_location,other,binary x binary (post x 400 m-1 km band),0.0056,0.002,,,**,,,high,"DiD term, third band (400 m-1 km)","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7527,se,"Stepwise treatment: three distance bands (<150 m, 150-400/500 m, 400 m-1 km) vs control; full specification (age/area controls not reported in table)",other,0.0056,0.002,not harmonised,reported,0.0030673466136334035,0.0010954809334405013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T9-c3,9,3,20,T=1 * M,transit_distance,D_location,other,km (post x distance to metro station in km),-0.0442,0.0055,,,***,,,high,DiD slope: post x continuous distance to nearest metro station; text: 0.44% price reduction per 100 m => distance in km,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,305468.0,0.7365,se,"Linear distance DiD, transactions within 1 km of station only; full specification (age/area controls not reported)",other,-0.0442,0.0055,not harmonised,reported,-0.014539113310096628,0.0018091656833830645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.036363636363637,305458.0,km,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T9-c4,9,4,20,T=1 * M,transit_distance,D_location,other,km (post x distance in km),-0.1434,0.0276,,,***,,,high,DiD linear term of quadratic distance specification,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,305468.0,0.7367,se,"Quadratic distance DiD, transactions within 1 km; full specification (age/area controls not reported)",other,-0.1434,0.0276,not harmonised,reported,-0.009400373896018851,0.0018092769841709922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.195652173913044,305458.0,km,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T9-c4,9,4,20,T=1 * M2,transit_distance,D_location,other,km squared (post x distance^2),0.0895,0.023,,,***,,,high,DiD quadratic term,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,305468.0,0.7367,se,"Quadratic distance DiD, transactions within 1 km; full specification (age/area controls not reported)",other,0.0895,0.023,not harmonised,reported,0.007040583980517206,0.0018093120843787232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.891304347826087,305458.0,km sqd,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c2,10,2,21,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0513,0.0032,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.4435,se,"Region FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",pct_premium,5.263324801442804,0.33684436400334555,100*(exp(b - se^2/2) - 1),reported,0.018397530968404793,0.0011476042709336323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.03125,759047.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c2,10,2,21,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0157,0.0045,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.4435,se,"Region FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",other,0.0157,0.0045,not harmonised,reported,0.004004509445201699,0.0011477893314272387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.488888888888889,759047.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c2,10,2,21,lnAge,age,A_structural,log,years (log of building age),-0.1259,0.0006,,,***,,,high,Log of building age in years (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.4435,se,"Region FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,-0.1259,0.0006,b (log-log),reported,-0.23415092524624476,0.0011158900329447724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-209.83333333333337,759047.0,years,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c2,10,2,21,lnArea,living_area,A_structural,log,m2 (log of surface area),0.0883,0.0019,,,***,,,high,Log of dwelling surface area in m2 (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.4435,se,"Region FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,0.0883,0.0019,b (log-log),reported,0.05326669754847317,0.0011461690299218463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),46.473684210526315,759047.0,m2,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c3,10,3,21,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.0413,0.0027,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.5708,se,"Quarter FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",pct_premium,-4.0462273124582175,0.2590761305871378,100*(exp(b - se^2/2) - 1),reported,-0.017554361110645848,0.0011476216706717624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.296296296296298,759047.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c3,10,3,21,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),-0.0063,0.0039,,,,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.5708,se,"Quarter FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",other,-0.0063,0.0039,not harmonised,reported,-0.001854132907216398,0.0011477965616101512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6153846153846154,759047.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c3,10,3,21,lnAge,age,A_structural,log,years (log of building age),-0.0785,0.0005,,,***,,,high,Log of building age in years (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.5708,se,"Quarter FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,-0.0785,0.0005,b (log-log),reported,-0.17734781303859223,0.0011296039047044091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-157.0,759047.0,years,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c3,10,3,21,lnArea,living_area,A_structural,log,m2 (log of surface area),0.5667,0.0013,,,***,,,high,Log of dwelling surface area in m2 (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.5708,se,"Quarter FE only; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,0.5667,0.0013,b (log-log),reported,0.4474653192172142,0.001026477704221596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),435.9230769230769,759047.0,m2,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c4,10,4,21,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0211,0.0022,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.7296,se,"Region + quarter FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",pct_premium,2.1321707347450536,0.22469131936877404,100*(exp(b - se^2/2) - 1),reported,0.01100776442886614,0.0011477289925831996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.59090909090909,759047.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c4,10,4,21,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0222,0.0031,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.7296,se,"Region + quarter FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",other,0.0222,0.0031,not harmonised,reported,0.008219440874344633,0.001147759761732809,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.161290322580646,759047.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c4,10,4,21,lnAge,age,A_structural,log,years (log of building age),-0.1069,0.0004,,,***,,,high,Log of building age in years (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.7296,se,"Region + quarter FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,-0.1069,0.0004,b (log-log),reported,-0.29326204181834953,0.0010973322425382583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-267.25,759047.0,years,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c4,10,4,21,lnArea,living_area,A_structural,log,m2 (log of surface area),0.1081,0.0013,,,***,,,high,Log of dwelling surface area in m2 (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759057.0,0.7296,se,"Region + quarter FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,0.1081,0.0013,b (log-log),reported,0.09501208608171699,0.0011426060305849406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),83.15384615384616,759047.0,m2,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c5,10,5,21,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0127,0.0022,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759056.0,0.736,se,"Region + quarter + region x quarter + frame FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",pct_premium,1.2778536556837583,0.22281181724644963,100*(exp(b - se^2/2) - 1),reported,0.006625786824444806,0.0011477740955731159,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7727272727272725,759046.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c5,10,5,21,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0371,0.0032,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,759056.0,0.736,se,"Region + quarter + region x quarter + frame FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",other,0.0371,0.0032,not harmonised,reported,0.013306119926422038,0.0011476976755943535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.59375,759046.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c5,10,5,21,lnAge,age,A_structural,log,years (log of building age),-0.1049,0.0005,,,***,,,high,Log of building age in years (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759056.0,0.736,se,"Region + quarter + region x quarter + frame FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,-0.1049,0.0005,b (log-log),reported,-0.23411591356754177,0.00111590044598447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-209.79999999999998,759046.0,years,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T10-c5,10,5,21,lnArea,living_area,A_structural,log,m2 (log of surface area),0.1076,0.0013,,,***,,,high,Log of dwelling surface area in m2 (elasticity),"ln nominal transaction price of the property (total price, Tomans)",log,OLS,759056.0,0.736,se,"Region + quarter + region x quarter + frame FE; lnAge, lnArea (log-form robustness; sample smaller because ln(age) drops age=0)",elasticity,0.1076,0.0013,b (log-log),reported,0.09457662390101962,0.0011426543779862965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),82.76923076923077,759046.0,m2,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c2,11,2,22,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0504,0.003,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.4667,se,Region FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),pct_premium,5.168695632552511,0.31550750667824307,100*(exp(b - se^2/2) - 1),reported,0.01840103914645345,0.0010952999491936578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.8,833272.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c2,11,2,22,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.021,0.0042,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.4667,se,Region FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),other,0.021,0.0042,not harmonised,reported,0.005477344982473835,0.0010954689964947667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.000000000000001,833272.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c3,11,3,22,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),-0.0419,0.0026,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.5759,se,Quarter FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),pct_premium,-4.103756895344423,0.2493310748097131,100*(exp(b - se^2/2) - 1),reported,-0.017651418547080983,0.0010953147547114693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.115384615384617,833272.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c3,11,3,22,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),-0.0051,0.0037,,,,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.5759,se,Quarter FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),other,-0.0051,0.0037,not harmonised,reported,-0.001509991708561081,0.0010954841807207841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3783783783783785,833272.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c4,11,4,22,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0227,0.0021,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.7444,se,Region + quarter FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),pct_premium,2.295735000429966,0.21482151718182607,100*(exp(b - se^2/2) - 1),reported,0.011840845670532769,0.001095408630313604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.80952380952381,833272.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c4,11,4,22,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.0238,0.0029,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833282.0,0.7444,se,Region + quarter FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),other,0.0238,0.0029,not harmonised,reported,0.008990172265521233,0.0010954411584038477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.206896551724139,833272.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c5,11,5,22,M=1,transit_distance,D_location,binary,binary (within 400 m of metro station),0.0141,0.0021,,,***,,,high,M=1: property within 400 m of nearest (newly opened) metro station vs 400 m-3 km control ring,"ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7504,se,Region + quarter + region x quarter + frame FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),pct_premium,1.4197637546791153,0.21298197350955997,100*(exp(b - se^2/2) - 1),reported,0.0073552076207961704,0.0010954564541611317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.714285714285714,833271.0,binary,,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab,602e1fb4b1e820fd13056357b14a38759c9354ab:T11-c5,11,5,22,T=1 * M=1,transit_distance,D_location,other,binary x binary (post x within 400 m),0.038,0.003,,,***,,,high,"Difference-in-differences term: post-opening x within 400 m of metro station; interaction, x_transform other","ln nominal transaction price of the property (total price, Tomans)",log,OLS,833281.0,0.7504,se,Region + quarter + region x quarter + frame FE; age and area decile FE (categorised age/area robustness; decile FE not extracted),other,0.038,0.003,not harmonised,reported,0.01387482138301748,0.00109538063550138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.666666666666666,833271.0,binary x binary,nonstandard_x_transform,2021.0,Iran,Asia,Lower middle income,2010.0,2018.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,No of Bedrooms,bedrooms,A_structural,level,count,0.014671,,15.866329,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",semi_elasticity,1.4671,0.09246625353602587,100*b/1 (per unit count),|b/t|,0.027746745360949617,0.0017487816722412358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.866329,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,No of Bedrooms,bedrooms,A_structural,level,count,0.017519,,19.144798,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",semi_elasticity,1.7519,0.09150788637205781,100*b/1 (per unit count),|b/t|,0.033474197097174904,0.0017484748126971566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.144798,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,No of Full Baths,bathrooms,A_structural,level,count (full bathrooms),0.164688,,152.781209,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",semi_elasticity,16.4688,0.10779336089688885,100*b/1 (per unit count),|b/t|,0.25821930785876646,0.0016901247839894136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),152.781209,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,No of Full Baths,bathrooms,A_structural,level,count (full bathrooms),0.146963,,136.717485,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",semi_elasticity,14.6963,0.10749393173813868,100*b/1 (per unit count),|b/t|,0.2326198417364866,0.001701463728187266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),136.717485,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,No of Half Baths,bathrooms,A_structural,level,count (half bathrooms),0.151836,,91.099069,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",semi_elasticity,15.1836,0.16667129715672505,100*b/1 (per unit count),|b/t|,0.1573874574059269,0.0017276516558684802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),91.099069,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,No of Half Baths,bathrooms,A_structural,level,count (half bathrooms),0.132745,,79.822351,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",semi_elasticity,13.2745,0.166300539055784,100*b/1 (per unit count),|b/t|,0.13830361849786582,0.0017326427593928652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),79.822351,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,No of Fireplaces,fireplace,B_interior,level,count,0.170455,,161.09964,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",semi_elasticity,17.0455,0.10580718864424526,100*b/1 (per unit count),|b/t|,0.271268762437529,0.0016838570367849921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),161.09964,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,True
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,No of Fireplaces,fireplace,B_interior,level,count,0.145072,,137.00185,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",semi_elasticity,14.507200000000001,0.10589054089415584,100*b/1 (per unit count),|b/t|,0.23307741979798047,0.0017012720616399008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),137.00185,326734.0,count,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,Garage Dummy,garage,A_structural,binary,binary,0.170616,,71.760975,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",pct_premium,18.60318718412024,0.2819869515218971,100*(exp(b - se^2/2) - 1),|b/t|,0.12456482150188128,0.0017358295578046602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.760975,326734.0,binary,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,Garage Dummy,garage,A_structural,binary,binary,0.170824,,72.495021,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",pct_premium,18.627865166874045,0.27953013073405475,100*(exp(b - se^2/2) - 1),|b/t|,0.12581893000096803,0.0017355527078331083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),72.495021,326734.0,binary,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,Basement Dummy,basement,A_structural,binary,binary,0.138199,,80.941398,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",pct_premium,14.820234651955344,0.19604386790974618,100*(exp(b - se^2/2) - 1),|b/t|,0.14020467173343504,0.0017321750698379957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),80.941398,326734.0,binary,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,Basement Dummy,basement,A_structural,binary,binary,0.128803,,75.68174,0.0,,,,high,t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",pct_premium,13.746437396654287,0.1935857129338164,100*(exp(b - se^2/2) - 1),|b/t|,0.13125633723647367,0.0017343197611005463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),75.68174,326734.0,binary,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,Stories Dummy,storeys,A_structural,binary,binary (multi-storey dummy; definition not printed),0.014131,,8.167266,0.0,,,,high,Dummy for number of stories (mean 0.48); exact definition not printed; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",pct_premium,1.4229796442986942,0.17548225692346803,100*(exp(b - se^2/2) - 1),|b/t|,0.014286808006754073,0.00174927668656244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.167266,326734.0,binary,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,Stories Dummy,storeys,A_structural,binary,binary (multi-storey dummy; definition not printed),0.026279,,15.247442,0.0,,,,high,Dummy for number of stories (mean 0.48); exact definition not printed; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",pct_premium,2.6625812770961987,0.1769394486197117,100*(exp(b - se^2/2) - 1),|b/t|,0.0266652322759925,0.0017488331666382138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.247442,326734.0,binary,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,Age,age,A_structural,level,years,-0.014724,,-160.94147,0.0,,,,high,Enters with Age Squared; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",semi_elasticity,-1.4724,0.009148667524908278,100*b/1 (per year),|b/t|,-0.27102199880989125,0.001683978646460053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-160.94147,326734.0,years,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,Age,age,A_structural,level,years,-0.01629,,-164.96856,0.0,,,,high,Enters with Age Squared; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",semi_elasticity,-1.629,0.009874608834556112,100*b/1 (per year),|b/t|,-0.277287995472513,0.0016808535849043784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-164.96856,326734.0,years,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,Age Squared,age,A_structural,squared,years squared,0.000122,,156.98142,0.0,,,,high,Quadratic term of age; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",other,0.000122,7.771620361186693e-07,not harmonised,|b/t|,0.26482652706561816,0.0016869928114143582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),156.98142,326734.0,years sqd,nonstandard_x_transform,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,False,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,Age Squared,age,A_structural,squared,years squared,0.000126,,151.223767,0.0,,,,high,Quadratic term of age; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",other,0.000126,8.332023629592563e-07,not harmonised,|b/t|,0.25576004993678264,0.001691268872681783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),151.223767,326734.0,years sqd,nonstandard_x_transform,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,False,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c1,2,1,42,Land Sq Feet (Log),lot_size,A_structural,log,sqft,0.183304,,159.507617,0.0,,,,high,Log of land square feet; Robinson column printed as .235341; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,OLS,326744.0,0.591,t,"Full sample; year dummies (2010 omitted), county dummies, lat/lon quadratic included; t and p in separate columns",elasticity,0.183304,0.001149186499350686,b (log-log),|b/t|,0.2687825868496196,0.0016850768126616773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),159.507617,326734.0,sqft,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,ols,other,True,True,False
617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232,617738a89ff272112e6f8804b981aa1981f5a232:T2-c2,2,2,42,Land Sq Feet (Log),lot_size,A_structural,log,sqft,0.235341,,196.40403,0.0,,,,high,Log of land square feet; Robinson column printed as .235341; t-value and p-value printed in separate columns; p printed as 0.00; no stars used,log sale price,log,other,326744.0,,t,"Robinson's semi-parametric (partially linear) method, location handled non-parametrically; county dummies included; t and p in separate columns",elasticity,0.235341,0.0011982493434579728,b (log-log),|b/t|,0.3249529474124439,0.0016545126258989894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),196.40403,326734.0,sqft,,2014.0,United States,North America,High income,2003.0,2010.0,single_family,transaction,working_paper,True,True,unknown,,2006.5,other,other,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,AREALOT,lot_size,A_structural,level,sqft,3.787e-06,,,0.003,**,,,high,"land area in square feet; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,0.004076292874807971,0.0013735353246660147,100*b/0.092903 (X per sqft->m2),|b/z(p)| (two-sided normal),0.0740591647932815,0.024954752291596768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417837,1597.0,sqft,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,AREALOT,lot_size,A_structural,level,sqft,3.3899e-06,,,0.007,**,,,high,"land area in square feet; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,0.0036488579921604286,0.0013530102739311742,100*b/0.092903 (X per sqft->m2),|b/z(p)| (two-sided normal),0.06733125885025346,0.024966684145242248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6968442608781262,1597.0,sqft,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,AREALOT,lot_size,A_structural,level,sqft,4.82604e-06,,,0.001,**,,,high,"land area in square feet; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,0.005194706222745779,0.0015786853129105404,100*b/0.092903 (X per sqft->m2),|b/z(p)| (two-sided normal),0.08206267849670865,0.02493907060876609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,1597.0,sqft,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,FRONT,other,other,level,feet,8.7561e-06,,,0.001,**,,,high,"street frontage in feet (Table 1); no taxonomy leaf; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,0.0008756100000000001,0.000266100254290597,100*b (unit as reported: ft; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.08206267849670865,0.02493907060876609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,1597.0,ft,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,FRONT,other,other,level,feet,1.06e-05,,,0.0,**,,,high,"street frontage in feet (Table 1); no taxonomy leaf; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,0.00106,,100*b (unit as reported: ft; no harmonised unit defined for this characteristic),,,,,,1597.0,ft,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,FRONT,other,other,level,feet,7.4217e-06,,,0.003,**,,,high,"street frontage in feet (Table 1); no taxonomy leaf; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,0.00074217,0.0002500793596572471,100*b (unit as reported: ft; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.07405916479328149,0.024954752291596768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,1597.0,ft,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,ATTIC,storage,B_interior,level,sqft,0.00014,,,0.067,,,,high,"attic square footage; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,0.013999999999999999,0.007643281374110712,100*b (unit as reported: sqft; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.045786771146122306,0.02499722679155903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8316740304001806,1597.0,sqft,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,ATTIC,storage,B_interior,level,sqft,0.00013,,,0.098,,,,high,"attic square footage; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,0.013,0.007856751346649095,100*b (unit as reported: sqft; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.04136908746136253,0.0250020487401311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.654627902351078,1597.0,sqft,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,ATTIC,storage,B_interior,level,sqft,0.00014,,,0.065,,,,high,"attic square footage; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,0.013999999999999999,0.007587014452274058,100*b (unit as reported: sqft; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.04612561583903591,0.024996836713629053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.845258116755501,1597.0,sqft,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,BEDROOMS,bedrooms,A_structural,level,count,0.08504,,,0.0,**,,,high,"stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,8.504000000000001,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,BEDROOMS,bedrooms,A_structural,level,count,0.08581,,,0.0,**,,,high,"stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,8.581,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,BEDROOMS,bedrooms,A_structural,level,count,0.07047,,,0.0,**,,,high,"stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,7.047000000000001,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,FULLBATHS,bathrooms,A_structural,level,count,0.1512,,,0.0,**,,,high,"number of full baths; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,15.120000000000001,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,FULLBATHS,bathrooms,A_structural,level,count,0.16015,,,0.0,**,,,high,"number of full baths; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,16.014999999999997,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,FULLBATHS,bathrooms,A_structural,level,count,0.14032,,,0.0,**,,,high,"number of full baths; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,14.032,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,HALFBATHS,bathrooms,A_structural,level,count,0.1048,,,0.002,**,,,high,"number of half baths; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,10.48,3.391330800303558,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.07709816968086972,0.024948988309710252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,HALFBATHS,bathrooms,A_structural,level,count,0.11255,,,0.0,**,,,high,"number of half baths; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,11.254999999999999,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,HALFBATHS,bathrooms,A_structural,level,count,0.10348,,,0.001,**,,,high,"number of half baths; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,10.348,3.144785271295551,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.08206267849670865,0.02493907060876609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,AGE,age,A_structural,level,years,-0.00698,,,0.0,**,,,high,"age of building in years; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,-0.698,,100*b/1 (per year),,,,,,1597.0,years,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,AGE,age,A_structural,level,years,-0.00672,,,0.0,**,,,high,"age of building in years; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,-0.672,,100*b/1 (per year),,,,,,1597.0,years,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,AGE,age,A_structural,level,years,-0.00643,,,0.0,**,,,high,"age of building in years; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,-0.643,,100*b/1 (per year),,,,,,1597.0,years,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,GARAGE,garage,A_structural,level,count,0.1272,,,0.0,**,,,high,"car capacity of basement garage; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,12.72,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,GARAGE,garage,A_structural,level,count,0.1273,,,0.0,**,,,high,"car capacity of basement garage; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,12.73,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,GARAGE,garage,A_structural,level,count,0.12189,,,0.0,**,,,high,"car capacity of basement garage; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,12.189,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,FIREPLACES,fireplace,B_interior,level,count,0.23866,,,0.0,**,,,high,"number of fireplace stacks; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,23.866,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,FIREPLACES,fireplace,B_interior,level,count,0.23163,,,0.0,**,,,high,"number of fireplace stacks; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,23.163,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,FIREPLACES,fireplace,B_interior,level,count,0.20293,,,0.0,**,,,high,"number of fireplace stacks; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,20.293,,100*b/1 (per unit count),,,,,,1597.0,count,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,CVPOOR,condition,A_structural,binary,binary,-0.83248,,,0.0,**,,,high,"1 = very poor condition of the house; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,pct_premium,-56.5030776077599,,100*(exp(b) - 1),,,,,,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,CVPOOR,condition,A_structural,binary,binary,-0.803,,,0.0,**,,,high,"1 = very poor condition of the house; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",pct_premium,-55.20170028152563,,100*(exp(b) - 1),,,,,,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,CVPOOR,condition,A_structural,binary,binary,-0.86721,,,0.0,**,,,high,"1 = very poor condition of the house; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",pct_premium,-57.98779438320848,,100*(exp(b) - 1),,,,,,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,AIRCOND,air_conditioning,A_structural,binary,binary,0.36702,,,0.0,**,,,high,"central air conditioning; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,pct_premium,44.34267873622417,,100*(exp(b) - 1),,,,,,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,AIRCOND,air_conditioning,A_structural,binary,binary,0.35357,,,0.0,**,,,high,"central air conditioning; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",pct_premium,42.414267344944136,,100*(exp(b) - 1),,,,,,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,AIRCOND,air_conditioning,A_structural,binary,binary,0.28278,,,0.0,**,,,high,"central air conditioning; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",pct_premium,32.681323079514726,,100*(exp(b) - 1),,,,,,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,HEATING,heating,A_structural,binary,binary,0.31162,,,0.062,,,,high,"presence of a heating system (mean 0.99); stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,pct_premium,34.67308970933223,22.802355073629485,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.046650351958594155,0.02499622694962295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.866295743458108,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,True,True,True
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,HEATING,heating,A_structural,binary,binary,0.26759,,,0.107,,,,high,"presence of a heating system (mean 0.99); stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",pct_premium,28.892587448099462,21.695258031999728,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04030069137018994,0.025003141371515908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6118251211466297,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,HEATING,heating,A_structural,binary,binary,0.31981,,,0.035,*,,,high,"presence of a heating system (mean 0.99); stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",pct_premium,36.11168382513961,20.885232737523197,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.05268517181817334,0.024988717211901088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.108358399169109,1597.0,binary,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,True
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,EDUCATION,education_level,F_neighbourhood,level,percent,0.01346,,,0.0,**,,,high,"percent of population 25+ with bachelor's degree or higher, by ZIP code; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,1.346,,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),,,,,,1597.0,percent,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,EDUCATION,education_level,F_neighbourhood,level,percent,0.0136,,,0.0,**,,,high,"percent of population 25+ with bachelor's degree or higher, by ZIP code; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,1.3599999999999999,,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),,,,,,1597.0,percent,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,EDUCATION,education_level,F_neighbourhood,level,percent,0.01346,,,0.0,**,,,high,"percent of population 25+ with bachelor's degree or higher, by ZIP code; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,1.346,,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),,,,,,1597.0,percent,unit_not_harmonisable,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,False,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,EXP-DISTBR,hazard_site,E_environment,other,feet,-0.22867,,,0.026,*,,,high,"exp(-distbr/500), distbr = Euclidean distance in feet to closest brownfield (sample within 1,000 ft); negative coefficient = discount near brownfield; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,other,-0.22867,0.10271709239509492,not harmonised,|b/z(p)| (two-sided normal),-0.05562130576692892,0.02498473259980524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.226211769317175,1597.0,ft,nonstandard_x_transform,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,False,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,EXP-DISTBR,hazard_site,E_environment,other,feet,-0.22844,,,0.025,*,,,high,"exp(-distbr/500), distbr = Euclidean distance in feet to closest brownfield (sample within 1,000 ft); negative coefficient = discount near brownfield; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",other,-0.22844,0.10191831980328675,not harmonised,|b/z(p)| (two-sided normal),-0.05599966158904793,0.024984203373789267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.241402727604945,1597.0,ft,nonstandard_x_transform,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,EXP-DISTBR,hazard_site,E_environment,other,feet,-0.25361,,,0.03,*,,,high,"exp(-distbr/500), distbr = Euclidean distance in feet to closest brownfield (sample within 1,000 ft); negative coefficient = discount near brownfield; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",other,-0.25361,0.11686610042586475,not harmonised,|b/z(p)| (two-sided normal),-0.054223303585858564,0.02498665684431646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1700903775845606,1597.0,ft,nonstandard_x_transform,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,False,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c1,4,OLS,9,DISTHW,highway_distance,D_location,level,feet,-6.6047e-06,,,0.017,*,,,high,"Euclidean distance to closest highway exit in feet; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,OLS,1607.0,,p,p-values in parentheses,semi_elasticity,-2.1668963254593177,0.9079018323625219,100*b/0.0003048 (X per ft->km),|b/z(p)| (two-sided normal),-0.059617479817791794,0.024978961167390214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3867077344922505,1597.0,ft,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,ols,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c2,4,SAR,9,DISTHW,highway_distance,D_location,level,feet,-5.6457e-06,,,0.041,*,,,high,"Euclidean distance to closest highway exit in feet; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_lag,1607.0,,p,"spatial autoregressive (ML), rho 0.01007; p-values in parentheses; Table 5 total impacts not extracted",semi_elasticity,-1.8522637795275592,0.9064040032610451,100*b/0.0003048 (X per ft->km),|b/z(p)| (two-sided normal),-0.051069485359920175,0.024990817445299105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0435300074398564,1597.0,ft,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,False
620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b,620cbd7217ca28bcea726920009e42496a17ea8b:T4-c3,4,SEM,9,DISTHW,highway_distance,D_location,level,feet,-6.7431e-06,,,0.029,*,,,high,"Euclidean distance to closest highway exit in feet; stars: ** 99% level, * 95% level; p-value printed (SE not printed)","ln(MKTVALU), 2009 appraised market value of land and improvements (USD)",log,spatial_error,1607.0,,p,"spatial error model (ML), lambda 0.32641; p-values in parentheses",semi_elasticity,-2.2123031496062993,1.0131975266300586,100*b/0.0003048 (X per ft->km),|b/z(p)| (two-sided normal),-0.05455703534770206,0.024986201951752177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1834865280065587,1597.0,ft,,2012.0,United States,North America,High income,2009.0,2009.0,single_family,appraisal,journal,False,False,unknown,,2009.0,spatial,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c1,3,ATE,31,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.6223,0.0946,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD),"log(sale price), nominal US$",log,OLS,2409.0,0.6394,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-46.56875934001226,5.07726323454315,100*(exp(b - se^2/2) - 1),reported,-0.13311026792472427,0.020234985289537072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.578224101479915,2399.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,True
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c2,3,Zones,31,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.4958,0.1071,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD),"log(sale price), nominal US$",log,OLS,2409.0,0.642,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-39.43997748022095,6.523283702174183,100*(exp(b - se^2/2) - 1),reported,-0.09409590751530952,0.02032608248263342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.629318394024277,2399.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c3,3,Exponential,31,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.5678,0.0888,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD),"log(sale price), nominal US$",log,OLS,2409.0,0.6393,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-43.545925552062194,5.032926162454833,100*(exp(b - se^2/2) - 1),reported,-0.129448712956592,0.02024488501328878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.394144144144144,2399.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c4,3,Gaussian,31,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.5686,0.0887,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD),"log(sale price), nominal US$",log,OLS,2409.0,0.6394,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-43.59057011966455,5.023238254343976,100*(exp(b - se^2/2) - 1),reported,-0.12977171903265064,0.020244023000696644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.410372040586245,2399.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c1,3,ATE,31,Treatment × After Harvey,other,other,other,binary interaction,0.113,0.11,,,,,,high,DiD ATE within 1.5 miles,"log(sale price), nominal US$",log,OLS,2409.0,0.6394,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.113,0.11,not harmonised,reported,0.020968875206871115,0.02041217940491878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0272727272727273,2399.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c2,3,Zones,31,Treatment × After Harvey × (<0.5 mile),other,other,other,binary interaction (buffer <0.5 mile; reference 1-1.5 miles),-0.2772,0.1268,,,**,,,high,DiD ATE by distance buffer,"log(sale price), nominal US$",log,OLS,2409.0,0.642,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.2772,0.1268,not harmonised,reported,-0.044588893290688734,0.020396362443215483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.186119873817035,2399.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c2,3,Zones,31,Treatment × After Harvey × (<1 mile),other,other,other,binary interaction (buffer 0.5-1 mile; reference 1-1.5 miles),-0.1333,0.0718,,,*,,,high,DiD ATE by distance buffer,"log(sale price), nominal US$",log,OLS,2409.0,0.642,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1333,0.0718,not harmonised,reported,-0.03787728300351214,0.02040201740174172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8565459610027855,2399.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c3,3,Exponential,31,Treatment × After Harvey × Distance to Superfund Sites,other,other,other,interaction with negative-exponential distance transform exp(-d/0.25),-0.4415,0.2296,,,*,,,high,distance-decay DiD effect,"log(sale price), nominal US$",log,OLS,2409.0,0.6393,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.4415,0.2296,not harmonised,reported,-0.03922918346892166,0.02040095249029312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9229094076655053,2399.0,interaction with negative-exponential distance transform exp,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T3-c4,3,Gaussian,31,Treatment × After Harvey × Distance to Superfund Sites,other,other,other,interaction with Gaussian distance transform [1-(d/1.5)]^2,-0.1699,0.0742,,,**,,,high,distance-decay DiD effect,"log(sale price), nominal US$",log,OLS,2409.0,0.6394,se,"DID without matching (transactions within 1.5 miles of impacted or non-impacted Superfund sites); specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1699,0.0742,not harmonised,reported,-0.04669821651711793,0.020394394735551213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2897574123989215,2399.0,interaction with gaussian distance transform [1-]^2,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c1,4,ATE,32,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.6067,0.1013,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD); matched sample,"log(sale price), nominal US$",log,OLS,1963.0,0.65,se,"DID with propensity-score matching; specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-45.76430120486249,5.522337982986996,100*(exp(b - se^2/2) - 1),reported,-0.1342954725300279,0.022423160321891917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.989141164856861,1953.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c2,4,Zones,32,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.4861,0.1117,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD); matched sample,"log(sale price), nominal US$",log,OLS,1963.0,0.6527,se,"DID with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-38.8804525228698,6.86977677566452,100*(exp(b - se^2/2) - 1),reported,-0.0979999305965116,0.022519218777268764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.35183527305282,1953.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c3,4,Exponential,32,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.5566,0.0907,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD); matched sample,"log(sale price), nominal US$",log,OLS,1963.0,0.65,se,"DID with propensity-score matching; specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-42.91982029021777,5.198511133593405,100*(exp(b - se^2/2) - 1),reported,-0.1375426698661734,0.022413079692529515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.136714443219404,1953.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c4,4,Gaussian,32,Treatment (home sales within 1.5 miles),hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs within 1.5 miles of non-impacted site),-0.558,0.0906,,,***,,,high,sale within 1.5 miles of a hurricane-impacted Superfund site (pre-Harvey level effect in DiD); matched sample,"log(sale price), nominal US$",log,OLS,1963.0,0.6502,se,"DID with propensity-score matching; specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-42.99915991905755,5.185514783919097,100*(exp(b - se^2/2) - 1),reported,-0.138031347628971,0.02241154138922002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.158940397350994,1953.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c1,4,ATE,32,Treatment × After Harvey,other,other,other,binary interaction,0.1029,0.1226,,,,,,high,DiD ATE within 1.5 miles,"log(sale price), nominal US$",log,OLS,1963.0,0.65,se,"DID with propensity-score matching; specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.1029,0.1226,not harmonised,reported,0.018988710432600076,0.022624061215128954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8393148450244698,1953.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c2,4,Zones,32,Treatment × After Harvey × (<0.5 mile),other,other,other,binary interaction (buffer <0.5 mile; reference 1-1.5 miles),-0.2876,0.1279,,,**,,,high,DiD ATE by distance buffer,"log(sale price), nominal US$",log,OLS,1963.0,0.6527,se,"DID with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.2876,0.1279,not harmonised,reported,-0.0508166164499318,0.022598905577003746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.248631743549648,1953.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c2,4,Zones,32,Treatment × After Harvey × (<1 mile),other,other,other,binary interaction (buffer 0.5-1 mile; reference 1-1.5 miles),-0.1262,0.0682,,,*,,,high,DiD ATE by distance buffer,"log(sale price), nominal US$",log,OLS,1963.0,0.6527,se,"DID with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1262,0.0682,not harmonised,reported,-0.04183535659599097,0.022608330585155184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8504398826979473,1953.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c3,4,Exponential,32,Treatment × After Harvey × Distance to Superfund Sites,other,other,other,interaction with negative-exponential distance transform exp(-d/0.25),-0.5238,0.2123,,,**,,,high,distance-decay DiD effect,"log(sale price), nominal US$",log,OLS,1963.0,0.65,se,"DID with propensity-score matching; specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.5238,0.2123,not harmonised,reported,-0.055742776340518954,0.02259295803186745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4672633066415455,1953.0,interaction with negative-exponential distance transform exp,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T4-c4,4,Gaussian,32,Treatment × After Harvey × Distance to Superfund Sites,other,other,other,interaction with Gaussian distance transform [1-(d/1.5)]^2,-0.1945,0.0688,,,***,,,high,distance-decay DiD effect,"log(sale price), nominal US$",log,OLS,1963.0,0.6502,se,"DID with propensity-score matching; specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1945,0.0688,not harmonised,reported,-0.06384005352700219,0.022581982944255785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8270348837209305,1953.0,interaction with gaussian distance transform [1-]^2,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c1,5,ATE,33,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.1283,0.052,,,**,,,high,DDD: pre-Harvey level effect of proximity to impacted site; reference 1.5-3 miles from impacted site,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-12.159893869287552,4.573865206145785,100*(exp(b - se^2/2) - 1),reported,-0.03435413007455108,0.013923731596856241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4673076923076924,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c2,5,Zones,33,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.0691,0.051,,,,,,high,DDD: pre-Harvey level effect of proximity to impacted site; reference 1.5-3 miles from impacted site,"log(sale price), nominal US$",log,OLS,5162.0,0.6846,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-6.797952905080507,4.759490095591027,100*(exp(b - se^2/2) - 1),reported,-0.018873071448491614,0.013929473862128398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3549019607843138,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c3,5,Exponential,33,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.1027,0.0527,,,*,,,high,DDD: pre-Harvey level effect of proximity to impacted site; reference 1.5-3 miles from impacted site,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-9.885458815450443,4.755635626953473,100*(exp(b - se^2/2) - 1),reported,-0.027140128184829276,0.013926823323666043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.94876660341556,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c4,5,Gaussian,33,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.102,0.0524,,,*,,,high,DDD: pre-Harvey level effect of proximity to impacted site; reference 1.5-3 miles from impacted site,"log(sale price), nominal US$",log,OLS,5162.0,0.6831,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-9.820934891651056,4.731874850744914,100*(exp(b - se^2/2) - 1),reported,-0.02710948779603058,0.013926834906980415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.946564885496183,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c1,5,ATE,33,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2878,0.1435,,,**,,,high,DDD: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,31.98312334798794,19.135589813470563,100*(exp(b - se^2/2) - 1),reported,0.027930679007349107,0.013926519935908953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.005574912891986,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c2,5,Zones,33,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2474,0.1428,,,*,,,high,DDD: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,5162.0,0.6846,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,26.769980355399838,18.28827157623752,100*(exp(b - se^2/2) - 1),reported,0.024129987032068905,0.0139278987396097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7324929971988794,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c3,5,Exponential,33,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2794,0.1413,,,*,,,high,DDD: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,30.92011669626322,18.684610118354406,100*(exp(b - se^2/2) - 1),reported,0.027537948230310316,0.013926671742816208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9773531493276713,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c4,5,Gaussian,33,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2789,0.1421,,,*,,,high,DDD: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,5162.0,0.6831,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,30.839840155212748,18.78100416206179,100*(exp(b - se^2/2) - 1),reported,0.02733406398685257,0.013926749704308895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9627023223082334,5152.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c1,5,ATE,33,Treatment × After Harvey,other,other,other,binary interaction,0.0678,0.1105,,,,,,high,DDD effect,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.0678,0.1105,not harmonised,reported,0.008547982444861749,0.013931446315003291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6135746606334842,5152.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c2,5,Zones,33,Treatment × After Harvey × (<.5 mile),other,other,other,binary interaction (buffer <0.5 mile),-0.3371,0.1013,,,***,,,high,DDD effect by buffer,"log(sale price), nominal US$",log,OLS,5162.0,0.6846,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.3371,0.1013,not harmonised,reported,-0.04631217084561806,0.013917006546013376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.327739387956565,5152.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c2,5,Zones,33,Treatment × After Harvey × (<1 mile),other,other,other,binary interaction (buffer 0.5-1 mile),-0.1181,0.0604,,,*,,,high,DDD effect by buffer,"log(sale price), nominal US$",log,OLS,5162.0,0.6846,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1181,0.0604,not harmonised,reported,-0.027231022637381863,0.013926788884825271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.955298013245033,5152.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c3,5,Exponential,33,Treatment × After × Distance to Superfund Site,other,other,other,interaction with negative-exponential distance transform,-0.6172,0.2429,,,**,,,high,DDD distance-decay effect,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.6172,0.2429,not harmonised,reported,-0.03537842676865619,0.013923233736400824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5409633594071632,5152.0,interaction with negative-exponential distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c4,5,Gaussian,33,Treatment × After × Distance to Superfund Site,other,other,other,interaction with Gaussian distance transform,-0.1885,0.0703,,,***,,,high,DDD distance-decay effect,"log(sale price), nominal US$",log,OLS,5162.0,0.6831,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1885,0.0703,not harmonised,reported,-0.037330626642594206,0.013922244312861393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.681365576102418,5152.0,interaction with gaussian distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c1,5,ATE,33,Control × After Harvey,other,other,other,binary interaction,-0.0657,0.052,,,,,,high,placebo: non-impacted sites after Harvey,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.0657,0.052,not harmonised,reported,-0.017599763283875875,0.013929797424072231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2634615384615384,5152.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c2,5,Zones,33,Control × After Harvey × (<.5 mile),other,other,other,binary interaction,0.0899,0.0582,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,5162.0,0.6846,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.0899,0.0582,not harmonised,reported,0.02151534116603804,0.013928730321061336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5446735395189002,5152.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c2,5,Zones,33,Control × After Harvey × (<1 mile),other,other,other,binary interaction,0.0022,0.0467,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,5162.0,0.6846,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.0022,0.0467,not harmonised,reported,0.0006563232353357348,0.013931952313717643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.047109207708779445,5152.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c3,5,Exponential,33,Control × After Harvey × Distance to Superfund Sites,other,other,other,interaction with negative-exponential distance transform,-0.1889,0.1902,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,5162.0,0.6832,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1889,0.1902,not harmonised,reported,-0.013835407278491935,0.013930621833611255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9931650893796005,5152.0,interaction with negative-exponential distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T5-c4,5,Gaussian,33,Control × After Harvey × Distance to Superfund Sites,other,other,other,interaction with Gaussian distance transform,-0.025,0.0841,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,5162.0,0.6831,se,"DDD without matching (adds control group 1.5-3 miles from impacted sites); specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.025,0.0841,not harmonised,reported,-0.0041414494162266245,0.013931835836186363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.29726516052318674,5152.0,interaction with gaussian distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c1,6,ATE,34,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.1727,0.0653,,,***,,,high,DDD matched: pre-Harvey level effect of proximity to impacted site,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-16.040197638613442,5.494276660715877,100*(exp(b - se^2/2) - 1),reported,-0.03968615619659654,0.015005825128186186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.644716692189893,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c2,6,Zones,34,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.1164,0.0614,,,*,,,high,DDD matched: pre-Harvey level effect of proximity to impacted site,"log(sale price), nominal US$",log,OLS,4444.0,0.6958,se,"DDD with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-11.155717190278146,5.465331301241179,100*(exp(b - se^2/2) - 1),reported,-0.028458422972404526,0.015011573629773522,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8957654723127035,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c3,6,Exponential,34,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.1483,0.0633,,,**,,,high,DDD matched: pre-Harvey level effect of proximity to impacted site,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-13.955316148434827,5.457551446534811,100*(exp(b - se^2/2) - 1),reported,-0.03516178873739154,0.015008369703822551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3428120063191153,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c4,6,Gaussian,34,Treatment,hazard_site,E_environment,dummy,binary (within 1.5 miles of impacted Superfund site vs 1.5-3 miles),-0.1476,0.0628,,,**,,,high,DDD matched: pre-Harvey level effect of proximity to impacted site,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,-13.892349282970128,5.418234262139949,100*(exp(b - se^2/2) - 1),reported,-0.035274308583150456,0.015008310155974583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3503184713375798,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c1,6,ATE,34,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2995,0.1424,,,**,,,high,DDD matched: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,33.557394391708485,19.212380827520615,100*(exp(b - se^2/2) - 1),reported,0.03156984605925037,0.0150101705470359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1032303370786516,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,True
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c2,6,Zones,34,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2656,0.1401,,,*,,,high,DDD matched: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,4444.0,0.6958,se,"DDD with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,29.147631597498737,18.272027899087075,100*(exp(b - se^2/2) - 1),reported,0.028458771709249205,0.01501157348066948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.895788722341185,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c3,6,Exponential,34,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2917,0.1398,,,**,,,high,DDD matched: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,32.56832408416239,18.71504483088682,100*(exp(b - se^2/2) - 1),reported,0.03131975118633932,0.015010288707062861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.086552217453505,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c4,6,Gaussian,34,Control,hazard_site,E_environment,dummy,binary (within 1.5 miles of non-impacted Superfund site vs 1.5-3 miles from impacted site),0.2907,0.1408,,,**,,,high,DDD matched: level effect of proximity to a non-flooded Superfund site,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",pct_premium,32.41724257969973,18.830075471936777,100*(exp(b - se^2/2) - 1),reported,0.03099102031620059,0.01501044258865168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0646306818181817,4434.0,binary,,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,True,True,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c1,6,ATE,34,Treatment × After Harvey,other,other,other,binary interaction,0.0629,0.1193,,,,,,high,DDD effect,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.0629,0.1193,not harmonised,reported,0.00791769456022447,0.015017185390060087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5272422464375524,4434.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c2,6,Zones,34,Treatment × After Harvey × (<.5 mile),other,other,other,binary interaction (buffer <0.5 mile),-0.3454,0.1054,,,***,,,high,DDD effect by buffer,"log(sale price), nominal US$",log,OLS,4444.0,0.6958,se,"DDD with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.3454,0.1054,not harmonised,reported,-0.04915396896467526,0.014999502978797838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2770398481973433,4434.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c2,6,Zones,34,Treatment × After Harvey × (<1 mile),other,other,other,binary interaction (buffer 0.5-1 mile),-0.0949,0.0592,,,,,,high,DDD effect by buffer,"log(sale price), nominal US$",log,OLS,4444.0,0.6958,se,"DDD with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.0949,0.0592,not harmonised,reported,-0.02406693856660705,0.01501330625019112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6030405405405403,4434.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c3,6,Exponential,34,Treatment × Distance to Superfond Site,other,other,other,interaction with negative-exponential distance transform (post-Harvey),-0.6705,0.2602,,,**,,,high,DDD distance-decay effect; row label printed 'Treatment × Distance to Superfond Site' (After omitted in label),"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.6705,0.2602,not harmonised,reported,-0.03866951236521447,0.015006423739640277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5768639508070716,4434.0,interaction with negative-exponential distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c4,6,Gaussian,34,Treatment × Distance to Superfond Site,other,other,other,interaction with Gaussian distance transform (post-Harvey),-0.2055,0.0728,,,***,,,high,DDD distance-decay effect; row label as printed,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.2055,0.0728,not harmonised,reported,-0.04235383337757707,0.015004180388747497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8228021978021975,4434.0,interaction with gaussian distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c1,6,ATE,34,Control × After Harvey,other,other,other,binary interaction,-0.0567,0.0559,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification ATE; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.0567,0.0559,not harmonised,reported,-0.015230810947554805,0.015015914144061969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0143112701252237,4434.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c2,6,Zones,34,Control × After Harvey × (<.5 mile),other,other,other,binary interaction,0.1155,0.0896,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,4444.0,0.6958,se,"DDD with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,0.1155,0.0896,not harmonised,reported,0.019355070943292432,0.015014842913584433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2890625,4434.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c2,6,Zones,34,Control × After Harvey × (<1 mile),other,other,other,binary interaction,-0.0182,0.0445,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,4444.0,0.6958,se,"DDD with propensity-score matching; specification Zones; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.0182,0.0445,not harmonised,reported,-0.0061419367668173125,0.01501737286392145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4089887640449439,4434.0,binary interaction,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c3,6,Exponential,34,Control × After Harvey × Distance to Superfund Sites,other,other,other,interaction with negative-exponential distance transform,-0.1927,0.205,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Exponential; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.1927,0.205,not harmonised,reported,-0.014115190401978818,0.015016160002105126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9400000000000001,4434.0,interaction with negative-exponential distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b,63065c2cad027c9578aace5fb84b3bd4ba000e2b:T6-c4,6,Gaussian,34,Control × After Harvey × Distance to Superfund Sites,other,other,other,interaction with Gaussian distance transform,-0.0128,0.0914,,,,,,high,placebo,"log(sale price), nominal US$",log,OLS,4444.0,0.6944,se,"DDD with propensity-score matching; specification Gaussian; structural and spatial controls, year FE, parallel-trend terms and census-tract FE included but not reported",other,-0.0128,0.0914,not harmonised,reported,-0.0021031244341278726,0.015017622912444337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14004376367614882,4434.0,interaction with gaussian distance transform,nonstandard_x_transform,2024.0,United States,North America,High income,2013.0,2019.0,single_family,transaction,journal,True,True,clustered,,2016.0,ols,journal,False,False,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A1,2,Panel A (1),30,Interim × Near,water_quality,E_environment,other,binary,0.0309,0.0109,,,***,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-500m.","Log(sales price), 2015 USD",log,OLS,70521.0,0.8651,se,"Sales market (transaction prices); treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0309,0.0109,not harmonised,reported,0.010675268245986632,0.0037657095107202034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.834862385321101,70511.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A1,2,Panel A (1),30,Post × Near,water_quality,E_environment,other,binary,0.0384,0.0155,,,**,,,high,DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-500m.,"Log(sales price), 2015 USD",log,OLS,70521.0,0.8651,se,"Sales market (transaction prices); treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0384,0.0155,not harmonised,reported,0.009329367231486868,0.00376576021062621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4774193548387093,70511.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A2,2,Panel A (2),30,Interim × Near,water_quality,E_environment,other,binary,0.0193,0.0102,,,*,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-600m.","Log(sales price), 2015 USD",log,OLS,73702.0,0.8642,se,"Sales market (transaction prices); treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0193,0.0102,not harmonised,reported,0.006970058383227131,0.003683657798389468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.892156862745098,73692.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A2,2,Panel A (2),30,Post × Near,water_quality,E_environment,other,binary,0.0272,0.0154,,,*,,,high,DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-600m.,"Log(sales price), 2015 USD",log,OLS,73702.0,0.8642,se,"Sales market (transaction prices); treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0272,0.0154,not harmonised,reported,0.006506221122203774,0.0036836693118359604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7662337662337662,73692.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A3,2,Panel A (3),30,Interim × Near,water_quality,E_environment,other,binary,0.0133,0.01,,,,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-700m.","Log(sales price), 2015 USD",log,OLS,75582.0,0.8639,se,"Sales market (transaction prices); treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0133,0.01,not harmonised,reported,0.004838002632645139,0.0036375959643948413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3299999999999998,75572.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A3,2,Panel A (3),30,Post × Near,water_quality,E_environment,other,binary,0.0253,0.0154,,,,,,high,DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-700m.,"Log(sales price), 2015 USD",log,OLS,75582.0,0.8639,se,"Sales market (transaction prices); treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0253,0.0154,not harmonised,reported,0.005976013739915119,0.0036375735808178984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6428571428571428,75572.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A4,2,Panel A (4),30,Interim × Near,water_quality,E_environment,other,binary,0.0117,0.0093,,,,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-800m.","Log(sales price), 2015 USD",log,OLS,77308.0,0.8642,se,"Sales market (transaction prices); treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0117,0.0093,not harmonised,reported,0.004524955769462422,0.0035967597141880786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2580645161290325,77298.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A4,2,Panel A (4),30,Post × Near,water_quality,E_environment,other,binary,0.0235,0.0141,,,,,,high,DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-800m.,"Log(sales price), 2015 USD",log,OLS,77308.0,0.8642,se,"Sales market (transaction prices); treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0235,0.0141,not harmonised,reported,0.005994553185933562,0.003596731911560137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,77298.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A5,2,Panel A (5),30,Interim × Near,water_quality,E_environment,other,binary,0.0128,0.0091,,,,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-900m.","Log(sales price), 2015 USD",log,OLS,78815.0,0.8638,se,"Sales market (transaction prices); treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0128,0.0091,not harmonised,reported,0.005010559675671558,0.003562194769422749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4065934065934065,78805.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A5,2,Panel A (5),30,Post × Near,water_quality,E_environment,other,binary,0.0233,0.0138,,,*,,,high,DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-900m.,"Log(sales price), 2015 USD",log,OLS,78815.0,0.8638,se,"Sales market (transaction prices); treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0233,0.0138,not harmonised,reported,0.006014397016528576,0.0035621750569997564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6884057971014494,78805.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A6,2,Panel A (6),30,Interim × Near,water_quality,E_environment,other,binary,0.0103,0.0085,,,,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-1,000m.","Log(sales price), 2015 USD",log,OLS,80924.0,0.8637,se,"Sales market (transaction prices); treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0103,0.0085,not harmonised,reported,0.0042599305891487735,0.0035154766997829686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2117647058823529,80914.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A6,2,Panel A (6),30,Post × Near,water_quality,E_environment,other,binary,0.0195,0.0132,,,,,,high,"DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-1,000m.","Log(sales price), 2015 USD",log,OLS,80924.0,0.8637,se,"Sales market (transaction prices); treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0195,0.0132,not harmonised,reported,0.005193294940375454,0.0035154611904079994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4772727272727273,80914.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A7,2,Panel A (7),30,Interim × Near,water_quality,E_environment,other,binary,0.0104,0.0083,,,,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-1,100m.","Log(sales price), 2015 USD",log,OLS,82574.0,0.864,se,"Sales market (transaction prices); treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0104,0.0083,not harmonised,reported,0.004360695433888526,0.003480170394353343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2530120481927711,82564.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A7,2,Panel A (7),30,Post × Near,water_quality,E_environment,other,binary,0.0175,0.0132,,,,,,high,"DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-1,100m.","Log(sales price), 2015 USD",log,OLS,82574.0,0.864,se,"Sales market (transaction prices); treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0175,0.0132,not harmonised,reported,0.0046138570237633835,0.003480166440781523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.325757575757576,82564.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A8,2,Panel A (8),30,Interim × Near,water_quality,E_environment,other,binary,0.0091,0.0079,,,,,,high,"DiD interaction: within buffer x period after BOWB listed for restoration, before completion (water-quality improvement announced). Buffer 0-1,200m.","Log(sales price), 2015 USD",log,OLS,84547.0,0.8645,se,"Sales market (transaction prices); treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0091,0.0079,not harmonised,reported,0.003961753742737792,0.0034393246777613806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1518987341772151,84537.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-A8,2,Panel A (8),30,Post × Near,water_quality,E_environment,other,binary,0.0149,0.0129,,,,,,high,"DiD interaction: within buffer x period after restoration completed (water-quality improvement realised). Buffer 0-1,200m.","Log(sales price), 2015 USD",log,OLS,84547.0,0.8645,se,"Sales market (transaction prices); treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0149,0.0129,not harmonised,reported,0.003972553139774207,0.0034393245304085414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1550387596899225,84537.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B1,2,Panel B (1),30,Interim × Near,water_quality,E_environment,other,binary,0.0593,0.0179,,,***,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-500m.","Log(monthly rent), 2015 USD",log,OLS,63283.0,0.8799,se,"Rental market (price_type = rent); treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0593,0.0179,not harmonised,reported,0.01316906033245375,0.0039751463735400025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3128491620111733,63273.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B1,2,Panel B (1),30,Post × Near,water_quality,E_environment,other,binary,0.0808,0.0223,,,***,,,high,DiD interaction (rent): within buffer x after restoration completed. Buffer 0-500m.,"Log(monthly rent), 2015 USD",log,OLS,63283.0,0.8799,se,"Rental market (price_type = rent); treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0808,0.0223,not harmonised,reported,0.014402975886261634,0.003975078740886565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.623318385650224,63273.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B2,2,Panel B (2),30,Interim × Near,water_quality,E_environment,other,binary,0.0415,0.019,,,**,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-600m.","Log(monthly rent), 2015 USD",log,OLS,65502.0,0.8794,se,"Rental market (price_type = rent); treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0415,0.019,not harmonised,reported,0.008534627123978754,0.003907419647122803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.18421052631579,65492.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B2,2,Panel B (2),30,Post × Near,water_quality,E_environment,other,binary,0.0547,0.0244,,,**,,,high,DiD interaction (rent): within buffer x after restoration completed. Buffer 0-600m.,"Log(monthly rent), 2015 USD",log,OLS,65502.0,0.8794,se,"Rental market (price_type = rent); treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0547,0.0244,not harmonised,reported,0.008759649130347005,0.003907412043518591,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2418032786885242,65492.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B3,2,Panel B (3),30,Interim × Near,water_quality,E_environment,other,binary,0.0336,0.0179,,,*,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-700m.","Log(monthly rent), 2015 USD",log,OLS,67124.0,0.8795,se,"Rental market (price_type = rent); treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0336,0.0179,not harmonised,reported,0.007245498730442611,0.0038599531927060336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8770949720670391,67114.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B3,2,Panel B (3),30,Post × Near,water_quality,E_environment,other,binary,0.048,0.0237,,,**,,,high,DiD interaction (rent): within buffer x after restoration completed. Buffer 0-700m.,"Log(monthly rent), 2015 USD",log,OLS,67124.0,0.8795,se,"Rental market (price_type = rent); treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.048,0.0237,not harmonised,reported,0.00781759303332939,0.0038599365602063865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0253164556962027,67114.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B4,2,Panel B (4),30,Interim × Near,water_quality,E_environment,other,binary,0.0302,0.0164,,,*,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-800m.","Log(monthly rent), 2015 USD",log,OLS,68711.0,0.8794,se,"Rental market (price_type = rent); treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0302,0.0164,not harmonised,reported,0.007025396625361731,0.0038151160482096822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8414634146341462,68701.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B4,2,Panel B (4),30,Post × Near,water_quality,E_environment,other,binary,0.0429,0.0215,,,*,,,high,DiD interaction (rent): within buffer x after restoration completed. Buffer 0-800m.,"Log(monthly rent), 2015 USD",log,OLS,68711.0,0.8794,se,"Rental market (price_type = rent); treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0429,0.0215,not harmonised,reported,0.007612454660856139,0.003815099655207622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9953488372093025,68701.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B5,2,Panel B (5),30,Interim × Near,water_quality,E_environment,other,binary,0.0292,0.0152,,,*,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-900m.","Log(monthly rent), 2015 USD",log,OLS,70282.0,0.879,se,"Rental market (price_type = rent); treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0292,0.0152,not harmonised,reported,0.0072466402676702926,0.0037722237009790564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9210526315789473,70272.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B5,2,Panel B (5),30,Post × Near,water_quality,E_environment,other,binary,0.039,0.0201,,,*,,,high,DiD interaction (rent): within buffer x after restoration completed. Buffer 0-900m.,"Log(monthly rent), 2015 USD",log,OLS,70282.0,0.879,se,"Rental market (price_type = rent); treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.039,0.0201,not harmonised,reported,0.007319236146855864,0.0037722217064564837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9402985074626866,70272.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B6,2,Panel B (6),30,Interim × Near,water_quality,E_environment,other,binary,0.0312,0.0135,,,**,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-1,000m.","Log(monthly rent), 2015 USD",log,OLS,71757.0,0.8784,se,"Rental market (price_type = rent); treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0312,0.0135,not harmonised,reported,0.008627853998720235,0.003733206057138563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.311111111111111,71747.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B6,2,Panel B (6),30,Post × Near,water_quality,E_environment,other,binary,0.0387,0.0182,,,**,,,high,"DiD interaction (rent): within buffer x after restoration completed. Buffer 0-1,000m.","Log(monthly rent), 2015 USD",log,OLS,71757.0,0.8784,se,"Rental market (price_type = rent); treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0387,0.0182,not harmonised,reported,0.007938236248981448,0.0037332273832419213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1263736263736264,71747.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B7,2,Panel B (7),30,Interim × Near,water_quality,E_environment,other,binary,0.0286,0.0127,,,**,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-1,100m.","Log(monthly rent), 2015 USD",log,OLS,73338.0,0.8791,se,"Rental market (price_type = rent); treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0286,0.0127,not harmonised,reported,0.008315959677350859,0.0036927513252572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.251968503937008,73328.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B7,2,Panel B (7),30,Post × Near,water_quality,E_environment,other,binary,0.0354,0.0169,,,**,,,high,"DiD interaction (rent): within buffer x after restoration completed. Buffer 0-1,100m.","Log(monthly rent), 2015 USD",log,OLS,73338.0,0.8791,se,"Rental market (price_type = rent); treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0354,0.0169,not harmonised,reported,0.007735148301782104,0.0036927685395513436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.094674556213018,73328.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B8,2,Panel B (8),30,Interim × Near,water_quality,E_environment,other,binary,0.0296,0.0124,,,**,,,high,"DiD interaction (rent): within buffer x after BOWB listing, before completion. Buffer 0-1,200m.","Log(monthly rent), 2015 USD",log,OLS,75001.0,0.8795,se,"Rental market (price_type = rent); treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0296,0.0124,not harmonised,reported,0.008716636872078064,0.003651564095059729,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3870967741935485,74991.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T2-B8,2,Panel B (8),30,Post × Near,water_quality,E_environment,other,binary,0.0361,0.0163,,,**,,,high,"DiD interaction (rent): within buffer x after restoration completed. Buffer 0-1,200m.","Log(monthly rent), 2015 USD",log,OLS,75001.0,0.8795,se,"Rental market (price_type = rent); treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; difference-in-differences",other,0.0361,0.0163,not harmonised,reported,0.008087249138969894,0.003651583406238484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.214723926380368,74991.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A1,3,Panel A (1),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0388,0.0182,,,**,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-500m.","Log(sales price), 2015 USD",log,OLS,101542.0,0.8478,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0388,0.0182,not harmonised,reported,0.006690354760757849,0.003138259191901877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.131868131868132,101532.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A1,3,Panel A (1),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.0486,0.019,,,**,,,high,Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-500m.,"Log(sales price), 2015 USD",log,OLS,101542.0,0.8478,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0486,0.019,not harmonised,reported,0.008027257692873215,0.003138228316143849,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.557894736842105,101532.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A2,3,Panel A (2),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0301,0.0163,,,*,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-600m.","Log(sales price), 2015 USD",log,OLS,108110.0,0.8476,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0301,0.0163,not harmonised,reported,0.005616415049413801,0.003041447352340364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8466257668711656,108100.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A2,3,Panel A (2),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.0353,0.0179,,,*,,,high,Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-600m.,"Log(sales price), 2015 USD",log,OLS,108110.0,0.8476,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0353,0.0179,not harmonised,reported,0.005997924785910171,0.003041440613818472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9720670391061452,108100.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A3,3,Panel A (3),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0261,0.0164,,,,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-700m.","Log(sales price), 2015 USD",log,OLS,113477.0,0.8476,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0261,0.0164,not harmonised,reported,0.004724512757303477,0.00296865935707958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5914634146341462,113467.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A3,3,Panel A (3),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.0353,0.0185,,,*,,,high,Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-700m.,"Log(sales price), 2015 USD",log,OLS,113477.0,0.8476,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0353,0.0185,not harmonised,reported,0.005664495330358101,0.002968644861519118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9081081081081082,113467.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A4,3,Panel A (4),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0247,0.0159,,,,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-800m.","Log(sales price), 2015 USD",log,OLS,118147.0,0.8497,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0247,0.0159,not harmonised,reported,0.004519625228285591,0.002909394377722304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5534591194968552,118137.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A4,3,Panel A (4),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.0333,0.017,,,*,,,high,Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-800m.,"Log(sales price), 2015 USD",log,OLS,118147.0,0.8497,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0333,0.017,not harmonised,reported,0.0056989558231076145,0.002909376846631515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9588235294117649,118137.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A5,3,Panel A (5),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0297,0.0143,,,**,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-900m.","Log(sales price), 2015 USD",log,OLS,122778.0,0.8499,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0297,0.0143,not harmonised,reported,0.0059274812900710055,0.0028539724729971503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.076923076923077,122768.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A5,3,Panel A (5),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.035,0.0162,,,**,,,high,Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-900m.,"Log(sales price), 2015 USD",log,OLS,122778.0,0.8499,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.035,0.0162,not harmonised,reported,0.006165981018233731,0.002853968357011041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.160493827160494,122768.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A6,3,Panel A (6),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0244,0.0136,,,*,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-1,000m.","Log(sales price), 2015 USD",log,OLS,127301.0,0.8499,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0244,0.0136,not harmonised,reported,0.005028594057175905,0.002802822917114438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7941176470588238,127291.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A6,3,Panel A (6),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.0306,0.016,,,*,,,high,"Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-1,000m.","Log(sales price), 2015 USD",log,OLS,127301.0,0.8499,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0306,0.016,not harmonised,reported,0.005360389590043174,0.002802818086297085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9124999999999999,127291.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A7,3,Panel A (7),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0239,0.0124,,,*,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-1,100m.","Log(sales price), 2015 USD",log,OLS,130953.0,0.8513,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0239,0.0124,not harmonised,reported,0.005326340322407608,0.0027634569036759137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9274193548387097,130943.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A7,3,Panel A (7),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.0275,0.0154,,,*,,,high,"Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-1,100m.","Log(sales price), 2015 USD",log,OLS,130953.0,0.8513,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0275,0.0154,not harmonised,reported,0.004934754385270492,0.0027634624557514756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7857142857142856,130943.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A8,3,Panel A (8),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.0219,0.0119,,,*,,,high,"Triple-difference interaction: BOWB (vs clean water body) x within buffer x after listing, before completion. Buffer 0-1,200m.","Log(sales price), 2015 USD",log,OLS,134272.0,0.852,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.0219,0.0119,not harmonised,reported,0.005022444192412474,0.0027290906798953622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8403361344537814,134262.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-A8,3,Panel A (8),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.023,0.0152,,,,,,high,"Triple-difference interaction: BOWB x within buffer x after restoration completed. Buffer 0-1,200m.","Log(sales price), 2015 USD",log,OLS,134272.0,0.852,se,"Sales market (transaction prices); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.023,0.0152,not harmonised,reported,0.0041295619807540955,0.0027291018307592286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.513157894736842,134262.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B1,3,Panel B (1),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1369,0.0364,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-500m.","Log(monthly rent), 2015 USD",log,OLS,93166.0,0.855,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1369,0.0364,not harmonised,reported,0.012321502761472517,0.0032761336779956146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7609890109890105,93156.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B1,3,Panel B (1),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1685,0.0457,,,***,,,high,Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-500m.,"Log(monthly rent), 2015 USD",log,OLS,93166.0,0.855,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-500m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1685,0.0457,not harmonised,reported,0.012079434470874475,0.003276143355008685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6870897155361053,93156.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B2,3,Panel B (2),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1139,0.0354,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-600m.","Log(monthly rent), 2015 USD",log,OLS,98950.0,0.8539,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1139,0.0354,not harmonised,reported,0.010228496230325982,0.003179005852094291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2175141242937855,98940.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B2,3,Panel B (2),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1399,0.0446,,,***,,,high,Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-600m.,"Log(monthly rent), 2015 USD",log,OLS,98950.0,0.8539,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-600m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1399,0.0446,not harmonised,reported,0.009971840173106802,0.0031790140937852994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1367713004484306,98940.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B3,3,Panel B (3),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1167,0.0334,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-700m.","Log(monthly rent), 2015 USD",log,OLS,104009.0,0.8542,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1167,0.0334,not harmonised,reported,0.010833887432151794,0.0031007012873510704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4940119760479043,103999.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B3,3,Panel B (3),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1453,0.0429,,,***,,,high,Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-700m.,"Log(monthly rent), 2015 USD",log,OLS,104009.0,0.8542,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-700m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1453,0.0429,not harmonised,reported,0.010501946215026979,0.003100712268579885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3869463869463874,103999.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B4,3,Panel B (4),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1189,0.0339,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-800m.","Log(monthly rent), 2015 USD",log,OLS,108356.0,0.8558,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1189,0.0339,not harmonised,reported,0.01065494130378636,0.003037868042038331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5073746312684366,108346.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B4,3,Panel B (4),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1448,0.0434,,,***,,,high,Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-800m.,"Log(monthly rent), 2015 USD",log,OLS,108356.0,0.8558,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-800m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1448,0.0434,not harmonised,reported,0.01013561445808618,0.0030378844439291446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3364055299539173,108346.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B5,3,Panel B (5),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1199,0.0343,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-900m.","Log(monthly rent), 2015 USD",log,OLS,113663.0,0.8567,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1199,0.0343,not harmonised,reported,0.010368388597028986,0.002966102826339401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4956268221574347,113653.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B5,3,Panel B (5),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1439,0.0442,,,***,,,high,Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-900m.,"Log(monthly rent), 2015 USD",log,OLS,113663.0,0.8567,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-900m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1439,0.0442,not harmonised,reported,0.009656679605563061,0.00296612396501659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.255656108597285,113653.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B6,3,Panel B (6),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1191,0.0334,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-1,000m.","Log(monthly rent), 2015 USD",log,OLS,117542.0,0.8548,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1191,0.0334,not harmonised,reported,0.010400728033374133,0.002916744889292158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.565868263473054,117532.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B6,3,Panel B (6),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1416,0.043,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-1,000m.","Log(monthly rent), 2015 USD",log,OLS,117542.0,0.8548,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,000m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1416,0.043,not harmonised,reported,0.009604985211887539,0.0029167681081296902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.293023255813954,117532.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B7,3,Panel B (7),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1164,0.0326,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-1,100m.","Log(monthly rent), 2015 USD",log,OLS,121283.0,0.8558,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1164,0.0326,not harmonised,reported,0.010252516886320845,0.0028714093685056658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.570552147239264,121273.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B7,3,Panel B (7),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1375,0.0416,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-1,100m.","Log(monthly rent), 2015 USD",log,OLS,121283.0,0.8558,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,100m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1375,0.0416,not harmonised,reported,0.009490907617093381,0.0028714309590624335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.305288461538462,121273.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B8,3,Panel B (8),31,Treat × Interim × Near,water_quality,E_environment,other,binary,0.1133,0.0318,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after listing, before completion. Buffer 0-1,200m.","Log(monthly rent), 2015 USD",log,OLS,124185.0,0.8563,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1133,0.0318,not harmonised,reported,0.010110287559182304,0.002837662351120894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.562893081761006,124175.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523,63d3e41634a76fe68a3edb5f056b73b7098ad523:T3-B8,3,Panel B (8),31,Treat × Post × Near,water_quality,E_environment,other,binary,0.1338,0.0405,,,***,,,high,"Triple-difference interaction (rent): BOWB x within buffer x after restoration completed. Buffer 0-1,200m.","Log(monthly rent), 2015 USD",log,OLS,124185.0,0.8563,se,"Rental market (price_type = rent); sample adds properties near connected clean water bodies as controls; treatment buffer Near = 0-1,200m of a BOWB, control 1,200-3,000 m; month, BOWB, ring-road and district x year FE; triple difference",other,0.1338,0.0405,not harmonised,reported,0.009374862795648815,0.002837682684781592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.303703703703704,124175.0,binary,nonstandard_x_transform,2023.0,China,Asia,Upper middle income,2015.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,,2016.5,ols,journal,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,APARTMENT,dwelling_type,A_structural,dummy,binary,-31.73,23.84,,0.1836,,,,high,Dwelling type dummy,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,-31.73,23.84,"b (currency per unit, as reported)",reported,-0.04745036160665776,0.03565132747250933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3309563758389262,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,SINGLE FAMILY HOME,dwelling_type,A_structural,dummy,binary,40.99,35.88,,0.2536,,,,high,Dwelling type dummy,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,40.99,35.88,"b (currency per unit, as reported)",reported,0.04074083555610246,0.03566189752995746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1424191750278707,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,LIVING AREA (m2),living_area,A_structural,level,m2,1.88,0.28,,0.0001,,,,high,,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,1.88,0.28,"b (currency per unit, as reported)",reported,0.23304479944099077,0.03470879991674331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.7142857142857135,785.0,m2,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,GARAGE,garage,A_structural,dummy,binary,112.63,27.5,,0.0001,,,,high,,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,112.63,27.5,"b (currency per unit, as reported)",reported,0.14464230752790516,0.035316198677238675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0956363636363635,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,TITLE & CONST. PERMIT,other,other,dummy,binary,38.83,19.73,,0.0494,,,,high,Legal title and construction permit (formal legal status),"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,38.83,19.73,"b (currency per unit, as reported)",reported,0.0700707357956624,0.03560380162885448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.968068930562595,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,BUILT BY A BUILDER,other,other,dummy,binary,28.9,20.47,,0.1585,,,,high,Property built by a professional builder,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,28.9,20.47,"b (currency per unit, as reported)",reported,0.05032624164966791,0.035646303341477587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4118221787982412,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,TENANT,other,other,dummy,binary,16.2,17.18,,0.3458,,,,high,Respondent is a tenant (occupancy status),"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,16.2,17.18,"b (currency per unit, as reported)",reported,0.033636531298819806,0.03567133380948916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9429569266589056,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,FAMILY INCOME < 3M,other,other,dummy,binary,-942.8,161.46,,0.0001,,,,high,Household income category dummy (million TL/month); occupant characteristic,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,-942.8,161.46,"b (currency per unit, as reported)",reported,-0.20402674550197258,0.03494077039536327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.839217143564969,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,"FAMILY INCOME [3M,5M]",other,other,dummy,binary,-937.5,160.96,,0.0001,,,,high,Household income category dummy,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,-937.5,160.96,"b (currency per unit, as reported)",reported,-0.2035314480109887,0.03494444999663866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.824428429423459,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,"FAMILY INCOME [5M,9M]",other,other,dummy,binary,-839.67,162.29,,0.0001,,,,high,Household income category dummy,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,-839.67,162.29,"b (currency per unit, as reported)",reported,-0.18159363368326775,0.0350981109369842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.173886253003882,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,"FAMILY INCOME [9M,14M]",other,other,dummy,binary,-904.74,165.56,,0.0001,,,,high,Household income category dummy,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,-904.74,165.56,"b (currency per unit, as reported)",reported,-0.19143705908838687,0.03503141178976649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.464725779173714,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,"FAMILY INCOME [15M,20M]",other,other,dummy,binary,-896.64,170.7,,0.0001,,,,high,Household income category dummy,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,monetary,-896.64,170.7,"b (currency per unit, as reported)",reported,-0.18426741982337685,0.03508035394790599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.252724077328647,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,GREEN AREA/VERY HAPPY,green_space,E_environment,other,binary,237.47,55.74,,0.0001,,,,high,Occupant very satisfied with green areas (satisfaction dummy),"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,237.47,55.74,not harmonised,reported,0.15032921080939832,0.03528593174091827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.260315751704342,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,RECREATIONAL FACILITIES/VERY HAPPY,neighbourhood_amenities,F_neighbourhood,other,binary,-108.77,79.89,,0.1738,,,,high,Occupant very satisfied with nearby recreational facilities,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,-108.77,79.89,not harmonised,reported,-0.0485366411481922,0.035649464570461295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.361497058455376,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,NOISE IN THE BUILDING/UNHAPPY,noise,E_environment,other,binary,-24.77,18.54,,0.182,,,,high,Occupant unhappy with nearby noise (satisfaction dummy),"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,-24.77,18.54,not harmonised,reported,-0.04763084074601475,0.035651020889427266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.336030204962244,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,NEIGHBOR. RELATIONS/HAPPY,other,other,other,binary,21.86,22.82,,0.3384,,,,high,Satisfaction with neighbourhood relations,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,21.86,22.82,not harmonised,reported,0.0341700804361037,0.03567068781115675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.957931638913234,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,NEIGHBOR. RELATIONS/UNHAPPY,other,other,other,binary,50.0,29.74,,0.0931,,,,high,Satisfaction with neighbourhood relations,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,50.0,29.74,not harmonised,reported,0.05989819444551367,0.03562744605619153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6812373907195697,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,VIEW/VERY HAPPY,view,E_environment,other,binary,-59.09,44.96,,0.1892,,,,high,Occupant very satisfied with view,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,-59.09,44.96,not harmonised,reported,-0.04685711750198867,0.03565232700777476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.314279359430605,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,VIEW/UNHAPPY,view,E_environment,other,binary,-27.92,16.99,,0.1007,,,,high,Occupant unhappy with view,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,-27.92,16.99,not harmonised,reported,-0.05855196502099865,0.035630296766001684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6433195997645675,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T2-value,2,1,23,SHOPPING FACILITIES/HAPPY,commercial_distance,D_location,other,binary,23.37,18.12,,0.1976,,,,high,Occupant satisfied with shopping facilities,"Self-assessed property value (price you would ask, thousand TL)",level,OLS,795.0,0.263,none,All surveyed households (owners and tenants); adj R2 0.235,other,23.37,18.12,not harmonised,reported,0.0459839254164712,0.03565377529081977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2897350993377483,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,LIVING AREA - M2,living_area,A_structural,level,m2,0.52,0.091,,0.0001,,,,high,,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,0.52,0.091,"b (currency per unit, as reported)",reported,0.1998376977790673,0.03497159711133677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.714285714285714,785.0,m2,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,GARAGE,garage,A_structural,dummy,binary,57.45,8.66,,0.0001,,,,high,,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,57.45,8.66,"b (currency per unit, as reported)",reported,0.2304052891477518,0.03473124114916502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.633949191685913,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,TITLE & CONST. PERMIT,other,other,dummy,binary,20.43,6.2,,0.001,,,,high,Legal title and construction permit,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,20.43,6.2,"b (currency per unit, as reported)",reported,0.1168043077965063,0.035447220182982825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2951612903225804,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,BUILT BY A COOPERATIVE,other,other,dummy,binary,59.59,11.44,,0.0001,,,,high,Built by a housing cooperative,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,59.59,11.44,"b (currency per unit, as reported)",reported,0.18278217198472568,0.035090250839155256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.208916083916084,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,BUILT BY A BUILDER,other,other,dummy,binary,23.38,6.16,,0.0002,,,,high,Built by a professional builder,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,23.38,6.16,"b (currency per unit, as reported)",reported,0.13423947204347994,0.035368483652174355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7954545454545454,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,OWNER,other,other,dummy,binary,12.76,5.51,,0.0209,,,,high,Respondent is owner-occupier,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,12.76,5.51,"b (currency per unit, as reported)",reported,0.08237317537000909,0.035570234818867556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3157894736842106,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,TENANT - RENT FREE,other,other,dummy,binary,13.38,12.25,,0.2749,,,,high,Rent-free tenant,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,13.38,12.25,"b (currency per unit, as reported)",reported,0.03895430305908554,0.0356644403941553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0922448979591837,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,FAMILY INCOME < 3M,other,other,dummy,binary,156.2,52.03,,0.0028,,,,high,Household income category dummy,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,156.2,52.03,"b (currency per unit, as reported)",reported,0.10654019371052931,0.03548838846836646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0021141649048624,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,"FAMILY INCOME [3M,5M]",other,other,dummy,binary,164.82,51.81,,0.0015,,,,high,Household income category dummy,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,164.82,51.81,"b (currency per unit, as reported)",reported,0.11281839099657777,0.03546366240463957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1812391430225824,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,"FAMILY INCOME [5M,9M]",other,other,dummy,binary,196.45,52.13,,0.0002,,,,high,Household income category dummy,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,196.45,52.13,"b (currency per unit, as reported)",reported,0.13330186108308684,0.03537300085650963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.768463456742758,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,"FAMILY INCOME [9M,14M]",other,other,dummy,binary,241.57,53.37,,0.0001,,,,high,Household income category dummy,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,241.57,53.37,"b (currency per unit, as reported)",reported,0.15948372123585688,0.03523469885481509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.526325651114859,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,"FAMILY INCOME [15M,20M]",other,other,dummy,binary,233.21,54.71,,0.0001,,,,high,Household income category dummy,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,monetary,233.21,54.71,"b (currency per unit, as reported)",reported,0.15040997786587834,0.03528549328520305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.262657649424237,785.0,binary,,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,TRANSPORTATION/VERY HAPPY,transit_distance,D_location,other,binary,61.96,15.43,,0.0001,,,,high,Occupant very satisfied with transportation (satisfaction dummy),"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,61.96,15.43,not harmonised,reported,0.141871587654908,0.03533051319424194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.015554115359689,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,TRANSPORTATION/HAPPY,transit_distance,D_location,other,binary,18.88,11.76,,0.109,,,,high,Occupant satisfied with transportation,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,18.88,11.76,not harmonised,reported,0.057206849809636,0.035633080178035986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6054421768707483,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,TRANSPORTATION/UNHAPPY,transit_distance,D_location,other,binary,15.33,12.3,,0.2131,,,,high,Occupant unhappy with transportation,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,15.33,12.3,not harmonised,reported,0.04443988702028621,0.03565626942919246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2463414634146341,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,GREEN AREA/VERY HAPPY,green_space,E_environment,other,binary,86.04,18.05,,0.0001,,,,high,Occupant very satisfied with green areas,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,86.04,18.05,not harmonised,reported,0.16772285322208916,0.03518593096999894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.766759002770083,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,RECREATIONAL FACILITIES/VERY HAPPY,neighbourhood_amenities,F_neighbourhood,other,binary,-34.58,25.87,,0.1817,,,,high,Very satisfied with recreational facilities,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,-34.58,25.87,not harmonised,reported,-0.047654075553711435,0.0356509813352954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.336683417085427,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,RECREATIONAL FACILITIES/UNHAPPY,neighbourhood_amenities,F_neighbourhood,other,binary,-10.83,5.63,,0.0547,,,,high,Unhappy with recreational facilities,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,-10.83,5.63,not harmonised,reported,-0.0684958171730143,0.0356077055109945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9236234458259325,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,NEIGHBOR. RELATIONS/UNHAPPY,other,other,other,binary,12.49,7.44,,0.0937,,,,high,Unhappy with neighbourhood relations,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,12.49,7.44,not harmonised,reported,0.05981036946517674,0.0356276340128835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.678763440860215,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,False,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,VIEW/VERY HAPPY,view,E_environment,other,binary,-38.34,14.35,,0.0077,,,,high,Very satisfied with view,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,-38.34,14.35,not harmonised,reported,-0.09492916835169125,0.03553034861363509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.671777003484321,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,VIEW/UNHAPPY,view,E_environment,other,binary,6.75,5.4,,0.2115,,,,high,Unhappy with view,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,6.75,5.4,not harmonised,reported,0.0445700780275668,0.03565606242205344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,SHOPPING FACILITIES/HAPPY,commercial_distance,D_location,other,binary,14.88,8.92,,0.0958,,,,high,Satisfied with shopping facilities,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,14.88,8.92,not harmonised,reported,0.059433983577719895,0.035628436392020256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6681614349775786,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd,63f327f2d844daf6ba970bc6d5f6ab1fc9861dfd:T3-rent,3,1,25,SHOPPING FACILITIES/UNHAPPY,commercial_distance,D_location,other,binary,12.2,10.41,,0.2415,,,,high,Unhappy with shopping facilities,"Self-assessed monthly rent (rent you would charge, thousand TL)",level,OLS,795.0,0.4443,none,All surveyed households; adj R2 0.4195,other,12.2,10.41,not harmonised,reported,0.041792146283796947,0.03566034777166609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1719500480307397,785.0,binary,nonstandard_x_transform,2000.0,Turkey,Asia,Upper middle income,1992.0,1992.0,mixed,appraisal,conference,False,True,unknown,,1992.0,ols,other,False,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Area (Sq. M.),living_area,A_structural,level,m2,0.018,0.001,23.435,0.0,,,,high,Floor area of flat in m2 (quadratic specification with Area^2),"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,1.7999999999999998,0.1,100*b/1 (X per m2->m2),reported,0.36969980804382985,0.015775541200931508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.435,3469.0,m2,,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Residents with university degree (%),education_level,F_neighbourhood,level,"percentage points (share of residents with university degree, census tract, 300 m buffer)",0.005,0.001,4.452,0.0,,,,high,"2001 census, census-tract level","ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,0.5,0.1,100*b (unit as reported: percentage points; no harmonised unit defined for this characteristic),reported,0.07537300931293293,0.016930145847469213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.452,3469.0,percentage points,unit_not_harmonisable,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,AC,air_conditioning,A_structural,binary,binary,0.101,0.013,7.503,0.0,,,,high,Air conditioning dummy,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",pct_premium,10.61831653366292,1.4381596342924505,100*(exp(b - se^2/2) - 1),reported,0.12636802948431225,0.016842333664442523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.503,3469.0,binary,,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Area (Sq. M.)^2,living_area,A_structural,squared,m2 squared,-4.104e-05,,-11.88,0.0,,,,high,"Squared floor area; SE printed as 0.000 (rounded), not recorded; t = -11.880","ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",other,-4.104e-05,3.454545454545454e-06,not harmonised,|b/t|,-0.19772189104315618,0.016643256821814494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.88,3469.0,m2 sqd,nonstandard_x_transform,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Built density,density,F_neighbourhood,level,"floor area ratio (m2 of floor space per m2 of land, zone)",0.038,0.006,6.264,0.0,,,,high,Built density of the zone used as a continuous centrality indicator; not population density,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,3.8,0.6,100*b (unit as reported: floor area ratio; no harmonised unit defined for this characteristic),reported,0.10575654242744868,0.016883228356872396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.264,3469.0,floor area ratio,unit_not_harmonisable,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Interaction lift*story level,floor_level,A_structural,other,"storey number x (+1 if lift present, -1 otherwise)",0.013,0.002,5.846,0.0,,,,high,Interaction of floor level with lift presence (coded +1/-1); x_transform other,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",other,0.013,0.002,not harmonised,reported,0.09877063598143955,0.016895421823715284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.846,3469.0,storey number x,nonstandard_x_transform,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Swiming pool,pool,C_exterior,binary,binary,0.136,0.026,5.204,0.0,,,,high,Condominium swimming pool dummy,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",pct_premium,14.52947185510951,2.9787729233466407,100*(exp(b - se^2/2) - 1),reported,0.08801293555441606,0.016912554872101474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.204,3469.0,binary,,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Number of baths,bathrooms,A_structural,level,count,0.062,0.012,5.189,0.0,,,,high,Number of bathrooms,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,6.2,1.2,100*b/1 (per unit count),reported,0.08776120394844175,0.01691293196154206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.189,3469.0,count,,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Heating,heating,A_structural,binary,binary,0.046,0.013,3.453,0.001,,,,high,Heating dummy,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",pct_premium,4.698593690729247,1.3611967342440183,100*(exp(b - se^2/2) - 1),reported,0.0585260692760948,0.0169493394949594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.453,3469.0,binary,,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,PCA factor for high professional profile,other,other,level,"PCA factor score (socio-professional status of residents, zone)",0.061,0.014,4.466,0.0,,,,high,Neighbourhood socio-professional status (managerial/professional residents); taxonomy other,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,6.1,1.4000000000000001,100*b (unit as reported: pca factor score; no harmonised unit defined for this characteristic),reported,0.07560867848915082,0.01692984292188778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.466,3469.0,pca factor score,unit_not_harmonisable,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Residential buildings with doorkeeper (%),other,other,level,"percentage points (share of residential buildings with doorkeeper service, zone)",0.005,0.001,4.128,0.0,,,,high,Neighbourhood status proxy; taxonomy other,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,0.5,0.1,100*b (unit as reported: percentage points; no harmonised unit defined for this characteristic),reported,0.06991550222572235,0.016936894919021884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.128,3469.0,percentage points,unit_not_harmonisable,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Centrality synthetic index,employment_access,D_location,level,"index (DP2 synthetic centrality index from spatio-temporal behaviour of population, range 2.52-20.41)",0.01,0.003,3.481,0.001,,,,high,"Continuous centrality indicator based on time-geography (activities, not only employment); mapped to employment_access as an accessibility index","ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,1.0,0.3,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.05899900544040265,0.016948866831485967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.481,3469.0,index,unit_not_harmonisable,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Renewal,renovation,A_structural,binary,binary,0.043,0.017,2.512,0.012,,,,high,Renewed/renovated flat dummy,"ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",pct_premium,4.378705672312377,1.7746944212460416,100*(exp(b - se^2/2) - 1),reported,0.04261111034721558,0.016963021635038048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.512,3469.0,binary,,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e,66b74975968370c75371207845d963de2413a23e:T3-m14,3,Model 14,7,Energy qualification,energy_efficiency,A_structural,level,"ordinal 1-7 EPC class (Table 1 codes 1=A most efficient, 7=G least efficient)",0.009,0.004,2.076,0.038,,,,medium,"Positive coefficient interpreted by the author as +0.9% per step up the efficiency ladder (A-G gap 5.11%), which implies the regression coding runs from least to most efficient, opposite to the Table 1 note; direction ambiguous","ln asking (listing) price of flat, euros",log,OLS,3479.0,0.653,se,"Final model (14) after stepwise family of models and Mahalanobis outlier removal; only variables significant at 95% retained; adjusted R2 0.652; SE, standardized beta, t and Sig. in separate columns",semi_elasticity,0.8999999999999999,0.4,100*b (unit as reported: ordinal 1-7 epc class; no harmonised unit defined for this characteristic),reported,0.035225372131678284,0.01696790565109744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.076,3469.0,ordinal 1-7 epc class,unit_not_harmonisable,2017.0,Spain,Europe,High income,2014.0,2014.0,condo_apartment,listing,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c1,2,1,19,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.0015,0.0279,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4541,se,"(1) full sample, no neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,0.11114121612580963,2.794188140319964,100*(exp(b - se^2/2) - 1),reported,0.00014939419503944283,0.0027787320277336365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.053763440860215055,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c1,2,1,19,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.0064,0.0162,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4541,se,"(1) full sample, no neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.6509937940466504,1.6096651069342212,100*(exp(b - se^2/2) - 1),reported,-0.0010977700284121328,0.002778730384418211,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39506172839506176,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c1,2,1,19,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0123,0.0142,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4541,se,"(1) full sample, no neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-1.2324246682465345,1.4026409768457313,100*(exp(b - se^2/2) - 1),reported,-0.002406922909851461,0.002778724009747215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8661971830985915,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c1,2,1,19,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0141,0.0208,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4541,se,"(1) full sample, no neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-1.4214328783897834,2.0508777940326186,100*(exp(b - se^2/2) - 1),reported,-0.0018836563711302414,0.0027787271290431927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6778846153846154,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c2,2,2,19,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.009,0.028,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4558,se,"(2) full sample + neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,0.8645155366612256,2.82531374096683,100*(exp(b - se^2/2) - 1),reported,0.0008931635197675098,0.0027787309503878085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3214285714285714,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c2,2,2,19,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.0005,0.0171,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4558,se,"(2) full sample + neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.06459962545155573,1.7091452137143797,100*(exp(b - se^2/2) - 1),reported,-8.12494751336406e-05,0.0027787320495705086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.029239766081871343,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c2,2,2,19,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0071,0.0143,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4558,se,"(2) full sample + neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.7176370987735714,1.4198829579990748,100*(exp(b - se^2/2) - 1),reported,-0.0013796488699768577,0.0027787294141787414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4965034965034965,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c2,2,2,19,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0106,0.0217,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4558,se,"(2) full sample + neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-1.077695301866366,2.147119480988287,100*(exp(b - se^2/2) - 1),reported,-0.0013573517368230942,0.0027787294989680324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48847926267281105,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c2,2,2,19,"DISTi,MOT 10 min",highway_distance,D_location,dummy,minutes,-0.0551,0.0194,,,***,,,high,Dummy: within 10 min driving distance of a motorway access point; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,129521.0,0.4558,se,"(2) full sample + neighbourhood controls; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-5.378757083851527,1.8359975780916555,100*(exp(b - se^2/2) - 1),reported,-0.007891926205348832,0.0027786455242063035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8402061855670104,129511.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c3,2,3,19,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.0077,0.0296,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,90745.0,0.4738,se,"(3) walking distance to station <= 40 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,0.7288351683896632,2.9828799748571697,100*(exp(b - se^2/2) - 1),reported,0.0008635976072683602,0.0033198037889796703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26013513513513514,90735.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c3,2,3,19,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.006,0.0168,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,90745.0,0.4738,se,"(3) walking distance to station <= 40 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.6122301863756063,1.669950179610611,100*(exp(b - se^2/2) - 1),reported,-0.0011856438191163174,0.0033198026935256893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35714285714285715,90735.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c3,2,3,19,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0066,0.0144,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,90745.0,0.4738,se,"(3) walking distance to station <= 40 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.6681260463003547,1.4305272943146583,100*(exp(b - se^2/2) - 1),reported,-0.0015215755426396735,0.0033198011839411052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45833333333333337,90735.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c3,2,3,19,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.016,0.0187,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,90745.0,0.4738,se,"(3) walking distance to station <= 40 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-1.6044734644099723,1.8403180885033832,100*(exp(b - se^2/2) - 1),reported,-0.0028404634305284693,0.003319791634430149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8556149732620321,90735.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c3,2,3,19,"DISTi,MOT 10 min",highway_distance,D_location,dummy,minutes,-0.0659,0.0235,,,***,,,high,Dummy: within 10 min driving distance of a motorway access point; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,90745.0,0.4738,se,"(3) walking distance to station <= 40 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-6.403399723830983,2.200127532838317,100*(exp(b - se^2/2) - 1),reported,-0.009309177508785614,0.0033196611753636106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.804255319148936,90735.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c4,2,4,19,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.0261,0.0797,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,13283.0,0.4458,se,"(4) <= 40 min walk & commuting 60-40 min to Berlin; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,2.318872820183704,8.180755393970315,100*(exp(b - se^2/2) - 1),reported,0.002842469964100446,0.008679879545548105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.32747804265997493,13273.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c4,2,4,19,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.0167,0.0306,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,13283.0,0.4458,se,"(4) <= 40 min walk & commuting 60-40 min to Berlin; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-1.7021646566881765,3.0093223362777426,100*(exp(b - se^2/2) - 1),reported,-0.004737024432872705,0.008679817224305674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5457516339869282,13273.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c4,2,4,19,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,0.0356,0.0348,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,13283.0,0.4458,se,"(4) <= 40 min walk & commuting 60-40 min to Berlin; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,3.561399219460859,3.6061196094166097,100*(exp(b - se^2/2) - 1),reported,0.008879102849681849,0.008679572448565404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0229885057471264,13273.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c4,2,4,19,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0055,0.0493,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,13283.0,0.4458,se,"(4) <= 40 min walk & commuting 60-40 min to Berlin; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.6692748127798742,4.902959429732971,100*(exp(b - se^2/2) - 1),reported,-0.0009683470177955147,0.008679910541330705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11156186612576065,13273.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c4,2,4,19,"DISTi,MOT 10 min",highway_distance,D_location,dummy,minutes,-0.0586,0.0555,,,,,,high,Dummy: within 10 min driving distance of a motorway access point; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,13283.0,0.4458,se,"(4) <= 40 min walk & commuting 60-40 min to Berlin; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-5.836742192567101,5.234115796467052,100*(exp(b - se^2/2) - 1),reported,-0.00916435381012495,0.008679550110271925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0558558558558557,13273.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c5,2,5,19,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.048,0.0289,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,34930.0,0.5127,se,"(5) <= 40 min walk & commuting 40-30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,4.873260788472034,3.0321031938877203,100*(exp(b - se^2/2) - 1),reported,0.008887694616586282,0.00535113280040299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6608996539792389,34920.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c5,2,5,19,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.0036,0.0233,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,34930.0,0.5127,se,"(5) <= 40 min walk & commuting 40-30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.3863960618378459,2.3216270802982146,100*(exp(b - se^2/2) - 1),reported,-0.0008268168406465103,0.005351342329739914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15450643776824033,34920.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c5,2,5,19,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0069,0.0208,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,34930.0,0.5127,se,"(5) <= 40 min walk & commuting 40-30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.7091058952247042,2.0656974007130575,100*(exp(b - se^2/2) - 1),reported,-0.001775202717105866,0.005351335726927828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3317307692307692,34920.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c5,2,5,19,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.009,0.0204,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,34930.0,0.5127,se,"(5) <= 40 min walk & commuting 40-30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-0.9165815455939863,2.021722372696683,100*(exp(b - se^2/2) - 1),reported,-0.0023608805494115216,0.005351329245332783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4411764705882352,34920.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c5,2,5,19,"DISTi,MOT 10 min",highway_distance,D_location,dummy,minutes,-0.0561,0.0392,,,,,,high,Dummy: within 10 min driving distance of a motorway access point; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,34930.0,0.5127,se,"(5) <= 40 min walk & commuting 40-30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-5.5281538296610755,3.7061427798810334,100*(exp(b - se^2/2) - 1),reported,-0.007658204178609713,0.005351187233538338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4311224489795917,34920.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c6,2,6,19,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,-0.0735,0.0578,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,36117.0,0.5258,se,"(6) <= 40 min walk & commuting <= 30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-7.241460624493645,5.370406922674981,100*(exp(b - se^2/2) - 1),reported,-0.006691971351515163,0.005262529851939815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2716262975778547,36107.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c6,2,6,19,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.0139,0.0236,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,36117.0,0.5258,se,"(6) <= 40 min walk & commuting <= 30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-1.4078438721649378,2.3274229351171387,100*(exp(b - se^2/2) - 1),reported,-0.003099595402491436,0.00526262240998546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5889830508474576,36107.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c6,2,6,19,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0284,0.0202,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,36117.0,0.5258,se,"(6) <= 40 min walk & commuting <= 30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-2.819879787261348,1.9634389682551536,100*(exp(b - se^2/2) - 1),reported,-0.007398767501167915,0.005262503645196897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.405940594059406,36107.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c6,2,6,19,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0269,0.0232,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,36117.0,0.5258,se,"(6) <= 40 min walk & commuting <= 30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-2.6803359409782512,2.2584239114358615,100*(exp(b - se^2/2) - 1),reported,-0.006101835665632111,0.005262549719058177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1594827586206897,36107.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T2-c6,2,6,19,"DISTi,MOT 10 min",highway_distance,D_location,dummy,minutes,-0.0527,0.0329,,,,,,high,Dummy: within 10 min driving distance of a motorway access point; pre-LEZ level effect of distance band (DiD base term); POST x DIST interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,36117.0,0.5258,se,"(6) <= 40 min walk & commuting <= 30 min; municipality and quarter FE; SE clustered at municipality level; within R2; property characteristics (living space, base area, rooms, age, under construction, detached) included but NOT printed",pct_premium,-5.184871393252621,3.1211064325607834,100*(exp(b - se^2/2) - 1),reported,-0.008429534333651942,0.005262460713038879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6018237082066868,36107.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c1,6,1,42,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,-0.0844,0.0583,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5337,se,"(1) commuting <= 30 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-8.24970144011371,5.35814053811093,100*(exp(b - se^2/2) - 1),reported,-0.006577523824300619,0.004543479134558366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4476843910806176,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c1,6,1,42,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.028,0.0253,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5337,se,"(1) commuting <= 30 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-2.7922791438630634,2.4601425680071545,100*(exp(b - se^2/2) - 1),reported,-0.005028401558607443,0.0045435199797417255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1067193675889329,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c1,6,1,42,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0385,0.0212,,,*,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5337,se,"(1) commuting <= 30 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-3.798450367478423,2.0399312140484134,100*(exp(b - se^2/2) - 1),reported,-0.00825102717684776,0.004543422757121365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8160377358490565,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c1,6,1,42,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0297,0.0222,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5337,se,"(1) commuting <= 30 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-2.950246858679395,2.155035498160236,100*(exp(b - se^2/2) - 1),reported,-0.006078457498676391,0.004543493483859121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3378378378378377,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c2,6,2,42,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,-0.1693,0.1056,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5336,se,"(2) commuting <= 30 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-16.043861144660355,8.915339016821099,100*(exp(b - se^2/2) - 1),reported,-0.007284159564731708,0.004543456881486523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.603219696969697,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c2,6,2,42,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.1354,0.0416,,,***,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5336,se,"(2) commuting <= 30 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-12.73888844950568,3.6332046200790638,100*(exp(b - se^2/2) - 1),reported,-0.014786853897625173,0.0045430806657400825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.254807692307692,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c2,6,2,42,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.0708,0.0375,,,*,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5336,se,"(2) commuting <= 30 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-6.900663176395982,3.493680761761865,100*(exp(b - se^2/2) - 1),reported,-0.008577958566104407,0.004543410257470555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8880000000000001,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c2,6,2,42,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0928,0.05,,,*,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,48450.0,0.5336,se,"(2) commuting <= 30 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-8.976275075951056,4.556878786106425,100*(exp(b - se^2/2) - 1),reported,-0.008432579865371692,0.004543415875739058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8559999999999999,48440.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c3,6,3,42,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.0474,0.0286,,,*,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5355,se,"(3) commuting > 30-40 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,4.811259698627457,2.9988282373884956,100*(exp(b - se^2/2) - 1),reported,0.007569294132673923,0.004567126839545869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6573426573426573,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c3,6,3,42,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.007,0.0249,,,,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5355,se,"(3) commuting > 30-40 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-0.728335189776308,2.4726308629037552,100*(exp(b - se^2/2) - 1),reported,-0.0012839669641721856,0.004567253915412488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.28112449799196787,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c3,6,3,42,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.009,0.0208,,,,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5355,se,"(3) commuting > 30-40 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-0.9173979895983009,2.061363987847598,100*(exp(b - se^2/2) - 1),reported,-0.00197621340646221,0.004567248761601552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43269230769230765,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c3,6,3,42,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0101,0.0211,,,,,,high,Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5355,se,"(3) commuting > 30-40 min, semi-annual post effects; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-1.0269509763771945,2.0887962591405014,100*(exp(b - se^2/2) - 1),reported,-0.0021862176460033422,0.004567246765412924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.47867298578199047,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c4,6,4,42,"DISTi,STN 5 min",transit_distance,D_location,dummy,minutes,0.0495,0.0594,,,,,,high,Dummy: house within 5 min walking distance of a train station (reference: beyond 20 min / beyond 40 min in restricted samples); pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5359,se,"(4) commuting > 30-40 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,4.889352202889574,6.241428817756057,100*(exp(b - se^2/2) - 1),reported,0.00380602049987201,0.004567224599846412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c4,6,4,42,"DISTi,STN 5−10 min",transit_distance,D_location,dummy,minutes,-0.0742,0.0416,,,*,,,high,Dummy: 5-10 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5359,se,"(4) commuting > 30-40 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-7.231707478949822,3.862501668790813,100*(exp(b - se^2/2) - 1),reported,-0.008146136427174542,0.004567106137068207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7836538461538463,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c4,6,4,42,"DISTi,STN 10−15 min",transit_distance,D_location,dummy,minutes,-0.1258,0.0403,,,***,,,high,Dummy: 10-15 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted,"log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5359,se,"(4) commuting > 30-40 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-11.892457608311036,3.5536184851665684,100*(exp(b - se^2/2) - 1),reported,-0.01425564841293555,0.004566793569485713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1215880893300243,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa,670518142f78514470ad2ea85cc37f0ccb4483aa:T6-c4,6,4,42,"DISTi,STN 15−20 min",transit_distance,D_location,dummy,minutes,-0.0044,0.0394,,,*,,,high,"Dummy: 15-20 min walking distance to train station; pre-LEZ level effect of distance band; POST x DIST and trend interactions not extracted; star placement for this cell ambiguous in flattened text (0.0044 (0.0394) *) - likely a transcription artefact, beta/se=0.11","log nominal posted (listing) house price, EUR",log,OLS,47949.0,0.5359,se,"(4) commuting > 30-40 min, with linear trend; extended post period to end 2012; walking distance <= 40 min; bootstrapped SEs (500 reps); municipality and quarter FE; property and neighbourhood controls included but not printed",pct_premium,-0.5162806664517294,3.922702083323984,100*(exp(b - se^2/2) - 1),reported,-0.0005100490146860797,0.0045672570860526215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11167512690355332,47939.0,minutes,,2022.0,Germany,Europe,High income,2008.0,2011.0,single_family,listing,journal,True,True,clustered,,2009.5,ols,journal,True,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,Surface (m2),living_area,A_structural,level,m2,-0.003,,,,***,-0.003,-0.003,high,"dependent is price per m2, hence negative",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,-0.003,0.0,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),,,,,1728.0,m2,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,False,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,Surface (m2),living_area,A_structural,level,m2,-0.003,,,,***,-0.003,-0.002,high,"dependent is price per m2, hence negative",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,-0.003,0.00025510672645007767,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.4073808436117727,0.034641864477423456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.759784,695.0,m2,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,Surface (m2),living_area,A_structural,level,m2,-0.003,,,,***,-0.004,-0.003,high,"dependent is price per m2, hence negative",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,-0.003,0.00025510672645007767,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.3450869455846205,0.02934466700958287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.759784,1023.0,m2,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,Bathrooms,bathrooms,A_structural,level,count,0.149,,,,***,0.118,0.179,high,,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.149,0.015561510313454737,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.22445906660779452,0.023442430066883346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.574906098360655,1728.0,count,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,Bathrooms,bathrooms,A_structural,level,count,0.091,,,,***,0.047,0.136,high,,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.091,0.022704498654056913,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.15030556851932048,0.03750123712246342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.008016269662921,695.0,count,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,Bathrooms,bathrooms,A_structural,level,count,0.17,,,,***,0.129,0.212,high,,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.17,0.02117385829535644,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.2434680907587196,0.030324463842153582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.028768192771087,1023.0,count,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,EPC: >=A,energy_efficiency,A_structural,dummy,binary,0.506,,,,***,0.459,0.553,high,EPC class A (A4-A1) vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.506,0.023980032286307306,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.45263294109157626,0.021450894350241563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.10088902127659,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,EPC: >=A,energy_efficiency,A_structural,dummy,binary,0.473,,,,***,0.41,0.536,high,EPC class A (A4-A1) vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.473,0.0321434475327098,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.48739482155386404,0.033121669924590036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.715285269841262,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,EPC: >=A,energy_efficiency,A_structural,dummy,binary,0.517,,,,***,0.449,0.585,high,EPC class A (A4-A1) vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.517,0.03469451479721055,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.422314074258893,0.02834039051923683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.901491000000005,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,EPC: B,energy_efficiency,A_structural,dummy,binary,0.361,,,,***,0.265,0.458,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.361,0.049235598204864985,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.17370144457547354,0.02369056657263889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.332093305699482,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,EPC: B,energy_efficiency,A_structural,dummy,binary,0.312,,,,***,0.202,0.421,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.312,0.055868373092567,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.2072355501200789,0.037108695615871594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.58455495890411,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,EPC: B,energy_efficiency,A_structural,dummy,binary,0.361,,,,***,0.2,0.522,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.361,0.082144365916925,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.13612260723391814,0.030974252792766856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.394701888198758,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,EPC: C,energy_efficiency,A_structural,dummy,binary,0.253,,,,***,0.167,0.339,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.253,0.04387835694941336,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.1373915887908407,0.023828131125737263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7659406046511625,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,EPC: C,energy_efficiency,A_structural,dummy,binary,0.183,,,,***,0.08,0.287,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.183,0.052807092375166065,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.13033056300111379,0.03760862338637823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.465443594202899,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,EPC: C,energy_efficiency,A_structural,dummy,binary,0.321,,,,***,0.185,0.457,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.321,0.06938902959442113,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.14314646799229083,0.030943285058734973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.626091499999999,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,EPC: D,energy_efficiency,A_structural,dummy,binary,0.245,,,,***,0.192,0.299,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.245,0.027296419730158304,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.2110541549513816,0.023514378772844007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.975536074766357,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,EPC: D,energy_efficiency,A_structural,dummy,binary,0.165,,,,***,0.089,0.241,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.165,0.0387762224204118,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.15934601928178765,0.03744749506350197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.255185,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,EPC: D,energy_efficiency,A_structural,dummy,binary,0.279,,,,***,0.205,0.352,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.279,0.037500688788161415,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.22656080119922073,0.030452279918883454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.439863346938776,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,EPC: E,energy_efficiency,A_structural,dummy,binary,0.091,,,,***,0.043,0.139,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.091,0.02449024573920746,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.0890324336667429,0.023960727244595838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7157650833333324,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,EPC: E,energy_efficiency,A_structural,dummy,binary,0.077,,,,**,0.016,0.139,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.077,0.031378127353359556,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.09268254215558669,0.037768891054430064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4539386666666663,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,EPC: E,energy_efficiency,A_structural,dummy,binary,0.095,,,,***,0.025,0.166,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.095,0.03597004842946095,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.08229418907593493,0.031159220700258904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6410862411347518,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,EPC: F,energy_efficiency,A_structural,dummy,binary,0.071,,,,***,0.029,0.113,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.071,0.021428965021806523,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.07945297018714254,0.02398021012702696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.313272476190476,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,EPC: F,energy_efficiency,A_structural,dummy,binary,0.026,,,,,-0.032,0.084,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.026,0.02959238026820901,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.033308877091676306,0.037911113738612355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8786045517241379,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,EPC: F,energy_efficiency,A_structural,dummy,binary,0.097,,,,***,0.039,0.155,high,vs G,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.097,0.029592380268209005,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.10194953338554685,0.0311023645372327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.277870827586207,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,Elevator (present),accessibility,B_interior,binary,binary,0.103,,,,***,0.07,0.135,high,,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.103,0.016581937219255046,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.14778652867722578,0.023792106217256248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.211578215384615,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,Elevator (present),accessibility,B_interior,binary,binary,0.093,,,,***,0.049,0.136,high,,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.093,0.02219428520115676,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.15697539003374533,0.037461898666363055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.190267862068965,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,Elevator (present),accessibility,B_interior,binary,binary,0.093,,,,***,0.047,0.139,high,,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.093,0.023469818833407145,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.12294979020601618,0.031028057007963607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.962535913043478,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c1,3,Full sample,10,Alarm System (present),other,other,binary,binary,0.033,,,,*,-0.005,0.07,high,alarm system present,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.55,none,full sample 2022-2023,monetary,0.033,0.019133004483755826,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.04145580861605778,0.024035580973598692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.72476832,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,False,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c2,3,year=2022,10,Alarm System (present),other,other,binary,binary,0.087,,,,***,0.037,0.138,high,alarm system present,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,0.622,none,2022 listings,monetary,0.087,0.025765779371457843,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.1270428546253755,0.03762480647123699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3765716435643562,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,False,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T3-c3,3,year=2023,10,Alarm System (present),other,other,binary,binary,0.005,,,,,-0.049,0.059,high,alarm system present,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,0.529,none,2023 listings,monetary,0.005,0.027551526456608386,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.0056738719650566505,0.03126476671133338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18147814814814817,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,False,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,Surface (m2),living_area,A_structural,level,m2,-0.003,,,,***,-0.003,-0.003,high,dependent is price per m2,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,-0.003,0.0,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),,,,,1728.0,m2,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,False,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,Bathrooms,bathrooms,A_structural,level,count,0.122,,,,***,0.081,0.163,high,2022 base level; 2023 x Bathrooms interaction 0.036 (ns) not extracted,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.122,0.02091875156890637,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.1389375010729963,0.02382294318484157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.832088,1728.0,count,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,>=A,energy_efficiency,A_structural,dummy,binary,0.466,,,,***,0.392,0.54,high,"EPC >=A vs G, 2022 base level",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.466,0.037755795514611494,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.28463249422016923,0.02306121512573079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.342476,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,B,energy_efficiency,A_structural,dummy,binary,0.311,,,,***,0.182,0.439,high,"EPC B vs G, 2022 base level",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.311,0.06556242869766996,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.11337677962693486,0.023901148007274456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.743570459143969,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,C,energy_efficiency,A_structural,dummy,binary,0.18,,,,***,0.059,0.301,high,"EPC C vs G, 2022 base level",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.18,0.06173582780091879,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.06996771794637657,0.02399730548200422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9156489256198346,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,D,energy_efficiency,A_structural,dummy,binary,0.157,,,,***,0.068,0.246,high,"EPC D vs G, 2022 base level",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.157,0.04540899730811382,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.08288746340195828,0.023973481544559912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.457464584269663,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,E,energy_efficiency,A_structural,dummy,binary,0.07,,,,*,-0.002,0.141,high,"EPC E vs G, 2022 base level",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.07,0.0364802618823611,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.04611115760158434,0.024030672928637458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9188458741258745,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,F,energy_efficiency,A_structural,dummy,binary,0.022,,,,,-0.046,0.09,high,"EPC F vs G, 2022 base level",ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.022,0.034694514797210564,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.015252445123007921,0.024053462864265473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.634106,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,Elevator (present),accessibility,B_interior,binary,binary,0.093,,,,***,0.042,0.144,high,2022 base level,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.093,0.026020886097907914,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.08566229197118881,0.02396783593836597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.574052000000001,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:T5-c1,5,β,13,Alarm System (present),other,other,binary,binary,0.089,,,,***,0.029,0.149,high,alarm system; 2022 base level (2023 interaction -0.087**),ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,0.56,none,pooled 2022-2023 with Year=2023 dummy and 2023 interactions on all variables except surface,monetary,0.089,0.030612807174009316,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.06976786364898081,0.02399764221155982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9072799333333332,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,False,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c1,A3,Full sample,19,EPC: >=A,energy_efficiency,A_structural,dummy,binary,0.261,,,,***,0.203,0.319,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,,none,full sample; EPC D reference,monetary,0.261,0.029592380268209005,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.2075520567542093,0.023532411451798692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.819838,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c2,A3,year=2022,19,EPC: >=A,energy_efficiency,A_structural,dummy,binary,0.308,,,,***,0.226,0.39,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,,none,2022 listings; EPC D reference,monetary,0.308,0.04183750313781274,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.2689596534567941,0.03653441670598589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.361815999999999,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c3,A3,year=2023,19,EPC: >=A,energy_efficiency,A_structural,dummy,binary,0.238,,,,***,0.159,0.317,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,,none,2023 listings; EPC D reference,monetary,0.238,0.04030686277911227,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.18154434813698306,0.030745727431431773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.904701670886076,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c1,A3,Full sample,19,EPC: B,energy_efficiency,A_structural,dummy,binary,0.116,,,,**,0.013,0.219,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,,none,full sample; EPC D reference,monetary,0.116,0.05255198564871599,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.05302559787569351,0.02402241774636233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.207338097087379,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c2,A3,year=2022,19,EPC: B,energy_efficiency,A_structural,dummy,binary,0.147,,,,**,0.024,0.269,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,,none,2022 listings; EPC D reference,monetary,0.147,0.06250114798026903,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.08886186913290059,0.03778210090121578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3519568,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c3,A3,year=2023,19,EPC: B,energy_efficiency,A_structural,dummy,binary,0.082,,,,,-0.083,0.248,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,,none,2023 listings; EPC D reference,monetary,0.082,0.0844403264549757,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.030347718751351168,0.031250869250340006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9710999879154079,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c1,A3,Full sample,19,EPC: C,energy_efficiency,A_structural,dummy,binary,0.008,,,,,-0.085,0.101,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,,none,full sample; EPC D reference,monetary,0.008,0.04744985111971444,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.004055829519034643,0.024056063355642105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16859905376344086,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c2,A3,year=2022,19,EPC: C,energy_efficiency,A_structural,dummy,binary,0.019,,,,,-0.097,0.134,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,,none,2022 listings; EPC D reference,monetary,0.019,0.05892965380996794,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.012229109447264115,0.03792932558586751,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.32241832034632034,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c3,A3,year=2023,19,EPC: C,energy_efficiency,A_structural,dummy,binary,0.042,,,,,-0.1,0.185,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,,none,2023 listings; EPC D reference,monetary,0.042,0.07270541703827214,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.01805817597556201,0.03126017179175578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5776735999999999,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c1,A3,Full sample,19,EPC: E,energy_efficiency,A_structural,dummy,binary,-0.154,,,,***,-0.214,-0.094,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,,none,full sample; EPC D reference,monetary,-0.154,0.030612807174009316,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.12014027493966659,0.023882019938704396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.030574266666667,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c2,A3,year=2022,19,EPC: E,energy_efficiency,A_structural,dummy,binary,-0.087,,,,**,-0.17,-0.005,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,,none,2022 listings; EPC D reference,monetary,-0.087,0.04209260986426282,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.07816104239810741,0.03781611797985793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.066871127272727,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c3,A3,year=2023,19,EPC: E,energy_efficiency,A_structural,dummy,binary,-0.183,,,,***,-0.267,-0.1,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,,none,2023 listings; EPC D reference,monetary,-0.183,0.04260282331716297,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.1331046535103463,0.030987071236029947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.295489964071856,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c1,A3,Full sample,19,EPC: F,energy_efficiency,A_structural,dummy,binary,-0.174,,,,***,-0.23,-0.119,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,,none,full sample; EPC D reference,monetary,-0.174,0.028316846635958622,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.14623077144633678,0.02379766855461975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.144751999999999,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c2,A3,year=2022,19,EPC: F,energy_efficiency,A_structural,dummy,binary,-0.138,,,,***,-0.219,-0.058,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,,none,2022 listings; EPC D reference,monetary,-0.138,0.0410721829584625,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.1264270552094435,0.03762779088740509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.359938285714286,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c3,A3,year=2023,19,EPC: F,energy_efficiency,A_structural,dummy,binary,-0.182,,,,***,-0.256,-0.107,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,,none,2023 listings; EPC D reference,monetary,-0.182,0.03801090224106157,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.1480514791450638,0.030920715936415304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.788099973154362,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c1,A3,Full sample,19,EPC: G,energy_efficiency,A_structural,dummy,binary,-0.245,,,,***,-0.299,-0.192,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1738.0,,none,full sample; EPC D reference,monetary,-0.245,0.027296419730158304,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.2110541549513816,0.023514378772844007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.975536074766357,1728.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c2,A3,year=2022,19,EPC: G,energy_efficiency,A_structural,dummy,binary,-0.165,,,,***,-0.241,-0.089,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,705.0,,none,2022 listings; EPC D reference,monetary,-0.165,0.0387762224204118,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.15934601928178765,0.03744749506350197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.255185,695.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef,670783d3e4613f6e96cb818330755422772653ef:TA3-c3,A3,year=2023,19,EPC: G,energy_efficiency,A_structural,dummy,binary,-0.279,,,,***,-0.352,-0.205,high,EPC class vs reference class D,ln(P/m2): log listing price per m2 (EUR),per_m2,OLS,1033.0,,none,2023 listings; EPC D reference,monetary,-0.279,0.037500688788161415,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),-0.22656080119922073,0.030452279918883454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.439863346938776,1023.0,binary,,2026.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,listing,journal,False,True,unknown,,2022.5,ols,journal,False,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,DCBD,cbd_distance,D_location,level,km,-0.326,,-55.863,0.0,***,,,high,Distance to CBD (Citizen Centre) in km; p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,-32.6,0.5835705207382347,100*b/1 (X per km->km),|b/t|,-0.3312895668415053,0.005930393406038081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-55.863,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,True
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,Fee,other,other,level,currency,0.293,,43.604,0.0,***,,,high,Property management fee (RMB per m2 per month presumably; unit printed as RMB); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,29.299999999999997,0.6719567012200716,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),|b/t|,0.2643185703572728,0.006061796403019741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),43.604,25313.0,currency,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,DPark,green_space,E_environment,level,km,-0.111,,-27.376,0.0,***,,,high,Distance to nearest park (km); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,-11.1,0.40546464056107534,100*b/1 (X per km->km),|b/t|,-0.16957522858264115,0.0061943026221011515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-27.376,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,NumWash,bathrooms,A_structural,level,count,0.094,,21.743,0.0,***,,,high,Number of bathrooms; p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,9.4,0.43232304649772346,100*b/1 (per unit count),|b/t|,0.13540338270148036,0.006227447118680972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.743,25313.0,count,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,GiZScore_C,neighbourhood_amenities,F_neighbourhood,level,index,0.083,,17.104,0.0,***,,,high,Getis-Ord Gi* z-score of social-media check-in activity at commercial/business facilities (hot-spot degree of activity); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,8.3,0.4852666043030871,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.106888420736522,0.006249323008449602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.104,25313.0,index,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,False,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,DHospital,hospital_distance,D_location,level,km,-0.094,,-24.942,0.0,***,,,high,Distance to nearest hospital (km); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,-9.4,0.3768743484884933,100*b/1 (X per km->km),|b/t|,-0.15487712681873642,0.006209491092083089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.942,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,SPrimary,school_distance,D_location,level,km,0.08,,21.966,0.0,***,,,high,Distance to nearest primary school (km); printed as SPrimary (DSprimary in Table 1); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,8.0,0.3641992169716835,100*b/1 (X per km->km),|b/t|,0.13676625678881987,0.0062262704538295485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.966,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,Floor,floor_level,A_structural,level,count,0.063,,20.077,0.0,***,,,high,Floor on which the unit is situated; p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,6.3,0.31379190118045525,100*b/1 (per unit count),|b/t|,0.12519770989125328,0.006235877366700866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.077,25313.0,count,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,True
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,GiZScore_G,green_space,E_environment,level,index,0.134,,24.466,0.0,***,,,high,Getis-Ord Gi* z-score of check-in activity in green space (degree of activity); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,13.4,0.5476988473800376,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.15199033785888566,0.006212308422254789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.466,25313.0,index,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,False,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,DBus,transit_distance,D_location,level,km,-0.049,,-14.273,0.0,***,,,high,Distance to nearest bus station (km); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,-4.9,0.34330554193231977,100*b/1 (X per km->km),|b/t|,-0.0893517070991853,0.006260191066992594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.273,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,DSub,transit_distance,D_location,level,km,-0.049,,-11.417,0.0,***,,,high,Distance to nearest metro station (km); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,-4.9,0.4291845493562232,100*b/1 (X per km->km),|b/t|,-0.0715755795157146,0.006269210783543365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.417,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,Area,living_area,A_structural,level,m2,0.031,,6.907,0.0,***,,,high,Living area in m2 (coefficient as printed; interpreted by authors as semi-elasticity per unit); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,3.1,0.4488200376429709,100*b/1 (X per m2->m2),|b/t|,0.0433719332524284,0.006279417004839786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.907,25313.0,m2,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,True,True,True
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T4-c1,4,1,13,RGreen,green_space,E_environment,level,share,0.023,,6.854,0.0,***,,,high,Green space ratio of the estate (%); p-value printed as .000; *** significant at 1%,ln(price) of new housing unit,log,OLS,25323.0,0.724,t,"Global hedonic price model (HPM), semi-log OLS; 25,323 new-housing transactions, Shenzhen, July-December 2015; t-ratio and p-value printed in separate columns (no parentheses); only variables significant at 1% listed",semi_elasticity,2.3,0.33557046979865773,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.04303974303240077,0.006279507299737491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.854,25313.0,share,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,ols,journal,False,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,Floor,floor_level,A_structural,level,count,0.040169,,,0.0,***,,,medium,"GWR local coefficient summary: min -0.35291, lower quartile 0.031246, median 0.040169 (recorded), upper quartile 0.056628, max 0.339561; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,4.016900000000001,,100*b/1 (per unit count),,,,,,25313.0,count,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,NumBed,bedrooms,A_structural,level,count,0.040887,,,0.0,***,,,medium,"GWR local coefficient summary: min -0.66773, lower quartile -0.02149, median 0.040887 (recorded), upper quartile 0.128011, max 0.493299; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,4.0887,,100*b/1 (per unit count),,,,,,25313.0,count,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,NumWash,bathrooms,A_structural,level,count,0.022523,,,0.0,***,,,medium,"GWR local coefficient summary: min -0.64065, lower quartile -0.0432, median 0.022523 (recorded), upper quartile 0.06847, max 0.358014; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,2.2523,,100*b/1 (per unit count),,,,,,25313.0,count,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,Area,living_area,A_structural,level,m2,0.074358,,,0.0,***,,,medium,"GWR local coefficient summary: min -0.68338, lower quartile -0.01717, median 0.074358 (recorded), upper quartile 0.142633, max 1.335995; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,7.4357999999999995,,100*b/1 (X per m2->m2),,,,,,25313.0,m2,implausible_magnitude,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,RPlot,density,F_neighbourhood,level,index,-0.1074,,,0.0,***,,,medium,"GWR local coefficient summary: min -10.9418, lower quartile -0.5452, median -0.1074 (recorded), upper quartile 0.145695, max 1.900368; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-10.74,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,25313.0,index,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,RGreen,green_space,E_environment,level,share,0.089782,,,0.0,***,,,medium,"GWR local coefficient summary: min -3.84742, lower quartile -0.0312, median 0.089782 (recorded), upper quartile 0.399657, max 1.858314; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,8.9782,,"100*b (unit as reported: share; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,25313.0,share,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,Fee,other,other,level,currency,0.25459,,,0.0,***,,,medium,"GWR local coefficient summary: min -2.68112, lower quartile 0.015945, median 0.25459 (recorded), upper quartile 0.621551, max 4.052185; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,25.459,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,25313.0,currency,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DSub,transit_distance,D_location,level,km,-0.02085,,,0.0,***,,,medium,"GWR local coefficient summary: min -12.302, lower quartile -0.40319, median -0.02085 (recorded), upper quartile 0.563174, max 15.22164; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-2.085,,100*b/1 (X per km->km),,,,,,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DBus,transit_distance,D_location,level,km,-0.03028,,,0.004,**,,,medium,"GWR local coefficient summary: min -4.17797, lower quartile -0.35394, median -0.03028 (recorded), upper quartile 0.509983, max 23.5197; p-value .004; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-3.028,1.0520604033015901,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.018087241405985936,0.006284303331636323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.878161739095483,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DSnursery,school_distance,D_location,level,km,-0.03278,,,0.0,***,,,medium,"GWR local coefficient summary: min -18.9376, lower quartile -0.1124, median -0.03278 (recorded), upper quartile 0.158163, max 4.604339; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-3.2779999999999996,,100*b/1 (X per km->km),,,,,,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DSprimary,school_distance,D_location,level,km,0.017652,,,0.0,***,,,medium,"GWR local coefficient summary: min -12.4242, lower quartile -0.13258, median 0.017652 (recorded), upper quartile 0.261109, max 2.282185; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,1.7652,,100*b/1 (X per km->km),,,,,,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DSmiddle,school_distance,D_location,level,km,0.017384,,,0.0,***,,,medium,"GWR local coefficient summary: min -3.55686, lower quartile -0.41095, median 0.017384 (recorded), upper quartile 0.249635, max 14.10076; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,1.7384,,100*b/1 (X per km->km),,,,,,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DHospital,hospital_distance,D_location,level,km,-0.12827,,,0.0,***,,,medium,"GWR local coefficient summary: min -4.868, lower quartile -0.62254, median -0.12827 (recorded), upper quartile 0.300655, max 5.447187; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-12.827,,100*b/1 (X per km->km),,,,,,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DPark,green_space,E_environment,level,km,0.012992,,,0.855,,,,medium,"GWR local coefficient summary: min -2.27624, lower quartile -0.07367, median 0.012992 (recorded), upper quartile 0.328673, max 11.93626; p-value .855; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,1.2992,7.109453972280937,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),0.0011485969771561127,0.0062853273874633655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18274258544544394,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,True,True,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,DCBD,cbd_distance,D_location,level,km,-0.09705,,,0.0,***,,,medium,"GWR local coefficient summary: min -16.3229, lower quartile -0.74499, median -0.09705 (recorded), upper quartile 0.430907, max 1.94271; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-9.705,,100*b/1 (X per km->km),,,,,,25313.0,km,,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,GiZScore_G,green_space,E_environment,level,index,-0.1252,,,0.0,***,,,medium,"GWR local coefficient summary: min -11.9198, lower quartile -2.41877, median -0.1252 (recorded), upper quartile 0.179357, max 4.508242; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,-12.520000000000001,,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,25313.0,index,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb,67d132eca470e26b7a8c41462ca1252d056a93bb:T5-c1,5,median,14,GiZScore_C,neighbourhood_amenities,F_neighbourhood,level,index,0.463302,,,0.0,***,,,medium,"GWR local coefficient summary: min -9.16523, lower quartile 0.015567, median 0.463302 (recorded), upper quartile 2.229864, max 11.68491; p-value .000; RPlot = plot (floor-area) ratio mapped to density; Fee = property management fee; DSnursery = distance to nursery school",ln(price) of new housing unit,log,GWR,25323.0,0.9399,none,GWR local coefficients: table reports min / lower quartile / median / upper quartile / max and a p-value per variable; beta recorded = median local coefficient (bandwidth 0.337),semi_elasticity,46.3302,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,25313.0,index,unit_not_harmonisable,2016.0,China,Asia,Upper middle income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,social media check-in hot-spot analysis (Getis-Ord Gi*),2015.0,spatial,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,Area,living_area,A_structural,level,m2,0.008,,315.66,,***,,,high,Building floor area in m2,log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,0.8,0.0025343724260280046,100*b/1 (X per m2->m2),|b/t|,0.7848179019255282,0.002486276062616512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),315.66,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,Floor,floor_level,A_structural,level,count,-0.0004,,-1.05,,,,,high,Trading floor (floor on which the unit is located),log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-0.04,0.0380952380952381,100*b/1 (per unit count),|b/t|,-0.0042124500969041865,0.0040118572351468445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.05,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,TStory,storeys,A_structural,level,count,-0.0003,,-1.01,,,,,high,Total number of storeys of the building,log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-0.03,0.0297029702970297,100*b/1 (per unit count),|b/t|,-0.004051978494422616,0.004011859895467937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.01,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,Bathroom,bathrooms,A_structural,level,count,0.059,,24.31,,***,,,high,Number of bathrooms,log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,5.8999999999999995,0.24269847799259564,100*b/1 (per unit count),|b/t|,0.0970685527674644,0.003992947460611452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.31,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,Age,age,A_structural,level,years,-0.011,,-118.36,,***,,,high,Dwelling age in years,log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-1.0999999999999999,0.00929368029739777,100*b/1 (per year),|b/t|,-0.4289445129167328,0.0036240665167010214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-118.36,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,Dist. MRT,transit_distance,D_location,level,100 m,-0.011,,-40.04,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1)",log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-10.999999999999998,0.2747252747252747,100*b/0.1 (X per 100m->km),|b/t|,-0.15860292813775728,0.00396111209135258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.04,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,Dist. CC,cbd_distance,D_location,level,100 m,-0.004,,-32.22,,***,,,high,"Distance to city center, unit = 100 m (Table 1)",log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-4.0,0.12414649286157667,100*b/0.1 (X per 100m->km),|b/t|,-0.1281966071692666,0.003978789794204425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.22,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,THA,liquidity,G_market,binary,binary,-0.102,,-34.74,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*),log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),pct_premium,-9.697434067988109,0.2651382103345579,100*(exp(b - se^2/2) - 1),|b/t|,-0.1380389107308333,0.003973486204111493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.74,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,FHA,other,other,binary,binary,0.014,,1.08,,,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA,log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),pct_premium,1.4013258772074932,1.3145720764017492,100*(exp(b - se^2/2) - 1),|b/t|,0.00433280358587904,0.004011855172110223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.08,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c1,2,1,8,THA × FHA,other,other,other,binary,0.042,,1.68,,*,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies,log of real housing price,log,OLS,62140.0,0.853,t,OLS model; district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),other,0.042,0.025,not harmonised,|b/t|,0.006739826870463385,0.004011801708609158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.68,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,ols,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,Area,living_area,A_structural,level,m2,0.008,,316.6,,***,,,high,Building floor area in m2; z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,0.8,0.002526847757422615,100*b/1 (X per m2->m2),|b/t|,0.7857130259297691,0.0024817214969354677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),316.6,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,Floor,floor_level,A_structural,level,count,-0.0001,,-0.37,,,,,high,Trading floor (floor on which the unit is located); z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-0.01,0.02702702702702703,100*b/1 (per unit count),|b/t|,-0.0014843987118146568,0.004011888410309883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,TStory,storeys,A_structural,level,count,-0.0004,,-1.33,,,,,high,Total number of storeys of the building; z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-0.04,0.03007518796992481,100*b/1 (per unit count),|b/t|,-0.005335741508022053,0.004011835720317333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.33,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,Bathroom,bathrooms,A_structural,level,count,0.06,,25.31,,***,,,high,Number of bathrooms; z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,6.0,0.2370604504148558,100*b/1 (per unit count),|b/t|,0.10102154789875127,0.003991368941080651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.31,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,Age,age,A_structural,level,years,-0.011,,-121.44,,***,,,high,Dwelling age in years; z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-1.0999999999999999,0.009057971014492754,100*b/1 (per year),|b/t|,-0.4379874255214203,0.0036066158228048447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-121.44,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,Dist. MRT,transit_distance,D_location,level,100 m,-0.011,,-39.9,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1); z-statistic recorded in t field",log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-10.999999999999998,0.2756892230576441,100*b/0.1 (X per 100m->km),|b/t|,-0.15806225098165366,0.003961459924352223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.9,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,Dist. CC,cbd_distance,D_location,level,100 m,-0.003,,-27.49,,***,,,high,"Distance to city center, unit = 100 m (Table 1); z-statistic recorded in t field",log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-2.9999999999999996,0.10913059294288834,100*b/0.1 (X per 100m->km),|b/t|,-0.10962226968601657,0.0039877144301934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-27.49,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,THA,liquidity,G_market,binary,binary,-0.099,,-34.56,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*); z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),pct_premium,-9.426100815764594,0.25945754656924563,100*(exp(b - se^2/2) - 1),|b/t|,-0.13733720794901302,0.003973877544820979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.56,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,FHA,other,other,binary,binary,0.022,,1.7,,*,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA; z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),pct_premium,2.2158188401727896,1.3229037210793906,100*(exp(b - se^2/2) - 1),|b/t|,0.006820059194552408,0.004011799526207299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c2,2,2,8,THA × FHA,other,other,other,binary,0.05,,2.02,,**,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies; z-statistic recorded in t field,log of real housing price,log,spatial_lag,62140.0,0.859,t,Spatial lag model (rho=0.196); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),other,0.05,0.024752475247524754,not harmonised,|b/t|,0.00810375741362985,0.004011761095856362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.02,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,Area,living_area,A_structural,level,m2,0.007,,179.92,,***,,,high,Building floor area in m2; z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,0.7000000000000001,0.0038906180524677643,100*b/1 (X per m2->m2),|b/t|,0.5852760679041914,0.00325297947923628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),179.92,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,Floor,floor_level,A_structural,level,count,-0.0008,,-2.33,,**,,,high,Trading floor (floor on which the unit is located); z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-0.08,0.034334763948497854,100*b/1 (per unit count),|b/t|,-0.009347301921344,0.004011717562808583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.33,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,TStory,storeys,A_structural,level,count,-0.0009,,-2.69,,***,,,high,Total number of storeys of the building; z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-0.09,0.03345724907063197,100*b/1 (per unit count),|b/t|,-0.010791363312472348,0.004011659223967416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.69,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,Bathroom,bathrooms,A_structural,level,count,0.061,,25.42,,***,,,high,Number of bathrooms; z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,6.1,0.2399685287175452,100*b/1 (per unit count),|b/t|,0.10145608886418755,0.0039911915367501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.42,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,Age,age,A_structural,level,years,-0.011,,-114.1,,***,,,high,Dwelling age in years; z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-1.0999999999999999,0.009640666082383873,100*b/1 (per year),|b/t|,-0.41622157714188324,0.0036478665831891606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-114.1,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,Dist. MRT,transit_distance,D_location,level,100 m,-0.011,,-37.84,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1); z-statistic recorded in t field",log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-10.999999999999998,0.29069767441860456,100*b/0.1 (X per 100m->km),|b/t|,-0.15009036366713763,0.003966447242789049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.84,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,Dist. CC,cbd_distance,D_location,level,100 m,-0.003,,-31.16,,***,,,high,"Distance to city center, unit = 100 m (Table 1); z-statistic recorded in t field",log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),semi_elasticity,-2.9999999999999996,0.0962772785622593,100*b/0.1 (X per 100m->km),|b/t|,-0.12404507193499618,0.0039809073149870405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.16,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,THA,liquidity,G_market,binary,binary,-0.101,,-34.42,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*); z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),pct_premium,-9.607085869753773,0.26524473074222044,100*(exp(b - se^2/2) - 1),|b/t|,-0.136791296075163,0.003974180594862376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.42,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,FHA,other,other,binary,binary,0.02,,1.55,,,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA; z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),pct_premium,2.011641525031038,1.3163888258409753,100*(exp(b - se^2/2) - 1),|b/t|,0.0062183136603811235,0.0040118152647620154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.55,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T2-c3,2,3,8,THA × FHA,other,other,other,binary,0.032,,1.3,,,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies; z-statistic recorded in t field,log of real housing price,log,spatial_error,62140.0,0.855,t,Spatial error model (lambda=0.056); district and trading year x quarter dummies included. Statistic printed = t value (OLS) or z-statistic (spatial models),other,0.032,0.024615384615384615,not harmonised,|b/t|,0.005215389747877738,0.004011838267598261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,spatial,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,Area,living_area,A_structural,level,m2,0.008,,131.06,,***,,,high,Building floor area in m2,log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.8,0.00610407446970853,100*b/1 (X per m2->m2),|b/t|,0.46538797713656815,0.0035509535871857782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),131.06,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,Floor,floor_level,A_structural,level,count,-0.002,,-2.15,,**,,,high,Trading floor (floor on which the unit is located),log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-0.2,0.09302325581395349,100*b/1 (per unit count),|b/t|,-0.008625248729903522,0.004011743595303963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.15,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,TStory,storeys,A_structural,level,count,-0.004,,-5.4,,***,,,high,Total number of storeys of the building,log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-0.4,0.07407407407407407,100*b/1 (per unit count),|b/t|,-0.021659139146064214,0.004010951693715595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.4,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,Bathroom,bathrooms,A_structural,level,count,0.069,,12.16,,***,,,high,Number of bathrooms,log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,6.9,0.5674342105263158,100*b/1 (per unit count),|b/t|,0.04872666802968347,0.004007127305072654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.16,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,Age,age,A_structural,level,years,-0.012,,-55.95,,***,,,high,Dwelling age in years,log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-1.2,0.021447721179624665,100*b/1 (per year),|b/t|,-0.21901568425555434,0.003914489441564867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-55.95,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,Dist. MRT,transit_distance,D_location,level,100 m,-0.012,,-17.8,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-11.999999999999998,0.6741573033707865,100*b/0.1 (X per 100m->km),|b/t|,-0.07123029926587827,0.004001702205948218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.8,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,Dist. CC,cbd_distance,D_location,level,100 m,-0.002,,-9.48,,***,,,high,"Distance to city center, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-2.0,0.21097046413502107,100*b/0.1 (X per 100m->km),|b/t|,-0.03800526685198405,0.004008994393669203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.48,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,THA,liquidity,G_market,binary,binary,-0.122,,-17.62,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*),log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-11.487284872939297,0.6128723096329199,100*(exp(b - se^2/2) - 1),|b/t|,-0.07051359255404434,0.004001906501364604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.62,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,FHA,other,other,binary,binary,0.069,,2.24,,**,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA,log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,7.0928008223223316,3.3004061799658877,100*(exp(b - se^2/2) - 1),|b/t|,0.008986277082675359,0.00401173084048007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.24,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c1,3,1,9,THA × FHA,other,other,other,binary,-0.023,,-0.4,,,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies,log of real housing price,log,quantile,62140.0,0.577,t,Spatial quantile regression at quantile 0.1 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,other,-0.023,0.057499999999999996,not harmonised,|b/t|,-0.0016047550657998192,0.004011887664499548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,Area,living_area,A_structural,level,m2,0.008,,230.96,,***,,,high,Building floor area in m2,log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.8,0.0034638032559750607,100*b/1 (X per m2->m2),|b/t|,0.6796689749703956,0.0029427995106096104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),230.96,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,Floor,floor_level,A_structural,level,count,0.001,,1.51,,,,,high,Trading floor (floor on which the unit is located),log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.1,0.06622516556291391,100*b/1 (per unit count),|b/t|,0.006057847016732288,0.004011819216378999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.51,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,TStory,storeys,A_structural,level,count,-0.001,,-3.01,,***,,,high,Total number of storeys of the building,log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-0.1,0.03322259136212625,100*b/1 (per unit count),|b/t|,-0.012074917039594813,0.004011600345380337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.01,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,Bathroom,bathrooms,A_structural,level,count,0.061,,17.98,,***,,,high,Number of bathrooms,log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,6.1,0.339265850945495,100*b/1 (per unit count),|b/t|,0.07194689566865423,0.004001495865887331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.98,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,Age,age,A_structural,level,years,-0.012,,-88.2,,***,,,high,Dwelling age in years,log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-1.2,0.013605442176870748,100*b/1 (per year),|b/t|,-0.3335809911284945,0.0037820974050849724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-88.2,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,Dist. MRT,transit_distance,D_location,level,100 m,-0.013,,-33.05,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-13.0,0.39334341906202724,100*b/0.1 (X per 100m->km),|b/t|,-0.13144265101108937,0.003977084750713749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.05,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,Dist. CC,cbd_distance,D_location,level,100 m,-0.003,,-16.04,,***,,,high,"Distance to city center, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-2.9999999999999996,0.18703241895261846,100*b/0.1 (X per 100m->km),|b/t|,-0.06421793447551657,0.004003611875032205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.04,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,THA,liquidity,G_market,binary,binary,-0.124,,-30.11,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*),log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-11.662765007773979,0.36379641404669916,100*(exp(b - se^2/2) - 1),|b/t|,-0.11992626924232185,0.003982938201339151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.11,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,FHA,other,other,binary,binary,0.05,,2.74,,***,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA,log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,5.10960764655346,1.9183779130949346,100*(exp(b - se^2/2) - 1),|b/t|,0.010991922259860154,0.0040116504598029755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.74,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c2,3,2,9,THA × FHA,other,other,other,binary,0.037,,1.07,,,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies,log of real housing price,log,quantile,62140.0,0.606,t,Spatial quantile regression at quantile 0.25 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,other,0.037,0.03457943925233645,not harmonised,|b/t|,0.004292685776883028,0.00401185586624582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.07,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,Area,living_area,A_structural,level,m2,0.008,,305.09,,***,,,high,Building floor area in m2,log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.8,0.002622177062506146,100*b/1 (X per m2->m2),|b/t|,0.7744051848532305,0.002538284391009967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),305.09,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,Floor,floor_level,A_structural,level,count,0.001,,3.91,,***,,,high,Trading floor (floor on which the unit is located),log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.1,0.025575447570332477,100*b/1 (per unit count),|b/t|,0.015684571362824928,0.0040113993255306716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.91,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,TStory,storeys,A_structural,level,count,0.001,,3.31,,***,,,high,Total number of storeys of the building,log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.1,0.030211480362537763,100*b/1 (per unit count),|b/t|,0.01327819457122899,0.004011539145386402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.31,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,Bathroom,bathrooms,A_structural,level,count,0.055,,21.54,,***,,,high,Number of bathrooms,log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,5.5,0.255338904363974,100*b/1 (per unit count),|b/t|,0.08609530020573562,0.003996996295530903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.54,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,Age,age,A_structural,level,years,-0.011,,-105.21,,***,,,high,Dwelling age in years,log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-1.0999999999999999,0.010455279916357762,100*b/1 (per year),|b/t|,-0.38886962304523837,0.003696127963551358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-105.21,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,Dist. MRT,transit_distance,D_location,level,100 m,-0.011,,-38.23,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-10.999999999999998,0.28773214752811926,100*b/0.1 (X per 100m->km),|b/t|,-0.15160190236468427,0.003965521903339897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.23,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,Dist. CC,cbd_distance,D_location,level,100 m,-0.003,,-26.2,,***,,,high,"Distance to city center, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-2.9999999999999996,0.11450381679389314,100*b/0.1 (X per 100m->km),|b/t|,-0.1045356999030926,0.003989912210041702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.2,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,THA,liquidity,G_market,binary,binary,-0.097,,-30.8,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*),log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-9.244849461162952,0.28582120972379044,100*(exp(b - se^2/2) - 1),|b/t|,-0.12263362214237468,0.003981611108518658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.8,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,FHA,other,other,binary,binary,0.014,,1.69,,*,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA,log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,1.4063663257676495,0.8400815636176859,100*(exp(b - se^2/2) - 1),|b/t|,0.006779943048875158,0.0040118006206361885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.69,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c3,3,3,9,THA × FHA,other,other,other,binary,0.056,,2.1,,**,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies,log of real housing price,log,quantile,62140.0,0.641,t,Spatial quantile regression at quantile 0.5 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,other,0.056,0.026666666666666665,not harmonised,|b/t|,0.008424675956318801,0.004011750455389906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,Area,living_area,A_structural,level,m2,0.008,,300.55,,***,,,high,Building floor area in m2,log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.8,0.0026617867243387125,100*b/1 (X per m2->m2),|b/t|,0.769729876557214,0.002561070958433584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),300.55,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,Floor,floor_level,A_structural,level,count,0.002,,5.064,,***,,,high,Trading floor (floor on which the unit is located),log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.2,0.03949447077409163,100*b/1 (per unit count),|b/t|,0.02031203383913802,0.004011065134110984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.064,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,TStory,storeys,A_structural,level,count,0.003,,7.32,,***,,,high,Total number of storeys of the building,log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.3,0.040983606557377046,100*b/1 (per unit count),|b/t|,0.029354400276794333,0.004010163972239663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.32,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,Bathroom,bathrooms,A_structural,level,count,0.054,,20.79,,***,,,high,Number of bathrooms,log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,5.4,0.2597402597402597,100*b/1 (per unit count),|b/t|,0.08311863443541102,0.0039980103143535855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.79,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,Age,age,A_structural,level,years,-0.01,,-100.58,,***,,,high,Dwelling age in years,log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-1.0,0.00994233446013124,100*b/1 (per year),|b/t|,-0.3741999428127673,0.0037204209864065145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-100.58,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,Dist. MRT,transit_distance,D_location,level,100 m,-0.01,,-32.26,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-10.0,0.3099814011159331,100*b/0.1 (X per 100m->km),|b/t|,-0.12835313841242058,0.003978708568270942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.26,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,Dist. CC,cbd_distance,D_location,level,100 m,-0.004,,-29.4,,***,,,high,"Distance to city center, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-4.0,0.13605442176870747,100*b/0.1 (X per 100m->km),|b/t|,-0.11713764713633111,0.003984273712120106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.4,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,THA,liquidity,G_market,binary,binary,-0.061,,-19.22,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*),log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-5.918149860359867,0.29859634561049214,100*(exp(b - se^2/2) - 1),|b/t|,-0.07688036386024712,0.004000018931334398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.22,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,FHA,other,other,binary,binary,-0.018,,-1.26,,,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA,log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-1.7939183042087636,1.4030871890832868,100*(exp(b - se^2/2) - 1),|b/t|,-0.005054920382713652,0.004011841573582263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.26,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c4,3,4,9,THA × FHA,other,other,other,binary,0.089,,3.3,,***,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies,log of real housing price,log,quantile,62140.0,0.671,t,Spatial quantile regression at quantile 0.75 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,other,0.089,0.02696969696969697,not harmonised,|b/t|,0.013238086220523988,0.004011541278946663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,Area,living_area,A_structural,level,m2,0.008,,265.16,,***,,,high,Building floor area in m2,log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.8,0.003017046311660884,100*b/1 (X per m2->m2),|b/t|,0.7286160106961107,0.00274783530960971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),265.16,62130.0,m2,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,Floor,floor_level,A_structural,level,count,0.002,,3.27,,***,,,high,Trading floor (floor on which the unit is located),log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.2,0.06116207951070336,100*b/1 (per unit count),|b/t|,0.013117760785789887,0.004011547640914338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.27,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,TStory,storeys,A_structural,level,count,0.003,,6.37,,***,,,high,Total number of storeys of the building,log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,0.3,0.04709576138147566,100*b/1 (per unit count),|b/t|,0.025547416223605634,0.004010583394600571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.37,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,Bathroom,bathrooms,A_structural,level,count,0.049,,16.63,,***,,,high,Number of bathrooms,log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,4.9,0.29464822609741437,100*b/1 (per unit count),|b/t|,0.06656978250903975,0.0040029935363222946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.63,62130.0,count,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,Age,age,A_structural,level,years,-0.01,,-86.66,,***,,,high,Dwelling age in years,log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-1.0,0.01153934918070621,100*b/1 (per year),|b/t|,-0.3283896357140622,0.0037894026738294743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-86.66,62130.0,years,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,Dist. MRT,transit_distance,D_location,level,100 m,-0.009,,-25.01,,***,,,high,"Distance to nearest MRT station, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-8.999999999999998,0.35985605757696915,100*b/0.1 (X per 100m->km),|b/t|,-0.09983614297336484,0.003991848979342856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.01,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,Dist. CC,cbd_distance,D_location,level,100 m,-0.004,,-26.04,,***,,,high,"Distance to city center, unit = 100 m (Table 1)",log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,semi_elasticity,-4.0,0.15360983102918585,100*b/0.1 (X per 100m->km),|b/t|,-0.10390422696447446,0.003990177686807775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.04,62130.0,100m,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,THA,liquidity,G_market,binary,binary,-0.038,,-10.64,,***,,,high,Dummy = 1 if located in a housing-transaction hotspot area (Getis-Ord Gi*),log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-3.7293198840120723,0.3438260503182855,100*(exp(b - se^2/2) - 1),|b/t|,-0.042647702341546294,0.004008242701273148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.64,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,True,True,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,FHA,other,other,binary,binary,-0.024,,-1.51,,,,,high,Dummy = 1 if located in a short-term flipping hotspot area (resales within 2 years); printed name FHA,log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,pct_premium,-2.383759734608326,1.5517123863701872,100*(exp(b - se^2/2) - 1),|b/t|,-0.006057847016732288,0.004011819216378999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.51,62130.0,binary,,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1,69288e963b8055b19e0fd077000b14eb34270ae1:T3-c5,3,5,9,THA × FHA,other,other,other,binary,0.045,,1.45,,,,,high,Interaction of transaction-hotspot and flipping-hotspot dummies,log of real housing price,log,quantile,62140.0,0.693,t,Spatial quantile regression at quantile 0.9 (with spatial lag term); district and trading year x quarter dummies included; pseudo R-square reported,other,0.045,0.03103448275862069,not harmonised,|b/t|,0.005817146177671859,0.004011824950118523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.45,62130.0,binary,nonstandard_x_transform,2024.0,Taiwan,Asia,High income,2012.0,2021.0,unknown,transaction,journal,True,True,unknown,,2016.5,quantile,journal,False,False,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Quality of the Corresponding Public Primary School,school_quality,F_neighbourhood,level,index,-0.031,0.001,-33.79,8.616e-110,,,,medium,"citywide rank of the corresponding public primary school (1 = best, up to 44); negative sign = better school raises price; entered linearly; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",semi_elasticity,-3.1,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.8795888614830695,0.026031040588430573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.79,334.0,index,unit_not_harmonisable,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,False,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Distance to the Corresponding Public Primary School,school_distance,D_location,log,m,-0.027,0.01,-2.657,0.008,,,,medium,"straight-line distance from neighbourhood centre to corresponding public primary school, metres, entered in logs; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,-0.027,0.01,b (log-log),reported,-0.14387203416943053,0.054148300402495485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.657,334.0,m,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Distance to the District's CBD,cbd_distance,D_location,log,m,-0.007,0.011,-0.663,0.508,,,,medium,"straight-line distance to district CBD (Wanda Plaza), metres, entered in logs; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,-0.007,0.011,b (log-log),reported,-0.03625389736018949,0.05468159481174884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.663,334.0,m,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Property Management Fee,public_services,F_neighbourhood,log,currency,0.029,0.019,1.536,0.125,,,,medium,RMB per m2 per month; continuous variables stated to enter in logarithmic form (estate upkeep fee); column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,0.029,0.019,b (log-log),reported,0.08375090299895833,0.054525327473280163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.536,334.0,currency,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Green Coverage Ratio,green_space,E_environment,log,share,0.002,0.032,0.072,0.942,,,,medium,% of open space covered by greenery in the estate; continuous -> log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,0.002,0.032,b (log-log),reported,0.0039396341438554215,0.05471714088688085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.072,334.0,share,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Elevator,accessibility,B_interior,dummy,binary,0.003,0.022,0.123,0.902,,,,medium,1 if the estate has elevators; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",pct_premium,0.27618067808961033,2.2066099099074292,100*(exp(b - se^2/2) - 1),reported,0.006730108135391152,0.054716326303993104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.123,334.0,binary,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Residential Area,living_area,A_structural,log,m2,-0.003,0.024,-0.106,0.916,,,,medium,"floor area within a house, m2, log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,-0.003,0.024,b (log-log),reported,-0.005799964388080212,0.05471664517056805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.106,334.0,m2,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,True
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Floor Area Ratio,density,F_neighbourhood,log,index,-0.016,0.02,-0.775,0.439,,,,medium,"building gross floor area / land area of the estate, log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,-0.016,0.02,b (log-log),reported,-0.0423680356235517,0.054668433062647355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.775,334.0,index,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T6-c1,6,1,16,Building Age,age,A_structural,log,years,-0.034,0.023,-1.466,0.144,,,,medium,"years since the building was built, log form (text); column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,344.0,0.849,se,"citywide, 344 neighbourhoods in 7 districts; residual df 334 (Table 7); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS",elasticity,-0.034,0.023,b (log-log),reported,-0.07995911114962143,0.05454236776918243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.466,334.0,years,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Quality of the Corresponding Public Primary School,school_quality,F_neighbourhood,level,index,-0.028,0.001,-22.069,6.56e-59,,,,medium,"citywide rank of the corresponding public primary school (1 = best, up to 44); negative sign = better school raises price; entered linearly; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,semi_elasticity,-2.8000000000000003,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.8230114920366978,0.037292649963147295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.069,232.0,index,unit_not_harmonisable,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,False,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Distance to the Corresponding Public Primary School,school_distance,D_location,log,m,-0.012,0.01,-1.146,0.253,,,,medium,"straight-line distance from neighbourhood centre to corresponding public primary school, metres, entered in logs; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.012,0.01,b (log-log),reported,-0.0750265289415271,0.06546817534164669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.146,232.0,m,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Distance to the District's CBD,cbd_distance,D_location,log,m,-0.024,0.012,-1.948,0.053,,,,medium,"straight-line distance to district CBD (Wanda Plaza), metres, entered in logs; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.024,0.012,b (log-log),reported,-0.1268591885967761,0.06512278675399184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.948,232.0,m,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Property Management Fee,public_services,F_neighbourhood,log,currency,0.016,0.02,0.823,0.411,,,,medium,RMB per m2 per month; continuous variables stated to enter in logarithmic form (estate upkeep fee); column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.016,0.02,b (log-log),reported,0.05395389474397995,0.06555758778126361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.823,232.0,currency,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Green Coverage Ratio,green_space,E_environment,log,share,0.032,0.031,1.005,0.316,,,,medium,% of open space covered by greenery in the estate; continuous -> log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.032,0.031,b (log-log),reported,0.06583832274267414,0.06551076889818322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.005,232.0,share,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Elevator,accessibility,B_interior,dummy,binary,-0.01,0.024,-0.429,0.668,,,,medium,1 if the estate has elevators; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,pct_premium,-1.023525954754645,2.3761196009980035,100*(exp(b - se^2/2) - 1),reported,-0.028154065031397404,0.06562719121537856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.429,232.0,binary,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Residential Area,living_area,A_structural,log,m2,-0.001,0.025,-0.041,0.968,,,,medium,"floor area within a house, m2, log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.001,0.025,b (log-log),reported,-0.0026917721217692685,0.06565297857973826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.041,232.0,m2,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Floor Area Ratio,density,F_neighbourhood,log,index,-0.054,0.023,-2.381,0.018,,,,medium,"building gross floor area / land area of the estate, log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.054,0.023,b (log-log),reported,-0.15444468913208453,0.064865472126033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.381,232.0,index,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T8-c1,8,1,17,Building Age,age,A_structural,log,years,-0.071,0.023,-3.122,0.002,,,,medium,"years since the building was built, log form (text); column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,242.0,0.726,se,urban districts; residual df 232 (Table 9); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.071,0.023,b (log-log),reported,-0.20079479750141924,0.06431607863594466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.122,232.0,years,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Quality of the Corresponding Public Primary School,school_quality,F_neighbourhood,level,index,-0.019,0.003,-6.246,1.289e-08,,,,medium,"citywide rank of the corresponding public primary school (1 = best, up to 44); negative sign = better school raises price; entered linearly; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,semi_elasticity,-1.9,0.3,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.5456894862848211,0.08736623219417562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.246,92.0,index,unit_not_harmonisable,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,False,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Distance to the Corresponding Public Primary School,school_distance,D_location,log,m,-0.042,0.021,-2.001,0.048,,,,medium,"straight-line distance from neighbourhood centre to corresponding public primary school, metres, entered in logs; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.042,0.021,b (log-log),reported,-0.20422195166123877,0.10205994585769054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.001,92.0,m,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Distance to the District's CBD,cbd_distance,D_location,log,m,0.042,0.023,1.806,0.074,,,,medium,"straight-line distance to district CBD (Wanda Plaza), metres, entered in logs; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.042,0.023,b (log-log),reported,0.1850370637005404,0.10245684590284629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.806,92.0,m,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Property Management Fee,public_services,F_neighbourhood,log,currency,0.016,0.039,0.407,0.685,,,,medium,RMB per m2 per month; continuous variables stated to enter in logarithmic form (estate upkeep fee); column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.016,0.039,b (log-log),reported,0.04239453405461633,0.10416347433566665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.407,92.0,currency,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Green Coverage Ratio,green_space,E_environment,log,share,0.069,0.095,0.725,0.47,,,,medium,% of open space covered by greenery in the estate; continuous -> log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.069,0.095,b (log-log),reported,0.07537147126758946,0.10396065002426132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.725,92.0,share,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Elevator,accessibility,B_interior,dummy,binary,0.075,0.049,1.511,0.134,,,,medium,1 if the estate has elevators; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD),"ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,pct_premium,7.659092737422291,5.281632339334695,100*(exp(b - se^2/2) - 1),reported,0.1556135782496125,0.10298714642595136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.511,92.0,binary,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Residential Area,living_area,A_structural,log,m2,0.049,0.057,0.868,0.387,,,,medium,"floor area within a house, m2, log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.049,0.057,b (log-log),reported,0.09012696568654247,0.10383290977712266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.868,92.0,m2,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Floor Area Ratio,density,F_neighbourhood,log,index,-0.043,0.043,-0.995,0.322,,,,medium,"building gross floor area / land area of the estate, log form per text; column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,-0.043,0.043,b (log-log),reported,-0.1031822273370634,0.10370073099202352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.995,92.0,index,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de,6a582c18a0b55c4939a0e4812ae293fd34b678de:T10-c1,10,1,18,Building Age,age,A_structural,log,years,0.049,0.062,0.786,0.434,,,,medium,"years since the building was built, log form (text); column headed 'Standard Deviation' is the coefficient standard error (Excel output; t = coef/SD)","ln(housing price, RMB per m2, representative house of the neighbourhood)",log,IV,102.0,0.435,se,suburban districts; residual df 92 (Table 11); N derived from ANOVA df (not printed directly); R2 computed from ANOVA SS regression / SS total; author states 2SLS but no instrument or first stage is reported and the Excel output looks like OLS,elasticity,0.049,0.062,b (log-log),reported,0.08167240135809607,0.10390890758027489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.786,92.0,years,,2018.0,China,Asia,Upper middle income,,,condo_apartment,listing,journal,False,False,OLS,,2015.0,iv,journal,True,True,False
6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f:T1-c1,1,1,12,PM10,air_pollution,E_environment,level,ug/m3,-0.015372,,,,***,,,medium,"Annual mean PM10 concentration (VNA interpolation, 2011). Printed SE in parentheses is rounded to '0' and unusable; stars only.","log monthly rent (reported or hypothetical imputed rent, COP/month)",log,other,6061.0,,se,"All dwelling types; includes structural, security, cadastral, distance, owner-bias, other pollutants (CO, SO2, O3) and locality fixed effects; frontier model with heteroskedastic variance",semi_elasticity,-1.5372000000000001,,100*b/1 (per ug/m3),,,,,,6051.0,ug/m3,,2018.0,Colombia,South America,Upper middle income,2011.0,2011.0,mixed,rent,journal,False,True,unknown,,2011.0,other,journal,False,False,False
6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f:T1-c2,1,2,12,PM10,air_pollution,E_environment,level,ug/m3,-0.0061217,,,,**,,,medium,"Annual mean PM10 concentration (VNA interpolation, 2011). Printed SE in parentheses is rounded to '0' and unusable; stars only.","log monthly rent (reported or hypothetical imputed rent, COP/month)",log,other,4153.0,,se,"Apartments submarket; includes structural, security, cadastral, distance, owner-bias, other pollutants (CO, SO2, O3) and locality fixed effects; frontier model with heteroskedastic variance",semi_elasticity,-0.61217,,100*b/1 (per ug/m3),,,,,,4143.0,ug/m3,,2018.0,Colombia,South America,Upper middle income,2011.0,2011.0,mixed,rent,journal,False,True,unknown,,2011.0,other,journal,False,False,False
6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f:T1-c3,1,3,12,PM10,air_pollution,E_environment,level,ug/m3,-0.024382,,,,*,,,medium,"Annual mean PM10 concentration (VNA interpolation, 2011). Printed SE in parentheses is rounded to '-0.01' and unusable; stars only.","log monthly rent (reported or hypothetical imputed rent, COP/month)",log,other,319.0,,se,"Condominiums submarket; includes structural, security, cadastral, distance, owner-bias, other pollutants (CO, SO2, O3) and locality fixed effects; frontier model with heteroskedastic variance",semi_elasticity,-2.4382,,100*b/1 (per ug/m3),,,,,,309.0,ug/m3,,2018.0,Colombia,South America,Upper middle income,2011.0,2011.0,mixed,rent,journal,False,True,unknown,,2011.0,other,journal,False,False,False
6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f,6c0fa6707be0f8c5edd0d4088f7dfb356412099f:T1-c4,1,4,12,PM10,air_pollution,E_environment,level,ug/m3,-0.021776,,,,***,,,medium,"Annual mean PM10 concentration (VNA interpolation, 2011). Printed SE in parentheses is rounded to '-0.01' and unusable; stars only.","log monthly rent (reported or hypothetical imputed rent, COP/month)",log,other,1562.0,,se,"Houses submarket; includes structural, security, cadastral, distance, owner-bias, other pollutants (CO, SO2, O3) and locality fixed effects; frontier model with heteroskedastic variance",semi_elasticity,-2.1776,,100*b/1 (per ug/m3),,,,,,1552.0,ug/m3,,2018.0,Colombia,South America,Upper middle income,2011.0,2011.0,mixed,rent,journal,False,True,unknown,,2011.0,other,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,Floor area,living_area,A_structural,other,index,-0.015,0.0107,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),other,-0.015,0.0107,not harmonised,reported,,,,-1.4018691588785046,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,Floor area,living_area,A_structural,other,index,0.0126,0.017,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,other,0.0126,0.017,not harmonised,reported,,,,0.7411764705882352,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,Floor area,living_area,A_structural,other,index,0.00578,0.0122,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,other,0.00578,0.0122,not harmonised,reported,,,,0.4737704918032787,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,Floor area,living_area,A_structural,other,index,-0.0059,0.0128,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,other,-0.0059,0.0128,not harmonised,reported,,,,-0.46093749999999994,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,Floor area,living_area,A_structural,other,index,-0.0127,0.0124,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,other,-0.0127,0.0124,not harmonised,reported,,,,-1.0241935483870968,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,Floor area,living_area,A_structural,other,index,-0.0228,0.0122,,,*,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,other,-0.0228,0.0122,not harmonised,reported,,,,-1.8688524590163933,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,Floor area,living_area,A_structural,other,index,-0.0292,0.0123,,,**,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,other,-0.0292,0.0123,not harmonised,reported,,,,-2.3739837398373984,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,Floor area,living_area,A_structural,other,index,-0.0239,0.0117,,,**,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,other,-0.0239,0.0117,not harmonised,reported,,,,-2.0427350427350426,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,Floor area,living_area,A_structural,other,index,-0.039,0.0133,,,***,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,other,-0.039,0.0133,not harmonised,reported,,,,-2.9323308270676693,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,Floor area,living_area,A_structural,other,index,-0.0469,0.016,,,***,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,other,-0.0469,0.016,not harmonised,reported,,,,-2.93125,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,Plot ratio,density,F_neighbourhood,other,index,-0.0056,0.0023,,,**,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),other,-0.0056,0.0023,not harmonised,reported,,,,-2.4347826086956523,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,Plot ratio,density,F_neighbourhood,other,index,-0.0042,0.0019,,,*,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,other,-0.0042,0.0019,not harmonised,reported,,,,-2.2105263157894735,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,Plot ratio,density,F_neighbourhood,other,index,-0.0051,0.0019,,,***,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,other,-0.0051,0.0019,not harmonised,reported,,,,-2.68421052631579,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,Plot ratio,density,F_neighbourhood,other,index,-0.0066,0.0019,,,***,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,other,-0.0066,0.0019,not harmonised,reported,,,,-3.473684210526316,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,Plot ratio,density,F_neighbourhood,other,index,-0.0072,0.0026,,,**,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,other,-0.0072,0.0026,not harmonised,reported,,,,-2.769230769230769,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,Plot ratio,density,F_neighbourhood,other,index,-0.0078,0.0028,,,**,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,other,-0.0078,0.0028,not harmonised,reported,,,,-2.7857142857142856,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,Plot ratio,density,F_neighbourhood,other,index,-0.0072,0.0043,,,*,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,other,-0.0072,0.0043,not harmonised,reported,,,,-1.6744186046511627,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,Plot ratio,density,F_neighbourhood,other,index,-0.0013,0.0045,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,other,-0.0013,0.0045,not harmonised,reported,,,,-0.2888888888888889,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,Plot ratio,density,F_neighbourhood,other,index,-0.0035,0.0042,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,other,-0.0035,0.0042,not harmonised,reported,,,,-0.8333333333333334,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,Plot ratio,density,F_neighbourhood,other,index,-0.0056,0.0033,,,***,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,other,-0.0056,0.0033,not harmonised,reported,,,,-1.696969696969697,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,PM,other,other,other,index,0.0129,0.0036,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),other,0.0129,0.0036,not harmonised,reported,,,,3.5833333333333335,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,PM,other,other,other,index,0.0207,0.0069,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,other,0.0207,0.0069,not harmonised,reported,,,,3.0,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,PM,other,other,other,index,0.0205,0.006,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,other,0.0205,0.006,not harmonised,reported,,,,3.4166666666666665,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,PM,other,other,other,index,0.0198,0.0036,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,other,0.0198,0.0036,not harmonised,reported,,,,5.500000000000001,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,PM,other,other,other,index,0.0158,0.0027,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,other,0.0158,0.0027,not harmonised,reported,,,,5.851851851851852,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,PM,other,other,other,index,0.0138,0.0038,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,other,0.0138,0.0038,not harmonised,reported,,,,3.631578947368421,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,PM,other,other,other,index,0.0148,0.004,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,other,0.0148,0.004,not harmonised,reported,,,,3.7,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,PM,other,other,other,index,0.00982,0.005,,,*,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,other,0.00982,0.005,not harmonised,reported,,,,1.9640000000000002,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,PM,other,other,other,index,0.00575,0.0068,,,,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,other,0.00575,0.0068,not harmonised,reported,,,,0.8455882352941176,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,PM,other,other,other,index,0.00663,0.006,,,,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,other,0.00663,0.006,not harmonised,reported,,,,1.105,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,Building age,age,A_structural,level,years,-0.0016,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-0.16,0.01,100*b/1 (per year),reported,,,,-16.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,Building age,age,A_structural,level,years,-0.0027,0.0002,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-0.27,0.02,100*b/1 (per year),reported,,,,-13.5,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,Building age,age,A_structural,level,years,-0.0026,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-0.26,0.01,100*b/1 (per year),reported,,,,-25.999999999999996,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,Building age,age,A_structural,level,years,-0.0028,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-0.27999999999999997,0.01,100*b/1 (per year),reported,,,,-28.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,Building age,age,A_structural,level,years,-0.0025,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-0.25,0.01,100*b/1 (per year),reported,,,,-25.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,Building age,age,A_structural,level,years,-0.0022,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-0.22,0.01,100*b/1 (per year),reported,,,,-22.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,Building age,age,A_structural,level,years,-0.0018,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-0.18,0.01,100*b/1 (per year),reported,,,,-18.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,Building age,age,A_structural,level,years,-0.0016,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-0.16,0.01,100*b/1 (per year),reported,,,,-16.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,Building age,age,A_structural,level,years,-0.0008,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-0.08,0.01,100*b/1 (per year),reported,,,,-8.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,Building age,age,A_structural,level,years,-0.00057,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-0.056999999999999995,0.01,100*b/1 (per year),reported,,,,-5.699999999999999,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0834,0.0109,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),pct_premium,8.691194228479237,1.1848043983051866,100*(exp(b - se^2/2) - 1),reported,,,,7.651376146788991,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0495,0.0189,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,pct_premium,5.055794055658791,1.985909169286018,100*(exp(b - se^2/2) - 1),reported,,,,2.619047619047619,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,QMS,school_quality,F_neighbourhood,binary,binary,0.051,0.0167,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,pct_premium,5.217616234803546,1.7573792317829502,100*(exp(b - se^2/2) - 1),reported,,,,3.053892215568862,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0644,0.015,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,pct_premium,6.639894438997174,1.5997783815296618,100*(exp(b - se^2/2) - 1),reported,,,,4.293333333333333,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,QMS,school_quality,F_neighbourhood,binary,binary,0.075,0.0171,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,pct_premium,7.772657035216302,1.84318189801272,100*(exp(b - se^2/2) - 1),reported,,,,4.385964912280701,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0739,0.0171,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,pct_premium,7.654172291034067,1.8411555126411874,100*(exp(b - se^2/2) - 1),reported,,,,4.321637426900584,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0781,0.0131,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,pct_premium,8.11380053151456,1.4164123170076843,100*(exp(b - se^2/2) - 1),reported,,,,5.961832061068702,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0827,0.0188,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,pct_premium,8.602395578245957,2.0420858824029122,100*(exp(b - se^2/2) - 1),reported,,,,4.398936170212766,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0845,0.0175,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,pct_premium,8.80062305219187,1.9043024774060073,100*(exp(b - se^2/2) - 1),reported,,,,4.828571428571428,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,QMS,school_quality,F_neighbourhood,binary,binary,0.0979,0.0167,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,pct_premium,10.269871780048746,1.841763665555527,100*(exp(b - se^2/2) - 1),reported,,,,5.862275449101797,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0294,0.0118,,,**,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),pct_premium,2.9764751960174474,1.2152070070798866,100*(exp(b - se^2/2) - 1),reported,,,,2.4915254237288136,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0427,0.0192,,,**,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,pct_premium,4.343241726571123,2.0037595400729575,100*(exp(b - se^2/2) - 1),reported,,,,2.2239583333333335,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0222,0.0232,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,pct_premium,2.217312942192229,2.3720799485226847,100*(exp(b - se^2/2) - 1),reported,,,,0.956896551724138,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0235,0.0164,,,*,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,pct_premium,2.3640632297778907,1.6789964132244721,100*(exp(b - se^2/2) - 1),reported,,,,1.4329268292682926,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0177,0.0187,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,pct_premium,1.7679621559664183,1.903393662089007,100*(exp(b - se^2/2) - 1),reported,,,,0.946524064171123,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0157,0.017,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,pct_premium,1.5677116574039918,1.726900617286876,100*(exp(b - se^2/2) - 1),reported,,,,0.9235294117647057,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0274,0.0167,,,***,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,pct_premium,2.763552345780651,1.7163906495819146,100*(exp(b - se^2/2) - 1),reported,,,,1.6407185628742516,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0389,0.0158,,,***,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,pct_premium,3.9536748725151005,1.6426730886447458,100*(exp(b - se^2/2) - 1),reported,,,,2.4620253164556956,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0468,0.0152,,,***,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,pct_premium,4.779135778964139,1.5928268565711012,100*(exp(b - se^2/2) - 1),reported,,,,3.0789473684210527,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,QPS,school_quality,F_neighbourhood,binary,binary,0.0299,0.0174,,,*,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,pct_premium,3.0195530848235785,1.7928115989548667,100*(exp(b - se^2/2) - 1),reported,,,,1.7183908045977012,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,Subway,transit_distance,D_location,binary,binary,0.0481,0.0129,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),pct_premium,4.918827629359845,1.3535654951504803,100*(exp(b - se^2/2) - 1),reported,,,,3.7286821705426356,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,Subway,transit_distance,D_location,binary,binary,0.0606,0.0264,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,pct_premium,6.210365326347933,2.804930936655136,100*(exp(b - se^2/2) - 1),reported,,,,2.2954545454545454,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,Subway,transit_distance,D_location,binary,binary,0.0417,0.0211,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,pct_premium,4.234959927246695,2.1998472969719907,100*(exp(b - se^2/2) - 1),reported,,,,1.9763033175355451,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,Subway,transit_distance,D_location,binary,binary,0.0451,0.0184,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,pct_premium,4.595539360884415,1.9248837409820394,100*(exp(b - se^2/2) - 1),reported,,,,2.4510869565217392,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,Subway,transit_distance,D_location,binary,binary,0.05,0.0197,,,**,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,pct_premium,5.106712226684573,2.0710040598607673,100*(exp(b - se^2/2) - 1),reported,,,,2.538071065989848,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,Subway,transit_distance,D_location,binary,binary,0.0612,0.0226,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,pct_premium,6.284005247522351,2.4026320244185078,100*(exp(b - se^2/2) - 1),reported,,,,2.7079646017699117,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,Subway,transit_distance,D_location,binary,binary,0.0553,0.018,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,pct_premium,5.668642734116203,1.9023437239361627,100*(exp(b - se^2/2) - 1),reported,,,,3.0722222222222224,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,Subway,transit_distance,D_location,binary,binary,0.069,0.0178,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,pct_premium,7.126648566824478,1.9071564522840563,100*(exp(b - se^2/2) - 1),reported,,,,3.876404494382023,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,Subway,transit_distance,D_location,binary,binary,0.048,0.0216,,,**,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,pct_premium,4.892593333947848,2.2662086155008567,100*(exp(b - se^2/2) - 1),reported,,,,2.2222222222222223,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,Subway,transit_distance,D_location,binary,binary,0.00169,0.0256,,,,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,pct_premium,0.13632483794265848,2.5643300578683093,100*(exp(b - se^2/2) - 1),reported,,,,0.066015625,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0081,0.0003,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,0.8099999999999999,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,27.0,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0097,0.0005,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,0.97,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,19.4,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0031,0.0006,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,0.31,0.06,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,5.166666666666667,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0035,0.0005,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,0.35000000000000003,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,7.0,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0041,0.0004,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,0.41000000000000003,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,10.25,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0045,0.0004,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,0.44999999999999996,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,11.249999999999998,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0034,0.0003,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,0.33999999999999997,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,11.333333333333334,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0039,0.0003,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,0.38999999999999996,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,13.0,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0083,0.0004,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,0.83,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,20.75,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0159,0.0005,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,1.59,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,31.8,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,Dcbd,cbd_distance,D_location,level,km,-0.0625,0.029,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-6.25,2.9000000000000004,100*b/1 (X per km->km),reported,,,,-2.155172413793103,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,Dcbd,cbd_distance,D_location,level,km,-0.103,0.0331,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-10.299999999999999,3.3099999999999996,100*b/1 (X per km->km),reported,,,,-3.11178247734139,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,Dcbd,cbd_distance,D_location,level,km,-0.0675,0.0395,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-6.75,3.95,100*b/1 (X per km->km),reported,,,,-1.7088607594936709,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,Dcbd,cbd_distance,D_location,level,km,-0.06,0.0302,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-6.0,3.02,100*b/1 (X per km->km),reported,,,,-1.9867549668874172,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,Dcbd,cbd_distance,D_location,level,km,-0.069,0.0297,,,*,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-6.9,2.97,100*b/1 (X per km->km),reported,,,,-2.3232323232323235,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,Dcbd,cbd_distance,D_location,level,km,-0.0705,0.0333,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-7.049999999999999,3.3300000000000005,100*b/1 (X per km->km),reported,,,,-2.117117117117117,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,Dcbd,cbd_distance,D_location,level,km,-0.0615,0.0323,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-6.15,3.2300000000000004,100*b/1 (X per km->km),reported,,,,-1.9040247678018574,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,Dcbd,cbd_distance,D_location,level,km,-0.0489,0.0195,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-4.89,1.95,100*b/1 (X per km->km),reported,,,,-2.5076923076923077,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,Dcbd,cbd_distance,D_location,level,km,-0.03295,0.0505,,,,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-3.295,5.050000000000001,100*b/1 (X per km->km),reported,,,,-0.6524752475247524,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,Dcbd,cbd_distance,D_location,level,km,-0.054,0.0813,,,,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-5.4,8.129999999999999,100*b/1 (X per km->km),reported,,,,-0.6642066420664207,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.Xriver,water_proximity,E_environment,level,km,-0.0168,0.0056,,,***,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-1.68,0.5599999999999999,100*b/1 (X per km->km),reported,,,,-3.0,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.Xriver,water_proximity,E_environment,level,km,-0.0125,0.0111,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-1.25,1.11,100*b/1 (X per km->km),reported,,,,-1.1261261261261262,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.Xriver,water_proximity,E_environment,level,km,-0.0085,0.0083,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-0.8500000000000001,0.83,100*b/1 (X per km->km),reported,,,,-1.0240963855421688,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.Xriver,water_proximity,E_environment,level,km,0.00265,0.0072,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,0.265,0.72,100*b/1 (X per km->km),reported,,,,0.3680555555555556,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.Xriver,water_proximity,E_environment,level,km,0.00066,0.0061,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,0.066,0.61,100*b/1 (X per km->km),reported,,,,0.1081967213114754,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.Xriver,water_proximity,E_environment,level,km,-0.0064,0.0078,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-0.64,0.7799999999999999,100*b/1 (X per km->km),reported,,,,-0.8205128205128206,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.Xriver,water_proximity,E_environment,level,km,-0.0087,0.0072,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-0.8699999999999999,0.72,100*b/1 (X per km->km),reported,,,,-1.2083333333333333,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.Xriver,water_proximity,E_environment,level,km,-0.013,0.0071,,,*,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-1.3,0.7100000000000001,100*b/1 (X per km->km),reported,,,,-1.8309859154929575,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.Xriver,water_proximity,E_environment,level,km,-0.0252,0.0097,,,**,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-2.52,0.97,100*b/1 (X per km->km),reported,,,,-2.597938144329897,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.Xriver,water_proximity,E_environment,level,km,-0.028,0.0083,,,**,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-2.8000000000000003,0.83,100*b/1 (X per km->km),reported,,,,-3.3734939759036147,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.Lriver,water_proximity,E_environment,level,km,-0.0245,0.012,,,**,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-2.45,1.2,100*b/1 (X per km->km),reported,,,,-2.0416666666666665,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.Lriver,water_proximity,E_environment,level,km,-0.0357,0.0224,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-3.5700000000000003,2.2399999999999998,100*b/1 (X per km->km),reported,,,,-1.5937500000000002,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.Lriver,water_proximity,E_environment,level,km,-0.0318,0.0158,,,**,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-3.18,1.58,100*b/1 (X per km->km),reported,,,,-2.0126582278481013,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.Lriver,water_proximity,E_environment,level,km,-0.0315,0.0119,,,***,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-3.15,1.1900000000000002,100*b/1 (X per km->km),reported,,,,-2.6470588235294117,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.Lriver,water_proximity,E_environment,level,km,-0.0263,0.0083,,,***,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-2.63,0.83,100*b/1 (X per km->km),reported,,,,-3.1686746987951806,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.Lriver,water_proximity,E_environment,level,km,-0.0223,0.0061,,,***,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-2.23,0.61,100*b/1 (X per km->km),reported,,,,-3.6557377049180326,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.Lriver,water_proximity,E_environment,level,km,-0.01385,0.0371,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-1.385,3.71,100*b/1 (X per km->km),reported,,,,-0.3733153638814016,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.Lriver,water_proximity,E_environment,level,km,-0.0075,0.0435,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-0.75,4.35,100*b/1 (X per km->km),reported,,,,-0.1724137931034483,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.Lriver,water_proximity,E_environment,level,km,-0.0025,0.0446,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-0.25,4.46,100*b/1 (X per km->km),reported,,,,-0.05605381165919283,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.Lriver,water_proximity,E_environment,level,km,-0.0171,0.0537,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-1.71,5.37,100*b/1 (X per km->km),reported,,,,-0.31843575418994413,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0251,0.0049,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-2.5100000000000002,0.49,100*b/1 (X per km->km),reported,,,,-5.122448979591837,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.YHwetland,water_proximity,E_environment,level,km,-0.00795,0.0097,,,,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-0.795,0.97,100*b/1 (X per km->km),reported,,,,-0.8195876288659794,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0183,0.0101,,,**,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-1.83,1.01,100*b/1 (X per km->km),reported,,,,-1.811881188118812,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0166,0.0092,,,*,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-1.66,0.9199999999999999,100*b/1 (X per km->km),reported,,,,-1.8043478260869565,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0235,0.0095,,,**,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-2.35,0.95,100*b/1 (X per km->km),reported,,,,-2.473684210526316,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0273,0.0087,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-2.73,0.8699999999999999,100*b/1 (X per km->km),reported,,,,-3.137931034482759,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0299,0.0086,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-2.9899999999999998,0.86,100*b/1 (X per km->km),reported,,,,-3.4767441860465116,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0312,0.0069,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-3.1199999999999997,0.69,100*b/1 (X per km->km),reported,,,,-4.521739130434782,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.YHwetland,water_proximity,E_environment,level,km,-0.0405,0.0067,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-4.05,0.67,100*b/1 (X per km->km),reported,,,,-6.044776119402985,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.YHwetland,water_proximity,E_environment,level,km,-0.042,0.0084,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-4.2,0.84,100*b/1 (X per km->km),reported,,,,-5.000000000000001,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.LL,water_proximity,E_environment,level,km,-0.0173,0.0066,,,***,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-1.73,0.66,100*b/1 (X per km->km),reported,,,,-2.621212121212121,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.LL,water_proximity,E_environment,level,km,0.00134,0.0181,,,,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,0.134,1.81,100*b/1 (X per km->km),reported,,,,0.07403314917127071,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.LL,water_proximity,E_environment,level,km,-0.0156,0.0131,,,,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-1.5599999999999998,1.31,100*b/1 (X per km->km),reported,,,,-1.1908396946564885,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.LL,water_proximity,E_environment,level,km,-0.0164,0.0121,,,,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-1.6400000000000001,1.21,100*b/1 (X per km->km),reported,,,,-1.3553719008264464,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.LL,water_proximity,E_environment,level,km,-0.0205,0.0089,,,*,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-2.0500000000000003,0.89,100*b/1 (X per km->km),reported,,,,-2.303370786516854,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.LL,water_proximity,E_environment,level,km,-0.0207,0.0069,,,**,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-2.07,0.69,100*b/1 (X per km->km),reported,,,,-3.0,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.LL,water_proximity,E_environment,level,km,-0.0223,0.0052,,,**,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-2.23,0.52,100*b/1 (X per km->km),reported,,,,-4.288461538461538,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.LL,water_proximity,E_environment,level,km,-0.0243,0.0068,,,***,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-2.4299999999999997,0.6799999999999999,100*b/1 (X per km->km),reported,,,,-3.573529411764706,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.LL,water_proximity,E_environment,level,km,-0.0209,0.0077,,,**,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-2.09,0.77,100*b/1 (X per km->km),reported,,,,-2.714285714285714,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.LL,water_proximity,E_environment,level,km,-0.0319,0.0105,,,***,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-3.19,1.05,100*b/1 (X per km->km),reported,,,,-3.0380952380952375,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.ML,water_proximity,E_environment,level,km,-0.0143,0.0045,,,***,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-1.43,0.44999999999999996,100*b/1 (X per km->km),reported,,,,-3.177777777777778,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.ML,water_proximity,E_environment,level,km,-0.0344,0.0108,,,***,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-3.44,1.08,100*b/1 (X per km->km),reported,,,,-3.185185185185185,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.ML,water_proximity,E_environment,level,km,-0.0156,0.0098,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-1.5599999999999998,0.98,100*b/1 (X per km->km),reported,,,,-1.5918367346938775,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.ML,water_proximity,E_environment,level,km,-0.0039,0.0048,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-0.38999999999999996,0.48,100*b/1 (X per km->km),reported,,,,-0.8125,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.ML,water_proximity,E_environment,level,km,-0.0014,0.0043,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-0.13999999999999999,0.43,100*b/1 (X per km->km),reported,,,,-0.32558139534883723,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.ML,water_proximity,E_environment,level,km,-0.004,0.0045,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-0.4,0.44999999999999996,100*b/1 (X per km->km),reported,,,,-0.888888888888889,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.ML,water_proximity,E_environment,level,km,-0.0082,0.0055,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-0.8200000000000001,0.5499999999999999,100*b/1 (X per km->km),reported,,,,-1.4909090909090912,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.ML,water_proximity,E_environment,level,km,-0.0066,0.0052,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-0.66,0.52,100*b/1 (X per km->km),reported,,,,-1.2692307692307694,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.ML,water_proximity,E_environment,level,km,-0.0053,0.0048,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-0.53,0.48,100*b/1 (X per km->km),reported,,,,-1.1041666666666667,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.ML,water_proximity,E_environment,level,km,-0.0019,0.0058,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-0.19,0.58,100*b/1 (X per km->km),reported,,,,-0.32758620689655177,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.SL,water_proximity,E_environment,level,km,-0.0124,0.0167,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-1.24,1.67,100*b/1 (X per km->km),reported,,,,-0.7425149700598802,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.SL,water_proximity,E_environment,level,km,-0.0214,0.026,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-2.1399999999999997,2.6,100*b/1 (X per km->km),reported,,,,-0.823076923076923,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.SL,water_proximity,E_environment,level,km,0.00524,0.0288,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,0.524,2.88,100*b/1 (X per km->km),reported,,,,0.18194444444444444,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.SL,water_proximity,E_environment,level,km,-0.0001,0.0212,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-0.01,2.12,100*b/1 (X per km->km),reported,,,,-0.0047169811320754715,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.SL,water_proximity,E_environment,level,km,0.0044,0.0241,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,0.44,2.41,100*b/1 (X per km->km),reported,,,,0.18257261410788383,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.SL,water_proximity,E_environment,level,km,0.00533,0.0216,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,0.5329999999999999,2.16,100*b/1 (X per km->km),reported,,,,0.24675925925925923,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.SL,water_proximity,E_environment,level,km,-0.0113,0.0139,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-1.13,1.39,100*b/1 (X per km->km),reported,,,,-0.8129496402877698,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.SL,water_proximity,E_environment,level,km,-0.0118,0.0183,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-1.18,1.83,100*b/1 (X per km->km),reported,,,,-0.6448087431693988,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.SL,water_proximity,E_environment,level,km,-0.0098,0.0293,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-0.98,2.93,100*b/1 (X per km->km),reported,,,,-0.33447098976109213,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.SL,water_proximity,E_environment,level,km,-0.0421,0.0378,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-4.21,3.7800000000000002,100*b/1 (X per km->km),reported,,,,-1.1137566137566137,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.YL,green_space,E_environment,level,km,-0.0234,0.0045,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-2.34,0.44999999999999996,100*b/1 (X per km->km),reported,,,,-5.2,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.YL,green_space,E_environment,level,km,-0.0277,0.0068,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-2.77,0.6799999999999999,100*b/1 (X per km->km),reported,,,,-4.073529411764706,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.YL,green_space,E_environment,level,km,-0.0323,0.0068,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-3.2300000000000004,0.6799999999999999,100*b/1 (X per km->km),reported,,,,-4.750000000000001,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.YL,green_space,E_environment,level,km,-0.0349,0.0056,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-3.49,0.5599999999999999,100*b/1 (X per km->km),reported,,,,-6.232142857142858,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.YL,green_space,E_environment,level,km,-0.0311,0.0037,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,-3.11,0.37,100*b/1 (X per km->km),reported,,,,-8.405405405405405,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.YL,green_space,E_environment,level,km,-0.0281,0.0052,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-2.81,0.52,100*b/1 (X per km->km),reported,,,,-5.403846153846154,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.YL,green_space,E_environment,level,km,-0.0251,0.0044,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-2.5100000000000002,0.44,100*b/1 (X per km->km),reported,,,,-5.704545454545454,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.YL,green_space,E_environment,level,km,-0.019,0.0078,,,**,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-1.9,0.7799999999999999,100*b/1 (X per km->km),reported,,,,-2.435897435897436,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.YL,green_space,E_environment,level,km,-0.0182,0.0061,,,**,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-1.82,0.61,100*b/1 (X per km->km),reported,,,,-2.983606557377049,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.YL,green_space,E_environment,level,km,-0.0212,0.0073,,,**,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-2.12,0.73,100*b/1 (X per km->km),reported,,,,-2.904109589041096,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c1,3,1,8,D.NWG,green_space,E_environment,level,km,-0.0124,0.0047,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,OLS,,0.519,se,Conditional mean (OLS),semi_elasticity,-1.24,0.47000000000000003,100*b/1 (X per km->km),reported,,,,-2.6382978723404253,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,ols,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c2,3,2,8,D.NWG,green_space,E_environment,level,km,-0.0082,0.0021,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.321,se,Quantile 0.1,semi_elasticity,-0.8200000000000001,0.21,100*b/1 (X per km->km),reported,,,,-3.9047619047619055,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c3,3,3,8,D.NWG,green_space,E_environment,level,km,-0.0108,0.0022,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.307,se,Quantile 0.2,semi_elasticity,-1.08,0.22,100*b/1 (X per km->km),reported,,,,-4.909090909090909,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c4,3,4,8,D.NWG,green_space,E_environment,level,km,-0.0861,0.0015,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.313,se,Quantile 0.3,semi_elasticity,-8.61,0.15,100*b/1 (X per km->km),reported,,,,-57.4,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c5,3,5,8,D.NWG,green_space,E_environment,level,km,0.0089,0.0022,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.346,se,Quantile 0.4,semi_elasticity,0.89,0.22,100*b/1 (X per km->km),reported,,,,4.045454545454545,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c6,3,6,8,D.NWG,green_space,E_environment,level,km,-0.0138,0.0024,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.331,se,Quantile 0.5,semi_elasticity,-1.38,0.24,100*b/1 (X per km->km),reported,,,,-5.75,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c7,3,7,8,D.NWG,green_space,E_environment,level,km,-0.0135,0.0022,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.358,se,Quantile 0.6,semi_elasticity,-1.35,0.22,100*b/1 (X per km->km),reported,,,,-6.136363636363636,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c8,3,8,8,D.NWG,green_space,E_environment,level,km,-0.0131,0.0026,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.7,semi_elasticity,-1.31,0.26,100*b/1 (X per km->km),reported,,,,-5.038461538461539,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c9,3,9,8,D.NWG,green_space,E_environment,level,km,-0.0095,0.0026,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.343,se,Quantile 0.8,semi_elasticity,-0.95,0.26,100*b/1 (X per km->km),reported,,,,-3.6538461538461537,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T3-c10,3,10,8,D.NWG,green_space,E_environment,level,km,-0.0054,0.0038,,,,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.9,semi_elasticity,-0.54,0.38,100*b/1 (X per km->km),reported,,,,-1.4210526315789473,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,Floor area,living_area,A_structural,other,index,-0.0153,0.0147,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),other,-0.0153,0.0147,not harmonised,reported,,,,-1.0408163265306123,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,Floor area,living_area,A_structural,other,index,0.0099,0.0138,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),other,0.0099,0.0138,not harmonised,reported,,,,0.7173913043478262,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,Floor area,living_area,A_structural,other,index,0.00319,0.0147,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),other,0.00319,0.0147,not harmonised,reported,,,,0.21700680272108844,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,Floor area,living_area,A_structural,other,index,-0.00587,0.012,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),other,-0.00587,0.012,not harmonised,reported,,,,-0.4891666666666667,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,Floor area,living_area,A_structural,other,index,-0.0131,0.0094,,,,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),other,-0.0131,0.0094,not harmonised,reported,,,,-1.3936170212765957,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,Floor area,living_area,A_structural,other,index,-0.0224,0.0098,,,*,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),other,-0.0224,0.0098,not harmonised,reported,,,,-2.2857142857142856,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,Floor area,living_area,A_structural,other,index,-0.0273,0.008,,,**,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),other,-0.0273,0.008,not harmonised,reported,,,,-3.4125,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,Floor area,living_area,A_structural,other,index,-0.0278,0.0125,,,**,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),other,-0.0278,0.0125,not harmonised,reported,,,,-2.2239999999999998,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,Floor area,living_area,A_structural,other,index,-0.0436,0.016,,,**,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),other,-0.0436,0.016,not harmonised,reported,,,,-2.725,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,Floor area,living_area,A_structural,other,index,-0.0566,0.0219,,,**,,,medium,"Ordinal score of floor area in 5 classes (1: <60 m2, 2: 60-90, 3: 90-120, 4: 120-144, 5: >144 m2); not a continuous area. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),other,-0.0566,0.0219,not harmonised,reported,,,,-2.584474885844749,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,Plot ratio,density,F_neighbourhood,other,index,-0.00549,0.0023,,,**,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),other,-0.00549,0.0023,not harmonised,reported,,,,-2.3869565217391306,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,Plot ratio,density,F_neighbourhood,other,index,-0.00525,0.0021,,,**,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),other,-0.00525,0.0021,not harmonised,reported,,,,-2.5000000000000004,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,Plot ratio,density,F_neighbourhood,other,index,-0.005,0.0024,,,**,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),other,-0.005,0.0024,not harmonised,reported,,,,-2.0833333333333335,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,Plot ratio,density,F_neighbourhood,other,index,-0.00677,0.0027,,,***,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),other,-0.00677,0.0027,not harmonised,reported,,,,-2.507407407407407,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,Plot ratio,density,F_neighbourhood,other,index,-0.00704,0.0023,,,*,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),other,-0.00704,0.0023,not harmonised,reported,,,,-3.0608695652173914,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,Plot ratio,density,F_neighbourhood,other,index,-0.00759,0.0041,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),other,-0.00759,0.0041,not harmonised,reported,,,,-1.8512195121951218,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,Plot ratio,density,F_neighbourhood,other,index,-0.00684,0.0045,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),other,-0.00684,0.0045,not harmonised,reported,,,,-1.52,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,Plot ratio,density,F_neighbourhood,other,index,-0.00135,0.0048,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),other,-0.00135,0.0048,not harmonised,reported,,,,-0.28125000000000006,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,Plot ratio,density,F_neighbourhood,other,index,-0.00263,0.0037,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),other,-0.00263,0.0037,not harmonised,reported,,,,-0.7108108108108108,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,Plot ratio,density,F_neighbourhood,other,index,-0.0065,0.004,,,,,,medium,"Community plot ratio (floor-area ratio) coded in 5 ordinal classes (<1=1, 1-2=2, 2-3=3, 3-5=4, >5=5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),other,-0.0065,0.004,not harmonised,reported,,,,-1.625,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,PM,other,other,other,index,0.0129,0.0036,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),other,0.0129,0.0036,not harmonised,reported,,,,3.5833333333333335,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,PM,other,other,other,index,0.0212,0.005,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),other,0.0212,0.005,not harmonised,reported,,,,4.24,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,PM,other,other,other,index,0.0211,0.0045,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),other,0.0211,0.0045,not harmonised,reported,,,,4.688888888888889,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,PM,other,other,other,index,0.0199,0.0037,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),other,0.0199,0.0037,not harmonised,reported,,,,5.378378378378378,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,PM,other,other,other,index,0.0159,0.0044,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),other,0.0159,0.0044,not harmonised,reported,,,,3.6136363636363638,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,PM,other,other,other,index,0.0143,0.0056,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),other,0.0143,0.0056,not harmonised,reported,,,,2.553571428571429,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,PM,other,other,other,index,0.0143,0.0053,,,***,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),other,0.0143,0.0053,not harmonised,reported,,,,2.69811320754717,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,PM,other,other,other,index,0.0103,0.0045,,,**,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),other,0.0103,0.0045,not harmonised,reported,,,,2.288888888888889,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,PM,other,other,other,index,0.00461,0.0072,,,,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),other,0.00461,0.0072,not harmonised,reported,,,,0.6402777777777778,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,PM,other,other,other,index,-0.000734,0.0046,,,,,,medium,"Property management level score 0-5 (ordinal). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),other,-0.000734,0.0046,not harmonised,reported,,,,-0.15956521739130433,,index,nonstandard_x_transform,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,Building age,age,A_structural,level,years,-0.0015,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-0.15,0.01,100*b/1 (per year),reported,,,,-15.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,Building age,age,A_structural,level,years,-0.0026,0.0002,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.26,0.02,100*b/1 (per year),reported,,,,-12.999999999999998,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,Building age,age,A_structural,level,years,-0.0024,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.24,0.01,100*b/1 (per year),reported,,,,-23.999999999999996,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,Building age,age,A_structural,level,years,-0.0026,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.26,0.01,100*b/1 (per year),reported,,,,-25.999999999999996,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,Building age,age,A_structural,level,years,-0.0024,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.24,0.01,100*b/1 (per year),reported,,,,-23.999999999999996,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,Building age,age,A_structural,level,years,-0.0021,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.21,0.01,100*b/1 (per year),reported,,,,-20.999999999999996,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,Building age,age,A_structural,level,years,-0.002,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.2,0.01,100*b/1 (per year),reported,,,,-20.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,Building age,age,A_structural,level,years,-0.0017,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.16999999999999998,0.01,100*b/1 (per year),reported,,,,-17.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,Building age,age,A_structural,level,years,-0.0009,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.09,0.01,100*b/1 (per year),reported,,,,-9.0,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,Building age,age,A_structural,level,years,-0.0006,0.0001,,,***,,,medium,"Age of house in years (2019 minus building year). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.06,0.01,100*b/1 (per year),reported,,,,-5.999999999999999,,years,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0817,0.0111,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),pct_premium,8.506337453899349,1.2044945463392225,100*(exp(b - se^2/2) - 1),reported,,,,7.360360360360359,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0623,0.021,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),pct_premium,6.4046933096782555,2.2349913207605465,100*(exp(b - se^2/2) - 1),reported,,,,2.9666666666666663,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0478,0.0145,,,**,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),pct_premium,4.885057596285547,1.520993221154359,100*(exp(b - se^2/2) - 1),reported,,,,3.296551724137931,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0653,0.0115,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),pct_premium,6.740863538876951,1.2276011031362366,100*(exp(b - se^2/2) - 1),reported,,,,5.678260869565217,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0705,0.0136,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),pct_premium,7.294533885742438,1.4593406144257695,100*(exp(b - se^2/2) - 1),reported,,,,5.1838235294117645,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0669,0.0099,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),pct_premium,6.913615967179632,1.0584966684333512,100*(exp(b - se^2/2) - 1),reported,,,,6.757575757575757,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0743,0.0127,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),pct_premium,7.704303436108373,1.3679549679187566,100*(exp(b - se^2/2) - 1),reported,,,,5.850393700787402,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0797,0.0134,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),pct_premium,8.28649063220912,1.4511692545989177,100*(exp(b - se^2/2) - 1),reported,,,,5.94776119402985,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0773,0.0124,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),pct_premium,8.028308226630054,1.3396540106516175,100*(exp(b - se^2/2) - 1),reported,,,,6.233870967741935,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,QMS,school_quality,F_neighbourhood,binary,binary,0.0981,0.0164,,,***,,,medium,"Residential district belongs to a quality middle-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),pct_premium,10.292475560730963,1.809039862519307,100*(exp(b - se^2/2) - 1),reported,,,,5.9817073170731705,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,QPS,school_quality,F_neighbourhood,binary,binary,0.028,0.0119,,,**,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),pct_premium,2.832287144278145,1.2237908644614959,100*(exp(b - se^2/2) - 1),reported,,,,2.3529411764705883,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0503,0.0136,,,***,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),pct_premium,5.148927879176846,1.4301576740229394,100*(exp(b - se^2/2) - 1),reported,,,,3.698529411764706,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0208,0.014,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),pct_premium,2.091777280593843,1.4294249587103929,100*(exp(b - se^2/2) - 1),reported,,,,1.4857142857142855,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0244,0.0149,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),pct_premium,2.4586375448556907,1.526803172798176,100*(exp(b - se^2/2) - 1),reported,,,,1.6375838926174497,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0182,0.0134,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),pct_premium,1.8275204497828934,1.364611283328583,100*(exp(b - se^2/2) - 1),reported,,,,1.3582089552238805,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0104,0.0178,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),pct_premium,1.029420447962237,1.7986085969791061,100*(exp(b - se^2/2) - 1),reported,,,,0.5842696629213483,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,True
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0236,0.0138,,,*,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),pct_premium,2.378319441860266,1.4129553435001572,100*(exp(b - se^2/2) - 1),reported,,,,1.710144927536232,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0348,0.0161,,,**,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),pct_premium,3.52784196139051,1.6670142949658915,100*(exp(b - se^2/2) - 1),reported,,,,2.1614906832298137,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0462,0.0156,,,**,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),pct_premium,4.715642103627604,1.63376280097994,100*(exp(b - se^2/2) - 1),reported,,,,2.9615384615384617,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,QPS,school_quality,F_neighbourhood,binary,binary,0.0342,0.0306,,,,,,medium,"Residential district belongs to a quality primary-school district (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),pct_premium,3.430718902888752,3.166462125688157,100*(exp(b - se^2/2) - 1),reported,,,,1.1176470588235294,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,Subway,transit_distance,D_location,binary,binary,0.0472,0.0129,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),pct_premium,4.824443163873826,1.352347834234449,100*(exp(b - se^2/2) - 1),reported,,,,3.6589147286821704,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,Subway,transit_distance,D_location,binary,binary,0.0534,0.022,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),pct_premium,5.459625816536873,2.3206733029546522,100*(exp(b - se^2/2) - 1),reported,,,,2.4272727272727277,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,Subway,transit_distance,D_location,binary,binary,0.047,0.0178,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),pct_premium,4.795597874258739,1.8656571761617873,100*(exp(b - se^2/2) - 1),reported,,,,2.640449438202247,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,Subway,transit_distance,D_location,binary,binary,0.0417,0.0142,,,**,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),pct_premium,4.247654954857705,1.4804659534124298,100*(exp(b - se^2/2) - 1),reported,,,,2.936619718309859,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,Subway,transit_distance,D_location,binary,binary,0.0475,0.0196,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),pct_premium,4.844479650249256,2.055346554197719,100*(exp(b - se^2/2) - 1),reported,,,,2.423469387755102,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,Subway,transit_distance,D_location,binary,binary,0.0573,0.0216,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),pct_premium,5.872644626755608,2.2873826623310527,100*(exp(b - se^2/2) - 1),reported,,,,2.6527777777777777,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,Subway,transit_distance,D_location,binary,binary,0.0553,0.0183,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),pct_premium,5.668067369922936,1.9340494526684322,100*(exp(b - se^2/2) - 1),reported,,,,3.021857923497268,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,Subway,transit_distance,D_location,binary,binary,0.0707,0.02,,,***,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),pct_premium,7.304456943071291,2.1465183996138424,100*(exp(b - se^2/2) - 1),reported,,,,3.5349999999999997,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,Subway,transit_distance,D_location,binary,binary,0.0444,0.0205,,,*,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),pct_premium,4.518078975139184,2.143070884452277,100*(exp(b - se^2/2) - 1),reported,,,,2.1658536585365855,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,Subway,transit_distance,D_location,binary,binary,0.0165,0.023,,,,,,medium,"Subway station within 1 km of property (1/0). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),pct_premium,1.636801189109538,2.338264816607928,100*(exp(b - se^2/2) - 1),reported,,,,0.7173913043478262,,binary,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0079,0.0003,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,0.79,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,26.33333333333334,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0113,0.0006,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,1.13,0.06,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,18.833333333333332,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0033,0.0007,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.33,0.06999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,4.714285714285714,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0031,0.0006,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.31,0.06,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,5.166666666666667,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0045,0.0005,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.44999999999999996,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,9.0,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0056,0.0004,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.5599999999999999,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,14.0,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0023,0.0003,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.22999999999999998,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,7.666666666666667,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0045,0.0003,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.44999999999999996,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,15.0,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0073,0.0004,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.73,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,18.25,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.0125,0.0007,,,***,,,medium,"Living facilities count (hospital, gym, bank, food market, supermarket within 1 km; 0-5). Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,1.25,0.06999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,,,,17.857142857142858,,count,unit_not_harmonisable,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,False,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.CBD,cbd_distance,D_location,level,km,-0.0575,0.0267,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-5.75,2.67,100*b/1 (X per km->km),reported,,,,-2.153558052434457,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.CBD,cbd_distance,D_location,level,km,-0.1365,0.045,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-13.65,4.5,100*b/1 (X per km->km),reported,,,,-3.0333333333333337,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.CBD,cbd_distance,D_location,level,km,-0.079,0.0271,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-7.9,2.71,100*b/1 (X per km->km),reported,,,,-2.915129151291513,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.CBD,cbd_distance,D_location,level,km,-0.0595,0.0202,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-5.949999999999999,2.02,100*b/1 (X per km->km),reported,,,,-2.9455445544554455,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.CBD,cbd_distance,D_location,level,km,-0.0672,0.0266,,,***,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-6.72,2.6599999999999997,100*b/1 (X per km->km),reported,,,,-2.526315789473684,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.CBD,cbd_distance,D_location,level,km,-0.0557,0.0291,,,*,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-5.57,2.91,100*b/1 (X per km->km),reported,,,,-1.9140893470790377,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.CBD,cbd_distance,D_location,level,km,-0.0555,0.0274,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-5.55,2.74,100*b/1 (X per km->km),reported,,,,-2.0255474452554743,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.CBD,cbd_distance,D_location,level,km,-0.0381,0.0224,,,**,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.81,2.2399999999999998,100*b/1 (X per km->km),reported,,,,-1.7008928571428572,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.CBD,cbd_distance,D_location,level,km,-0.00635,0.0507,,,,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.635,5.07,100*b/1 (X per km->km),reported,,,,-0.1252465483234714,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.CBD,cbd_distance,D_location,level,km,-0.0417,0.0984,,,,,,medium,"Walking distance to CBD (Wuyi Square), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-4.17,9.84,100*b/1 (X per km->km),reported,,,,-0.42378048780487804,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.Xriver,water_proximity,E_environment,level,km,-0.0172,0.0057,,,***,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-1.72,0.5700000000000001,100*b/1 (X per km->km),reported,,,,-3.017543859649123,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.Xriver,water_proximity,E_environment,level,km,-0.0128,0.0106,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.28,1.06,100*b/1 (X per km->km),reported,,,,-1.2075471698113207,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.Xriver,water_proximity,E_environment,level,km,-0.00706,0.0084,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.7060000000000001,0.84,100*b/1 (X per km->km),reported,,,,-0.8404761904761906,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.Xriver,water_proximity,E_environment,level,km,0.0004,0.0071,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.04,0.7100000000000001,100*b/1 (X per km->km),reported,,,,0.056338028169014086,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.Xriver,water_proximity,E_environment,level,km,-0.0007,0.0086,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.06999999999999999,0.86,100*b/1 (X per km->km),reported,,,,-0.08139534883720931,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.Xriver,water_proximity,E_environment,level,km,-0.0073,0.0091,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.73,0.91,100*b/1 (X per km->km),reported,,,,-0.8021978021978021,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.Xriver,water_proximity,E_environment,level,km,-0.0066,0.0097,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.66,0.97,100*b/1 (X per km->km),reported,,,,-0.6804123711340206,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.Xriver,water_proximity,E_environment,level,km,-0.0115,0.0135,,,,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.15,1.35,100*b/1 (X per km->km),reported,,,,-0.8518518518518519,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.Xriver,water_proximity,E_environment,level,km,-0.0236,0.0132,,,**,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.36,1.32,100*b/1 (X per km->km),reported,,,,-1.7878787878787878,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.Xriver,water_proximity,E_environment,level,km,-0.0314,0.0097,,,***,,,medium,"Walking distance to Xiangjiang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.1399999999999997,0.97,100*b/1 (X per km->km),reported,,,,-3.2371134020618553,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.Lriver,water_proximity,E_environment,level,km,-0.0236,0.0118,,,**,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-2.36,1.18,100*b/1 (X per km->km),reported,,,,-2.0,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.Lriver,water_proximity,E_environment,level,km,-0.0312,0.0512,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.1199999999999997,5.12,100*b/1 (X per km->km),reported,,,,-0.6093749999999999,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.Lriver,water_proximity,E_environment,level,km,-0.0325,0.0529,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.25,5.29,100*b/1 (X per km->km),reported,,,,-0.6143667296786389,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.Lriver,water_proximity,E_environment,level,km,-0.0221,0.0111,,,**,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.21,1.11,100*b/1 (X per km->km),reported,,,,-1.990990990990991,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.Lriver,water_proximity,E_environment,level,km,-0.0252,0.0104,,,***,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.52,1.04,100*b/1 (X per km->km),reported,,,,-2.4230769230769234,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.Lriver,water_proximity,E_environment,level,km,-0.0214,0.0081,,,***,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.1399999999999997,0.8099999999999999,100*b/1 (X per km->km),reported,,,,-2.6419753086419755,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.Lriver,water_proximity,E_environment,level,km,-0.0112,0.0261,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.1199999999999999,2.6100000000000003,100*b/1 (X per km->km),reported,,,,-0.4291187739463601,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.Lriver,water_proximity,E_environment,level,km,-0.0062,0.0263,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.62,2.63,100*b/1 (X per km->km),reported,,,,-0.23574144486692014,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.Lriver,water_proximity,E_environment,level,km,-0.0015,0.0363,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.15,3.63,100*b/1 (X per km->km),reported,,,,-0.04132231404958678,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.Lriver,water_proximity,E_environment,level,km,-0.0083,0.0543,,,,,,medium,"Walking distance to Liuyang River, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.83,5.43,100*b/1 (X per km->km),reported,,,,-0.15285451197053407,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0227,0.0056,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-2.27,0.5599999999999999,100*b/1 (X per km->km),reported,,,,-4.053571428571429,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0125,0.0099,,,,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.25,0.9900000000000001,100*b/1 (X per km->km),reported,,,,-1.2626262626262625,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0153,0.0081,,,*,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.53,0.8099999999999999,100*b/1 (X per km->km),reported,,,,-1.8888888888888888,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0171,0.0069,,,**,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.71,0.69,100*b/1 (X per km->km),reported,,,,-2.4782608695652177,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0222,0.0067,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.22,0.67,100*b/1 (X per km->km),reported,,,,-3.3134328358208958,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0237,0.0071,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.37,0.7100000000000001,100*b/1 (X per km->km),reported,,,,-3.338028169014084,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0235,0.0095,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.35,0.95,100*b/1 (X per km->km),reported,,,,-2.473684210526316,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0215,0.0086,,,*,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.15,0.86,100*b/1 (X per km->km),reported,,,,-2.5,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.YHwetland,water_proximity,E_environment,level,km,-0.026,0.0071,,,**,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.6,0.7100000000000001,100*b/1 (X per km->km),reported,,,,-3.661971830985915,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.YHwetland,water_proximity,E_environment,level,km,-0.0285,0.0073,,,***,,,medium,"Walking distance to Yanghu National Wetland Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.85,0.73,100*b/1 (X per km->km),reported,,,,-3.904109589041096,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.LL,water_proximity,E_environment,level,km,-0.0166,0.0008,,,**,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-1.66,0.08,100*b/1 (X per km->km),reported,,,,-20.75,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.LL,water_proximity,E_environment,level,km,-0.0074,0.0011,,,,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.74,0.11,100*b/1 (X per km->km),reported,,,,-6.7272727272727275,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.LL,water_proximity,E_environment,level,km,-0.0189,0.0184,,,,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.8900000000000001,1.8399999999999999,100*b/1 (X per km->km),reported,,,,-1.0271739130434783,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.LL,water_proximity,E_environment,level,km,-0.0152,0.0181,,,,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.52,1.81,100*b/1 (X per km->km),reported,,,,-0.8397790055248618,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.LL,water_proximity,E_environment,level,km,-0.021,0.0103,,,**,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.1,1.03,100*b/1 (X per km->km),reported,,,,-2.0388349514563107,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.LL,water_proximity,E_environment,level,km,-0.0179,0.0087,,,**,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.79,0.8699999999999999,100*b/1 (X per km->km),reported,,,,-2.057471264367816,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.LL,water_proximity,E_environment,level,km,-0.0231,0.0095,,,***,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.31,0.95,100*b/1 (X per km->km),reported,,,,-2.431578947368421,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.LL,water_proximity,E_environment,level,km,-0.0235,0.0093,,,***,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.35,0.9299999999999999,100*b/1 (X per km->km),reported,,,,-2.526881720430108,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.LL,water_proximity,E_environment,level,km,-0.0195,0.0077,,,***,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.95,0.77,100*b/1 (X per km->km),reported,,,,-2.5324675324675323,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.LL,water_proximity,E_environment,level,km,-0.0143,0.0081,,,*,,,medium,"Walking distance to nearest large lake (>200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.43,0.8099999999999999,100*b/1 (X per km->km),reported,,,,-1.7654320987654322,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.ML,water_proximity,E_environment,level,km,-0.0145,0.0085,,,*,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-1.4500000000000002,0.8500000000000001,100*b/1 (X per km->km),reported,,,,-1.7058823529411764,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.ML,water_proximity,E_environment,level,km,-0.0332,0.0137,,,**,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.32,1.37,100*b/1 (X per km->km),reported,,,,-2.423357664233577,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.ML,water_proximity,E_environment,level,km,-0.0159,0.0122,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.59,1.22,100*b/1 (X per km->km),reported,,,,-1.3032786885245902,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.ML,water_proximity,E_environment,level,km,-0.0051,0.0066,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.51,0.66,100*b/1 (X per km->km),reported,,,,-0.7727272727272728,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.ML,water_proximity,E_environment,level,km,-0.0013,0.0066,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.13,0.66,100*b/1 (X per km->km),reported,,,,-0.19696969696969696,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.ML,water_proximity,E_environment,level,km,-0.0038,0.0047,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.38,0.47000000000000003,100*b/1 (X per km->km),reported,,,,-0.8085106382978723,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.ML,water_proximity,E_environment,level,km,-0.0071,0.005,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.7100000000000001,0.5,100*b/1 (X per km->km),reported,,,,-1.4200000000000002,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.ML,water_proximity,E_environment,level,km,-0.0051,0.0048,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.51,0.48,100*b/1 (X per km->km),reported,,,,-1.0625000000000002,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.ML,water_proximity,E_environment,level,km,-0.0049,0.0055,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.49,0.5499999999999999,100*b/1 (X per km->km),reported,,,,-0.890909090909091,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.ML,water_proximity,E_environment,level,km,-0.005,0.0041,,,,,,medium,"Walking distance to nearest medium lake (18,000-200,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.5,0.41000000000000003,100*b/1 (X per km->km),reported,,,,-1.2195121951219512,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.SL,water_proximity,E_environment,level,km,-0.0128,0.0167,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-1.28,1.67,100*b/1 (X per km->km),reported,,,,-0.7664670658682635,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.SL,water_proximity,E_environment,level,km,-0.0207,0.0267,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.07,2.67,100*b/1 (X per km->km),reported,,,,-0.7752808988764044,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.SL,water_proximity,E_environment,level,km,0.00136,0.0216,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.136,2.16,100*b/1 (X per km->km),reported,,,,0.06296296296296297,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.SL,water_proximity,E_environment,level,km,-0.0065,0.0231,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.65,2.31,100*b/1 (X per km->km),reported,,,,-0.2813852813852814,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.SL,water_proximity,E_environment,level,km,0.00368,0.022,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.368,2.1999999999999997,100*b/1 (X per km->km),reported,,,,0.1672727272727273,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.SL,water_proximity,E_environment,level,km,-0.0008,0.0228,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.08,2.2800000000000002,100*b/1 (X per km->km),reported,,,,-0.03508771929824561,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.SL,water_proximity,E_environment,level,km,-0.0107,0.0227,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.0699999999999998,2.27,100*b/1 (X per km->km),reported,,,,-0.47136563876651977,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.SL,water_proximity,E_environment,level,km,-0.0137,0.0284,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.37,2.8400000000000003,100*b/1 (X per km->km),reported,,,,-0.48239436619718307,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.SL,water_proximity,E_environment,level,km,-0.0218,0.0284,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.18,2.8400000000000003,100*b/1 (X per km->km),reported,,,,-0.7676056338028169,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.SL,water_proximity,E_environment,level,km,-0.029,0.0345,,,,,,medium,"Walking distance to nearest small lake (<18,000 m2), km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.9000000000000004,3.45,100*b/1 (X per km->km),reported,,,,-0.8405797101449275,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.YL,green_space,E_environment,level,km,-0.0203,0.0058,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-2.03,0.58,100*b/1 (X per km->km),reported,,,,-3.5,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.YL,green_space,E_environment,level,km,-0.0424,0.0084,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-4.24,0.84,100*b/1 (X per km->km),reported,,,,-5.047619047619048,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.YL,green_space,E_environment,level,km,-0.0373,0.0085,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.73,0.8500000000000001,100*b/1 (X per km->km),reported,,,,-4.388235294117647,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.YL,green_space,E_environment,level,km,-0.032,0.0073,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-3.2,0.73,100*b/1 (X per km->km),reported,,,,-4.383561643835616,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.YL,green_space,E_environment,level,km,-0.0285,0.0077,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.85,0.77,100*b/1 (X per km->km),reported,,,,-3.7012987012987013,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.YL,green_space,E_environment,level,km,-0.0211,0.0072,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.11,0.72,100*b/1 (X per km->km),reported,,,,-2.930555555555556,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.YL,green_space,E_environment,level,km,-0.0214,0.0066,,,***,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-2.1399999999999997,0.66,100*b/1 (X per km->km),reported,,,,-3.242424242424242,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.YL,green_space,E_environment,level,km,-0.0176,0.0103,,,*,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.76,1.03,100*b/1 (X per km->km),reported,,,,-1.70873786407767,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.YL,green_space,E_environment,level,km,-0.0066,0.0118,,,,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.66,1.18,100*b/1 (X per km->km),reported,,,,-0.5593220338983051,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.YL,green_space,E_environment,level,km,-0.00744,0.0111,,,,,,medium,"Walking distance to Yuelu Mountain National Forest Park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.744,1.11,100*b/1 (X per km->km),reported,,,,-0.6702702702702703,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c1,4,1,10,D.NWG,green_space,E_environment,level,km,-0.0121,0.0017,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,IV,,0.531,se,Conditional mean (spatial 2SLS with spatial lag WlnP instrumented),semi_elasticity,-1.21,0.16999999999999998,100*b/1 (X per km->km),reported,,,,-7.11764705882353,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,iv,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c2,4,2,10,D.NWG,green_space,E_environment,level,km,0.0043,0.0032,,,,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.335,se,Quantile 0.1 (spatial quantile regression with instrumented spatial lag),semi_elasticity,0.43,0.32,100*b/1 (X per km->km),reported,,,,1.34375,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c3,4,3,10,D.NWG,green_space,E_environment,level,km,-0.005,0.0034,,,,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.311,se,Quantile 0.2 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.5,0.33999999999999997,100*b/1 (X per km->km),reported,,,,-1.4705882352941178,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c4,4,4,10,D.NWG,green_space,E_environment,level,km,-0.0082,0.0025,,,**,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.329,se,Quantile 0.3 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.8200000000000001,0.25,100*b/1 (X per km->km),reported,,,,-3.2800000000000002,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c5,4,5,10,D.NWG,green_space,E_environment,level,km,-0.0091,0.0014,,,**,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.352,se,Quantile 0.4 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.91,0.13999999999999999,100*b/1 (X per km->km),reported,,,,-6.5,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c6,4,6,10,D.NWG,green_space,E_environment,level,km,-0.0155,0.0018,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.341,se,Quantile 0.5 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.55,0.18,100*b/1 (X per km->km),reported,,,,-8.61111111111111,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c7,4,7,10,D.NWG,green_space,E_environment,level,km,-0.0164,0.0021,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.367,se,Quantile 0.6 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.6400000000000001,0.21,100*b/1 (X per km->km),reported,,,,-7.809523809523811,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c8,4,8,10,D.NWG,green_space,E_environment,level,km,-0.0183,0.0022,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.339,se,Quantile 0.7 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.83,0.22,100*b/1 (X per km->km),reported,,,,-8.318181818181818,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c9,4,9,10,D.NWG,green_space,E_environment,level,km,-0.0114,0.003,,,***,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.361,se,Quantile 0.8 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-1.1400000000000001,0.3,100*b/1 (X per km->km),reported,,,,-3.8000000000000003,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178,6c554bbc30f053c6546ae8df71b70b126d3f4178:T4-c10,4,10,10,D.NWG,green_space,E_environment,level,km,-0.00907,0.0301,,,,,,medium,"Walking distance to nearest non-water green space park, km. Bracketed values are not defined in the table note (only star legend ***p<0.01, **p<0.05, *p<0.10); magnitudes are consistent with standard errors and are recorded as SE with medium confidence.","ln property price (per m2, RMB; inferred from text)",log,quantile,,0.353,se,Quantile 0.9 (spatial quantile regression with instrumented spatial lag),semi_elasticity,-0.907,3.01,100*b/1 (X per km->km),reported,,,,-0.30132890365448506,,km,,2021.0,China,Asia,Upper middle income,2019.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,,2019.0,quantile,journal,True,False,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,7,lnroom,bedrooms,A_structural,log,count,0.766,,113.44,,***,,,high,"Log number of rooms in the house (all rooms, not only bedrooms); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,elasticity,0.766,0.0067524682651622005,b (log-log),|b/t|,0.7474548956486194,0.006588988854448337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),113.44,10165.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,True
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,7,lnroom,bedrooms,A_structural,log,count,0.764,,113.22,,***,,,high,"Log number of rooms in the house (all rooms, not only bedrooms); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,elasticity,0.764,0.006747924394983219,b (log-log),|b/t|,0.7468141399751687,0.006596132661854519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),113.22,10165.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,7,lnroom,bedrooms,A_structural,log,count,0.765,,113.23,,***,,,high,"Log number of rooms in the house (all rooms, not only bedrooms); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",elasticity,0.765,0.006756160028261062,b (log-log),|b/t|,0.7468433104459178,0.006595807740403759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),113.23,10165.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,7,lnsize,living_area,A_structural,log,m2,0.0309,,11.21,,***,,,high,"Log residential building area in square metres; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,elasticity,0.0309,0.0027564674397859055,b (log-log),|b/t|,0.11050550442300827,0.009857761322302252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.21,10165.0,m2,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,7,lnsize,living_area,A_structural,log,m2,0.0308,,11.18,,***,,,high,"Log residential building area in square metres; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,elasticity,0.0308,0.0027549194991055457,b (log-log),|b/t|,0.11021336860715875,0.009858083059674307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.18,10165.0,m2,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,7,lnsize,living_area,A_structural,log,m2,0.031,,11.26,,***,,,high,"Log residential building area in square metres; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",elasticity,0.031,0.002753108348134991,b (log-log),|b/t|,0.11099233377015312,0.00985722324779335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.26,10165.0,m2,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,7,lntfloors,storeys,A_structural,log,count,0.124,,23.33,,***,,,high,"Log total number of floors of the building (excluding ground floor); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,elasticity,0.124,0.005315045006429491,b (log-log),|b/t|,0.22544178869138676,0.009663171396973286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.33,10165.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,7,lntfloors,storeys,A_structural,log,count,0.118,,22.23,,***,,,high,"Log total number of floors of the building (excluding ground floor); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,elasticity,0.118,0.005308142150247413,b (log-log),|b/t|,0.2153166971176818,0.009685861318834088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.23,10165.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,7,lntfloors,storeys,A_structural,log,count,0.118,,22.14,,***,,,high,"Log total number of floors of the building (excluding ground floor); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",elasticity,0.118,0.005329719963866305,b (log-log),|b/t|,0.21448515019237782,0.009687676160450668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.14,10165.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,7,lnbage,age,A_structural,log,years,-0.191,,-30.31,,***,,,high,"Log building age in years (built 2021 = 1 year); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,elasticity,-0.191,0.006301550643352029,b (log-log),|b/t|,-0.2879013037811516,0.009498558356356039,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.31,10165.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,7,lnbage,age,A_structural,log,years,-0.199,,-31.88,,***,,,high,"Log building age in years (built 2021 = 1 year); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,elasticity,-0.199,0.006242158092848181,b (log-log),|b/t|,-0.3014890162376341,0.009456995490515497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.88,10165.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,7,lnbage,age,A_structural,log,years,-0.198,,-31.08,,***,,,high,"Log building age in years (built 2021 = 1 year); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",elasticity,-0.198,0.006370656370656372,b (log-log),|b/t|,-0.2945876759634679,0.00947836795249253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.08,10165.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,7,lnorientation,sunlight,E_environment,other,ordinal_grade_1_4,0.0421,,8.57,,***,,,high,"Log of orientation grade: west 1, east 2, north 3, south 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,other,0.0421,0.004912485414235706,not harmonised,|b/t|,0.08469618003318767,0.009882868148563323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.57,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,7,lnorientation,sunlight,E_environment,other,ordinal_grade_1_4,0.0417,,8.47,,***,,,high,"Log of orientation grade: west 1, east 2, north 3, south 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,other,0.0417,0.004923258559622196,not harmonised,|b/t|,0.08371485990411875,0.0098836906616433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.47,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,7,lnorientation,sunlight,E_environment,other,ordinal_grade_1_4,0.0423,,8.6,,***,,,high,"Log of orientation grade: west 1, east 2, north 3, south 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",other,0.0423,0.00491860465116279,not harmonised,|b/t|,0.08499052815187251,0.009882619552543315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.6,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,7,lndecoration,condition,A_structural,other,ordinal_grade_1_3,0.08,,12.16,,***,,,high,"Log of decoration grade: vacant 1, minimally 2, exquisitely decorated 3; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,other,0.08,0.006578947368421052,not harmonised,|b/t|,0.1197412799023221,0.009847144728809383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.16,10165.0,ordinal_grade_1_3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,7,lndecoration,condition,A_structural,other,ordinal_grade_1_3,0.0788,,11.99,,***,,,high,"Log of decoration grade: vacant 1, minimally 2, exquisitely decorated 3; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,other,0.0788,0.006572143452877397,not harmonised,|b/t|,0.11809077331014045,0.009849105363648077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.99,10165.0,ordinal_grade_1_3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,7,lndecoration,condition,A_structural,other,ordinal_grade_1_3,0.0775,,11.75,,***,,,high,"Log of decoration grade: vacant 1, minimally 2, exquisitely decorated 3; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",other,0.0775,0.006595744680851064,not harmonised,|b/t|,0.11575898214862562,0.009851828267968137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.75,10165.0,ordinal_grade_1_3,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,8,lnfloorno,floor_level,A_structural,other,ordinal_grade_1_4,0.0044,,0.56,,,,,high,"Log of floor-level grade: basement 1, lower floors 2, high-rise 3, middle 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,other,0.0044,0.007857142857142858,not harmonised,|b/t|,0.005554278298871561,0.009918354105127787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,8,lnfloorno,floor_level,A_structural,other,ordinal_grade_1_4,0.00482,,0.61,,,,,high,"Log of floor-level grade: basement 1, lower floors 2, high-rise 3, middle 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,other,0.00482,0.007901639344262294,not harmonised,|b/t|,0.006050178595174693,0.009918325565860153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.61,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,lnfloorno,floor_level,A_structural,other,ordinal_grade_1_4,0.00556,,0.7,,,,,high,"Log of floor-level grade: basement 1, lower floors 2, high-rise 3, middle 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",other,0.00556,0.007942857142857144,not harmonised,|b/t|,0.00694278763430546,0.0099182680490078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,8,lnstructure,other,other,other,ordinal_grade_1_4,0.164,,14.45,,***,,,high,"Log of building structure grade: single-storey 1, tower 2, slab-tower 3, slab 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,other,0.164,0.011349480968858133,not harmonised,|b/t|,0.1418727083016507,0.009818180505304546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.45,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,False,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,8,lnstructure,other,other,other,ordinal_grade_1_4,0.161,,14.14,,***,,,high,"Log of building structure grade: single-storey 1, tower 2, slab-tower 3, slab 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,other,0.161,0.011386138613861386,not harmonised,|b/t|,0.13888841496132223,0.009822377295708786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.14,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,False,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,lnstructure,other,other,other,ordinal_grade_1_4,0.164,,14.42,,***,,,high,"Log of building structure grade: single-storey 1, tower 2, slab-tower 3, slab 4; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",other,0.164,0.011373092926490986,not harmonised,|b/t|,0.14158407337715026,0.00981859038676493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.42,10165.0,ordinal_grade_1_4,nonstandard_x_transform,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,False,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,8,subway,transit_distance,D_location,dummy,binary,0.0124,,2.04,,*,,,high,"1 if a subway station within 1 km of the community; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,pct_premium,1.2458494767417339,0.6154273168922415,100*(exp(b - se^2/2) - 1),|b/t|,0.02022961385564631,0.009916477380218778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.04,10165.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,8,subway,transit_distance,D_location,dummy,binary,0.0123,,2.04,,*,,,high,"1 if a subway station within 1 km of the community; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,pct_premium,1.2357554408119364,0.6104031500017993,100*(exp(b - se^2/2) - 1),|b/t|,0.02022961385564631,0.009916477380218778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.04,10165.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,subway,transit_distance,D_location,dummy,binary,0.012,,1.99,,*,,,high,"1 if a subway station within 1 km of the community; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",pct_premium,1.2053888183677808,0.6102948475574338,100*(exp(b - se^2/2) - 1),|b/t|,0.01973398550018569,0.009916575628234016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.99,10165.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,8,lncbd2,cbd_distance,D_location,log,km,-0.0487,,-11.28,,***,,,high,"Log Euclidean distance (km) from community to Saigao New Block (Weiyang District CBD); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,elasticity,-0.0487,0.004317375886524823,b (log-log),|b/t|,-0.11118704315909769,0.009857007372260434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.28,10165.0,km,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,8,lncbd2,cbd_distance,D_location,log,km,-0.0413,,-8.07,,***,,,high,"Log Euclidean distance (km) from community to Saigao New Block (Weiyang District CBD); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,elasticity,-0.0413,0.0051177199504337054,b (log-log),|b/t|,-0.07978717100904319,0.009886886122558015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.07,10165.0,km,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,lncbd2,cbd_distance,D_location,log,km,-0.0684,,-17.63,,***,,,high,"Log Euclidean distance (km) from community to Saigao New Block (Weiyang District CBD); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",elasticity,-0.0684,0.0038797504254112314,b (log-log),|b/t|,-0.17224965580750795,0.009770258412223936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.63,10165.0,km,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c1,5,1,8,lndshop,commercial_distance,D_location,log,km,-0.0239,,-5.63,,***,,,high,"Log Euclidean distance (km) to nearest shopping mall; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,All transactions; mall proximity = ln distance to nearest mall,elasticity,-0.0239,0.004245115452930728,b (log-log),|b/t|,-0.05575433495662941,0.009903079033149097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.63,10165.0,km,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c2,5,2,8,shop3km,commercial_distance,D_location,level,count,0.00478,,5.98,,***,,,high,"Number of shopping malls within 3 km of the flat (mean 7.0); stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.686,t,All transactions; mall proximity = number of malls within 3 km,semi_elasticity,0.47800000000000004,0.07993311036789297,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.05920861613899275,0.009901106377757984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.98,10165.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,False,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,bshop500m,commercial_distance,D_location,dummy,binary,-0.0393,,-3.86,,***,,,high,"1 if a shopping mall within 500 m; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",pct_premium,-3.858760400595085,0.9788980707066692,100*(exp(b - se^2/2) - 1),|b/t|,-0.03825740929867575,0.009911245932299417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.86,10165.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,bshop800m,commercial_distance,D_location,dummy,binary,0.0553,,5.01,,***,,,high,"1 if a shopping mall within 800 m; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",pct_premium,5.679324483893056,1.1665514297368575,100*(exp(b - se^2/2) - 1),|b/t|,0.04963048288821304,0.00990628400962336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.01,10165.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3,6e83e47fe67686f424f9c68dca8dac1370a653d3:T5-c3,5,3,8,bshop1km,commercial_distance,D_location,dummy,binary,-0.0334,,-3.74,,***,,,high,"1 if a shopping mall within 1 km; stars: * p<0.05, ** p<0.01, *** p<0.001",ln total transaction price (tprice),log,OLS,10175.0,0.685,t,"All transactions; mall proximity = binary indicators within 500 m, 800 m and 1 km",pct_premium,-3.2886944534556095,0.8637129454555059,100*(exp(b - se^2/2) - 1),|b/t|,-0.03706972033078823,0.009911689928018242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.74,10165.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,conference,False,False,unknown,,2019.0,ols,other,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T6-c1,6,1,15,Building age,age,A_structural,level,years,-0.009,,-87.388,0.001,***,,,high,p printed as <0.001,log of housing unit price (per ping),log,OLS,108671.0,0.533,t,"Full sample, baseline; t values and p values printed in separate columns (classical SE)",semi_elasticity,-0.8999999999999999,0.010298896873712635,100*b/1 (per year),|b/t|,-0.2562514355403295,0.0029323412315229716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-87.388,108661.0,years,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T7-c2,7,2,16,Building age,age,A_structural,level,years,-0.009,,,,***,,,high,"stars only, no SE printed",log of housing unit price (per ping),log,OLS,107518.0,0.557,none,Model 2: observations with |standardized residual| > 3 removed; stars only,semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,107508.0,years,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c1,8,1,17,Building age,age,A_structural,level,years,-0.008,0.0,,,***,,,medium,SE printed as 0.000 (rounded),log of housing unit price (per ping),log,OLS,27708.0,0.591,se,Small units (< 25 ping); HC1 robust SE,semi_elasticity,-0.8,,100*b/1 (per year),,,,,,27698.0,years,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c2,8,2,17,Building age,age,A_structural,level,years,-0.01,0.0,,,***,,,medium,SE printed as 0.000 (rounded),log of housing unit price (per ping),log,OLS,80963.0,0.526,se,Large units (>= 25 ping); HC1 robust SE,semi_elasticity,-1.0,,100*b/1 (per year),,,,,,80953.0,years,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T6-c1,6,1,15,Floor area,living_area,A_structural,level,ping (1 ping = 3.306 m2),-0.001,,-19.028,0.001,***,,,high,"Dependent variable is price per ping, so this is the effect of size on unit price; p printed as <0.001",log of housing unit price (per ping),log,OLS,108671.0,0.533,t,"Full sample, baseline; t values and p values printed in separate columns (classical SE)",semi_elasticity,-0.03024986387561256,0.0015897553014301326,100*b/3.3058 (X per pyeong->m2),|b/t|,-0.0576280533450121,0.0030285922506312858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.028,108661.0,pyeong,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,True
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T7-c2,7,2,16,Floor area,living_area,A_structural,level,ping (1 ping = 3.306 m2),-0.001,,,,***,,,high,"Dependent variable is price per ping, so this is the effect of size on unit price; stars only, no SE printed",log of housing unit price (per ping),log,OLS,107518.0,0.557,none,Model 2: observations with |standardized residual| > 3 removed; stars only,semi_elasticity,-0.03024986387561256,,100*b/3.3058 (X per pyeong->m2),,,,,,107508.0,pyeong,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c1,8,1,17,Floor area,living_area,A_structural,level,ping (1 ping = 3.306 m2),-0.003,0.0,,,***,,,medium,"Dependent variable is price per ping, so this is the effect of size on unit price; SE printed as 0.000 (rounded)",log of housing unit price (per ping),log,OLS,27708.0,0.591,se,Small units (< 25 ping); HC1 robust SE,semi_elasticity,-0.09074959162683767,,100*b/3.3058 (X per pyeong->m2),,,,,,27698.0,pyeong,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c2,8,2,17,Floor area,living_area,A_structural,level,ping (1 ping = 3.306 m2),-0.0,0.0,,,***,,,medium,"Dependent variable is price per ping, so this is the effect of size on unit price; SE printed as 0.000 (rounded); coefficient printed as -0.000",log of housing unit price (per ping),log,OLS,80963.0,0.526,se,Large units (>= 25 ping); HC1 robust SE,semi_elasticity,-0.0,,100*b/3.3058 (X per pyeong->m2),,,,,,80953.0,pyeong,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T6-c1,6,1,15,Floor level,floor_level,A_structural,level,count,0.006,,32.799,0.001,***,,,high,p printed as <0.001,log of housing unit price (per ping),log,OLS,108671.0,0.533,t,"Full sample, baseline; t values and p values printed in separate columns (classical SE)",semi_elasticity,0.6,0.018293240647580717,100*b/1 (per unit count),|b/t|,0.09901124183364539,0.0030187274561311443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.799,108661.0,count,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T7-c2,7,2,16,Floor level,floor_level,A_structural,level,count,0.006,,,,***,,,high,"stars only, no SE printed",log of housing unit price (per ping),log,OLS,107518.0,0.557,none,Model 2: observations with |standardized residual| > 3 removed; stars only,semi_elasticity,0.6,,100*b/1 (per unit count),,,,,,107508.0,count,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c1,8,1,17,Floor level,floor_level,A_structural,level,count,0.004,0.0,,,***,,,medium,SE printed as 0.000 (rounded),log of housing unit price (per ping),log,OLS,27708.0,0.591,se,Small units (< 25 ping); HC1 robust SE,semi_elasticity,0.4,,100*b/1 (per unit count),,,,,,27698.0,count,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c2,8,2,17,Floor level,floor_level,A_structural,level,count,0.005,0.0,,,***,,,medium,SE printed as 0.000 (rounded),log of housing unit price (per ping),log,OLS,80963.0,0.526,se,Large units (>= 25 ping); HC1 robust SE,semi_elasticity,0.5,,100*b/1 (per unit count),,,,,,80953.0,count,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T6-c1,6,1,15,Has parking,parking,A_structural,binary,binary,-0.08,,-30.929,0.001,***,,,high,p printed as <0.001,log of housing unit price (per ping),log,OLS,108671.0,0.533,t,"Full sample, baseline; t values and p values printed in separate columns (classical SE)",pct_premium,-7.688674158950592,0.23877043457897398,100*(exp(b - se^2/2) - 1),|b/t|,-0.09341695949647111,0.0030203679231941257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.929,108661.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T7-c2,7,2,16,Has parking,parking,A_structural,binary,binary,-0.078,,,,***,,,high,"stars only, no SE printed",log of housing unit price (per ping),log,OLS,107518.0,0.557,none,Model 2: observations with |standardized residual| > 3 removed; stars only,pct_premium,-7.5035573456460725,,100*(exp(b) - 1),,,,,,107508.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c1,8,1,17,Has parking,parking,A_structural,binary,binary,-0.015,0.009,,,*,,,high,HC1 robust SE,log of housing unit price (per ping),log,OLS,27708.0,0.591,se,Small units (< 25 ping); HC1 robust SE,pct_premium,-1.492795662258728,0.8866007456427563,100*(exp(b - se^2/2) - 1),reported,-0.010013888881664363,0.006008333328998618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6666666666666667,27698.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c2,8,2,17,Has parking,parking,A_structural,binary,binary,-0.076,0.003,,,***,,,high,HC1 robust SE,log of housing unit price (per ping),log,OLS,80963.0,0.526,se,Large units (>= 25 ping); HC1 robust SE,pct_premium,-7.318796410416328,0.27804486196781464,100*(exp(b - se^2/2) - 1),reported,-0.08868724326235537,0.0035008122340403442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.333333333333332,80953.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T6-c1,6,1,15,Has management,other,other,binary,binary,-0.116,,-27.854,0.001,***,,,high,Building has a management organization (condominium management committee); p printed as <0.001,log of housing unit price (per ping),log,OLS,108671.0,0.533,t,"Full sample, baseline; t values and p values printed in separate columns (classical SE)",pct_premium,-10.953249872030835,0.37084485496699515,100*(exp(b - se^2/2) - 1),|b/t|,-0.08419877801355367,0.003022861277143451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-27.854,108661.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T7-c2,7,2,16,Has management,other,other,binary,binary,-0.115,,,,***,,,high,"Building has a management organization (condominium management committee); stars only, no SE printed",log of housing unit price (per ping),log,OLS,107518.0,0.557,none,Model 2: observations with |standardized residual| > 3 removed; stars only,pct_premium,-10.863385609316866,,100*(exp(b) - 1),,,,,,107508.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c1,8,1,17,Has management,other,other,binary,binary,-0.094,0.011,,,***,,,high,Building has a management organization (condominium management committee); HC1 robust SE,log of housing unit price (per ping),log,OLS,27708.0,0.591,se,Small units (< 25 ping); HC1 robust SE,pct_premium,-8.9772308200451,1.0013110384648436,100*(exp(b - se^2/2) - 1),reported,-0.05127896079999664,0.006000729455318755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.545454545454547,27698.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c2,8,2,17,Has management,other,other,binary,binary,-0.114,0.005,,,***,,,high,Building has a management organization (condominium management committee); HC1 robust SE,log of housing unit price (per ping),log,OLS,80963.0,0.526,se,Large units (>= 25 ping); HC1 robust SE,pct_premium,-10.77531972723328,0.44612897794120415,100*(exp(b - se^2/2) - 1),reported,-0.07987822750783771,0.0035034310310455134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.8,80953.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T6-c1,6,1,15,Elevator building,accessibility,B_interior,binary,binary,0.15,,33.041,0.001,***,,,high,Elevator building vs walk-up apartment; p printed as <0.001,log of housing unit price (per ping),log,OLS,108671.0,0.533,t,"Full sample, baseline; t values and p values printed in separate columns (classical SE)",pct_premium,16.182227013881413,0.5274511558646604,100*(exp(b - se^2/2) - 1),|b/t|,0.09973453364074052,0.003018508327252218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.041,108661.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,True
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T7-c2,7,2,16,Elevator building,accessibility,B_interior,binary,binary,0.149,,,,***,,,high,"Elevator building vs walk-up apartment; stars only, no SE printed",log of housing unit price (per ping),log,OLS,107518.0,0.557,none,Model 2: observations with |standardized residual| > 3 removed; stars only,pct_premium,16.067298920908545,,100*(exp(b) - 1),,,,,,107508.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,False,False,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c1,8,1,17,Elevator building,accessibility,B_interior,binary,binary,0.13,0.013,,,***,,,high,Elevator building vs walk-up apartment; HC1 robust SE,log of housing unit price (per ping),log,OLS,27708.0,0.591,se,Small units (< 25 ping); HC1 robust SE,pct_premium,13.87321563918762,1.4804768983220082,100*(exp(b - se^2/2) - 1),reported,0.059978171917410325,0.005997817191741033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,27698.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620,6edd1ccf2791c69eb6ab0393c5323dfff21ae620:T8-c2,8,2,17,Elevator building,accessibility,B_interior,binary,binary,0.149,0.006,,,***,,,high,Elevator building vs walk-up apartment; HC1 robust SE,log of housing unit price (per ping),log,OLS,80963.0,0.526,se,Large units (>= 25 ping); HC1 robust SE,pct_premium,16.065209728330764,0.6964037935254512,100*(exp(b - se^2/2) - 1),reported,0.08695020300420381,0.0035013504565451206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.833333333333332,80953.0,binary,,2026.0,Taiwan,Asia,High income,2021.0,2024.0,mixed,transaction,journal,True,True,robust,,2022.5,ols,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Higher net residential density,density,F_neighbourhood,log,per_km2,-0.003,0.001,,,***,,,high,"ln(1+net residential density per km2) at census-tract level; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,-0.003,0.001,b (log-log),reported,-0.011236033862680541,0.0037453446208935135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Higher net residential density,density,F_neighbourhood,log,per_km2,-0.01,0.001,,,***,,,high,"ln(1+net residential density per km2) at census-tract level; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.01,0.001,b (log-log),reported,-0.04953713264795739,0.0049537132647957385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Higher net residential density,density,F_neighbourhood,log,per_km2,-0.004,0.001,,,**,,,high,"ln(1+net residential density per km2) at census-tract level; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.004,0.001,b (log-log),reported,-0.02285378499664151,0.005713446249160378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Higher net residential density,density,F_neighbourhood,log,per_km2,0.001,0.001,,,,,,high,"ln(1+net residential density per km2) at census-tract level; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.001,0.001,b (log-log),reported,0.0037455547914624562,0.0037455547914624562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Higher net residential density,density,F_neighbourhood,log,per_km2,-0.009,0.001,,,***,,,high,"ln(1+net residential density per km2) at census-tract level; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,-0.009,0.001,b (log-log),reported,-0.033691092088245736,0.003743454676471749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Higher intersection density,walkability,D_location,log,per_km2,-0.006,0.003,,,,,,high,"ln(1+four-way intersection density per km2) within 1,200 m network buffer; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,-0.006,0.003,b (log-log),reported,-0.007490951946705858,0.0037454759733529294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Higher intersection density,walkability,D_location,log,per_km2,0.001,0.004,,,,,,high,"ln(1+four-way intersection density per km2) within 1,200 m network buffer; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.001,0.004,b (log-log),reported,0.0012399496719601717,0.004959798687840687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Higher intersection density,walkability,D_location,log,per_km2,-0.099,0.004,,,***,,,high,"ln(1+four-way intersection density per km2) within 1,200 m network buffer; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.099,0.004,b (log-log),reported,-0.14005070521928892,0.005658614352294503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.75,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Higher intersection density,walkability,D_location,log,per_km2,-0.006,0.003,,,,,,high,"ln(1+four-way intersection density per km2) within 1,200 m network buffer; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,-0.006,0.003,b (log-log),reported,-0.007490951946705858,0.0037454759733529294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Higher intersection density,walkability,D_location,log,per_km2,-0.01,0.004,,,**,,,high,"ln(1+four-way intersection density per km2) within 1,200 m network buffer; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,-0.01,0.004,b (log-log),reported,-0.00936354215759,0.0037454168630360004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Shorter time distance to the nearest station,transit_distance,D_location,level,minutes,-0.013,0.0,,,***,,,high,"Walking time (minutes) to nearest railway station; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,semi_elasticity,-1.3,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,71279.0,minutes,unit_not_harmonisable,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Shorter time distance to the nearest station,transit_distance,D_location,level,minutes,-0.015,0.0,,,***,,,high,"Walking time (minutes) to nearest railway station; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,semi_elasticity,-1.5,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,40651.0,minutes,unit_not_harmonisable,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Shorter time distance to the nearest station,transit_distance,D_location,level,minutes,-0.009,0.0,,,***,,,high,"Walking time (minutes) to nearest railway station; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,semi_elasticity,-0.8999999999999999,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,30618.0,minutes,unit_not_harmonisable,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Shorter time distance to the nearest station,transit_distance,D_location,level,minutes,-0.01,0.0,,,***,,,high,"Walking time (minutes) to nearest railway station; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,semi_elasticity,-1.0,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,71279.0,minutes,unit_not_harmonisable,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Shorter time distance to the nearest station,transit_distance,D_location,level,minutes,-0.014,0.0,,,***,,,high,"Walking time (minutes) to nearest railway station; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,semi_elasticity,-1.4000000000000001,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,71279.0,minutes,unit_not_harmonisable,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Bars,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.077,0.003,,,***,,,high,"ln(1+density of bars per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,-0.077,0.003,b (log-log),reported,-0.09569537782958157,0.003728391344009672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.666666666666664,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Bars,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.086,0.004,,,***,,,high,"ln(1+density of bars per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.086,0.004,b (log-log),reported,-0.1060345885156302,0.004931841326308382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.499999999999996,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Bars,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.049,0.003,,,***,,,high,"ln(1+density of bars per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.049,0.003,b (log-log),reported,-0.09293998218256713,0.005690202990769416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.333333333333332,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Bars,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.068,0.002,,,***,,,high,"ln(1+density of bars per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,-0.068,0.002,b (log-log),reported,-0.12632947311258536,0.0037155727386054523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Bars,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.087,0.003,,,***,,,high,"ln(1+density of bars per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,-0.087,0.003,b (log-log),reported,-0.10798666748707428,0.0037236781892094587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.999999999999996,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Cafés,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.104,0.003,,,***,,,high,"ln(1+density of cafés per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.104,0.003,b (log-log),reported,0.12876583904177072,0.0037143992031280024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.666666666666664,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Cafés,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.095,0.004,,,***,,,high,"ln(1+density of cafés per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.095,0.004,b (log-log),reported,0.11698646892201965,0.0049257460598745115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.75,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Cafés,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.104,0.004,,,***,,,high,"ln(1+density of cafés per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.104,0.004,b (log-log),reported,0.1469747766399546,0.005652876024613639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Cafés,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.082,0.003,,,***,,,high,"ln(1+density of cafés per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.082,0.003,b (log-log),reported,0.1018468530483572,0.003726104379817946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.333333333333332,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Cafés,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.148,0.004,,,***,,,high,"ln(1+density of cafés per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.148,0.004,b (log-log),reported,0.13727450701569202,0.0037101218112349193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Restaurants,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.014,0.004,,,***,,,high,"ln(1+density of restaurants per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.014,0.004,b (log-log),reported,0.01310840737221821,0.0037452592492052033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Restaurants,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.048,0.006,,,***,,,high,"ln(1+density of restaurants per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.048,0.006,b (log-log),reported,0.039647222441540515,0.004955902805192565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Restaurants,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.006,0.004,,,,,,high,"ln(1+density of restaurants per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.006,0.004,b (log-log),reported,-0.00857209337179556,0.005714728914530375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Restaurants,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.001,0.003,,,,,,high,"ln(1+density of restaurants per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,-0.001,0.003,b (log-log),reported,-0.001248526048643995,0.003745578145931986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Restaurants,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.019,0.005,,,***,,,high,"ln(1+density of restaurants per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.019,0.005,b (log-log),reported,0.014231766555431718,0.0037452017251136103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Hobby classes,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.032,0.003,,,***,,,high,"ln(1+density of hobby classes per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.032,0.003,b (log-log),reported,0.0399210158112802,0.0037425952323075187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.666666666666666,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Hobby classes,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.074,0.004,,,***,,,high,"ln(1+density of hobby classes per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.074,0.004,b (log-log),reported,0.0913725095172389,0.0049390545684994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.5,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Hobby classes,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.018,0.004,,,***,,,high,"ln(1+density of hobby classes per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.018,0.004,b (log-log),reported,0.02570872483645304,0.005713049963656231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Hobby classes,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.028,0.003,,,***,,,high,"ln(1+density of hobby classes per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.028,0.003,b (log-log),reported,0.0349374143646734,0.0037432943962150077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.333333333333334,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Hobby classes,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.045,0.003,,,***,,,high,"ln(1+density of hobby classes per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.045,0.003,b (log-log),reported,0.05609525032454107,0.0037396833549694047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Cultural institutions,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.06,0.003,,,***,,,high,"ln(1+density of cultural institutions per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,-0.06,0.003,b (log-log),reported,-0.0747023091671727,0.0037351154583586343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Cultural institutions,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.05,0.004,,,***,,,high,"ln(1+density of cultural institutions per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.05,0.004,b (log-log),reported,-0.06187872387696933,0.004950297910157547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.5,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Cultural institutions,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.029,0.004,,,***,,,high,"ln(1+density of cultural institutions per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.029,0.004,b (log-log),reported,-0.04139778798872427,0.005710039722582658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.25,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Cultural institutions,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.04,0.003,,,***,,,high,"ln(1+density of cultural institutions per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,-0.04,0.003,b (log-log),reported,-0.049878917813824294,0.003740918836036822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.333333333333334,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Cultural institutions,neighbourhood_amenities,F_neighbourhood,log,per_km2,-0.081,0.004,,,***,,,high,"ln(1+density of cultural institutions per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,-0.081,0.004,b (log-log),reported,-0.07563077958708246,0.0037348533129423437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.25,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Parks,green_space,E_environment,log,per_km2,0.052,0.005,,,***,,,high,"ln(1+density of parks per km2) within 1,200 m; density not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.052,0.005,b (log-log),reported,0.038924521898753096,0.0037427424902647215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.399999999999999,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Parks,green_space,E_environment,log,per_km2,0.023,0.005,,,***,,,high,"ln(1+density of parks per km2) within 1,200 m; density not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.023,0.005,b (log-log),reported,0.022809155868500495,0.004958512145326195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Parks,green_space,E_environment,log,per_km2,0.043,0.011,,,***,,,high,"ln(1+density of parks per km2) within 1,200 m; density not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.043,0.011,b (log-log),reported,0.02233464290105564,0.005713513300270048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.909090909090909,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Parks,green_space,E_environment,log,per_km2,0.057,0.004,,,***,,,high,"ln(1+density of parks per km2) within 1,200 m; density not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.057,0.004,b (log-log),reported,0.053298664477777986,0.0037402571563352974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.25,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Parks,green_space,E_environment,log,per_km2,0.047,0.006,,,***,,,high,"ln(1+density of parks per km2) within 1,200 m; density not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.047,0.006,b (log-log),reported,0.02932776420384673,0.003743969898363412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.833333333333333,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Gyms and fitness clubs,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.063,0.003,,,***,,,high,"ln(1+density of gyms per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.063,0.003,b (log-log),reported,0.07841500135341449,0.0037340476834959277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Gyms and fitness clubs,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.07,0.003,,,***,,,high,"ln(1+density of gyms per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.07,0.003,b (log-log),reported,0.114961438371154,0.004926918787335171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.333333333333336,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Gyms and fitness clubs,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.045,0.003,,,***,,,high,"ln(1+density of gyms per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.045,0.003,b (log-log),reported,0.08541083194193988,0.0056940554627959925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Gyms and fitness clubs,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.043,0.003,,,***,,,high,"ln(1+density of gyms per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.043,0.003,b (log-log),reported,0.0536094593871932,0.0037401948409669676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.333333333333332,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Gyms and fitness clubs,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.082,0.003,,,***,,,high,"ln(1+density of gyms per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.082,0.003,b (log-log),reported,0.1018468530483572,0.003726104379817946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.333333333333332,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Convenience stores,commercial_distance,D_location,log,per_km2,0.092,0.004,,,***,,,high,"ln(1+density of convenience stores per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.092,0.004,b (log-log),reported,0.085830456141314,0.003731758962665826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Convenience stores,commercial_distance,D_location,log,per_km2,0.067,0.006,,,***,,,high,"ln(1+density of convenience stores per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.067,0.006,b (log-log),reported,0.05529971196396517,0.004952213011698373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.166666666666668,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Convenience stores,commercial_distance,D_location,log,per_km2,0.05,0.005,,,***,,,high,"ln(1+density of convenience stores per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.05,0.005,b (log-log),reported,0.05705629040480525,0.005705629040480525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Convenience stores,commercial_distance,D_location,log,per_km2,0.108,0.004,,,***,,,high,"ln(1+density of convenience stores per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.108,0.004,b (log-log),reported,0.10061746928335115,0.003726572936420413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,True
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Convenience stores,commercial_distance,D_location,log,per_km2,0.094,0.005,,,***,,,high,"ln(1+density of convenience stores per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.094,0.005,b (log-log),reported,0.07024298793317335,0.0037363291453815606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.8,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Schools,school_distance,D_location,log,per_km2,0.144,0.004,,,***,,,high,"ln(1+density of schools per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.144,0.004,b (log-log),reported,0.13363154159114599,0.003711987266420722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Schools,school_distance,D_location,log,per_km2,0.099,0.005,,,***,,,high,"ln(1+density of schools per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.099,0.005,b (log-log),reported,0.0977339451215354,0.00493605783442098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.8,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Schools,school_distance,D_location,log,per_km2,0.114,0.005,,,***,,,high,"ln(1+density of schools per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.114,0.005,b (log-log),reported,0.12920835746685017,0.005667033222230271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.8,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Schools,school_distance,D_location,log,per_km2,0.167,0.004,,,***,,,high,"ln(1+density of schools per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.167,0.004,b (log-log),reported,0.15450033738090313,0.0037006068833749257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.75,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,True
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Schools,school_distance,D_location,log,per_km2,0.142,0.004,,,***,,,high,"ln(1+density of schools per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.142,0.004,b (log-log),reported,0.13180801586701318,0.0037129018554088226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.49999999999999,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Childcare facilities,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.063,0.003,,,***,,,high,"ln(1+density of childcare facilities per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.063,0.003,b (log-log),reported,0.07841500135341449,0.0037340476834959277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Childcare facilities,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.052,0.005,,,***,,,high,"ln(1+density of childcare facilities per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.052,0.005,b (log-log),reported,0.05151346067186511,0.004953217372294723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.399999999999999,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Childcare facilities,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.059,0.004,,,***,,,high,"ln(1+density of childcare facilities per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.059,0.004,b (log-log),reported,0.08399744628480181,0.005694742121003513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.749999999999998,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Childcare facilities,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.048,0.003,,,***,,,high,"ln(1+density of childcare facilities per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.048,0.003,b (log-log),reported,0.05982196741164862,0.0037388729632280384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Childcare facilities,neighbourhood_amenities,F_neighbourhood,log,per_km2,0.059,0.004,,,***,,,high,"ln(1+density of childcare facilities per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.059,0.004,b (log-log),reported,0.05516319846486195,0.0037398778620245394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.749999999999998,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Retail shops,commercial_distance,D_location,log,per_km2,0.036,0.003,,,***,,,high,"ln(1+density of retail shops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.036,0.003,b (log-log),reported,0.04490163983939006,0.003741803319949172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.999999999999998,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Retail shops,commercial_distance,D_location,log,per_km2,0.042,0.003,,,***,,,high,"ln(1+density of retail shops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.042,0.003,b (log-log),reported,0.06927044107629034,0.004947888648306453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.0,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Retail shops,commercial_distance,D_location,log,per_km2,0.015,0.003,,,***,,,high,"ln(1+density of retail shops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.015,0.003,b (log-log),reported,0.028563035769473815,0.005712607153894763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Retail shops,commercial_distance,D_location,log,per_km2,0.033,0.003,,,***,,,high,"ln(1+density of retail shops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.033,0.003,b (log-log),reported,0.04116646537108159,0.0037424059428255987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Retail shops,commercial_distance,D_location,log,per_km2,0.035,0.003,,,***,,,high,"ln(1+density of retail shops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.035,0.003,b (log-log),reported,0.04365678314529263,0.0037420099838822245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.666666666666668,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Welfare facilities for older adults,public_services,F_neighbourhood,log,per_km2,0.02,0.003,,,***,,,high,"ln(1+density of welfare facilities for older adults per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.02,0.003,b (log-log),reported,0.02496275915964519,0.003744413873946778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.666666666666667,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Welfare facilities for older adults,public_services,F_neighbourhood,log,per_km2,0.012,0.004,,,**,,,high,"ln(1+density of welfare facilities for older adults per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.012,0.004,b (log-log),reported,0.014877760648974864,0.004959253549658288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Welfare facilities for older adults,public_services,F_neighbourhood,log,per_km2,0.005,0.003,,,***,,,high,"ln(1+density of welfare facilities for older adults per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.005,0.003,b (log-log),reported,0.009524466108276916,0.005714679664966149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Welfare facilities for older adults,public_services,F_neighbourhood,log,per_km2,0.028,0.003,,,***,,,high,"ln(1+density of welfare facilities for older adults per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.028,0.003,b (log-log),reported,0.0349374143646734,0.0037432943962150077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.333333333333334,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Welfare facilities for older adults,public_services,F_neighbourhood,log,per_km2,0.015,0.003,,,***,,,high,"ln(1+density of welfare facilities for older adults per km2) within 1,200 m; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.015,0.003,b (log-log),reported,0.01872462192935807,0.0037449243858716135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Hospitals,hospital_distance,D_location,log,per_km2,0.028,0.005,,,***,,,high,"ln(1+density of hospitals per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.028,0.005,b (log-log),reported,0.020970641337717085,0.0037447573817351943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Hospitals,hospital_distance,D_location,log,per_km2,0.017,0.006,,,**,,,high,"ln(1+density of hospitals per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.017,0.006,b (log-log),reported,0.014051386383218874,0.0049593128411360736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8333333333333335,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Hospitals,hospital_distance,D_location,log,per_km2,0.021,0.006,,,***,,,high,"ln(1+density of hospitals per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.021,0.006,b (log-log),reported,0.019998285934662397,0.0057137959813321135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Hospitals,hospital_distance,D_location,log,per_km2,0.018,0.004,,,***,,,high,"ln(1+density of hospitals per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.018,0.004,b (log-log),reported,0.016852721077709756,0.0037450491283799454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Hospitals,hospital_distance,D_location,log,per_km2,0.073,0.006,,,***,,,high,"ln(1+density of hospitals per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.073,0.006,b (log-log),reported,0.045523990120411695,0.0037416978181160298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.166666666666666,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,True
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Pharmacies,commercial_distance,D_location,log,per_km2,0.01,0.004,,,**,,,high,"ln(1+density of pharmacies per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.01,0.004,b (log-log),reported,0.00936354215759,0.0037454168630360004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Pharmacies,commercial_distance,D_location,log,per_km2,-0.016,0.005,,,***,,,high,"ln(1+density of pharmacies per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.016,0.005,b (log-log),reported,-0.015869369378237533,0.004959177930699229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Pharmacies,commercial_distance,D_location,log,per_km2,0.037,0.004,,,***,,,high,"ln(1+density of pharmacies per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.037,0.004,b (log-log),reported,0.052789475643591606,0.0057069703398477415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.25,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Pharmacies,commercial_distance,D_location,log,per_km2,0.029,0.004,,,***,,,high,"ln(1+density of pharmacies per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.029,0.004,b (log-log),reported,0.027145455778165198,0.003744200796988303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.25,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Pharmacies,commercial_distance,D_location,log,per_km2,-0.029,0.005,,,***,,,high,"ln(1+density of pharmacies per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,-0.029,0.005,b (log-log),reported,-0.02171924560305469,0.00374469751776805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Bus stops,transit_distance,D_location,log,per_km2,0.029,0.003,,,***,,,high,"ln(1+density of bus stops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.029,0.003,b (log-log),reported,0.03618357365664109,0.0037431283093076993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.666666666666666,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Bus stops,transit_distance,D_location,log,per_km2,-0.004,0.005,,,,,,high,"ln(1+density of bus stops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,-0.004,0.005,b (log-log),reported,-0.003967810766475637,0.004959763458094546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Bus stops,transit_distance,D_location,log,per_km2,0.122,0.004,,,***,,,high,"ln(1+density of bus stops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.122,0.004,b (log-log),reported,0.17171656903082813,0.005630051443633709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.5,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Bus stops,transit_distance,D_location,log,per_km2,0.061,0.004,,,***,,,high,"ln(1+density of bus stops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.061,0.004,b (log-log),reported,0.057027155552538636,0.0037394856100025336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.25,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Bus stops,transit_distance,D_location,log,per_km2,0.03,0.004,,,***,,,high,"ln(1+density of bus stops per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.03,0.004,b (log-log),reported,0.02808078016589886,0.0037441040221198483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Facility density: Stations,transit_distance,D_location,log,per_km2,0.056,0.005,,,***,,,high,"ln(1+density of railway stations per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,elasticity,0.056,0.005,b (log-log),reported,0.041913643382590605,0.0037422895877313044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.2,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Facility density: Stations,transit_distance,D_location,log,per_km2,0.009,0.006,,,,,,high,"ln(1+density of railway stations per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,elasticity,0.009,0.006,b (log-log),reported,0.007439497868694415,0.004959665245796277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4999999999999998,40651.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Facility density: Stations,transit_distance,D_location,log,per_km2,0.0,0.006,,,,,,high,"ln(1+density of railway stations per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,elasticity,0.0,0.006,b (log-log),reported,0.0,0.005714938887484885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,30618.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Facility density: Stations,transit_distance,D_location,log,per_km2,0.068,0.005,,,***,,,high,"ln(1+density of railway stations per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,elasticity,0.068,0.005,b (log-log),reported,0.050873939528383774,0.003740730847675277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.600000000000001,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Facility density: Stations,transit_distance,D_location,log,per_km2,0.062,0.006,,,***,,,high,"ln(1+density of railway stations per km2) within 1,200 m; density (access), not distance; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,elasticity,0.062,0.006,b (log-log),reported,0.03867538015741264,0.003742778724910901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.333333333333334,71279.0,per_km2,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,"Maximum flooding depth (0.0, 3.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.122,0.003,,,***,,,high,"Hazard-map maximum flooding depth between 0 and 3 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,pct_premium,-11.485561465606908,0.26554451055078815,100*(exp(b - se^2/2) - 1),reported,-0.1505834345976316,0.0037028713425647113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.666666666666664,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,"Maximum flooding depth (0.0, 3.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.118,0.005,,,***,,,high,"Hazard-map maximum flooding depth between 0 and 3 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,pct_premium,-11.131505601661118,0.44434802630730874,100*(exp(b - se^2/2) - 1),reported,-0.11625762465350402,0.004926170536165424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.599999999999998,40651.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,"Maximum flooding depth (0.0, 3.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.028,0.004,,,***,,,high,"Hazard-map maximum flooding depth between 0 and 3 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,pct_premium,-2.761941227457121,0.38895534672049875,100*(exp(b - se^2/2) - 1),reported,-0.03997259960861241,0.005710371372658915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,30618.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,"Maximum flooding depth (0.0, 3.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.078,0.004,,,***,,,high,"Hazard-map maximum flooding depth between 0 and 3 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,pct_premium,-7.504297314227426,0.3699857706174157,100*(exp(b - se^2/2) - 1),reported,-0.0728447877364425,0.0037356301403303847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.5,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,"Maximum flooding depth (0.0, 3.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.134,0.004,,,***,,,high,"Hazard-map maximum flooding depth between 0 and 3 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,pct_premium,-12.541693208919602,0.3498360258413336,100*(exp(b - se^2/2) - 1),reported,-0.12450069473675465,0.0037164386488583476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.5,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,"Maximum flooding depth [3.0, 5.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.188,0.005,,,***,,,high,"Hazard-map maximum flooding depth 3-5 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,pct_premium,-17.139565038642456,0.4143073536163403,100*(exp(b - se^2/2) - 1),reported,-0.1394576252920635,0.0037089793960655183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.6,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,"Maximum flooding depth [3.0, 5.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.248,0.005,,,***,,,high,"Hazard-map maximum flooding depth 3-5 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,pct_premium,-21.964981116029136,0.39017997163901713,100*(exp(b - se^2/2) - 1),reported,-0.23888386780369467,0.004816207012171263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-49.6,40651.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,"Maximum flooding depth [3.0, 5.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.006,0.005,,,,,,high,"Hazard-map maximum flooding depth 3-5 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,pct_premium,-0.5994461092958092,0.49700898202696764,100*(exp(b - se^2/2) - 1),reported,-0.006857765402553355,0.005714804502127796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,30618.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,"Maximum flooding depth [3.0, 5.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.114,0.003,,,***,,,high,"Hazard-map maximum flooding depth 3-5 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,pct_premium,-10.774605926935909,0.26767738676472247,100*(exp(b - se^2/2) - 1),reported,-0.14091191060298913,0.0037082081737628714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.0,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,"Maximum flooding depth [3.0, 5.0) m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.189,0.006,,,***,,,high,"Hazard-map maximum flooding depth 3-5 m; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,pct_premium,-17.22283933282862,0.4966719040168402,100*(exp(b - se^2/2) - 1),reported,-0.11717305969369665,0.003719779672815767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.5,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,"Maximum flooding depth [5.0, inf] m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.219,0.007,,,***,,,high,"Hazard-map maximum flooding depth 5 m or more; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,pct_premium,-19.66979629873733,0.5623252027075386,100*(exp(b - se^2/2) - 1),reported,-0.11638679788983075,0.0037201259599489284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.285714285714285,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,"Maximum flooding depth [5.0, inf] m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.213,0.007,,,***,,,high,"Hazard-map maximum flooding depth 5 m or more; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,pct_premium,-19.186366236632757,0.565709296051542,100*(exp(b - se^2/2) - 1),reported,-0.1492297878632147,0.004904265328838042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.428571428571427,40651.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,"Maximum flooding depth [5.0, inf] m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.085,0.01,,,***,,,high,"Hazard-map maximum flooding depth 5 m or more; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,pct_premium,-8.153364006464148,0.9185122844014574,100*(exp(b - se^2/2) - 1),reported,-0.048519767668567894,0.0057082079610079875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.5,30618.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,"Maximum flooding depth [5.0, inf] m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.113,0.005,,,***,,,high,"Hazard-map maximum flooding depth 5 m or more; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,pct_premium,-10.686050419745886,0.44657533005800776,100*(exp(b - se^2/2) - 1),reported,-0.08434846594285564,0.0037322330063210454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.6,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,"Maximum flooding depth [5.0, inf] m (ref: 0.0 m)",flood_risk,E_environment,dummy,binary,-0.278,0.016,,,***,,,high,"Hazard-map maximum flooding depth 5 m or more; reference no flood risk; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,pct_premium,-24.280010627530746,1.2116749144229368,100*(exp(b - se^2/2) - 1),reported,-0.06494209048152816,0.0037376742723181674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.375,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,True,True,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-ca,3,a,11,Located inside sediment-hazardous areas,other,other,dummy,binary,-0.026,0.005,,,***,,,high,"Designated sediment (landslide) disaster hazard area dummy; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.509,se,All samples (target area); 0.50 conditional quantile; pseudo R2,pct_premium,-2.567708950375114,0.4871675448043747,100*(exp(b - se^2/2) - 1),reported,-0.01947332824358021,0.003744870816073117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.199999999999999,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cb,3,b,11,Located inside sediment-hazardous areas,other,other,dummy,binary,-0.078,0.007,,,***,,,high,"Designated sediment (landslide) disaster hazard area dummy; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,40661.0,0.543,se,Central cities; 0.50 conditional quantile; pseudo R2,pct_premium,-7.505823480730833,0.6474750985804775,100*(exp(b - se^2/2) - 1),reported,-0.055182161556586076,0.004952245267898751,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.142857142857142,40651.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T3-cc,3,c,11,Located inside sediment-hazardous areas,other,other,dummy,binary,-0.008,0.009,,,,,,high,"Designated sediment (landslide) disaster hazard area dummy; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,30628.0,0.463,se,Other (non-central) cities; 0.50 conditional quantile; pseudo R2,pct_premium,-0.8008261641911041,0.8928287233533545,100*(exp(b - se^2/2) - 1),reported,-0.005079880132879691,0.005714865149489652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.888888888888889,30618.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-ca,4,a,12,Located inside sediment-hazardous areas,other,other,dummy,binary,-0.004,0.004,,,,,,high,"Designated sediment (landslide) disaster hazard area dummy; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.437,se,All samples; 0.15 conditional quantile; pseudo R2,pct_premium,-0.3999978688051131,0.39840319573759664,100*(exp(b - se^2/2) - 1),reported,-0.0037455547914624562,0.0037455547914624562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937,7059eb726206d6598f1ef88ae9a43a35a98c6937:T4-cc,4,c,12,Located inside sediment-hazardous areas,other,other,dummy,binary,-0.041,0.007,,,***,,,high,"Designated sediment (landslide) disaster hazard area dummy; stars: * p<0.05, ** p<0.01, *** p<0.001",log sale price (JPY) of single-family detached house,log,quantile,71289.0,0.531,se,All samples; 0.85 conditional quantile; pseudo R2,pct_premium,-4.01943855778184,0.6718803909634593,100*(exp(b - se^2/2) - 1),reported,-0.02193312588130243,0.003744680028515049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.857142857142858,71279.0,binary,,2023.0,Japan,Asia,High income,2015.0,2020.0,single_family,transaction,journal,True,False,unknown,,2017.5,quantile,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,Area,living_area,A_structural,level,m2,0.0032,0.0008,,,***,,,high,Size of the property in m2,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,0.32,0.08,100*b/1 (X per m2->m2),reported,0.13289109883701908,0.03322277470925477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,890.0,m2,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,Area,living_area,A_structural,level,m2,0.0104,0.0011,,,***,,,high,Size of the property in m2,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,1.04,0.11,100*b/1 (X per m2->m2),reported,0.23707679659554992,0.025075430409144703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.454545454545453,1501.0,m2,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,Age,age,A_structural,level,years,-0.0018,0.0025,,,,,,high,Age of property in years,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,-0.18,0.25,100*b/1 (per year),reported,-0.024127429081618888,0.03351031816891512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.72,890.0,years,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,Age,age,A_structural,level,years,-0.004,0.002,,,*,,,high,Age of property in years,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,-0.4,0.2,100*b/1 (per year),reported,-0.0515539262226467,0.025776963111323353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1501.0,years,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,State_p,condition,A_structural,level,index,0.3427,0.0368,,,***,,,high,"State of property, score 1 (worst) to 5 (best)","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,34.27,3.6799999999999997,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.29797556700521727,0.031997376322707896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.3125,890.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,State_p,condition,A_structural,level,index,0.238,0.0246,,,***,,,high,"State of property, score 1 (worst) to 5 (best)","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,23.799999999999997,2.46,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2422789816974007,0.02504228130149604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.674796747967479,1501.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,View,view,E_environment,level,index,0.1568,0.0379,,,***,,,high,"View quality score 1 (worst) to 5 (best), not a binary","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,15.68,3.7900000000000005,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.13736475852317379,0.03320232364813958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.137203166226913,890.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,View,view,E_environment,level,index,0.1489,0.0247,,,***,,,high,"View quality score 1 (worst) to 5 (best), not a binary","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,14.89,2.4699999999999998,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.15374912611618974,0.02550438828119467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0283400809716605,1501.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,F_right,other,other,dummy,binary,-0.6878,0.3477,,,*,,,high,Printed name F_right: full-right ownership dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-52.68049676852995,17.47820971653656,100*(exp(b - se^2/2) - 1),reported,-0.06616218556413045,0.03344662971888363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.978142076502732,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,F_right,other,other,dummy,binary,-0.3506,0.2494,,,,,,high,Printed name F_right: full-right ownership dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,-31.73002904492582,17.564379167863333,100*(exp(b - se^2/2) - 1),reported,-0.03626096944347073,0.025794311977186537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4057738572574179,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,P_right,other,other,dummy,binary,-0.6146,0.3457,,,,,,high,Printed name P_right: partial-right ownership dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-49.051468290621536,18.697433228539253,100*(exp(b - se^2/2) - 1),reported,-0.05948786304281404,0.033460713071755306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7778420595892392,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,P_right,other,other,dummy,binary,-0.3471,0.2478,,,,,,high,Printed name P_right: partial-right ownership dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,-31.463409811596232,17.51288465819387,100*(exp(b - se^2/2) - 1),reported,-0.03613094382583282,0.025794433535123514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.400726392251816,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,N_bed,bedrooms,A_structural,level,count,0.1361,0.0337,,,***,,,high,Number of bedrooms,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,13.61,3.37,100*b/1 (per unit count),reported,0.13414973403878797,0.03321709064737072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0385756676557865,890.0,count,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,N_bed,bedrooms,A_structural,level,count,-0.0201,0.0268,,,,,,high,Number of bedrooms,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,-2.01,2.68,100*b/1 (per unit count),reported,-0.01935483870967742,0.025806451612903226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,1501.0,count,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,N_bath,bathrooms,A_structural,level,count,0.06,0.053,,,,,,high,Number of bathrooms,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,6.0,5.3,100*b/1 (per unit count),reported,0.03791996358471805,0.03349596783316761,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1320754716981132,890.0,count,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,N_bath,bathrooms,A_structural,level,count,-0.0436,0.037,,,,,,high,Number of bathrooms,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,-4.36,3.6999999999999997,100*b/1 (per unit count),reported,-0.03040140317835012,0.025799355908232898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1783783783783783,1501.0,count,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,N_balcony,terrace_balcony,C_exterior,level,count,0.0491,0.0514,,,,,,high,Number of balconies,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",semi_elasticity,4.91,5.140000000000001,100*b/1 (per unit count),reported,0.03200374821190966,0.03350290546012539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9552529182879377,890.0,count,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,N_balcony,terrace_balcony,C_exterior,level,count,0.0229,0.0345,,,,,,high,Number of balconies,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",semi_elasticity,2.29,3.45,100*b/1 (per unit count),reported,0.017130195180799494,0.02580749928985077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6637681159420289,1501.0,count,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,Parking,parking,A_structural,dummy,binary,-0.0572,0.0692,,,,,,high,Parking dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-5.785333387324276,6.535283772547442,100*(exp(b - se^2/2) - 1),reported,-0.027696716884101212,0.033507216929716845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8265895953757226,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,Parking,parking,A_structural,dummy,binary,0.2003,0.0734,,,**,,,high,Parking dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,21.848248530559268,8.967786177238793,100*(exp(b - se^2/2) - 1),reported,0.07026190007486811,0.025747496083351577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7288828337874658,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,Elevator,accessibility,B_interior,dummy,binary,-0.0411,0.0799,,,,,,high,Elevator dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-4.332543707301772,7.668267883151981,100*(exp(b - se^2/2) - 1),reported,-0.017239929685718667,0.0335150944498521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5143929912390488,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,True
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,Elevator,accessibility,B_interior,dummy,binary,0.0432,0.042,,,,,,high,Elevator dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,4.322617193142664,4.385416153900877,100*(exp(b - se^2/2) - 1),reported,0.026539400660646785,0.02580219508673993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0285714285714285,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,SDB,other,other,dummy,binary,-2.2511,0.4437,,,***,,,high,"Printed name SDB: severely damaged building (60-90% destroyed), war damage dummy","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-90.45864660227267,4.671422202140571,100*(exp(b - se^2/2) - 1),reported,-0.16765605245781964,0.03304561790925973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.073473067387875,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,SDB,other,other,dummy,binary,-0.6999,0.0974,,,***,,,high,"Printed name SDB: severely damaged building (60-90% destroyed), war damage dummy","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,-50.571518532054505,4.837224557198732,100*(exp(b - se^2/2) - 1),reported,-0.18236529192064838,0.02537845325485234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.185831622176591,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,MDB,other,other,dummy,binary,-1.345,0.1674,,,***,,,high,"Printed name MDB: moderately damaged building (30-60%), war damage dummy","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-74.3085357601581,4.361434709575992,100*(exp(b - se^2/2) - 1),reported,-0.26005560858534676,0.032366772399395574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.034647550776583,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,MDB,other,other,dummy,binary,-0.6056,0.0596,,,***,,,high,"Printed name MDB: moderately damaged building (30-60%), war damage dummy","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,-45.52215496025953,3.2526514063345013,100*(exp(b - se^2/2) - 1),reported,-0.25369034076619895,0.024966882941983914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.161073825503356,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,LDB,other,other,dummy,binary,-1.3786,0.1184,,,***,,,high,"Printed name LDB: lightly damaged building (5-30%), war damage dummy","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-74.98286710396103,2.9828631548020335,100*(exp(b - se^2/2) - 1),reported,-0.3635827637166559,0.03122602584074572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.64358108108108,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,LDB,other,other,dummy,binary,-0.5678,0.0552,,,***,,,high,"Printed name LDB: lightly damaged building (5-30%), war damage dummy","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,-43.40918652231645,3.1285757226070587,100*(exp(b - se^2/2) - 1),reported,-0.25661055238150393,0.024946992764105353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.28623188405797,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M1,3,Model 1,16,UB,other,other,dummy,binary,-1.2019,0.0826,,,***,,,high,Printed name UB: unaffected building dummy (all four damage dummies printed; reference category unclear),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.603,se,"Damascus Governorate districts (12 districts), property attributes + building war-damage dummies",pct_premium,-70.04013040543757,2.48314173618521,100*(exp(b - se^2/2) - 1),reported,-0.43838053221361667,0.03012749143925845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.550847457627118,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T3-M2,3,Model 2,16,UB,other,other,dummy,binary,-0.4687,0.0539,,,***,,,high,Printed name UB: unaffected building dummy (all four damage dummies printed; reference category unclear),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.474,se,"Rural Damascus Governorate districts (37 districts), property attributes + building war-damage dummies",pct_premium,-37.50931029112049,3.373144464775625,100*(exp(b - se^2/2) - 1),reported,-0.21899956654461616,0.02518471652817327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.695732838589981,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,State_n,other,other,level,index,0.1616,0.0516,,,**,,,high,Printed name State_n: state of neighbourhood score 1 (worst) to 5 (best),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,16.16,5.16,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1044038981308353,0.0333368882645489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.131782945736434,890.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,State_n,other,other,level,index,0.3971,0.0294,,,***,,,high,Printed name State_n: state of neighbourhood score 1 (worst) to 5 (best),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,39.71,2.94,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.3291959970556241,0.024372607185684583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.506802721088436,1501.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,HD,density,F_neighbourhood,level,index,-0.0184,0.0505,,,,,,high,Housing density score 1 (densest) to 5 (least dense); inverted density scale,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,-1.8399999999999999,5.050000000000001,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.012212344686577779,0.033517576449574885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36435643564356435,890.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,HD,density,F_neighbourhood,level,index,0.1048,0.0217,,,***,,,high,Housing density score 1 (densest) to 5 (least dense); inverted density scale,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,10.48,2.17,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.12369806457325573,0.02561305344694322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.829493087557604,1501.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,QI,public_services,F_neighbourhood,level,index,0.3872,0.0496,,,***,,,high,Quality of infrastructure score 1 (worst) to 5 (best),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,38.72,4.96,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.25314943208226215,0.03242823303533111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.806451612903226,890.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,QI,public_services,F_neighbourhood,level,index,0.1436,0.0262,,,***,,,high,Quality of infrastructure score 1 (worst) to 5 (best),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,14.360000000000001,2.62,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14007473756902095,0.025556811450615242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.480916030534351,1501.0,index,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,Regular,other,other,dummy,binary,-0.3343,0.5304,,,,,,high,Printed name Regular: formal settlement dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-37.80913612065206,37.96810047045461,100*(exp(b - se^2/2) - 1),reported,-0.021122287800842535,0.033512597815037036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6302790346907994,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,Regular,other,other,dummy,binary,-0.101,0.6822,,,,,,high,Printed name Regular: formal settlement dummy,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-28.373023770715033,61.66631150347365,100*(exp(b - se^2/2) - 1),reported,-0.003821344058974465,0.025811098188439404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14805042509528,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,Slums,other,other,dummy,binary,-0.2968,0.5293,,,***,,,medium,Printed name Slums: informal settlement dummy; stars printed *** in Model 3 despite SE 0.5293 (as printed),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-35.39505595992824,39.337186224871175,100*(exp(b - se^2/2) - 1),reported,-0.018792748271159756,0.033514156536134966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5607406007935009,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,Slums,other,other,dummy,binary,-0.0509,0.68,,,,,,high,Printed name Slums: informal settlement dummy; stars printed *** in Model 3 despite SE 0.5293 (as printed),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-24.580173906412174,64.62541181427018,100*(exp(b - se^2/2) - 1),reported,-0.0019320471150121338,0.025811238471674876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07485294117647058,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,Accessibility,cbd_distance,D_location,other,index,0.18214,0.06331,,,**,,,high,"Accessibility to city centre ordinal score 1 (<2 km) to 5 (>8 km), i.e. higher = farther","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",other,0.18214,0.06331,not harmonised,reported,0.09599042414057041,0.033365289076202446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.876954667509082,890.0,index,nonstandard_x_transform,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,Accessibility,cbd_distance,D_location,other,index,-0.0496,0.0214,,,*,,,high,"Accessibility to city centre ordinal score 1 (<2 km) to 5 (>8 km), i.e. higher = farther","natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",other,-0.0496,0.0214,not harmonised,reported,-0.05971752309993953,0.025765221660054554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3177570093457946,1501.0,index,nonstandard_x_transform,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,D_city_center,cbd_distance,D_location,level,m,6e-05,4e-05,,,***,,,medium,Distance to city centre (Great Umayyad Mosque) in metres; Model 3 stars *** printed despite SE 0.00004 (as printed),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,6.0,4.0,100*b/0.001 (X per m->km),reported,0.050216678166132296,0.0334777854440882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,890.0,m,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,True
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,D_city_center,cbd_distance,D_location,level,m,4e-05,9e-06,,,**,,,high,Distance to city centre (Great Umayyad Mosque) in metres; Model 3 stars *** printed despite SE 0.00004 (as printed),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,4.0,0.8999999999999999,100*b/0.001 (X per m->km),reported,0.1139693650206361,0.025643107129643117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.444444444444445,1501.0,m,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,True,True,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,SDD,other,other,dummy,binary,-0.4491,0.1621,,,**,,,high,Printed name SDD: severely damaged district dummy (war),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-37.012771665674805,10.345258882209752,100*(exp(b - se^2/2) - 1),reported,-0.09246988058231873,0.033376458789565494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7705120296113512,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,SDD,other,other,dummy,binary,-0.6483,0.0526,,,***,,,high,Printed name SDD: severely damaged district dummy (war),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-47.77889069353029,2.7506328892615173,100*(exp(b - se^2/2) - 1),reported,-0.30315584132836365,0.024596633123356363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.32509505703422,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,MDD,other,other,dummy,binary,-1.2068,0.2198,,,***,,,high,Printed name MDD: moderately damaged district dummy (war),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-70.79867263378681,6.575383799945346,100*(exp(b - se^2/2) - 1),reported,-0.18100037687615758,0.03296642595076188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.490445859872612,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,MDD,other,other,dummy,binary,-0.4209,0.0709,,,***,,,high,Printed name MDD: moderately damaged district dummy (war),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-34.519212370054916,4.654271222842729,100*(exp(b - se^2/2) - 1),reported,-0.1514616968409036,0.02551350512240453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.936530324400564,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,UD,other,other,dummy,binary,-0.7933,0.0931,,,***,,,high,Printed name UD: unaffected district dummy (war),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-54.96065770726199,4.211374551878248,100*(exp(b - se^2/2) - 1),reported,-0.27463973834910066,0.032231135308586005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.52094522019334,890.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,UD,other,other,dummy,binary,-0.5484,0.0733,,,***,,,high,Printed name UD: unaffected district dummy (war),"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",pct_premium,-42.36766734745031,4.235813993527192,100*(exp(b - se^2/2) - 1),reported,-0.18960630746868634,0.02534307501359356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.481582537517053,1501.0,binary,,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M3,4,Model 3,17,D_conflict_point,other,other,level,m,5e-05,2e-05,,,***,,,high,Printed name D_conflict_point: distance to nearest conflict point in metres,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,900.0,0.427,se,"Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,0.005,0.002,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.08350748886986166,0.03340299554794467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,890.0,m,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7,71a2f8a0af3e00fb77bb5a3c7e34234f9d6c71e7:T4-M4,4,Model 4,17,D_conflict_point,other,other,level,m,2e-06,1.6e-05,,,,,,high,Printed name D_conflict_point: distance to nearest conflict point in metres,"natural log of property price (USD, inflation-adjusted to 2010)",log,OLS,1511.0,0.432,se,"Rural Damascus Governorate districts, district attributes + district war-damage dummies",semi_elasticity,0.00019999999999999998,0.0015999999999999999,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,0.0032263940378513024,0.025811152302810423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.125,1501.0,m,unit_not_harmonisable,2024.0,Syria,,,2010.0,2022.0,mixed,transaction,journal,True,True,unknown,,2016.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,homesize,living_area,A_structural,other,"sqft (printed; scale ambiguous, coefficient magnitude implies 1,000 sqft or log)",0.2331,,,,***,,,medium,"Table 5 lists 'Home Size (square feet)'; level-per-sqft scale inconsistent with coefficient size; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",other,0.2331,,not harmonised,,,,,,384358.0,sqft,nonstandard_x_transform,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,homesize,living_area,A_structural,other,"sqft (printed; scale ambiguous, coefficient magnitude implies 1,000 sqft or log)",0.2877,,,,***,,,medium,"Table 5 lists 'Home Size (square feet)'; level-per-sqft scale inconsistent with coefficient size; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,other,0.2877,,not harmonised,,,,,,384358.0,sqft,nonstandard_x_transform,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,homesize,living_area,A_structural,other,"sqft (printed; scale ambiguous, coefficient magnitude implies 1,000 sqft or log)",0.248,,,,***,,,medium,"Table 5 lists 'Home Size (square feet)'; level-per-sqft scale inconsistent with coefficient size; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",other,0.248,,not harmonised,,,,,,19062.0,sqft,nonstandard_x_transform,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,homesize,living_area,A_structural,other,"sqft (printed; scale ambiguous, coefficient magnitude implies 1,000 sqft or log)",0.283,,,,***,,,medium,"Table 5 lists 'Home Size (square feet)'; level-per-sqft scale inconsistent with coefficient size; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,other,0.283,,not harmonised,,,,,,19062.0,sqft,nonstandard_x_transform,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,homesize,living_area,A_structural,other,"sqft (printed; scale ambiguous, coefficient magnitude implies 1,000 sqft or log)",0.2474,,,,***,,,medium,"Table 5 lists 'Home Size (square feet)'; level-per-sqft scale inconsistent with coefficient size; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),other,0.2474,,not harmonised,,,,,,19062.0,sqft,nonstandard_x_transform,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,homesize,living_area,A_structural,other,"sqft (printed; scale ambiguous, coefficient magnitude implies 1,000 sqft or log)",0.2619,,,,***,,,medium,"Table 5 lists 'Home Size (square feet)'; level-per-sqft scale inconsistent with coefficient size; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),other,0.2619,,not harmonised,,,,,,19062.0,sqft,nonstandard_x_transform,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,homeage,age,A_structural,level,years,-0.0092,,,,***,,,high,"stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",semi_elasticity,-0.9199999999999999,,100*b/1 (per year),,,,,,384358.0,years,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,homeage,age,A_structural,level,years,-0.0097,,,,***,,,high,"stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,semi_elasticity,-0.97,,100*b/1 (per year),,,,,,384358.0,years,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,homeage,age,A_structural,level,years,-0.0099,,,,***,,,high,"stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",semi_elasticity,-0.9900000000000001,,100*b/1 (per year),,,,,,19062.0,years,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,homeage,age,A_structural,level,years,-0.0101,,,,***,,,high,"stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,semi_elasticity,-1.01,,100*b/1 (per year),,,,,,19062.0,years,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,homeage,age,A_structural,level,years,-0.0086,,,,***,,,high,"stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),semi_elasticity,-0.86,,100*b/1 (per year),,,,,,19062.0,years,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,homeage,age,A_structural,level,years,-0.0099,,,,***,,,high,"stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),semi_elasticity,-0.9900000000000001,,100*b/1 (per year),,,,,,19062.0,years,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Raillineop,transaction_timing,G_market,binary,binary,0.035,,,,***,,,high,"Home sold after (1) vs before (0) the METRORail opening in 2004; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,3.5619708799623284,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Raillineop,transaction_timing,G_market,binary,binary,0.0398,,,,***,,,high,"Home sold after (1) vs before (0) the METRORail opening in 2004; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,4.060263285237764,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Raillineop,transaction_timing,G_market,binary,binary,0.0301,,,,***,,,high,"Home sold after (1) vs before (0) the METRORail opening in 2004; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,3.0557584559356643,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Raillineop,transaction_timing,G_market,binary,binary,0.0375,,,,***,,,high,"Home sold after (1) vs before (0) the METRORail opening in 2004; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,3.82119970818251,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Raillineop,transaction_timing,G_market,binary,binary,0.038,,,,***,,,high,"Home sold after (1) vs before (0) the METRORail opening in 2004; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,3.873123287849767,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Raillineop,transaction_timing,G_market,binary,binary,0.0353,,,,***,,,high,"Home sold after (1) vs before (0) the METRORail opening in 2004; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,3.5930441319810713,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,RailQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRORail stop),0.4288,,,,***,,,high,"Distance-band dummy; reference = beyond 3 miles; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,53.541392090284305,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,RailQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRORail stop),0.3911,,,,***,,,high,"Distance-band dummy; reference = beyond 3 miles; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,47.86063666570275,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,RailQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRORail stop),0.6012,,,,***,,,high,"Distance-band dummy; reference = beyond 3 miles; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,82.43066554014416,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,RailQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRORail stop),0.4246,,,,**,,,high,"Distance-band dummy; reference = beyond 3 miles; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,52.89787058464328,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,RailQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRORail stop),0.3593,,,,*,,,high,"Distance-band dummy; reference = beyond 3 miles; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,43.23264350539302,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,RailQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRORail stop),0.4394,,,,**,,,high,"Distance-band dummy; reference = beyond 3 miles; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,55.17758736116396,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,RailHMI,transit_distance,D_location,dummy,binary (1/4-1/2 mile of METRORail stop),0.2462,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,27.91553790361434,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,RailHMI,transit_distance,D_location,dummy,binary (1/4-1/2 mile of METRORail stop),0.3598,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,43.30427773421055,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,RailHMI,transit_distance,D_location,dummy,binary (1/4-1/2 mile of METRORail stop),0.5019,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,65.18568189420108,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,RailHMI,transit_distance,D_location,dummy,binary (1/4-1/2 mile of METRORail stop),0.461,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,58.56588512805472,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,RailHMI,transit_distance,D_location,dummy,binary (1/4-1/2 mile of METRORail stop),0.3226,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,38.07129552517638,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,RailHMI,transit_distance,D_location,dummy,binary (1/4-1/2 mile of METRORail stop),0.4727,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,60.43200147702073,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Rail1MI,transit_distance,D_location,dummy,binary (1/2-1 mile of METRORail stop),0.2381,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,26.883607008795064,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Rail1MI,transit_distance,D_location,dummy,binary (1/2-1 mile of METRORail stop),0.3277,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,38.7772578059983,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Rail1MI,transit_distance,D_location,dummy,binary (1/2-1 mile of METRORail stop),0.374,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,45.35371504568524,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Rail1MI,transit_distance,D_location,dummy,binary (1/2-1 mile of METRORail stop),0.34,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,40.49475905635938,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Rail1MI,transit_distance,D_location,dummy,binary (1/2-1 mile of METRORail stop),0.2381,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,26.883607008795064,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Rail1MI,transit_distance,D_location,dummy,binary (1/2-1 mile of METRORail stop),0.3627,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,43.72046331705821,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Rail2MI,transit_distance,D_location,dummy,binary (1-2 miles of METRORail stop),0.1791,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,19.614035222260352,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Rail2MI,transit_distance,D_location,dummy,binary (1-2 miles of METRORail stop),0.3021,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,35.26964895951848,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Rail2MI,transit_distance,D_location,dummy,binary (1-2 miles of METRORail stop),0.2518,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,28.633874380758904,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Rail2MI,transit_distance,D_location,dummy,binary (1-2 miles of METRORail stop),0.2095,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,23.30613751108239,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Rail2MI,transit_distance,D_location,dummy,binary (1-2 miles of METRORail stop),0.1417,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,15.223092740320677,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Rail2MI,transit_distance,D_location,dummy,binary (1-2 miles of METRORail stop),0.2249,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,25.219749018164993,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Rail3Mi,transit_distance,D_location,dummy,binary (2-3 miles of METRORail stop),0.0884,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,9.242500464770753,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Rail3Mi,transit_distance,D_location,dummy,binary (2-3 miles of METRORail stop),0.2121,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,23.627150604795677,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Rail3Mi,transit_distance,D_location,dummy,binary (2-3 miles of METRORail stop),0.1668,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,18.151793808880967,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Rail3Mi,transit_distance,D_location,dummy,binary (2-3 miles of METRORail stop),0.2175,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,24.2965429507731,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Rail3Mi,transit_distance,D_location,dummy,binary (2-3 miles of METRORail stop),0.1502,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,16.206663281506284,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Rail3Mi,transit_distance,D_location,dummy,binary (2-3 miles of METRORail stop),0.2155,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,24.048198292311575,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,BusStQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRO bus stop),-0.0146,,,,***,,,high,"Bus stop proximity dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,-1.4493936801632024,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,BusStQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRO bus stop),0.0945,,,,***,,,high,"Bus stop proximity dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,9.910916313299012,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,BusStQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRO bus stop),0.0416,,,,***,,,high,"Bus stop proximity dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,4.247740437969805,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,BusStQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRO bus stop),0.1131,,,,***,,,high,"Bus stop proximity dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,11.974390174022709,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,BusStQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRO bus stop),0.0859,,,,***,,,high,"Bus stop proximity dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,8.969735312114825,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,BusStQMI,transit_distance,D_location,dummy,binary (within 1/4 mile of METRO bus stop),0.0935,,,,***,,,high,"Bus stop proximity dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,9.801060334129952,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Hwyhmi,highway_distance,D_location,dummy,binary (within 1/2 mile of highway intersection),-0.0053,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,-0.5285979779991123,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Hwyhmi,highway_distance,D_location,dummy,binary (within 1/2 mile of highway intersection),-0.0015,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,-0.14988755622891148,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Hwyhmi,highway_distance,D_location,dummy,binary (within 1/2 mile of highway intersection),0.0067,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,0.6722495211242352,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Hwyhmi,highway_distance,D_location,dummy,binary (within 1/2 mile of highway intersection),-0.0097,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,-0.9653106744009099,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Hwyhmi,highway_distance,D_location,dummy,binary (within 1/2 mile of highway intersection),0.0056,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,0.5615709310356376,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Hwyhmi,highway_distance,D_location,dummy,binary (within 1/2 mile of highway intersection),-0.0144,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,-1.4296815877557045,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Hwy1mi,highway_distance,D_location,dummy,binary (1/2-1 mile of highway intersection),-0.0088,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,-0.8761393329232758,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Hwy1mi,highway_distance,D_location,dummy,binary (1/2-1 mile of highway intersection),0.0034,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,0.3405786556238466,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Hwy1mi,highway_distance,D_location,dummy,binary (1/2-1 mile of highway intersection),0.0187,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,1.8875939981410905,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Hwy1mi,highway_distance,D_location,dummy,binary (1/2-1 mile of highway intersection),0.0251,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,2.541765716322786,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Hwy1mi,highway_distance,D_location,dummy,binary (1/2-1 mile of highway intersection),0.031,,,,*,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,3.1485503886522714,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Hwy1mi,highway_distance,D_location,dummy,binary (1/2-1 mile of highway intersection),0.0139,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,1.399705416292174,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Hwy2mi,highway_distance,D_location,dummy,binary (1-2 miles of highway intersection),0.0136,,,,**,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,1.3692900671977704,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Hwy2mi,highway_distance,D_location,dummy,binary (1-2 miles of highway intersection),0.0129,,,,***,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,1.2983563938328668,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Hwy2mi,highway_distance,D_location,dummy,binary (1-2 miles of highway intersection),0.0293,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,2.9733468182212786,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Hwy2mi,highway_distance,D_location,dummy,binary (1-2 miles of highway intersection),0.0219,,,,*,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,2.214156520303967,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Hwy2mi,highway_distance,D_location,dummy,binary (1-2 miles of highway intersection),0.0223,,,,*,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,2.2550503611350026,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Hwy2mi,highway_distance,D_location,dummy,binary (1-2 miles of highway intersection),0.0164,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,1.6535218181391809,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Hwy3mi,highway_distance,D_location,dummy,binary (2-3 miles of highway intersection),0.0076,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",pct_premium,0.7628953301887398,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Hwy3mi,highway_distance,D_location,dummy,binary (2-3 miles of highway intersection),-0.0055,,,,*,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,pct_premium,-0.5484902691080928,,100*(exp(b) - 1),,,,,,384358.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Hwy3mi,highway_distance,D_location,dummy,binary (2-3 miles of highway intersection),0.0085,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",pct_premium,0.8536227572039534,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Hwy3mi,highway_distance,D_location,dummy,binary (2-3 miles of highway intersection),0.0047,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,pct_premium,0.47110623241843896,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Hwy3mi,highway_distance,D_location,dummy,binary (2-3 miles of highway intersection),0.0079,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),pct_premium,0.7931287335715353,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Hwy3mi,highway_distance,D_location,dummy,binary (2-3 miles of highway intersection),-0.0025,,,,,,,high,"Distance-band dummy; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),pct_premium,-0.24968776025399153,,100*(exp(b) - 1),,,,,,19062.0,binary,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Distcbd,cbd_distance,D_location,log,miles (log of Euclidean distance to Downtown Houston; unit assumed miles),0.0075,,,,**,,,medium,"Logarithm of Euclidean distance to Downtown CBD (p. 253); distance unit not printed; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",elasticity,0.0075,,b (log-log),,,,,,384358.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Distcbd,cbd_distance,D_location,log,miles (log of Euclidean distance to Downtown Houston; unit assumed miles),-0.0049,,,,**,,,medium,"Logarithm of Euclidean distance to Downtown CBD (p. 253); distance unit not printed; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,elasticity,-0.0049,,b (log-log),,,,,,384358.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Distcbd,cbd_distance,D_location,log,miles (log of Euclidean distance to Downtown Houston; unit assumed miles),-0.0071,,,,,,,medium,"Logarithm of Euclidean distance to Downtown CBD (p. 253); distance unit not printed; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",elasticity,-0.0071,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Distcbd,cbd_distance,D_location,log,miles (log of Euclidean distance to Downtown Houston; unit assumed miles),-0.007,,,,***,,,medium,"Logarithm of Euclidean distance to Downtown CBD (p. 253); distance unit not printed; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,elasticity,-0.007,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Distcbd,cbd_distance,D_location,log,miles (log of Euclidean distance to Downtown Houston; unit assumed miles),-0.0087,,,,***,,,medium,"Logarithm of Euclidean distance to Downtown CBD (p. 253); distance unit not printed; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),elasticity,-0.0087,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Distcbd,cbd_distance,D_location,log,miles (log of Euclidean distance to Downtown Houston; unit assumed miles),-0.0062,,,,*,,,medium,"Logarithm of Euclidean distance to Downtown CBD (p. 253); distance unit not printed; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),elasticity,-0.0062,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Distmed,employment_access,D_location,log,miles (log of Euclidean distance to Texas Medical Center; unit assumed miles),-0.0267,,,,***,,,medium,"Logarithm of Euclidean distance to the Texas Medical Center employment centre; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",elasticity,-0.0267,,b (log-log),,,,,,384358.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Distmed,employment_access,D_location,log,miles (log of Euclidean distance to Texas Medical Center; unit assumed miles),-0.0192,,,,***,,,medium,"Logarithm of Euclidean distance to the Texas Medical Center employment centre; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,elasticity,-0.0192,,b (log-log),,,,,,384358.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Distmed,employment_access,D_location,log,miles (log of Euclidean distance to Texas Medical Center; unit assumed miles),-0.0188,,,,***,,,medium,"Logarithm of Euclidean distance to the Texas Medical Center employment centre; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",elasticity,-0.0188,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Distmed,employment_access,D_location,log,miles (log of Euclidean distance to Texas Medical Center; unit assumed miles),-0.0171,,,,***,,,medium,"Logarithm of Euclidean distance to the Texas Medical Center employment centre; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,elasticity,-0.0171,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Distmed,employment_access,D_location,log,miles (log of Euclidean distance to Texas Medical Center; unit assumed miles),-0.0112,,,,***,,,medium,"Logarithm of Euclidean distance to the Texas Medical Center employment centre; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),elasticity,-0.0112,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Distmed,employment_access,D_location,log,miles (log of Euclidean distance to Texas Medical Center; unit assumed miles),-0.0183,,,,***,,,medium,"Logarithm of Euclidean distance to the Texas Medical Center employment centre; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),elasticity,-0.0183,,b (log-log),,,,,,19062.0,miles,,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Medinc,income,F_neighbourhood,level,"currency (median household income, $1000, TAZ level)",0.0062,,,,***,,,high,"Level-2 (TAZ) variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",semi_elasticity,0.62,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,384358.0,currency,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Medinc,income,F_neighbourhood,level,"currency (median household income, $1000, TAZ level)",0.0034,,,,***,,,high,"Level-2 (TAZ) variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,semi_elasticity,0.33999999999999997,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,384358.0,currency,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Medinc,income,F_neighbourhood,level,"currency (median household income, $1000, TAZ level)",0.0048,,,,***,,,high,"Level-2 (TAZ) variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",semi_elasticity,0.48,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,19062.0,currency,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Medinc,income,F_neighbourhood,level,"currency (median household income, $1000, TAZ level)",0.0033,,,,***,,,high,"Level-2 (TAZ) variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,semi_elasticity,0.33,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,19062.0,currency,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Medinc,income,F_neighbourhood,level,"currency (median household income, $1000, TAZ level)",0.0021,,,,***,,,high,"Level-2 (TAZ) variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),semi_elasticity,0.21,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,19062.0,currency,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Medinc,income,F_neighbourhood,level,"currency (median household income, $1000, TAZ level)",0.0035,,,,***,,,high,"Level-2 (TAZ) variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),semi_elasticity,0.35000000000000003,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,19062.0,currency,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Popdens,density,F_neighbourhood,level,"per unit area (population density, TAZ; unit not printed)",0.003,,,,*,,,medium,"Level-2 variable; density unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",semi_elasticity,0.3,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,384358.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Popdens,density,F_neighbourhood,level,"per unit area (population density, TAZ; unit not printed)",-0.0071,,,,***,,,medium,"Level-2 variable; density unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,semi_elasticity,-0.7100000000000001,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,384358.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Popdens,density,F_neighbourhood,level,"per unit area (population density, TAZ; unit not printed)",-0.0042,,,,*,,,medium,"Level-2 variable; density unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",semi_elasticity,-0.42,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Popdens,density,F_neighbourhood,level,"per unit area (population density, TAZ; unit not printed)",-0.0064,,,,***,,,medium,"Level-2 variable; density unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,semi_elasticity,-0.64,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Popdens,density,F_neighbourhood,level,"per unit area (population density, TAZ; unit not printed)",-0.0034,,,,**,,,medium,"Level-2 variable; density unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),semi_elasticity,-0.33999999999999997,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Popdens,density,F_neighbourhood,level,"per unit area (population density, TAZ; unit not printed)",-0.0066,,,,***,,,medium,"Level-2 variable; density unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),semi_elasticity,-0.66,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Jobdens,employment_access,D_location,level,"per unit area (job density, TAZ; unit not printed)",0.0026,,,,***,,,medium,"Level-2 variable; total job density, unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",semi_elasticity,0.26,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,384358.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Jobdens,employment_access,D_location,level,"per unit area (job density, TAZ; unit not printed)",0.0034,,,,***,,,medium,"Level-2 variable; total job density, unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,semi_elasticity,0.33999999999999997,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,384358.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Jobdens,employment_access,D_location,level,"per unit area (job density, TAZ; unit not printed)",0.0026,,,,**,,,medium,"Level-2 variable; total job density, unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",semi_elasticity,0.26,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Jobdens,employment_access,D_location,level,"per unit area (job density, TAZ; unit not printed)",0.0045,,,,***,,,medium,"Level-2 variable; total job density, unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,semi_elasticity,0.44999999999999996,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Jobdens,employment_access,D_location,level,"per unit area (job density, TAZ; unit not printed)",0.0029,,,,***,,,medium,"Level-2 variable; total job density, unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),semi_elasticity,0.29,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Jobdens,employment_access,D_location,level,"per unit area (job density, TAZ; unit not printed)",0.0038,,,,***,,,medium,"Level-2 variable; total job density, unit not stated; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),semi_elasticity,0.38,,100*b (unit as reported: per unit area; no harmonised unit defined for this characteristic),,,,,,19062.0,per unit area,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Pctminor,demographics,F_neighbourhood,level,"share (percentage of minority population, TAZ)",-0.5325,,,,***,,,high,"Level-2 variable; magnitude (-0.5 to -0.67) suggests share in 0-1; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",semi_elasticity,-53.25,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,384358.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Pctminor,demographics,F_neighbourhood,level,"share (percentage of minority population, TAZ)",-0.6479,,,,***,,,high,"Level-2 variable; magnitude (-0.5 to -0.67) suggests share in 0-1; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,semi_elasticity,-64.79,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,384358.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Pctminor,demographics,F_neighbourhood,level,"share (percentage of minority population, TAZ)",-0.6615,,,,***,,,high,"Level-2 variable; magnitude (-0.5 to -0.67) suggests share in 0-1; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",semi_elasticity,-66.14999999999999,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,19062.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Pctminor,demographics,F_neighbourhood,level,"share (percentage of minority population, TAZ)",-0.6687,,,,***,,,high,"Level-2 variable; magnitude (-0.5 to -0.67) suggests share in 0-1; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,semi_elasticity,-66.86999999999999,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,19062.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Pctminor,demographics,F_neighbourhood,level,"share (percentage of minority population, TAZ)",-0.5012,,,,***,,,high,"Level-2 variable; magnitude (-0.5 to -0.67) suggests share in 0-1; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),semi_elasticity,-50.12,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,19062.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Pctminor,demographics,F_neighbourhood,level,"share (percentage of minority population, TAZ)",-0.6629,,,,***,,,high,"Level-2 variable; magnitude (-0.5 to -0.67) suggests share in 0-1; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),semi_elasticity,-66.29,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,19062.0,share,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c1,7,1,18,Jobacess,employment_access,D_location,level,"index (Hansen total job accessibility, TAZ)",0.0004,,,,***,,,high,"Level-2 variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,384368.0,0.4798,none,"100% sample, multilevel regression (level 2 = TAZ, 1,427 zones) (pseudo R2 reported)",semi_elasticity,0.04,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,384358.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c2,7,2,18,Jobacess,employment_access,D_location,level,"index (Hansen total job accessibility, TAZ)",0.0001,,,,***,,,high,"Level-2 variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,384368.0,0.436,none,100% sample OLS,semi_elasticity,0.01,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,384358.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c3,7,3,18,Jobacess,employment_access,D_location,level,"index (Hansen total job accessibility, TAZ)",0.0001,,,,,,,high,"Level-2 variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,other,19072.0,0.5063,none,"5% random sample, multilevel regression (1,207 zones) (pseudo R2 reported)",semi_elasticity,0.01,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,19062.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,other,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c4,7,4,18,Jobacess,employment_access,D_location,level,"index (Hansen total job accessibility, TAZ)",0.0001,,,,***,,,high,"Level-2 variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,OLS,19072.0,0.4447,none,5% random sample OLS,semi_elasticity,0.01,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,19062.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,ols,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c5,7,5,18,Jobacess,employment_access,D_location,level,"index (Hansen total job accessibility, TAZ)",0.0001,,,,***,,,high,"Level-2 variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_lag,19072.0,0.4691,none,5% random sample spatial lag model (rho=0.2420***),semi_elasticity,0.01,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,19062.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a,721bde276de0a5af885925a9444c628cfce6d18a:T7-c6,7,6,18,Jobacess,employment_access,D_location,level,"index (Hansen total job accessibility, TAZ)",0.0001,,,,***,,,high,"Level-2 variable; stars only, no SE/t printed","Lgval1000: log of home sale price ($1000, 2010 CPI-adjusted)",log,spatial_error,19072.0,0.4581,none,5% random sample spatial error model (lambda=0.2049***),semi_elasticity,0.01,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,19062.0,index,unit_not_harmonisable,2019.0,United States,North America,High income,1982.0,2010.0,single_family,transaction,journal,False,False,unknown,,1996.0,spatial,journal,False,False,False
74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615:T4-M1,4,M1,7,X1 (number of bedrooms),bedrooms,A_structural,level,count,88540000.0,39390000.0,2.248,0.025,,,,high,VIF 10.08,housing price (IDR),level,OLS,1713.0,0.428,none,Complete cases from Kaggle dataset; adj R2 0.427,monetary,88540000.0,39390000.0,"b (currency per unit, as reported)",reported,0.054393320495889776,0.02419631694657018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.248,1703.0,count,,2025.0,Indonesia,Asia,Upper middle income,,,single_family,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615:T4-M1,4,M1,7,X2 (number of bathrooms),bathrooms,A_structural,level,count,164200000.0,39390000.0,4.169,0.001,,,,high,p printed as < .001; VIF 10.26,housing price (IDR),level,OLS,1713.0,0.428,none,Complete cases from Kaggle dataset; adj R2 0.427,monetary,164200000.0,39390000.0,"b (currency per unit, as reported)",reported,0.10051239739105247,0.02410947406837431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.169,1703.0,count,,2025.0,Indonesia,Asia,Upper middle income,,,single_family,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615:T4-M1,4,M1,7,X3 (land area),lot_size,A_structural,level,m2,3729000.0,243986.024,15.282,0.001,,,,high,p printed as < .001,housing price (IDR),level,OLS,1713.0,0.428,none,Complete cases from Kaggle dataset; adj R2 0.427,monetary,3729000.0,243986.024,"b (currency per unit, as reported)",reported,0.34726979665852165,0.022724106573650156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.282,1703.0,m2,,2025.0,Indonesia,Asia,Upper middle income,,,single_family,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615:T4-M1,4,M1,7,X4 (house area),living_area,A_structural,level,m2,0.301,0.722,0.417,0.677,,,,medium,"Variable badly scaled in source data (mean 1.19e6 m2, max 2.4e9 m2); coefficient printed as 0.301 (SE 0.722)",housing price (IDR),level,OLS,1713.0,0.428,none,Complete cases from Kaggle dataset; adj R2 0.427,monetary,0.301,0.722,"b (currency per unit, as reported)",reported,0.010104307623962291,0.024230953534681757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.417,1703.0,m2,,2025.0,Indonesia,Asia,Upper middle income,,,single_family,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615,74099967a079fff42f0df3135c0f69cc4e059615:T4-M1,4,M1,7,X5 (vehicle slots),parking,A_structural,level,count,160600000.0,44080000.0,3.643,0.001,,,,high,p printed as < .001,housing price (IDR),level,OLS,1713.0,0.428,none,Complete cases from Kaggle dataset; adj R2 0.427,monetary,160600000.0,44080000.0,"b (currency per unit, as reported)",reported,0.08793589382160363,0.024138318369915906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.643,1703.0,count,,2025.0,Indonesia,Asia,Upper middle income,,,single_family,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T5-c1,5,1,8,Size (m2),living_area,A_structural,level,m2,30.032,0.039,1.7,0.0,,,,low,Property area in m2; Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,Asking price per m2 (EUR/m2),per_m2,OLS,1000.0,0.721,none,"Linear HPM, 1000 Idealista listings, Seville, August 2022; r = 0.85, R2 = 0.721, p <= 0.001; SE and t printed in separate columns; rooms variable not retained",monetary,30.032,0.039,"b (currency per unit, as reported)",reported,0.053950857655340564,0.03173579862078857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,990.0,m2,,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T5-c1,5,1,8,Baths (n),bathrooms,A_structural,level,count,98.725,0.042,0.14,0.0,,,,low,Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,Asking price per m2 (EUR/m2),per_m2,OLS,1000.0,0.721,none,"Linear HPM, 1000 Idealista listings, Seville, August 2022; r = 0.85, R2 = 0.721, p <= 0.001; SE and t printed in separate columns; rooms variable not retained",monetary,98.725,0.042,"b (currency per unit, as reported)",reported,0.004449448038322909,0.03178177170230649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.14,990.0,count,,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T5-c1,5,1,8,Age (years),age,A_structural,level,years,-53.31,1.42,-1.42,0.0,,,,low,Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,Asking price per m2 (EUR/m2),per_m2,OLS,1000.0,0.721,none,"Linear HPM, 1000 Idealista listings, Seville, August 2022; r = 0.85, R2 = 0.721, p <= 0.001; SE and t printed in separate columns; rooms variable not retained",monetary,-53.31,1.42,"b (currency per unit, as reported)",reported,-0.045084672410888385,0.031749769303442525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.42,990.0,years,,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T5-c1,5,1,8,Green area (m2/inh),green_space,E_environment,level,m2 per inhabitant,11.145,0.87,0.872,0.0,,,,low,Green area per inhabitant in the set of districts (district-level variable); Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,Asking price per m2 (EUR/m2),per_m2,OLS,1000.0,0.721,none,"Linear HPM, 1000 Idealista listings, Seville, August 2022; r = 0.85, R2 = 0.721, p <= 0.001; SE and t printed in separate columns; rooms variable not retained",monetary,11.145,0.87,"b (currency per unit, as reported)",reported,0.02770334232389635,0.03176988798611967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.872,990.0,m2 per inhabitant,,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T6-c1,6,1,9,Room,bedrooms,A_structural,other,count,1.39,1.08,1.29,0.0,,,,low,Number of bedrooms; transformation in 'logarithmic model' not stated; Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,"Asking price per m2 (EUR/m2), 'logarithmic model' (functional form not written out; likely regressors in logs)",other,OLS,1000.0,0.667,none,"Logarithmic HPM; r = 0.816, R2 = 0.667, p <= 0.001; size variable not retained (rooms instead)",other,1.39,1.08,not harmonised,reported,0.04096447701241906,0.031755408536758956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.29,990.0,count,nonstandard_x_transform,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T6-c1,6,1,9,Baths (n),bathrooms,A_structural,other,count,0.26,0.6,0.432,0.0,,,,low,transformation not stated; Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,"Asking price per m2 (EUR/m2), 'logarithmic model' (functional form not written out; likely regressors in logs)",other,OLS,1000.0,0.667,none,"Logarithmic HPM; r = 0.816, R2 = 0.667, p <= 0.001; size variable not retained (rooms instead)",other,0.26,0.6,not harmonised,reported,0.013728567366235678,0.03177909112554555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.432,990.0,count,nonstandard_x_transform,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T6-c1,6,1,9,Age (years),age,A_structural,other,years,-0.365,0.205,-1.78,0.0,,,,low,transformation not stated; Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,"Asking price per m2 (EUR/m2), 'logarithmic model' (functional form not written out; likely regressors in logs)",other,OLS,1000.0,0.667,none,"Logarithmic HPM; r = 0.816, R2 = 0.667, p <= 0.001; size variable not retained (rooms instead)",other,-0.365,0.205,not harmonised,reported,-0.05648180353193417,0.03173135029883942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.78,990.0,years,nonstandard_x_transform,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76,755578f10943061d8e5adfd02909b14166725d76:T6-c1,6,1,9,Green area (m2/inh),green_space,E_environment,other,m2 per inhabitant,-0.013,0.13,-0.1,0.0,,,,low,district-level; transformation not stated; Printed statistics are internally inconsistent (Student's t differs from coefficient/SE by orders of magnitude; all p printed 0.000) - table likely garbled at source; do not use without checking the publisher version,"Asking price per m2 (EUR/m2), 'logarithmic model' (functional form not written out; likely regressors in logs)",other,OLS,1000.0,0.667,none,"Logarithmic HPM; r = 0.816, R2 = 0.667, p <= 0.001; size variable not retained (rooms instead)",other,-0.013,0.13,not harmonised,reported,-0.0031781925793815014,0.03178192579381501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1,990.0,m2 per inhabitant,nonstandard_x_transform,2022.0,Spain,Europe,High income,2022.0,2022.0,unknown,listing,journal,False,False,unknown,,2022.0,ols,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,FREEHOLD,other,other,dummy,binary,0.294,,18.733,,,,,high,"freehold (vs leasehold) tenure dummy; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.159; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,34.16186672252024,2.105826443592001,100*(exp(b - se^2/2) - 1),|b/t|,0.27226802016199264,0.014534138694389187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.733,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,FREEHOLD,other,other,dummy,binary,0.155,,,,,,,medium,"freehold (vs leasehold) tenure dummy; GWR local-coefficient median; lower quartile 0.084, upper quartile 0.232; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,16.765796110512497,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,FLOORSIZE,living_area,A_structural,level,m2,0.005,,49.578,,,,,high,"floor area in square metres; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.542; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,0.5,0.010085118399290008,100*b/1 (X per m2->m2),|b/t|,0.5994181701761168,0.012090406433823808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.578,4383.0,m2,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,FLOORSIZE,living_area,A_structural,level,m2,0.005,,,,,,,medium,"floor area in square metres; GWR local-coefficient median; lower quartile 0.004, upper quartile 0.005; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,0.5,,100*b/1 (X per m2->m2),,,,,,4383.0,m2,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,LOTSIZE,lot_size,A_structural,level,m2,0.000186,,9.593,,,,,high,"lot size in square metres; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.086; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,0.0186,0.0019389137913061606,100*b/1 (X per m2->m2),|b/t|,0.14340248593168906,0.014948659015082777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.593,4383.0,m2,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,LOTSIZE,lot_size,A_structural,level,m2,0.000403,,,,,,,medium,"lot size in square metres; GWR local-coefficient median; lower quartile 0.00048, upper quartile 0.001; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,0.040299999999999996,,100*b/1 (X per m2->m2),,,,,,4383.0,m2,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,BEDROOMS,bedrooms,A_structural,dummy,binary,0.19,,11.497,,,,,high,"dummy = 1 if four or more bedrooms; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.108; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,20.908447964840214,1.998411964467929,100*(exp(b - se^2/2) - 1),|b/t|,0.17109879409801748,0.01488203827937875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.497,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,BEDROOMS,bedrooms,A_structural,dummy,binary,0.132,,,,,,,medium,"dummy = 1 if four or more bedrooms; GWR local-coefficient median; lower quartile 0.076, upper quartile 0.173; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,14.11083192672351,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,TYPE_TERRACED,dwelling_type,A_structural,dummy,binary,-0.084,,-2.841,,,,,high,"dummy = 1 if cluster/terraced house (reference: detached and other types); t-ratio printed in separate column, no SE; printed standardised coefficient = -0.043; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,-8.097054439277906,2.7184873464269788,100*(exp(b - se^2/2) - 1),|b/t|,-0.04287320891015263,0.01509088662800163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.841,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,TYPE_TERRACED,dwelling_type,A_structural,dummy,binary,0.29,,,,,,,medium,"dummy = 1 if cluster/terraced house (reference: detached and other types); GWR local-coefficient median; lower quartile 0.038, upper quartile 0.391; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,33.6427488025472,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,TYPE_TOWNHOUSE,dwelling_type,A_structural,dummy,binary,-0.223,,-5.645,,,,,high,"dummy = 1 if town house; t-ratio printed in separate column, no SE; printed standardised coefficient = -0.062; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,-20.05092227709564,3.1607725667437654,100*(exp(b - se^2/2) - 1),|b/t|,-0.08495817639633119,0.015050164109181788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.645,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,TYPE_TOWNHOUSE,dwelling_type,A_structural,dummy,binary,0.024,,,,,,,medium,"dummy = 1 if town house; GWR local-coefficient median; lower quartile -0.21, upper quartile 0.179; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,2.4290317890621527,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,TYPE_SEMIDETACHED,dwelling_type,A_structural,dummy,binary,0.357,,10.454,,,,,high,"dummy = 1 if semi-detached; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.124; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,42.82028449922131,4.880101449567955,100*(exp(b - se^2/2) - 1),|b/t|,0.155972772236994,0.014919913165964607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.454,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,TYPE_SEMIDETACHED,dwelling_type,A_structural,dummy,binary,0.571,,,,,,,medium,"dummy = 1 if semi-detached; GWR local-coefficient median; lower quartile 0.338, upper quartile 0.635; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,77.00362029134793,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,TYPE_BUNGALOW,dwelling_type,A_structural,dummy,binary,0.442,,9.869,,,,,high,"dummy = 1 if bungalow; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.096; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,55.42561568975282,6.967986191831751,100*(exp(b - se^2/2) - 1),|b/t|,0.14743985404422366,0.014939695414350355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.869,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,TYPE_BUNGALOW,dwelling_type,A_structural,dummy,binary,0.361,,,,,,,medium,"dummy = 1 if bungalow; GWR local-coefficient median; lower quartile 0.182, upper quartile 0.497; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,43.476346087855575,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,PRIMARY_SCHOOL,school_distance,D_location,level,m,2.832e-05,,2.783,,,,,high,"network distance in metres to nearest primary school; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.031; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,2.832,1.017606899029824,100*b/0.001 (X per m->km),|b/t|,0.041999497487108864,0.015091447174670812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.783,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,PRIMARY_SCHOOL,school_distance,D_location,level,m,1.4e-05,,,,,,,medium,"network distance in metres to nearest primary school; GWR local-coefficient median; lower quartile 2.7e-05, upper quartile 8.5e-05; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,1.4,,100*b/0.001 (X per m->km),,,,,,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,SECONDARY_SCHOOL,school_distance,D_location,level,m,5.244e-05,,6.881,,,,,high,"network distance in metres to nearest secondary school; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.07; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,5.244,0.7620985321900886,100*b/0.001 (X per m->km),|b/t|,0.10337907202756663,0.015023844212696792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.881,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,SECONDARY_SCHOOL,school_distance,D_location,level,m,3.9e-05,,,,,,,medium,"network distance in metres to nearest secondary school; GWR local-coefficient median; lower quartile 9e-06, upper quartile 0.000107; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,3.9,,100*b/0.001 (X per m->km),,,,,,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,SHOPMALL,commercial_distance,D_location,level,m,-8.112e-05,,-11.385,,,,,high,"network distance in metres to nearest shopping mall; t-ratio printed in separate column, no SE; printed standardised coefficient = -0.112; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,-8.112,0.7125164690382082,100*b/0.001 (X per m->km),|b/t|,-0.16948011047854353,0.01488626354664414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.385,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,SHOPMALL,commercial_distance,D_location,level,m,-5.6e-05,,,,,,,medium,"network distance in metres to nearest shopping mall; GWR local-coefficient median; lower quartile -8e-05, upper quartile -4e-06; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,-5.6,,100*b/0.001 (X per m->km),,,,,,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,FOREST,green_space,E_environment,level,m,-2.973e-05,,-5.934,,,,,high,"network distance in metres to nearest forest; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.052; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,-2.973,0.5010111223458038,100*b/0.001 (X per m->km),|b/t|,-0.08927384683434206,0.01504446357167881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.934,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,FOREST,green_space,E_environment,level,m,-4e-06,,,,,,,medium,"network distance in metres to nearest forest; GWR local-coefficient median; lower quartile -2.2e-05, upper quartile 8e-06; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,-0.39999999999999997,,100*b/0.001 (X per m->km),,,,,,4383.0,m,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,RAIL_STATION,transit_distance,D_location,dummy,binary,-0.036,,-2.132,,,,,high,"dummy = 1 if within 2 km radius of nearest rail station; t-ratio printed in separate column, no SE; printed standardised coefficient = -0.018; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",pct_premium,-3.5497216759221795,1.6288485255812581,100*(exp(b - se^2/2) - 1),|b/t|,-0.03218669520917442,0.015096948972408262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.132,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,RAIL_STATION,transit_distance,D_location,dummy,binary,-0.019,,,,,,,medium,"dummy = 1 if within 2 km radius of nearest rail station; GWR local-coefficient median; lower quartile -0.073, upper quartile 0.03; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",pct_premium,-1.8820637757194003,,100*(exp(b) - 1),,,,,,4383.0,binary,,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,MALAY,demographics,F_neighbourhood,level,share,0.041,,12.629,,,,,high,"percentage of residents of Malay ethnicity in the area; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.117; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,4.1000000000000005,0.3246496159632592,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.18737942194799526,0.014837233506057111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.629,4383.0,share,unit_not_harmonisable,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,MALAY,demographics,F_neighbourhood,level,share,0.033,,,,,,,medium,"percentage of residents of Malay ethnicity in the area; GWR local-coefficient median; lower quartile 0.006, upper quartile 0.07; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,3.3000000000000003,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,4383.0,share,unit_not_harmonisable,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-HPM,2,Hedonic price model (HPM),7,CHINESE,demographics,F_neighbourhood,level,share,0.025,,7.416,,,,,high,"percentage of residents of Chinese ethnicity in the area; t-ratio printed in separate column, no SE; printed standardised coefficient = 0.067; text states all variables significant at 1% level",ln selling price (MYR),log,OLS,4393.0,0.73,none,"global semi-log OLS, landed residential sales Kuala Lumpur 2010; r2 = adjusted R2; AICc 5663.63; t-ratio in separate column; variables retained by significance (stepwise-type selection)",semi_elasticity,2.5,0.337108953613808,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,0.11132077373488468,0.01501089181969858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.416,4383.0,share,unit_not_harmonisable,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,False,True,False
75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65,75ba68147bf6e34f9b631b70a8cea636eda11e65:T2-GWR,2,GWR model (median of local coefficients),7,CHINESE,demographics,F_neighbourhood,level,share,-0.002,,,,,,,medium,"percentage of residents of Chinese ethnicity in the area; GWR local-coefficient median; lower quartile -0.037, upper quartile 0.025; no inference printed",ln selling price (MYR),log,GWR,4393.0,0.85,none,"geographically weighted regression; beta = median of local coefficients, lower/upper quartiles in notes; no SE or t printed; r2 = adjusted R2; AICc 3185.55",semi_elasticity,-0.2,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,4383.0,share,unit_not_harmonisable,2017.0,Malaysia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,spatial,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Income ('000' BDT/month),other,other,level,currency,0.0031,0.0009,,,***,,,high,"Tenant household monthly income, thousand BDT",Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,0.31,0.09,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.2418335107730399,0.07020972893410836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.444444444444444,191.0,currency,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Income ('000' BDT/month),other,other,level,currency,0.0015,0.0016,,,,,,high,"Tenant household monthly income, thousand BDT",Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,0.15,0.16,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.11822757644062748,0.12610941487000266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9375,62.0,currency,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Income ('000' BDT/month),other,other,level,currency,0.0037,0.0013,,,***,,,high,"Tenant household monthly income, thousand BDT",Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,0.37,0.13,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.2524553540189075,0.08870052979042696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8461538461538463,119.0,currency,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Age of house (Years),age,A_structural,level,years,-0.0023,0.0018,,,,,,high,,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,-0.22999999999999998,0.18,100*b/1 (per year),reported,-0.0920640989270479,0.07205016437768964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.277777777777778,191.0,years,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Age of house (Years),age,A_structural,level,years,-0.0085,0.0064,,,,,,high,,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,-0.8500000000000001,0.64,100*b/1 (per year),reported,-0.166322674985009,0.12523119057694795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.328125,62.0,years,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Age of house (Years),age,A_structural,level,years,-0.0019,0.0018,,,,,,high,,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,-0.19,0.18,100*b/1 (per year),reported,-0.09631278113658055,0.09124368739255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0555555555555556,119.0,years,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Two rooms (=1),bedrooms,A_structural,dummy,binary,0.3915,0.0924,,,***,,,medium,Two-room flat (ref. one room); planned-area column blank,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,47.2896867861802,13.667788850173668,100*(exp(b - se^2/2) - 1),reported,0.29311378190031734,0.06917934469371473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.237012987012988,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Two rooms (=1),bedrooms,A_structural,dummy,binary,0.4072,0.0835,,,***,,,medium,Two-room flat (ref. one room); planned-area column blank,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,pct_premium,49.737544910871236,12.546748381035254,100*(exp(b - se^2/2) - 1),reported,0.4081173380526394,0.08368810836786687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.876646706586826,119.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Three rooms (=1),bedrooms,A_structural,dummy,binary,0.4772,0.0908,,,***,,,high,Three-room flat (ref. one room),Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,60.4926048789646,14.632925964566411,100*(exp(b - se^2/2) - 1),reported,0.35544245031779265,0.06763238576876691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.255506607929515,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Three rooms (=1),bedrooms,A_structural,dummy,binary,-0.0137,0.1151,,,,,,high,Three-room flat (ref. one room),Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,pct_premium,-2.0118864519689805,11.353388240086181,100*(exp(b - se^2/2) - 1),reported,-0.015114708813437953,0.1269856193012196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11902693310165074,62.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Three rooms (=1),bedrooms,A_structural,dummy,binary,0.4943,0.0814,,,***,,,high,Three-room flat (ref. one room),Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,pct_premium,63.39281440806119,13.34431137784058,100*(exp(b - se^2/2) - 1),reported,0.4863824848328258,0.08009616480961364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.072481572481573,119.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Four rooms (=1),bedrooms,A_structural,dummy,binary,0.6137,0.103,,,***,,,high,Four-room flat (ref. one room),Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,83.74807943576994,19.02671216509515,100*(exp(b - se^2/2) - 1),reported,0.3958986814229461,0.06644543618472128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.958252427184466,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Four rooms (=1),bedrooms,A_structural,dummy,binary,0.0777,0.1291,,,,,,high,Four-room flat (ref. one room),Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,pct_premium,7.182906431040825,13.953106963389148,100*(exp(b - se^2/2) - 1),reported,0.0762138574396928,0.12663074640237248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6018590240123936,62.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Four rooms (=1),bedrooms,A_structural,dummy,binary,0.6826,0.1178,,,***,,,high,Four-room flat (ref. one room),Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,pct_premium,96.53327708178183,23.312814273720736,100*(exp(b - se^2/2) - 1),reported,0.46911189707930867,0.08095719524749863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.794567062818336,119.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,True
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Garage (=1),garage,A_structural,dummy,binary,0.1549,0.0425,,,***,,,high,,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,16.648724150011397,4.9620501048752175,100*(exp(b - se^2/2) - 1),reported,0.25500308688959855,0.0699653401730661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.644705882352941,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Garage (=1),garage,A_structural,dummy,binary,0.183,0.0803,,,**,,,high,,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,pct_premium,19.694916260330398,9.64253969688907,100*(exp(b - se^2/2) - 1),reported,0.2780170875690565,0.12199329033767889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2789539227895395,62.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Garage (=1),garage,A_structural,dummy,binary,0.124,0.0569,,,**,,,high,,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,pct_premium,13.018484048765178,6.441170305985307,100*(exp(b - se^2/2) - 1),reported,0.19590172035472098,0.08989361200148083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1792618629173988,119.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Ventilation (1 = Poor to 5 = Excellent),other,other,level,index,0.054,0.0148,,,***,,,high,Perceived ventilation amenity score 1-5,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,5.4,1.48,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2552609786186117,0.06996041636213803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6486486486486482,191.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Ventilation (1 = Poor to 5 = Excellent),other,other,level,index,0.0785,0.0324,,,**,,,high,Perceived ventilation amenity score 1-5,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,7.85,3.2399999999999998,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.29409337271530434,0.12138376147739949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.42283950617284,62.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Ventilation (1 = Poor to 5 = Excellent),other,other,level,index,0.044,0.0174,,,**,,,high,Perceived ventilation amenity score 1-5,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,4.3999999999999995,1.7399999999999998,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2258209226104423,0.08930191030503853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.528735632183908,119.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Open space (meters),green_space,E_environment,level,m,-0.0002,0.0001,,,***,,,high,Distance to nearest open space in metres,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,-20.0,10.0,100*b/0.001 (X per m->km),reported,-0.1432229748078866,0.07161148740394328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,191.0,m,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Open space (meters),green_space,E_environment,level,m,-0.0002,0.0002,,,,,,high,Distance to nearest open space in metres,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,-20.0,20.0,100*b/0.001 (X per m->km),reported,-0.1259881576697424,0.12598815766974242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,62.0,m,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Open space (meters),green_space,E_environment,level,m,-0.0003,0.0001,,,***,,,high,Distance to nearest open space in metres,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,-30.0,10.0,100*b/0.001 (X per m->km),reported,-0.26516504294495524,0.08838834764831845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,119.0,m,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Waterlogging (1 = No to 5 = Extreme),flood_risk,E_environment,level,index,-0.0369,0.0146,,,**,,,high,Perceived waterlogging severity 1-5,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,-3.6900000000000004,1.46,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.1798926529161404,0.07117703882318833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5273972602739727,191.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Waterlogging (1 = No to 5 = Extreme),flood_risk,E_environment,level,index,-0.0601,0.0309,,,*,,,high,Perceived waterlogging severity 1-5,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,-6.01,3.09,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.239805577642699,0.12329438184957403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9449838187702266,62.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Waterlogging (1 = No to 5 = Extreme),flood_risk,E_environment,level,index,-0.0201,0.0164,,,,,,high,Perceived waterlogging severity 1-5,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,-2.01,1.6400000000000001,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.11164900753346732,0.0910967026641226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.225609756097561,119.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Landfill (1 = No to 5 = Extreme),hazard_site,E_environment,level,index,-0.098,0.0163,,,***,,,high,Perceived landfilling (waste) severity 1-5,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,-9.8,1.63,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.3989188845198218,0.06635079405788873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.012269938650308,191.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Landfill (1 = No to 5 = Extreme),hazard_site,E_environment,level,index,-0.1218,0.0381,,,***,,,high,Perceived landfilling (waste) severity 1-5,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,-12.18,3.81,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.37617853800248713,0.1176716116411721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1968503937007875,62.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Landfill (1 = No to 5 = Extreme),hazard_site,E_environment,level,index,-0.0941,0.0183,,,***,,,high,Perceived landfilling (waste) severity 1-5,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,-9.41,1.83,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.42637838595077937,0.08291949482358409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.14207650273224,119.0,index,unit_not_harmonisable,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Stationary shop distance within 0.5-1 km,commercial_distance,D_location,dummy,binary,0.0276,0.0512,,,,,,high,ref. < 0.5 km,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,2.6637891330760555,5.263280171050715,100*(exp(b - se^2/2) - 1),reported,0.03897555602958569,0.07230248075053576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5390625,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Stationary shop distance within 0.5-1 km,commercial_distance,D_location,dummy,binary,0.0309,0.0925,,,,,,high,ref. < 0.5 km,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,pct_premium,2.697940274957489,9.540286834563853,100*(exp(b - se^2/2) - 1),reported,0.04238677903412955,0.12688598901802536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3340540540540541,62.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Stationary shop distance within 0.5-1 km,commercial_distance,D_location,dummy,binary,-0.0006,0.0646,,,,,,high,ref. < 0.5 km,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,pct_premium,-0.2682974373609204,6.456125162567475,100*(exp(b - se^2/2) - 1),reported,-0.0008514224440509584,0.09166981647615322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.009287925696594425,119.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Stationary shop distance above 1 km,commercial_distance,D_location,dummy,binary,-0.0563,0.0895,,,,,,high,ref. < 0.5 km,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,-5.852277848378473,8.46003687441699,100*(exp(b - se^2/2) - 1),reported,-0.04546940450757936,0.0722826235067203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.629050279329609,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Stationary shop distance above 1 km,commercial_distance,D_location,dummy,binary,0.0329,0.2432,,,,,,high,ref. < 0.5 km,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,pct_premium,0.3332420207431852,25.13343564511267,100*(exp(b - se^2/2) - 1),reported,0.017177992017679937,0.12698138780242435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13527960526315788,62.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Stationary shop distance above 1 km,commercial_distance,D_location,dummy,binary,-0.1015,0.0983,,,,,,high,ref. < 0.5 km,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,pct_premium,-10.087341232851344,8.88121999293403,100*(exp(b - se^2/2) - 1),reported,-0.09423282305649858,0.09126193602417546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0325534079348933,119.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Hospital distance (km),hospital_distance,D_location,level,km,-0.1665,0.0505,,,***,,,high,,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,-16.650000000000002,5.050000000000001,100*b/1 (X per km->km),reported,-0.23205265019350957,0.07038233534397738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.297029702970297,191.0,km,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,Hospital distance (km),hospital_distance,D_location,level,km,-0.1722,0.0756,,,**,,,high,,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,-17.22,7.5600000000000005,100*b/1 (X per km->km),reported,-0.2778846880589063,0.12199815573317838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2777777777777777,62.0,km,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,Hospital distance (km),hospital_distance,D_location,level,km,-0.186,0.0882,,,**,,,high,,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,-18.6,8.82,100*b/1 (X per km->km),reported,-0.18980327427609814,0.09000348812447236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.108843537414966,119.0,km,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,True
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,School distance (km),school_distance,D_location,level,km,-0.2411,0.0438,,,***,,,high,,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,semi_elasticity,-24.11,4.38,100*b/1 (X per km->km),reported,-0.3700259211367352,0.06722163146324762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.504566210045662,191.0,km,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c2,3,2 (Planned areas),9,School distance (km),school_distance,D_location,level,km,-0.0476,0.1283,,,,,,high,,Log of rent,log,OLS,72.0,0.6072,se,planned residential areas,semi_elasticity,-4.760000000000001,12.83,100*b/1 (X per km->km),reported,-0.047065524341536595,0.12685938598779717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3710054559625877,62.0,km,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,False
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c3,3,3 (Unplanned areas),9,School distance (km),school_distance,D_location,level,km,-0.254,0.0745,,,***,,,high,,Log of rent,log,OLS,129.0,0.7954,se,unplanned residential areas,semi_elasticity,-25.4,7.449999999999999,100*b/1 (X per km->km),reported,-0.29830874586797146,0.08749606916206247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.409395973154363,119.0,km,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,True,True,True
75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884,75d0711aebd70fde400d1a6a49265e443bbe1884:T3-c1,3,1 (Overall),9,Planned areas (=1),other,other,dummy,binary,0.0343,0.0448,,,,,,high,Planned residential area indicator,Log of rent,log,OLS,201.0,0.7904,se,Khulna city overall,pct_premium,3.385701173441724,4.636329728446526,100*(exp(b - se^2/2) - 1),reported,0.05531386623195913,0.07224668242541601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7656249999999999,191.0,binary,,2020.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,unknown,,2017.0,ols,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Leq 16h,noise,E_environment,level,db,-0.0098,0.001,,,,,,high,"Aircraft noise Leq 16h daytime, dB(A) above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",semi_elasticity,-0.98,0.1,100*b/1 (per dB),reported,-0.15849722199910846,0.016173185918276375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.799999999999999,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Leq 16h,noise,E_environment,level,db,-0.007,0.0013,,,,,,high,"Aircraft noise Leq 16h daytime, dB(A) above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",semi_elasticity,-0.7000000000000001,0.13,100*b/1 (per dB),reported,-0.08786021020997507,0.016316896181852513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.384615384615385,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Leq 16h,noise,E_environment,level,db,-0.0098,0.0009,,,,,,high,"Aircraft noise Leq 16h daytime, dB(A) above 50",log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",semi_elasticity,-0.98,0.09,100*b/1 (per dB),reported,-0.17559143343714675,0.016125743887084903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.88888888888889,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Leq 16h,noise,E_environment,level,db,-0.0099,0.0011,,,,,,high,"Aircraft noise Leq 16h daytime, dB(A) above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",semi_elasticity,-0.9900000000000001,0.11,100*b/1 (per dB),reported,-0.145845837799237,0.01620509308880411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Leq 16h,noise,E_environment,level,db,-0.0099,0.001,,,,,,high,"Aircraft noise Leq 16h daytime, dB(A) above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",semi_elasticity,-0.9900000000000001,0.1,100*b/1 (per dB),reported,-0.16007330327796845,0.016169020533128128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.9,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Leq 16h,noise,E_environment,level,db,-0.0086,0.0023,,,,,,high,"Aircraft noise Leq 16h daytime, dB(A) above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",semi_elasticity,-0.86,0.22999999999999998,100*b/1 (per dB),reported,-0.06113330261550045,0.0163496041878664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.739130434782609,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Age of building,age,A_structural,level,years,-0.0028,,,,,,,medium,SE printed as 0.000 (3-decimal rounding in Model 1) — recorded as null,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",semi_elasticity,-0.27999999999999997,,100*b/1 (per year),,,,,,3727.0,years,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Age of building,age,A_structural,level,years,-0.0029,0.0003,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",semi_elasticity,-0.29,0.03,100*b/1 (per year),reported,-0.15639389604861728,0.016178678901581096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.666666666666666,3727.0,years,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Age of building,age,A_structural,level,years,-0.0026,0.0003,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",semi_elasticity,-0.26,0.03,100*b/1 (per year),reported,-0.1405528612729212,0.016217637839183213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.666666666666668,3727.0,years,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Age of building,age,A_structural,level,years,-0.003,0.0003,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",semi_elasticity,-0.3,0.03,100*b/1 (per year),reported,-0.16164816125656176,0.016164816125656175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.000000000000002,3727.0,years,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Age of building,age,A_structural,level,years,-0.003,0.0003,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",semi_elasticity,-0.3,0.03,100*b/1 (per year),reported,-0.16164816125656176,0.016164816125656175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.000000000000002,3727.0,years,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Age of building,age,A_structural,level,years,-0.0034,0.0003,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",semi_elasticity,-0.33999999999999997,0.03,100*b/1 (per year),reported,-0.1825241932628583,0.016105075876134554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.333333333333334,3727.0,years,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Age squared,age,A_structural,squared,years,0.0007,,,,,,,medium,Scaling of the squared term not stated; SE printed as 0.000 (3-decimal rounding in Model 1) — recorded as null,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",other,0.0007,,not harmonised,,,,,,3727.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Age squared,age,A_structural,squared,years,0.0007,0.0002,,,,,,high,Scaling of the squared term not stated,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",other,0.0007,0.0002,not harmonised,reported,0.057236859042299075,0.016353388297799736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,3727.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Age squared,age,A_structural,squared,years,0.0006,0.0002,,,,,,high,Scaling of the squared term not stated,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",other,0.0006,0.0002,not harmonised,reported,0.049081499325276626,0.016360499775092213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,3727.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Age squared,age,A_structural,squared,years,0.0008,0.0002,,,,,,high,Scaling of the squared term not stated,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",other,0.0008,0.0002,not harmonised,reported,0.06538077703635556,0.01634519425908889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,3727.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Age squared,age,A_structural,squared,years,0.0008,0.0002,,,,,,high,Scaling of the squared term not stated,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",other,0.0008,0.0002,not harmonised,reported,0.06538077703635556,0.01634519425908889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,3727.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Age squared,age,A_structural,squared,years,0.001,0.0002,,,,,,high,Scaling of the squared term not stated,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",other,0.001,0.0002,not harmonised,reported,0.0816278935560424,0.01632557871120848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,3727.0,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,New,condition,A_structural,dummy,binary,0.1377,0.012,,,,,,high,Condition: new (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,14.754858279035243,1.3771574511153968,100*(exp(b - se^2/2) - 1),reported,0.18472835308971755,0.01609833142394053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.475,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,New,condition,A_structural,dummy,binary,0.1373,0.0116,,,,,,high,Condition: new (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,14.709506942483364,1.3307198083497631,100*(exp(b - se^2/2) - 1),reported,0.19033562514266122,0.01608079571489344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.836206896551726,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,New,condition,A_structural,dummy,binary,0.1358,0.0119,,,,,,high,Condition: new (ref. needs renovation),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,14.53716792153652,1.3630888083526214,100*(exp(b - se^2/2) - 1),reported,0.1837448316580981,0.016101351227771486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.411764705882353,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,New,condition,A_structural,dummy,binary,0.1359,0.0116,,,,,,high,Condition: new (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,14.549025995638566,1.3288581041151162,100*(exp(b - se^2/2) - 1),reported,0.1884641160147208,0.01608670894606888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.715517241379311,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,New,condition,A_structural,dummy,binary,0.1367,0.0116,,,,,,high,Condition: new (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,14.64070188190021,1.3299216159464202,100*(exp(b - se^2/2) - 1),reported,0.1895338026113303,0.016083336578576676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.784482758620689,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,New,condition,A_structural,dummy,binary,0.1271,0.0117,,,,,,high,Condition: new (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,13.545284643076606,1.3285707612378306,100*(exp(b - se^2/2) - 1),reported,0.1751906577913077,0.016126913423747447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.863247863247862,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Renovated,renovation,A_structural,dummy,binary,0.1451,0.009,,,,,,high,Condition: renovated (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,15.610835666882771,1.040539662004895,100*(exp(b - se^2/2) - 1),reported,0.25533233263965127,0.01583729148006107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.122222222222224,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Renovated,renovation,A_structural,dummy,binary,0.1452,0.0093,,,,,,high,Condition: renovated (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,15.62207994547784,1.0753318452130993,100*(exp(b - se^2/2) - 1),reported,0.24776884619677073,0.01586949221508242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.612903225806452,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Renovated,renovation,A_structural,dummy,binary,0.1419,0.0096,,,,,,high,Condition: renovated (ref. needs renovation),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,15.240829243721565,1.1063629407694486,100*(exp(b - se^2/2) - 1),reported,0.2353211406101963,0.015920246299209895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.781250000000002,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Renovated,renovation,A_structural,dummy,binary,0.1443,0.0093,,,,,,high,Condition: renovated (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,15.518066886424364,1.0743644819311815,100*(exp(b - se^2/2) - 1),reported,0.2463265465361347,0.015875515473222815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.516129032258068,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Renovated,renovation,A_structural,dummy,binary,0.1448,0.0092,,,,,,high,Condition: renovated (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,15.575947269734659,1.0633437146353608,100*(exp(b - se^2/2) - 1),reported,0.24964763263482145,0.015861589918786997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.739130434782611,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Renovated,renovation,A_structural,dummy,binary,0.1491,0.0093,,,,,,high,Condition: renovated (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,16.073886507396274,1.0795338279497553,100*(exp(b - se^2/2) - 1),reported,0.25399970667924454,0.015843040054439796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.03225806451613,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Well maintained,condition,A_structural,dummy,binary,0.1126,0.008,,,,,,high,Condition: well maintained (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,11.914835641360444,0.8953473357872158,100*(exp(b - se^2/2) - 1),reported,0.22465843512571632,0.015961522921898137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.075000000000001,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Well maintained,condition,A_structural,dummy,binary,0.1125,0.008,,,,,,high,Condition: well maintained (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,11.903644717351813,0.8952578055302246,100*(exp(b - se^2/2) - 1),reported,0.22446897336190988,0.015962238105735813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.0625,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Well maintained,condition,A_structural,dummy,binary,0.1096,0.0082,,,,,,high,Condition: well maintained (ref. needs renovation),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,11.579413489161205,0.9149819517872426,100*(exp(b - se^2/2) - 1),reported,0.21387019270294325,0.0160012370452932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.365853658536585,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Well maintained,condition,A_structural,dummy,binary,0.1119,0.008,,,,,,high,Condition: well maintained (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,11.836522669149542,0.8947208119610869,100*(exp(b - se^2/2) - 1),reported,0.22333166874463536,0.015966517872717453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.987499999999999,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Well maintained,condition,A_structural,dummy,binary,0.1119,0.008,,,,,,high,Condition: well maintained (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,11.836522669149542,0.8947208119610869,100*(exp(b - se^2/2) - 1),reported,0.22333166874463536,0.015966517872717453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.987499999999999,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Well maintained,condition,A_structural,dummy,binary,0.1111,0.008,,,,,,high,Condition: well maintained (ref. needs renovation),log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,11.74708922915999,0.8940053215458436,100*(exp(b - se^2/2) - 1),reported,0.22181384325426934,0.015972193933700758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.887500000000001,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Brick building,roof_materials,A_structural,binary,binary,0.0284,0.009,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,2.876545913299644,0.925926412480037,100*(exp(b - se^2/2) - 1),reported,0.051619851337817775,0.01635840359297042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.155555555555556,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Brick building,roof_materials,A_structural,binary,binary,0.029,0.0092,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,2.9381030148120324,0.9470706269171203,100*(exp(b - se^2/2) - 1),reported,0.05156468023014001,0.016358450279906487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1521739130434785,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Brick building,roof_materials,A_structural,binary,binary,0.0298,0.0094,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,3.02029482827606,0.9684335558351763,100*(exp(b - se^2/2) - 1),reported,0.051858976565356506,0.016358200661555408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1702127659574466,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Brick building,roof_materials,A_structural,binary,binary,0.029,0.0093,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,2.9380077971107754,0.9573648728618716,100*(exp(b - se^2/2) - 1),reported,0.051011671931503806,0.016358915481482255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1182795698924735,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Brick building,roof_materials,A_structural,binary,binary,0.0278,0.0093,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,2.8145562734826157,0.9562167240415074,100*(exp(b - se^2/2) - 1),reported,0.04890600149118458,0.0163606407866193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.989247311827957,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Brick building,roof_materials,A_structural,binary,binary,0.028,0.0093,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,2.835121241165517,0.9564079865119253,100*(exp(b - se^2/2) - 1),reported,0.04925699261347977,0.016360358260905778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0107526881720434,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Detached,dwelling_type,A_structural,binary,binary,0.0256,0.006,,,,,,high,Detached house,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,2.5912027607686294,0.6155582965142293,100*(exp(b - se^2/2) - 1),reported,0.06971896799153866,0.016340383123016874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.266666666666667,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Detached,dwelling_type,A_structural,binary,binary,0.0264,0.0056,,,,,,high,Detached house,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,2.673546763270518,0.5749808775037913,100*(exp(b - se^2/2) - 1),reported,0.07699192482602739,0.016331620417642172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.714285714285714,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Detached,dwelling_type,A_structural,binary,binary,0.0253,0.0057,,,,,,high,Detached house,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,2.5606100097955276,0.5846049738864971,100*(exp(b - se^2/2) - 1),reported,0.07251387929796531,0.01633711905132025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.43859649122807,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Detached,dwelling_type,A_structural,binary,binary,0.0277,0.0056,,,,,,high,Detached house,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,2.8071091708176343,0.5757288387139952,100*(exp(b - se^2/2) - 1),reported,0.08075904372784713,0.016326738082164043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.946428571428571,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Detached,dwelling_type,A_structural,binary,binary,0.0271,0.0056,,,,,,high,Detached house,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,2.745443406894288,0.5753835050212345,100*(exp(b - se^2/2) - 1),reported,0.0790207935976936,0.016329020079228197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.839285714285714,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Detached,dwelling_type,A_structural,binary,binary,0.0267,0.0057,,,,,,high,Detached house,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,2.704295420127867,0.5854239940302656,100*(exp(b - se^2/2) - 1),reported,0.07650363220747551,0.016332236089236347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.684210526315789,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Cellar,basement,A_structural,binary,binary,0.0476,0.01,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,4.869863474217095,1.0487510709848031,100*(exp(b - se^2/2) - 1),reported,0.07773402340261264,0.01633067718542282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.760000000000001,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Cellar,basement,A_structural,binary,binary,0.0478,0.0103,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,4.890520152328892,1.0804296674406826,100*(exp(b - se^2/2) - 1),reported,0.07579835526408402,0.01633311839372522,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.640776699029126,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Cellar,basement,A_structural,binary,binary,0.0487,0.0106,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,4.9846349865271256,1.112899651803396,100*(exp(b - se^2/2) - 1),reported,0.07504418583990098,0.016334052770081115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.59433962264151,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Cellar,basement,A_structural,binary,binary,0.0468,0.0103,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,4.785682059959262,1.079349777808049,100*(exp(b - se^2/2) - 1),reported,0.0742214439763655,0.016335061387960786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.543689320388349,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Cellar,basement,A_structural,binary,binary,0.0458,0.0103,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,4.680948753280423,1.078270967525283,100*(exp(b - se^2/2) - 1),reported,0.07264397573720444,0.016336963975834187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.446601941747573,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Cellar,basement,A_structural,binary,binary,0.0441,0.0104,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,4.503034157994623,1.0868903326829793,100*(exp(b - se^2/2) - 1),reported,0.06929157753377348,0.01634087089231846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.240384615384616,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Double glazing,insulation,B_interior,binary,binary,0.0312,0.005,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,3.1678925551215764,0.5158459108091927,100*(exp(b - se^2/2) - 1),reported,0.10168292482603558,0.01629534051699288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.239999999999999,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Double glazing,insulation,B_interior,binary,binary,0.0317,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,3.2192184487312536,0.5677142880816927,100*(exp(b - se^2/2) - 1),reported,0.0939918015494865,0.016307725820888824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.763636363636364,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Double glazing,insulation,B_interior,binary,binary,0.0322,0.0056,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,3.2707836472077645,0.5783254564964275,100*(exp(b - se^2/2) - 1),reported,0.09377138115476108,0.016308066287784535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Double glazing,insulation,B_interior,binary,binary,0.034,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,3.4568958754279233,0.5690215337004648,100*(exp(b - se^2/2) - 1),reported,0.10074449972773084,0.016296904367721163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1818181818181825,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Double glazing,insulation,B_interior,binary,binary,0.0326,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,3.312157561662543,0.5682254609342458,100*(exp(b - se^2/2) - 1),reported,0.09663575931318147,0.016303579025230004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.927272727272727,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Double glazing,insulation,B_interior,binary,binary,0.0291,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,2.9511970595558656,0.5662401481450301,100*(exp(b - se^2/2) - 1),reported,0.08634271328109008,0.016319069520480945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.290909090909091,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Floor heating,heating,A_structural,binary,binary,0.0356,0.006,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,3.6222614898801186,0.6217447602442431,100*(exp(b - se^2/2) - 1),reported,0.09673364475035996,0.016303423272532577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.933333333333334,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Floor heating,heating,A_structural,binary,binary,0.0359,0.0064,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,3.653095771798931,0.663393399097204,100*(exp(b - se^2/2) - 1),reported,0.09149749713981345,0.016311531523532204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.609375,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Floor heating,heating,A_structural,binary,binary,0.0357,0.0066,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,3.6322325035784697,0.6839876316120044,100*(exp(b - se^2/2) - 1),reported,0.08825647105257256,0.016316322379467195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.409090909090909,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Floor heating,heating,A_structural,binary,binary,0.0333,0.0064,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,3.38394776681874,0.6616708165872022,100*(exp(b - se^2/2) - 1),reported,0.08492057598209958,0.016321071660223344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.203125,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Floor heating,heating,A_structural,binary,binary,0.0349,0.0064,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,3.549494485303817,0.6627303372842683,100*(exp(b - se^2/2) - 1),reported,0.08896928114898875,0.016315283649098223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.453125,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Floor heating,heating,A_structural,binary,binary,0.0326,0.0065,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,3.3115376905767935,0.6715391811041086,100*(exp(b - se^2/2) - 1),reported,0.08187737509955736,0.01632524350144549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.015384615384615,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Garage,garage,A_structural,binary,binary,0.0253,0.005,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,2.5609940999992142,0.5128113806021904,100*(exp(b - se^2/2) - 1),reported,0.08260078437834147,0.01632426568741926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.06,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Garage,garage,A_structural,binary,binary,0.0257,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,2.601757373646252,0.5643182008032935,100*(exp(b - se^2/2) - 1),reported,0.07631717866657951,0.0163324701426532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.672727272727273,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Garage,garage,A_structural,binary,binary,0.0253,0.0056,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,2.5606679565565615,0.5743487462744533,100*(exp(b - se^2/2) - 1),reported,0.07380177865153938,0.016335571559234013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.517857142857143,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Garage,garage,A_structural,binary,binary,0.0254,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,2.5709814630515693,0.5641489307348323,100*(exp(b - se^2/2) - 1),reported,0.07543141533533147,0.016333574186784373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.618181818181818,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Garage,garage,A_structural,binary,binary,0.0259,0.0055,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,2.622279777292924,0.5644310757305707,100*(exp(b - se^2/2) - 1),reported,0.07690758732182051,0.016331727037452233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.709090909090909,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Garage,garage,A_structural,binary,binary,0.0278,0.0056,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,2.817390395787389,0.5757864144766066,100*(exp(b - se^2/2) - 1),reported,0.0810486808919141,0.016326352985421542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.964285714285714,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Underground garage,garage,A_structural,binary,binary,0.029,0.007,,,,,,high,Underground garage,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,2.9399373881519697,0.7205972161325915,100*(exp(b - se^2/2) - 1),reported,0.06770528473816853,0.0163426549367993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.142857142857143,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Underground garage,garage,A_structural,binary,binary,0.0293,0.0071,,,,,,high,Underground garage,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,2.9707514077235375,0.7311107624093711,100*(exp(b - se^2/2) - 1),reported,0.06744342277965115,0.016342945451724343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.126760563380281,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Underground garage,garage,A_structural,binary,binary,0.0264,0.0072,,,,,,high,Underground garage,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,2.6724953915346905,0.7392611282191603,100*(exp(b - se^2/2) - 1),reported,0.059952849027080264,0.016350777007385527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.666666666666667,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Underground garage,garage,A_structural,binary,binary,0.027,0.0071,,,,,,high,Underground garage,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,2.734190828435734,0.7294311399620775,100*(exp(b - se^2/2) - 1),reported,0.06217056009786972,0.01634855469240278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8028169014084505,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Underground garage,garage,A_structural,binary,binary,0.0289,0.0072,,,,,,high,Underground garage,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,2.9294977491051144,0.741111593157097,100*(exp(b - se^2/2) - 1),reported,0.06560681816939823,0.016344951239434854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.013888888888888,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Underground garage,garage,A_structural,binary,binary,0.0287,0.0073,,,,,,high,Underground garage,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,2.908839299072463,0.751254543793872,100*(exp(b - se^2/2) - 1),reported,0.06426590708509713,0.01634638054777732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.931506849315068,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Modern kitchen,kitchen_quality,B_interior,binary,binary,0.0589,0.005,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,6.06559102380424,0.5303345842598922,100*(exp(b - se^2/2) - 1),reported,0.18946429344178622,0.01608355631933669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.78,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Modern kitchen,kitchen_quality,B_interior,binary,binary,0.0586,0.0052,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,6.03366796457494,0.5513825280571756,100*(exp(b - se^2/2) - 1),reported,0.18152592867939932,0.01610810288622656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.26923076923077,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Modern kitchen,kitchen_quality,B_interior,binary,binary,0.0592,0.0054,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,6.097194792145633,0.5729332051828222,100*(exp(b - se^2/2) - 1),reported,0.17674873883505374,0.016122351177521792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.962962962962964,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Modern kitchen,kitchen_quality,B_interior,binary,binary,0.0587,0.0053,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,6.04421618832931,0.5620422396259663,100*(exp(b - se^2/2) - 1),reported,0.17850513005261423,0.016117158250065676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.075471698113208,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Modern kitchen,kitchen_quality,B_interior,binary,binary,0.0585,0.0052,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,6.023065127929161,0.5513273925611906,100*(exp(b - se^2/2) - 1),reported,0.1812263396911068,0.016109007972542822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.250000000000002,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Modern kitchen,kitchen_quality,B_interior,binary,binary,0.0634,0.0053,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,6.543797099911819,0.5646900556456685,100*(exp(b - se^2/2) - 1),reported,0.1922881550412152,0.01607456185675774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.962264150943396,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Swimming pool,pool,C_exterior,binary,binary,0.0637,0.015,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,6.5652726335688305,1.5986589285168566,100*(exp(b - se^2/2) - 1),reported,0.06939373775627673,0.016340754573691538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.246666666666667,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Swimming pool,pool,C_exterior,binary,binary,0.0641,0.0147,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,6.608382208265695,1.567312549598071,100*(exp(b - se^2/2) - 1),reported,0.07124526015531388,0.016338616603480717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.3605442176870755,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Swimming pool,pool,C_exterior,binary,binary,0.0653,0.0152,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,6.735591204137004,1.6225684145800694,100*(exp(b - se^2/2) - 1),reported,0.07019678784996061,0.01633983423153754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.296052631578947,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Swimming pool,pool,C_exterior,binary,binary,0.0598,0.0147,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,6.150950348095563,1.5605875746929077,100*(exp(b - se^2/2) - 1),reported,0.06648782055469657,0.016343996022642804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.068027210884353,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Swimming pool,pool,C_exterior,binary,binary,0.0624,0.0147,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,6.427301920366513,1.5646503817475659,100*(exp(b - se^2/2) - 1),reported,0.06936497483225325,0.016340787340290427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.244897959183674,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Swimming pool,pool,C_exterior,binary,binary,0.0587,0.0149,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,6.0339346395852145,1.580081013288094,100*(exp(b - se^2/2) - 1),reported,0.06439760903698316,0.016346241476167787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9395973154362416,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,18,Sauna,recreation_private,C_exterior,binary,binary,0.0396,0.011,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,4.03315911712312,1.1444339864501194,100*(exp(b - se^2/2) - 1),reported,0.05886660941064001,0.0163518359474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6000000000000005,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Sauna,recreation_private,C_exterior,binary,binary,0.0396,0.0109,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,4.033273033494722,1.1340300411187547,100*(exp(b - se^2/2) - 1),reported,0.059404772845442266,0.01635131373776062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6330275229357802,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,18,Sauna,recreation_private,C_exterior,binary,binary,0.0379,0.0113,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,3.856105590135095,1.173648922392023,100*(exp(b - se^2/2) - 1),reported,0.05485631649286043,0.01635557721291089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3539823008849563,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Sauna,recreation_private,C_exterior,binary,binary,0.0391,0.0109,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,3.981269398970011,1.1334631678283278,100*(exp(b - se^2/2) - 1),reported,0.05865730974282897,0.016352037754394772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5871559633027528,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Sauna,recreation_private,C_exterior,binary,binary,0.0378,0.0109,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,3.8461815748618955,1.1319906230716263,100*(exp(b - se^2/2) - 1),reported,0.05671344700551246,0.016353877575663645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.467889908256881,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Sauna,recreation_private,C_exterior,binary,binary,0.0526,0.011,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",pct_premium,5.394419205192591,1.1594087533648771,100*(exp(b - se^2/2) - 1),reported,0.07808816134634139,0.016330223855698767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.781818181818182,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Log lot size,lot_size,A_structural,log,m2,0.1691,0.006,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",elasticity,0.1691,0.006,b (log-log),reported,0.4191415176672579,0.014871963962173551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.183333333333334,3727.0,m2,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Log lot size,lot_size,A_structural,log,m2,0.1692,0.0059,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",elasticity,0.1692,0.0059,b (log-log),reported,0.4251772874044693,0.01482592196032133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.677966101694913,3727.0,m2,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Log lot size,lot_size,A_structural,log,m2,0.1646,0.006,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",elasticity,0.1646,0.006,b (log-log),reported,0.40988266850055105,0.014941045024321423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.433333333333334,3727.0,m2,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Log lot size,lot_size,A_structural,log,m2,0.17,0.0059,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",elasticity,0.17,0.0059,b (log-log),reported,0.42682205768302484,0.014813236119587334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.8135593220339,3727.0,m2,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Log lot size,lot_size,A_structural,log,m2,0.1693,0.006,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",elasticity,0.1693,0.006,b (log-log),reported,0.4195500324020521,0.01486887297349269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.21666666666667,3727.0,m2,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Log lot size,lot_size,A_structural,log,m2,0.1786,0.0059,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",elasticity,0.1786,0.0059,b (log-log),reported,0.4442362344064352,0.01467521715004461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.27118644067797,3727.0,m2,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Log size,living_area,A_structural,log,m3,0.4163,0.011,,,,,,high,Log of building volume in m3 (not floor area),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",elasticity,0.4163,0.011,b (log-log),reported,0.5268891346007526,0.01392212462312822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.84545454545455,3727.0,m3,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Log size,living_area,A_structural,log,m3,0.4155,0.0111,,,,,,high,Log of building volume in m3 (not floor area),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",elasticity,0.4155,0.0111,b (log-log),reported,0.5227163959223781,0.013964264728612267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.43243243243243,3727.0,m3,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Log size,living_area,A_structural,log,m3,0.4206,0.0113,,,,,,high,Log of building volume in m3 (not floor area),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",elasticity,0.4206,0.0113,b (log-log),reported,0.5205680811138584,0.013985780591028535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.2212389380531,3727.0,m3,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,22,Log size,living_area,A_structural,log,m3,0.4165,0.0111,,,,,,high,Log of building volume in m3 (not floor area),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",elasticity,0.4165,0.0111,b (log-log),reported,0.5236297983499207,0.013955079859985883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.52252252252252,3727.0,m3,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,22,Log size,living_area,A_structural,log,m3,0.4158,0.0112,,,,,,high,Log of building volume in m3 (not floor area),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",elasticity,0.4158,0.0112,b (log-log),reported,0.5195858205824543,0.013995577658786645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.125,3727.0,m3,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,22,Log size,living_area,A_structural,log,m3,0.4223,0.0113,,,,,,high,Log of building volume in m3 (not floor area),log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",elasticity,0.4223,0.0113,b (log-log),reported,0.5220994373537756,0.013970456173567755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.37168141592921,3727.0,m3,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Log rooms,bedrooms,A_structural,log,count,0.1243,0.013,,,,,,high,Number of rooms (all rooms),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",elasticity,0.1243,0.013,b (log-log),reported,0.1547340014688453,0.01618296073286395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.561538461538461,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Log rooms,bedrooms,A_structural,log,count,0.1246,0.013,,,,,,high,Number of rooms (all rooms),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",elasticity,0.1246,0.013,b (log-log),reported,0.15509848135249624,0.016182024539185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.584615384615386,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Log rooms,bedrooms,A_structural,log,count,0.1223,0.0134,,,,,,high,Number of rooms (all rooms),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",elasticity,0.1223,0.0134,b (log-log),reported,0.14785706861390796,0.016200202121229492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.126865671641792,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Log rooms,bedrooms,A_structural,log,count,0.1199,0.013,,,,,,high,Number of rooms (all rooms),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",elasticity,0.1199,0.013,b (log-log),reported,0.14938110708912286,0.016196450309913235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.223076923076924,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Log rooms,bedrooms,A_structural,log,count,0.1207,0.013,,,,,,high,Number of rooms (all rooms),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",elasticity,0.1207,0.013,b (log-log),reported,0.15035535248369988,0.016194031336272563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.284615384615385,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,23,Log rooms,bedrooms,A_structural,log,count,0.1246,0.0131,,,,,,high,Number of rooms (all rooms),log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",elasticity,0.1246,0.0131,b (log-log),reported,0.15394268666671762,0.01618498551632425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.511450381679388,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Log bathrooms,bathrooms,A_structural,log,count,0.1005,0.008,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",elasticity,0.1005,0.008,b (log-log),reported,0.2015537232544312,0.016044077472989547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5625,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Log bathrooms,bathrooms,A_structural,log,count,0.0998,0.0078,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",elasticity,0.0998,0.0078,b (log-log),reported,0.20512640940909657,0.016031923781472477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.794871794871796,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Log bathrooms,bathrooms,A_structural,log,count,0.1018,0.008,,,,,,high,,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",elasticity,0.1018,0.008,b (log-log),reported,0.20405299441639382,0.016035598775355115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.725,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Log bathrooms,bathrooms,A_structural,log,count,0.096,0.0077,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",elasticity,0.096,0.0077,b (log-log),reported,0.20009128207317384,0.016048988249619152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.467532467532468,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Log bathrooms,bathrooms,A_structural,log,count,0.0969,0.0077,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",elasticity,0.0969,0.0077,b (log-log),reported,0.20189101872499673,0.016042939568446594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.584415584415584,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c3,6,3,23,Log bathrooms,bathrooms,A_structural,log,count,0.0969,0.0078,,,,,,high,,log house price (CHF),log,spatial_error,3737.0,,se,"Model 6, 'No GIS': location variables replaced by community dummies; rho 0.1453",elasticity,0.0969,0.0078,b (log-log),reported,0.19940623375587835,0.016051275782206924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.423076923076923,3727.0,count,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Share Swiss residents,demographics,F_neighbourhood,level,share,0.0149,0.006,,,,,,high,Share of Swiss residents within 300 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",semi_elasticity,1.49,0.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04064398778636256,0.016366706491152707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4833333333333334,3727.0,share,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Share Swiss residents,demographics,F_neighbourhood,level,share,0.0157,0.0064,,,,,,high,Share of Swiss residents within 300 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",semi_elasticity,1.5699999999999998,0.64,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04015037877699501,0.01636703338680052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4531249999999996,3727.0,share,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Share Swiss residents,demographics,F_neighbourhood,level,share,0.0159,0.0063,,,,,,high,Share of Swiss residents within 300 m,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",semi_elasticity,1.59,0.63,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.041305328517565194,0.016366262242808852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5238095238095237,3727.0,share,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Share Swiss residents,demographics,F_neighbourhood,level,share,0.016,0.0064,,,,,,high,Share of Swiss residents within 300 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",semi_elasticity,1.6,0.64,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04091631107705446,0.01636652443082178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,3727.0,share,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Share Swiss residents,demographics,F_neighbourhood,level,share,0.0169,0.0066,,,,,,high,Share of Swiss residents within 300 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",semi_elasticity,1.69,0.66,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04190650027956452,0.01636585218018496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5606060606060606,3727.0,share,unit_not_harmonisable,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Road traffic noise,noise,E_environment,level,db,-0.005,0.001,,,,,,high,"Road traffic noise Lr 16h, dB above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",semi_elasticity,-0.5,0.1,100*b/1 (per dB),reported,-0.0816278935560424,0.01632557871120848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Road traffic noise,noise,E_environment,level,db,-0.0051,0.0009,,,,,,high,"Road traffic noise Lr 16h, dB above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",semi_elasticity,-0.51,0.09,100*b/1 (per dB),reported,-0.09242406915919278,0.016310129851622255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.666666666666667,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Road traffic noise,noise,E_environment,level,db,-0.0053,0.0009,,,,,,high,"Road traffic noise Lr 16h, dB above 50",log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",semi_elasticity,-0.53,0.09,100*b/1 (per dB),reported,-0.0960157532081422,0.01630456186553358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.888888888888889,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Road traffic noise,noise,E_environment,level,db,-0.0053,0.0009,,,,,,high,"Road traffic noise Lr 16h, dB above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",semi_elasticity,-0.53,0.09,100*b/1 (per dB),reported,-0.0960157532081422,0.01630456186553358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.888888888888889,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Road traffic noise,noise,E_environment,level,db,-0.0054,0.0009,,,,,,high,"Road traffic noise Lr 16h, dB above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",semi_elasticity,-0.54,0.09,100*b/1 (per dB),reported,-0.0978101987120808,0.016301699785346797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.000000000000001,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Log travel dist. to CBD,cbd_distance,D_location,log,minutes,-0.231,0.015,,,,,,high,Car travel time to Zurich main station,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",elasticity,-0.231,0.015,b (log-log),reported,-0.2445936248766915,0.015882702914070873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.400000000000002,3727.0,minutes,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Log travel dist. to CBD,cbd_distance,D_location,log,minutes,-0.2354,0.0151,,,,,,high,Car travel time to Zurich main station,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",elasticity,-0.2354,0.0151,b (log-log),reported,-0.2474187699085575,0.0158709576279491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.589403973509933,3727.0,minutes,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Log travel dist. to CBD,cbd_distance,D_location,log,minutes,-0.229,0.0133,,,,,,high,Car travel time to Zurich main station,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",elasticity,-0.229,0.0133,b (log-log),reported,-0.27144630805605924,0.015765222258277672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.218045112781958,3727.0,minutes,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Log travel dist. to CBD,cbd_distance,D_location,log,minutes,-0.2335,0.0162,,,,,,high,Car travel time to Zurich main station,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",elasticity,-0.2335,0.0162,b (log-log),reported,-0.22978053000758497,0.015941946835644007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.413580246913583,3727.0,minutes,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Log travel dist. to CBD,cbd_distance,D_location,log,minutes,-0.2307,0.0159,,,,,,high,Car travel time to Zurich main station,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",elasticity,-0.2307,0.0159,b (log-log),reported,-0.23122717924282335,0.015936333549895498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.509433962264149,3727.0,minutes,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Low density (< 40%),density,F_neighbourhood,dummy,binary,-0.0034,0.006,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,-0.3412165287575375,0.5979634640729387,100*(exp(b - se^2/2) - 1),reported,-0.009281737078453995,0.016379536020801166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5666666666666667,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Low density (< 40%),density,F_neighbourhood,dummy,binary,-0.004,0.0059,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,-0.40093460242015544,0.587644713712955,100*(exp(b - se^2/2) - 1),reported,-0.011104563833412308,0.016379231654283153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6779661016949153,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Low density (< 40%),density,F_neighbourhood,dummy,binary,-0.0044,0.0056,,,,,,high,Maximum allowed built density category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,-0.4405945218899143,0.5575414128582312,100*(exp(b - se^2/2) - 1),reported,-0.012869124056204903,0.0163788851624426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7857142857142858,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Low density (< 40%),density,F_neighbourhood,dummy,binary,-0.0077,0.0059,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,-0.7687702288432741,0.5854744457438733,100*(exp(b - se^2/2) - 1),reported,-0.021372720341008775,0.016376500001552177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.305084745762712,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Low density (< 40%),density,F_neighbourhood,dummy,binary,-0.0045,0.006,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,-0.4507809191134693,0.5973060658977424,100*(exp(b - se^2/2) - 1),reported,-0.012284254245407904,0.016379005660543875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7499999999999999,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Middle density (40–50%),density,F_neighbourhood,dummy,binary,-0.0024,0.007,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,-0.24215632737187853,0.6983220143881672,100*(exp(b - se^2/2) - 1),reported,-0.005615994276585901,0.016379983306708878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3428571428571428,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,True
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Middle density (40–50%),density,F_neighbourhood,dummy,binary,-0.0029,0.0068,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,-0.2918851844522563,0.6780308566379161,100*(exp(b - se^2/2) - 1),reported,-0.006985520834972782,0.016379841957867215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4264705882352941,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Middle density (40–50%),density,F_neighbourhood,dummy,binary,-0.0034,0.0065,,,,,,high,Maximum allowed built density category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,-0.34152796196927904,0.6477937527456835,100*(exp(b - se^2/2) - 1),reported,-0.008567811898222142,0.016379640393659977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5230769230769231,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Middle density (40–50%),density,F_neighbourhood,dummy,binary,-0.0065,0.0068,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,-0.6501890638372254,0.6755943339263444,100*(exp(b - se^2/2) - 1),reported,-0.015655664948352504,0.016378234099814926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9558823529411765,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Middle density (40–50%),density,F_neighbourhood,dummy,binary,-0.0027,0.0069,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,-0.27200988089052247,0.6881395127879821,100*(exp(b - se^2/2) - 1),reported,-0.006409528102080653,0.01637990514976167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.391304347826087,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Higher density (> 50%),density,F_neighbourhood,dummy,binary,-0.0117,0.007,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,-1.1656035880287186,0.691857725190424,100*(exp(b - se^2/2) - 1),reported,-0.02736814850500455,0.016374105943165115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6714285714285715,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Higher density (> 50%),density,F_neighbourhood,dummy,binary,-0.0126,0.0072,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,-1.2546547473644476,0.7109849143091207,100*(exp(b - se^2/2) - 1),reported,-0.02865365280678767,0.016373515889592953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.75,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Higher density (> 50%),density,F_neighbourhood,dummy,binary,-0.0132,0.007,,,,,,high,Maximum allowed built density category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,-1.3137440495242236,0.6908207165535551,100*(exp(b - se^2/2) - 1),reported,-0.030873730875751997,0.016372433040171514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8857142857142857,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Higher density (> 50%),density,F_neighbourhood,dummy,binary,-0.0145,0.0072,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,-1.442092780825055,0.7096353254873153,100*(exp(b - se^2/2) - 1),reported,-0.032970052348743034,0.016371336338686197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.013888888888889,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,True
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Higher density (> 50%),density,F_neighbourhood,dummy,binary,-0.012,0.0073,,,,,,high,Maximum allowed built density category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,-1.1954613958118032,0.7212923503892092,100*(exp(b - se^2/2) - 1),reported,-0.026916668621506667,0.016374306744749892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.643835616438356,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Lake view > 0–20 km2,view,E_environment,dummy,binary,0.0552,0.007,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,5.672605382517926,0.7397263607514554,100*(exp(b - se^2/2) - 1),reported,0.12810561397020753,0.016245277133903127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.885714285714285,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Lake view > 0–20 km2,view,E_environment,dummy,binary,0.0514,0.0066,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,5.272097812498044,0.6948109783807992,100*(exp(b - se^2/2) - 1),reported,0.1265418567088346,0.016248565258332848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.787878787878788,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Lake view > 0–20 km2,view,E_environment,dummy,binary,0.0563,0.0059,,,,,,high,Lake view area category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,5.78965977878656,0.6241698562766487,100*(exp(b - se^2/2) - 1),reported,0.15443124979787595,0.016183736657326253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.542372881355933,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Lake view > 0–20 km2,view,E_environment,dummy,binary,0.0545,0.0067,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,5.598877447968764,0.7075283591971957,100*(exp(b - se^2/2) - 1),reported,0.1320750247439219,0.016236746161179393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.134328358208954,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Lake view > 0–20 km2,view,E_environment,dummy,binary,0.0557,0.0067,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,5.7256721625196905,0.7083779028524803,100*(exp(b - se^2/2) - 1),reported,0.13493071358501896,0.016230444901609103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.313432835820896,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Lake view 20–40 km2,view,E_environment,dummy,binary,0.139,0.012,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,14.904136604686302,1.3789489200043263,100*(exp(b - se^2/2) - 1),reported,0.18641201009888037,0.01609312317400406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.583333333333334,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Lake view 20–40 km2,view,E_environment,dummy,binary,0.1346,0.0118,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,14.399941317664911,1.3500132922022055,100*(exp(b - se^2/2) - 1),reported,0.18366727393667404,0.016101588651209163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.40677966101695,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Lake view 20–40 km2,view,E_environment,dummy,binary,0.1411,0.0107,,,,,,high,Lake view area category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,15.14738781979801,1.2321475819413379,100*(exp(b - se^2/2) - 1),reported,0.21113543125214343,0.01601097884052399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.186915887850468,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Lake view 20–40 km2,view,E_environment,dummy,binary,0.1318,0.0123,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,14.079382181138755,1.4032825481215527,100*(exp(b - se^2/2) - 1),reported,0.17287880290268726,0.016133606037200706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.715447154471544,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Lake view 20–40 km2,view,E_environment,dummy,binary,0.1357,0.0122,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,14.52530076770051,1.3973126535043223,100*(exp(b - se^2/2) - 1),reported,0.1792458232862573,0.0161149524251462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.122950819672129,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Lake view > 40 km2,view,E_environment,dummy,binary,0.1121,0.014,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,11.85150976730036,1.5660746045334046,100*(exp(b - se^2/2) - 1),reported,0.13004514272275852,0.016241141820861903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.007142857142858,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Lake view > 40 km2,view,E_environment,dummy,binary,0.1074,0.0138,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,11.32735062773611,1.5364637337742675,100*(exp(b - se^2/2) - 1),reported,0.12645759510473173,0.016248741270440393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.782608695652174,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Lake view > 40 km2,view,E_environment,dummy,binary,0.1103,0.012,,,,,,high,Lake view area category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,11.653261087544076,1.339935604941055,100*(exp(b - se^2/2) - 1),reported,0.14888366805999664,0.01619767920870317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.191666666666666,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Lake view > 40 km2,view,E_environment,dummy,binary,0.108,0.0146,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,11.392901650493869,1.6265097082642428,100*(exp(b - se^2/2) - 1),reported,0.12028909169995122,0.016261303137215626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.397260273972603,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Lake view > 40 km2,view,E_environment,dummy,binary,0.1119,0.0144,,,,,,high,Lake view area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,11.82850651450702,1.6104974615299563,100*(exp(b - se^2/2) - 1),reported,0.12626931319421025,0.016249134137592738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.770833333333334,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,View 50–100 km2,view,E_environment,dummy,binary,0.0359,0.009,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,3.651020657593529,0.9328969674804429,100*(exp(b - se^2/2) - 1),reported,0.06519993688127489,0.016345388076085626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9888888888888894,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,View 50–100 km2,view,E_environment,dummy,binary,0.0357,0.0091,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,3.630198740971946,0.9430738557074607,100*(exp(b - se^2/2) - 1),reported,0.06412867560487417,0.016346525154183614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9230769230769234,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,View 50–100 km2,view,E_environment,dummy,binary,0.0406,0.0084,,,,,,high,View on Alps area category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,4.139870684582947,0.874805776353867,100*(exp(b - se^2/2) - 1),reported,0.0789242028602801,0.016329145419368297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.833333333333333,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,View 50–100 km2,view,E_environment,dummy,binary,0.0319,0.0092,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,3.237056787007919,0.949821118019643,100*(exp(b - se^2/2) - 1),reported,0.05670531911958078,0.016353885137935523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4673913043478257,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,View 50–100 km2,view,E_environment,dummy,binary,0.0344,0.0093,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,3.4953765805249004,0.9625486267142113,100*(exp(b - se^2/2) - 1),reported,0.060478372629965714,0.016350257716822126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.698924731182796,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,View 100–250 km2,view,E_environment,dummy,binary,0.0236,0.008,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,2.3847920038109205,0.8191045469566129,100*(exp(b - se^2/2) - 1),reported,0.04826539607651095,0.016361151212376593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9499999999999997,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,View 100–250 km2,view,E_environment,dummy,binary,0.0244,0.0078,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,2.4668945068514025,0.7992660904581054,100*(exp(b - se^2/2) - 1),reported,0.05117361876558323,0.016358779769325784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1282051282051286,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,View 100–250 km2,view,E_environment,dummy,binary,0.027,0.0071,,,,,,high,View on Alps area category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,2.734190828435734,0.7294311399620775,100*(exp(b - se^2/2) - 1),reported,0.06217056009786972,0.01634855469240278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8028169014084505,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,View 100–250 km2,view,E_environment,dummy,binary,0.0213,0.0081,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,2.1494953521001614,0.8274380560122125,100*(exp(b - se^2/2) - 1),reported,0.04303406521771963,0.016365067054625773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6296296296296298,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,View 100–250 km2,view,E_environment,dummy,binary,0.0216,0.008,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,2.180227052942474,0.8174679749802,100*(exp(b - se^2/2) - 1),reported,0.044183462129314885,0.016364245233079586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,View > 250 km2,view,E_environment,dummy,binary,0.0163,0.009,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,1.639240501609729,0.9147902127678722,100*(exp(b - se^2/2) - 1),reported,0.029653391532631013,0.016373038269550867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8111111111111111,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,View > 250 km2,view,E_environment,dummy,binary,0.0183,0.009,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,1.8427223966807071,0.916621623994164,100*(exp(b - se^2/2) - 1),reported,0.033288032832421544,0.016371163688076167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0333333333333337,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,View > 250 km2,view,E_environment,dummy,binary,0.0159,0.0081,,,,,,high,View on Alps area category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,1.5993747396448876,0.8229819328706028,100*(exp(b - se^2/2) - 1),reported,0.032137199116066634,0.016371780681769792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9629629629629632,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,View > 250 km2,view,E_environment,dummy,binary,0.0135,0.0093,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,1.3547704631972524,0.9426401288986916,100*(exp(b - se^2/2) - 1),reported,0.023771051176960403,0.016375613033017166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4516129032258065,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,View > 250 km2,view,E_environment,dummy,binary,0.0137,0.0092,,,,,,high,View on Alps area category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,1.3751372164790165,0.932690733028224,100*(exp(b - se^2/2) - 1),reported,0.024385063029903647,0.016375370793803908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4891304347826089,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Slope 0–9%,other,other,dummy,binary,0.0194,0.006,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,1.9571050378180965,0.6117536416933556,100*(exp(b - se^2/2) - 1),reported,0.05288865535875558,0.016357316090336776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2333333333333334,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Slope 0–9%,other,other,dummy,binary,0.0191,0.0061,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,1.926460828390253,0.6217629788457922,100*(exp(b - se^2/2) - 1),reported,0.05122162681964514,0.016358739455488762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1311475409836063,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Slope 0–9%,other,other,dummy,binary,0.0191,0.006,,,,,,high,Terrain slope category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,1.9265224939176928,0.6115701431270087,100*(exp(b - se^2/2) - 1),reported,0.052073024647786605,0.01635801821396438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.183333333333333,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Slope 0–9%,other,other,dummy,binary,0.0171,0.006,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,1.7228731661407481,0.6103482251660217,100*(exp(b - se^2/2) - 1),reported,0.04663290117564504,0.01636242146513861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.85,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Slope 0–9%,other,other,dummy,binary,0.0177,0.0061,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,1.7838636245640638,0.620893119718874,100*(exp(b - se^2/2) - 1),reported,0.04747595832256302,0.016361770947323977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.901639344262295,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Slope > 9%,other,other,dummy,binary,0.0357,0.008,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,3.6311733875743712,0.8290759171054599,100*(exp(b - se^2/2) - 1),reported,0.0729023238095219,0.016336655195411068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.4625,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Slope > 9%,other,other,dummy,binary,0.036,0.008,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,3.662267403459807,0.829324677192739,100*(exp(b - se^2/2) - 1),reported,0.07351165149914103,0.01633592255536467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Slope > 9%,other,other,dummy,binary,0.0353,0.0076,,,,,,high,Terrain slope category,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,3.590052408067246,0.7873071354030562,100*(exp(b - se^2/2) - 1),reported,0.07586266465632521,0.016333038282948204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.644736842105263,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Slope > 9%,other,other,dummy,binary,0.0357,0.008,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,3.6311733875743712,0.8290759171054599,100*(exp(b - se^2/2) - 1),reported,0.0729023238095219,0.016336655195411068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.4625,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Slope > 9%,other,other,dummy,binary,0.0345,0.0081,,,,,,high,Terrain slope category,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,3.5068072411039264,0.8384326429846526,100*(exp(b - se^2/2) - 1),reported,0.06959851493541513,0.016340520897880077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2592592592592595,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,False,False,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Near power line,hazard_site,E_environment,binary,binary,-0.0103,0.015,,,,,,high,Power line within 200 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,-1.0358477587250614,1.4846292950202462,100*(exp(b - se^2/2) - 1),reported,-0.011247054490002387,0.01637920556796464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6866666666666666,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Near power line,hazard_site,E_environment,binary,binary,-0.0133,0.015,,,,,,high,Power line within 200 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,-1.332295321768684,1.4801820812911892,100*(exp(b - se^2/2) - 1),reported,-0.014522282643600688,0.016378514259700022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8866666666666666,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Near power line,hazard_site,E_environment,binary,binary,-0.0118,0.014,,,,,,high,Power line within 200 m,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,-1.182749868323374,1.3835770857541565,100*(exp(b - se^2/2) - 1),reported,-0.01380488803043391,0.016378680714074133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8428571428571429,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Near power line,hazard_site,E_environment,binary,binary,-0.009,0.0151,,,,,,high,Power line within 200 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,-0.9072598345397753,1.4964709719470544,100*(exp(b - se^2/2) - 1),reported,-0.009762592658409354,0.016379461015775695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5960264900662251,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Near power line,hazard_site,E_environment,binary,binary,-0.0064,0.0154,,,,,,high,Power line within 200 m,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,-0.6497380146716969,1.5301754720238894,100*(exp(b - se^2/2) - 1),reported,-0.006807215418377473,0.016379862100470793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4155844155844156,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Near railway,transit_distance,D_location,binary,binary,-0.0243,0.008,,,,,,high,"Railway line within 100 m (nuisance proximity, not station access)",log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,-2.4038363298626186,0.7807942943787568,100*(exp(b - se^2/2) - 1),reported,-0.04969351234530489,0.016360004064297908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0374999999999996,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Near railway,transit_distance,D_location,binary,binary,-0.0237,0.0082,,,,,,high,"Railway line within 100 m (nuisance proximity, not station access)",log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,-2.345419261386028,0.8007944843146316,100*(exp(b - se^2/2) - 1),reported,-0.047289926497936306,0.01636191549717628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.890243902439024,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Near railway,transit_distance,D_location,binary,binary,-0.0247,0.0078,,,,,,high,"Railway line within 100 m (nuisance proximity, not station access)",log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,-2.4427128482865768,0.7609699881383156,100*(exp(b - se^2/2) - 1),reported,-0.051801124504128486,0.016358249843408994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.166666666666667,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Near railway,transit_distance,D_location,binary,binary,-0.0244,0.008,,,,,,high,"Railway line within 100 m (nuisance proximity, not station access)",log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,-2.413595458265083,0.7807162188531602,100*(exp(b - se^2/2) - 1),reported,-0.049897504280637106,0.016359837469061344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0500000000000003,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Near railway,transit_distance,D_location,binary,binary,-0.0228,0.0083,,,,,,high,"Railway line within 100 m (nuisance proximity, not station access)",log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,-2.257571214285381,0.8112901033277651,100*(exp(b - se^2/2) - 1),reported,-0.04495084408933207,0.016363684471116503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.746987951807229,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Aspect: East,sunlight,E_environment,dummy,binary,-0.0032,0.007,,,,,,high,Orientation east (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,-0.3219306883109674,0.697763580180123,100*(exp(b - se^2/2) - 1),reported,-0.007487900527855812,0.016379782404684587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45714285714285713,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Aspect: East,sunlight,E_environment,dummy,binary,-0.0034,0.0068,,,,,,high,Orientation east (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,-0.3417267804226687,0.6776919259493304,100*(exp(b - se^2/2) - 1),reported,-0.00818984613512058,0.01637969227024116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Aspect: East,sunlight,E_environment,dummy,binary,-0.0002,0.0064,,,,,,high,Orientation east (slope > 1%),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,-0.02204556960709869,0.6398720127991467,100*(exp(b - se^2/2) - 1),reported,-0.0005118824835677614,0.016380239474168366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.03125,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Aspect: East,sunlight,E_environment,dummy,binary,-0.0032,0.0068,,,,,,high,Orientation east (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,-0.321793132480408,0.6778274778892623,100*(exp(b - se^2/2) - 1),reported,-0.007708119998110557,0.016379754995984933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4705882352941177,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Aspect: East,sunlight,E_environment,dummy,binary,-0.0031,0.0069,,,,,,high,Orientation east (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,-0.3118930997626168,0.6878643120266885,100*(exp(b - se^2/2) - 1),reported,-0.00735903971456922,0.01637979807436375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4492753623188406,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Aspect: West,sunlight,E_environment,dummy,binary,0.0102,0.006,,,,,,high,Orientation west (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",pct_premium,1.0234012944043247,0.606151318391961,100*(exp(b - se^2/2) - 1),reported,0.02783562066105109,0.01637389450650064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7000000000000002,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Aspect: West,sunlight,E_environment,dummy,binary,0.0093,0.0063,,,,,,high,Orientation west (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",pct_premium,0.9323349151155957,0.6358863290042145,100*(exp(b - se^2/2) - 1),reported,0.024173290773639725,0.01637545504020756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.476190476190476,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Aspect: West,sunlight,E_environment,dummy,binary,0.0139,0.006,,,,,,high,Orientation west (slope > 1%),log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",pct_premium,1.3978802380213429,0.608398232497753,100*(exp(b - se^2/2) - 1),reported,0.03792026665566546,0.016368460426905956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3166666666666664,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Aspect: West,sunlight,E_environment,dummy,binary,0.0093,0.0063,,,,,,high,Orientation west (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",pct_premium,0.9323349151155957,0.6358863290042145,100*(exp(b - se^2/2) - 1),reported,0.024173290773639725,0.01637545504020756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.476190476190476,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Aspect: West,sunlight,E_environment,dummy,binary,0.0106,0.0065,,,,,,high,Orientation west (slope > 1%),log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",pct_premium,1.0635029139317798,0.6569266463693804,100*(exp(b - se^2/2) - 1),reported,0.026702868782990222,0.01637440066881476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6307692307692307,3727.0,binary,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c1,5,1,19,Log tax revenue,public_services,F_neighbourhood,log,index,0.1559,0.01,,,,,,high,Communal per-capita tax revenue index,log house price (CHF),log,spatial_error,3737.0,,se,"Model 1, ML SAR error, W cut-off 300 m; rho 0.1852; residual SE 0.1279",elasticity,0.1559,0.01,b (log-log),reported,0.24742765031466277,0.015870920482018137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.590000000000002,3727.0,index,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,19,Log tax revenue,public_services,F_neighbourhood,log,index,0.1568,0.0099,,,,,,high,Communal per-capita tax revenue index,log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",elasticity,0.1568,0.0099,b (log-log),reported,0.2511229602645681,0.015855339965683828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.838383838383837,3727.0,index,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c3,5,3,19,Log tax revenue,public_services,F_neighbourhood,log,index,0.1549,0.0089,,,,,,high,Communal per-capita tax revenue index,log house price (CHF),log,OLS,3737.0,,se,"Model 3, OLS non-spatial; residual SE 0.1309",elasticity,0.1549,0.0089,b (log-log),reported,0.2741658708928448,0.01575259038700012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.404494382022474,3727.0,index,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,ols,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c1,6,1,23,Log tax revenue,public_services,F_neighbourhood,log,index,0.1588,0.0107,,,,,,high,Communal per-capita tax revenue index,log house price (CHF),log,spatial_error,3737.0,,se,"Model 4, ML SAR error, W cut-off 600 m; rho 0.2579",elasticity,0.1588,0.0107,b (log-log),reported,0.2362212236093421,0.015916669348992194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.841121495327103,3727.0,index,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T6-c2,6,2,23,Log tax revenue,public_services,F_neighbourhood,log,index,0.1599,0.0105,,,,,,high,Communal per-capita tax revenue index,log house price (CHF),log,spatial_error,3737.0,,se,"Model 5, ML SAR error, 4 nearest neighbours; rho 0.2233",elasticity,0.1599,0.0105,b (log-log),reported,0.24203121660334245,0.015893231859506543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.228571428571426,3727.0,index,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,True
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Leq Evening,noise,E_environment,level,db,-0.0064,0.0019,,,,,,high,"Aircraft noise Leq evening (21-23h), dB(A) above 50",log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",semi_elasticity,-0.64,0.19,100*b/1 (per dB),reported,-0.05509175539172371,0.016355364881917973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.368421052631579,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317,7670a6d2dc2183fc23719395205238727f178317:T5-c2,5,2,18,Leq 1h Peak,noise,E_environment,level,db,-0.0063,0.0023,,,,,,high,"Highest daytime 1-hour Leq in excess of Leq 16h, dB(A)",log house price (CHF),log,spatial_error,3737.0,,se,"Model 2, ML SAR error with three noise metrics; rho 0.1853; residual SE 0.1275",semi_elasticity,-0.63,0.22999999999999998,100*b/1 (per dB),reported,-0.04482252485039164,0.01636377891363504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.739130434782609,3727.0,db,,2008.0,Switzerland,Europe,High income,1995.0,2005.0,single_family,transaction,journal,True,False,unknown,,2000.0,spatial,journal,True,True,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Distance from the waterfront (Dl),water_proximity,E_environment,other,km (walking distance to Araldo di Crollalanza waterfront),-3.56,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-3.56,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Maintenance conditions (Sc),condition,A_structural,other,ordinal 1/3/5 (to be restructured / fit for habitation / restructured),20.22,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,20.22,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Sound level in the street where the property is located (Ld),noise,E_environment,other,ordinal rating 1-7 of dB(A) classes (<=40 to 70-75 dB(A)),-3.31,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-3.31,,not harmonised,,,,,,190.0,ordinal rating 1-7 of db classes ),nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Lift (A),accessibility,B_interior,other,binary,30.95,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,30.95,,not harmonised,,,,,,190.0,binary,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Distance from the nearest railway station (Ds),transit_distance,D_location,other,km (walking),-3.01,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-3.01,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Floor level (L),floor_level,A_structural,other,count,2.67,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,2.67,,not harmonised,,,,,,190.0,count,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T3-c1,3,1,16,Size (S),living_area,A_structural,other,m2 (gross floor area),22.8,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8673,none,"OMI central area of Bari (200 sales 2017-2019); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,22.8,,not harmonised,,,,,,190.0,m2,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Maintenance conditions (Sc),condition,A_structural,other,ordinal 1/3/5 (to be restructured / fit for habitation / restructured),19.49,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,19.49,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Distance from the waterfront (Dl),water_proximity,E_environment,other,km (walking distance to Araldo di Crollalanza waterfront),0.63,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,0.63,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Distance from the nearest railway station (Ds),transit_distance,D_location,other,km (walking),2.48,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,2.48,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Perceived environmental quality level (Qn),other,other,other,ordinal score 1-5 (survey of residents),0.52,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,0.52,,not harmonised,,,,,,190.0,ordinal score 1-5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Presence of parking (P),parking,A_structural,other,binary,4.3,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,4.3,,not harmonised,,,,,,190.0,binary,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Maintenance conditions of the building facades adjacent to the property (Sf),other,other,other,ordinal 1/3/5,9.01,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,9.01,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Distance from the nearest public green space (Dv),green_space,E_environment,other,km (walking),-1.14,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-1.14,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Sound level in the street where the property is located (Ld),noise,E_environment,other,ordinal rating 1-7 of dB(A) classes (<=40 to 70-75 dB(A)),-3.46,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-3.46,,not harmonised,,,,,,190.0,ordinal rating 1-7 of db classes ),nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Floor level (L),floor_level,A_structural,other,count,0.77,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: parabolic); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,0.77,,not harmonised,,,,,,190.0,count,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Bathroom number (B),bathrooms,A_structural,other,count,0.09,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,0.09,,not harmonised,,,,,,190.0,count,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Lift (A),accessibility,B_interior,other,binary,8.85,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,8.85,,not harmonised,,,,,,190.0,binary,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Road private and public vehicular traffic level (T),other,other,other,ordinal 1 (high traffic) / 3 / 5 (low traffic),3.08,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,3.08,,not harmonised,,,,,,190.0,ordinal 1 / 3 / 5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Size (S),living_area,A_structural,other,m2 (gross floor area),19.91,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,19.91,,not harmonised,,,,,,190.0,m2,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T4-c1,4,1,16,Distance from the nearest highway (Dt),highway_distance,D_location,other,km (by car),-1.77,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8315,none,"OMI semi-central area (200 sales); EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-1.77,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,16,Distance from the nearest public green space (Dv),green_space,E_environment,other,km (walking),3.73,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,3.73,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,16,Distance from the nearest railway station (Ds),transit_distance,D_location,other,km (walking),7.11,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,7.11,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,16,Perceived environmental quality level (Qn),other,other,other,ordinal score 1-5 (survey of residents),4.73,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,4.73,,not harmonised,,,,,,190.0,ordinal score 1-5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Maintenance conditions (Sc),condition,A_structural,other,ordinal 1/3/5 (to be restructured / fit for habitation / restructured),8.69,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,8.69,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Maintenance conditions of the public spaces adjacent to the property (Sa),other,other,other,ordinal 1/3/5,11.26,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,11.26,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Presence of parking (P),parking,A_structural,other,binary,3.59,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,3.59,,not harmonised,,,,,,190.0,binary,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Lift (A),accessibility,B_interior,other,binary,16.72,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,16.72,,not harmonised,,,,,,190.0,binary,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Floor level (L),floor_level,A_structural,other,count,1.38,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,1.38,,not harmonised,,,,,,190.0,count,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Maintenance conditions of the building facades adjacent to the property (Sf),other,other,other,ordinal 1/3/5,2.52,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,2.52,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Distance from the nearest highway (Dt),highway_distance,D_location,other,km (by car),-2.85,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: parabolic); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-2.85,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Sound level in the street where the property is located (Ld),noise,E_environment,other,ordinal rating 1-7 of dB(A) classes (<=40 to 70-75 dB(A)),-2.47,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: negative); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,-2.47,,not harmonised,,,,,,190.0,ordinal rating 1-7 of db classes ),nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T5-c1,5,1,17,Size (S),living_area,A_structural,other,m2 (gross floor area),21.57,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8314,none,"OMI peripheral area (200 sales); table continues p.17; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,21.57,,not harmonised,,,,,,190.0,m2,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T6-c1,6,1,17,Distance from the waterfront (Dl),water_proximity,E_environment,other,km (walking distance to Araldo di Crollalanza waterfront),5.52,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8171,none,"OMI suburban area (200 sales); noise variable not selected by the model; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,5.52,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T6-c1,6,1,17,Distance from the nearest public green space (Dv),green_space,E_environment,other,km (walking),1.2,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8171,none,"OMI suburban area (200 sales); noise variable not selected by the model; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,1.2,,not harmonised,,,,,,190.0,km,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T6-c1,6,1,17,Maintenance conditions (Sc),condition,A_structural,other,ordinal 1/3/5 (to be restructured / fit for habitation / restructured),17.74,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8171,none,"OMI suburban area (200 sales); noise variable not selected by the model; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,17.74,,not harmonised,,,,,,190.0,ordinal 1/3/5,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T6-c1,6,1,17,Lift (A),accessibility,B_interior,other,binary,17.73,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8171,none,"OMI suburban area (200 sales); noise variable not selected by the model; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,17.73,,not harmonised,,,,,,190.0,binary,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d,78349a30196c9ee010a5b6087b25ddd2eb8a9b3d:T6-c1,6,1,17,Size (S),living_area,A_structural,other,m2 (gross floor area),0.88,,,,,,,medium,"Value is the average percentage variation (%) of the total selling price for a one-level/one-unit passage of the variable, derived from the EPR-MOGA polynomial (functional correlation: direct); not a regression coefficient, no SE",ln(Pr): natural log of total selling price (euro),log,ML_marginal,200.0,0.8171,none,"OMI suburban area (200 sales); noise variable not selected by the model; EPR-MOGA evolutionary polynomial regression (Table 2 p.15 gives the fitted polynomial); table reports the average percentage variation of the total selling price for a one-level increase of each selected variable, other variables at sample means",other,0.88,,not harmonised,,,,,,190.0,m2,nonstandard_x_transform,2021.0,Italy,Europe,High income,2017.0,2019.0,condo_apartment,transaction,journal,False,False,unknown,genetic algorithm (EPR-MOGA evolutionary polynomial regression),2018.0,ml,journal,False,False,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Padua,6,Padua,7,Zone (OMI),cbd_distance,D_location,other,"ordinal (1=centre, 2=semi-central, 3=suburbs)",-0.244,,,0.001,,,,high,OMI macro-area ordinal code; higher = farther from centre; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.519,p,Padua; EPC as ordinal variable,other,-0.244,0.07415226190530677,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Padua,6,Padua,7,Proximity to local transport infrastructure,transit_distance,D_location,other,"ordinal (1=<=200 m, 2=201-500 m, 3=>500 m)",-0.115,,,0.001,,,,high,Ordinal distance class to local public transport; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.519,p,Padua; EPC as ordinal variable,other,-0.115,0.034948811963566716,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Padua,6,Padua,7,Energy class (EPC),energy_efficiency,A_structural,other,ordinal (1=A4 ... 12=G),-0.049,,,0.001,,,,high,EPC class ordinal code; higher = less efficient; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.519,p,Padua; EPC as ordinal variable,other,-0.049,0.014891232923606688,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Padua,6,Padua,7,Gross floor area,living_area,A_structural,level,m2,-0.001,,,0.001,,,,high,Gross floor area in sqm; dependent variable is price per sqm; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.519,p,Padua; EPC as ordinal variable,monetary,-0.001,0.0003039027127266671,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.17468545567307894,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,344.0,m2,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Padua,6,Padua,7,Maintenance status,condition,A_structural,other,ordinal (1=new ... 4=very bad),-0.114,,,0.001,,,,high,Maintenance status ordinal code; higher = worse condition; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.519,p,Padua; EPC as ordinal variable,other,-0.114,0.03464490925084005,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307894,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Mestre,6,Mestre,7,Proximity to local transport infrastructure,transit_distance,D_location,other,"ordinal (1=<=200 m, 2=201-500 m, 3=>500 m)",-0.032,,,0.034,,,,high,Ordinal distance class to local public transport,ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.522,p,Mestre; EPC as ordinal variable,other,-0.032,0.015093829210979147,not harmonised,|b/z(p)| (two-sided normal),-0.13449061455572692,0.06343682395574275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.120071689742151,244.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Mestre,6,Mestre,7,Energy class (EPC),energy_efficiency,A_structural,other,ordinal (1=A4 ... 12=G),-0.039,,,0.001,,,,high,EPC class ordinal code; higher = less efficient; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.522,p,Mestre; EPC as ordinal variable,other,-0.039,0.011852205796340016,not harmonised,|b/z(p)| (two-sided normal),-0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,244.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Mestre,6,Mestre,7,No. of bathrooms,bathrooms,A_structural,level,count,0.161,,,0.001,,,,high,Number of bathrooms; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.522,p,Mestre; EPC as ordinal variable,monetary,0.161,0.048928336748993405,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,244.0,count,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Mestre,6,Mestre,7,Gross floor area,living_area,A_structural,level,m2,-0.003,,,0.001,,,,high,Gross floor area in sqm; dependent variable is price per sqm; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.522,p,Mestre; EPC as ordinal variable,monetary,-0.003,0.0009117081381800013,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,244.0,m2,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Mestre,6,Mestre,7,Maintenance status,condition,A_structural,other,ordinal (1=new ... 4=very bad),-0.097,,,0.001,,,,high,Maintenance status ordinal code; higher = worse condition; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.522,p,Mestre; EPC as ordinal variable,other,-0.097,0.02947856313448671,not harmonised,|b/z(p)| (two-sided normal),-0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,244.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Bergamo,6,Bergamo,7,Zone (OMI),cbd_distance,D_location,other,"ordinal (1=centre, 2=semi-central, 3=suburbs)",-0.061,,,0.002,,,,high,OMI macro-area ordinal code; higher = farther from centre,ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.359,p,Bergamo; EPC as ordinal variable,other,-0.061,0.019739616299476816,not harmonised,|b/z(p)| (two-sided normal),-0.18067223701239754,0.05846558417365346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.090232306167813,283.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Bergamo,6,Bergamo,7,Energy class (EPC),energy_efficiency,A_structural,other,ordinal (1=A4 ... 12=G),-0.043,,,0.001,,,,high,EPC class ordinal code; higher = less efficient; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.359,p,Bergamo; EPC as ordinal variable,other,-0.043,0.013067816647246683,not harmonised,|b/z(p)| (two-sided normal),-0.1919637186633534,0.05833829484689183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,283.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Bergamo,6,Bergamo,7,No. of bathrooms,bathrooms,A_structural,level,count,0.275,,,0.001,,,,high,Number of bathrooms; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.359,p,Bergamo; EPC as ordinal variable,monetary,0.275,0.08357324599983346,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.1919637186633534,0.05833829484689183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,283.0,count,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Bergamo,6,Bergamo,7,Gross floor area,living_area,A_structural,level,m2,-0.002,,,0.001,,,,high,Gross floor area in sqm; dependent variable is price per sqm; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.359,p,Bergamo; EPC as ordinal variable,monetary,-0.002,0.0006078054254533342,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.19196371866335338,0.05833829484689183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,283.0,m2,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T6-Bergamo,6,Bergamo,7,Maintenance status,condition,A_structural,other,ordinal (1=new ... 4=very bad),-0.066,,,0.024,,,,high,Maintenance status ordinal code; higher = worse condition,ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.359,p,Bergamo; EPC as ordinal variable,other,-0.066,0.02924068267744372,not harmonised,|b/z(p)| (two-sided normal),-0.13298077029616026,0.05891588646109175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2571292444862254,283.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Padua,7,Padua,8,Zone (OMI),cbd_distance,D_location,other,"ordinal (1=centre, 2=semi-central, 3=suburbs)",-0.235,,,0.001,,,,high,OMI macro-area ordinal code; higher = farther from centre; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.509,p,"Padua; EPC as two dummies (A-B, C-D vs E-G)",other,-0.235,0.07141713749076677,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307894,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Padua,7,Padua,8,Proximity to local transport infrastructure,transit_distance,D_location,other,"ordinal (1=<=200 m, 2=201-500 m, 3=>500 m)",-0.119,,,0.001,,,,high,Ordinal distance class to local public transport; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.509,p,"Padua; EPC as two dummies (A-B, C-D vs E-G)",other,-0.119,0.03616442281447338,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Padua,7,Padua,8,High energy efficiency (Classes A and B),energy_efficiency,A_structural,dummy,binary,0.316,,,0.001,,,,high,"Dummy for EPC classes A and B; base = classes E, F, G; p-value printed as <0.001 (recorded as 0.001 upper bound)",ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.509,p,"Padua; EPC as two dummies (A-B, C-D vs E-G)",monetary,0.316,0.0960332572216268,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,344.0,binary,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Padua,7,Padua,8,Medium energy efficiency (Classes C and D),energy_efficiency,A_structural,dummy,binary,0.179,,,0.001,,,,high,"Dummy for EPC classes C and D; base = classes E, F, G; p-value printed as <0.001 (recorded as 0.001 upper bound)",ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.509,p,"Padua; EPC as two dummies (A-B, C-D vs E-G)",monetary,0.179,0.05439858557807341,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,344.0,binary,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Padua,7,Padua,8,Gross floor area,living_area,A_structural,level,m2,-0.001,,,0.001,,,,high,Gross floor area in sqm; dependent variable is price per sqm; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.509,p,"Padua; EPC as two dummies (A-B, C-D vs E-G)",monetary,-0.001,0.0003039027127266671,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.17468545567307894,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,344.0,m2,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Padua,7,Padua,8,Maintenance status,condition,A_structural,other,ordinal (1=new ... 4=very bad),-0.131,,,0.001,,,,high,Maintenance status ordinal code; higher = worse condition; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,354.0,0.509,p,"Padua; EPC as two dummies (A-B, C-D vs E-G)",other,-0.131,0.03981125536719339,not harmonised,|b/z(p)| (two-sided normal),-0.17468545567307897,0.053087383852942656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,344.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Mestre,7,Mestre,8,High energy efficiency (Classes A and B),energy_efficiency,A_structural,dummy,binary,0.317,,,0.001,,,,high,"Dummy for EPC classes A and B; base = classes E, F, G; p-value printed as <0.001 (recorded as 0.001 upper bound)",ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.513,p,Mestre; EPC as two dummies,monetary,0.317,0.09633715993435346,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,244.0,binary,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Mestre,7,Mestre,8,Medium energy efficiency (Classes C and D),energy_efficiency,A_structural,dummy,binary,0.179,,,0.001,,,,high,"Dummy for EPC classes C and D; base = classes E, F, G; p-value printed as <0.001 (recorded as 0.001 upper bound)",ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.513,p,Mestre; EPC as two dummies,monetary,0.179,0.05439858557807341,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,244.0,binary,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Mestre,7,Mestre,8,Number of bathrooms,bathrooms,A_structural,level,count,0.172,,,0.001,,,,high,Number of bathrooms; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.513,p,Mestre; EPC as two dummies,monetary,0.172,0.05227126658898673,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,244.0,count,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Mestre,7,Mestre,8,Gross floor area,living_area,A_structural,level,m2,-0.003,,,0.001,,,,high,Gross floor area in sqm; dependent variable is price per sqm; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.513,p,Mestre; EPC as two dummies,monetary,-0.003,0.0009117081381800013,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.20613048261817754,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,244.0,m2,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Mestre,7,Mestre,8,Maintenance status,condition,A_structural,other,ordinal (1=new ... 4=very bad),-0.103,,,0.001,,,,high,Maintenance status ordinal code; higher = worse condition; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,254.0,0.513,p,Mestre; EPC as two dummies,other,-0.103,0.03130197941084671,not harmonised,|b/z(p)| (two-sided normal),-0.2061304826181775,0.06264361284332125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,244.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Bergamo,7,Bergamo,8,Zone (OMI),cbd_distance,D_location,other,"ordinal (1=centre, 2=semi-central, 3=suburbs)",-0.061,,,0.002,,,,high,OMI macro-area ordinal code; higher = farther from centre,ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.349,p,Bergamo; EPC as two dummies,other,-0.061,0.019739616299476816,not harmonised,|b/z(p)| (two-sided normal),-0.18067223701239754,0.05846558417365346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.090232306167813,283.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Bergamo,7,Bergamo,8,High energy efficiency (Classes A and B),energy_efficiency,A_structural,dummy,binary,0.231,,,0.001,,,,high,"Dummy for EPC classes A and B; base = classes E, F, G; p-value printed as <0.001 (recorded as 0.001 upper bound)",ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.349,p,Bergamo; EPC as two dummies,monetary,0.231,0.0702015266398601,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.1919637186633534,0.05833829484689183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,283.0,binary,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Bergamo,7,Bergamo,8,Medium energy efficiency (Classes C and D),energy_efficiency,A_structural,dummy,binary,0.111,,,0.024,,,,high,"Dummy for EPC classes C and D; base = classes E, F, G",ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.349,p,Bergamo; EPC as two dummies,monetary,0.111,0.0491775117757008,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.13298077029616026,0.05891588646109175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2571292444862254,283.0,binary,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Bergamo,7,Bergamo,8,Number of bathrooms,bathrooms,A_structural,level,count,0.286,,,0.001,,,,high,Number of bathrooms; p-value printed as <0.001 (recorded as 0.001 upper bound),ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.349,p,Bergamo; EPC as two dummies,monetary,0.286,0.08691617583982678,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.1919637186633534,0.05833829484689183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,283.0,count,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Bergamo,7,Bergamo,8,Gross floor area,living_area,A_structural,level,m2,-0.002,,,0.002,,,,high,Gross floor area in sqm; dependent variable is price per sqm,ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.349,p,Bergamo; EPC as two dummies,monetary,-0.002,0.000647200534409076,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),-0.18067223701239754,0.05846558417365346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.090232306167813,283.0,m2,,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192,784bd147866d1ec15bbfa3c42d3c2fc6a9ac3192:T7-Bergamo,7,Bergamo,8,Maintenance status,condition,A_structural,other,ordinal (1=new ... 4=very bad),-0.082,,,0.006,,,,high,Maintenance status ordinal code; higher = worse condition,ln ask price per sqm (EUR/sqm),per_m2,OLS,293.0,0.349,p,Bergamo; EPC as two dummies,other,-0.082,0.029842257624407197,not harmonised,|b/z(p)| (two-sided normal),-0.16120240288903917,0.05866638581327061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7477813854449926,283.0,ordinal,nonstandard_x_transform,2023.0,Italy,Europe,High income,2022.0,2023.0,mixed,listing,journal,False,False,unknown,,2022.5,ols,journal,False,True,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,CBD,cbd_distance,D_location,level,m,-6.57e-05,,,0.0,,,,high,"distance in metres to CBD (Central MTR station); p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",semi_elasticity,-6.569999999999999,,100*b/0.001 (X per m->km),,,,,,167408.0,m,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,CBD,cbd_distance,D_location,level,m,-5.98e-05,,,0.0,,,,high,"distance in metres to CBD (Central MTR station); p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",semi_elasticity,-5.9799999999999995,,100*b/0.001 (X per m->km),,,,,,167408.0,m,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,CBD,cbd_distance,D_location,level,m,-5.8e-05,,,0.0,,,,high,"distance in metres to CBD (Central MTR station); p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,semi_elasticity,-5.8,,100*b/0.001 (X per m->km),,,,,,167408.0,m,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,CBD,cbd_distance,D_location,level,m,-5.96e-05,,,0.0,,,,high,"distance in metres to CBD (Central MTR station); p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,semi_elasticity,-5.96,,100*b/0.001 (X per m->km),,,,,,167408.0,m,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,CBD,cbd_distance,D_location,level,m,-5.78e-05,,,0.0,,,,high,"distance in metres to CBD (Central MTR station); p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,semi_elasticity,-5.78,,100*b/0.001 (X per m->km),,,,,,167408.0,m,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Age,age,A_structural,level,years,-0.0305,,,0.0,,,,high,"building age in years; with Age2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",semi_elasticity,-3.05,,100*b/1 (per year),,,,,,167408.0,years,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Age,age,A_structural,level,years,-0.0307,,,0.0,,,,high,"building age in years; with Age2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",semi_elasticity,-3.0700000000000003,,100*b/1 (per year),,,,,,167408.0,years,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Age,age,A_structural,level,years,-0.0307,,,0.0,,,,high,"building age in years; with Age2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,semi_elasticity,-3.0700000000000003,,100*b/1 (per year),,,,,,167408.0,years,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Age,age,A_structural,level,years,-0.0306,,,0.0,,,,high,"building age in years; with Age2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,semi_elasticity,-3.06,,100*b/1 (per year),,,,,,167408.0,years,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Age,age,A_structural,level,years,-0.0306,,,0.0,,,,high,"building age in years; with Age2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,semi_elasticity,-3.06,,100*b/1 (per year),,,,,,167408.0,years,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Age2,age,A_structural,squared,years,0.0003,,,0.0,,,,high,"building age squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",other,0.0003,,not harmonised,,,,,,167408.0,years,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Age2,age,A_structural,squared,years,0.0003,,,0.0,,,,high,"building age squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",other,0.0003,,not harmonised,,,,,,167408.0,years,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Age2,age,A_structural,squared,years,0.0003,,,0.0,,,,high,"building age squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,other,0.0003,,not harmonised,,,,,,167408.0,years,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Age2,age,A_structural,squared,years,0.0003,,,0.0,,,,high,"building age squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,other,0.0003,,not harmonised,,,,,,167408.0,years,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Age2,age,A_structural,squared,years,0.0003,,,0.0,,,,high,"building age squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,other,0.0003,,not harmonised,,,,,,167408.0,years,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Area,living_area,A_structural,level,sqft,0.0027,,,0.0,,,,high,"unit size in square feet; with Area2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",semi_elasticity,2.906255812511625,,100*b/0.092903 (X per sqft->m2),,,,,,167408.0,sqft,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Area,living_area,A_structural,level,sqft,0.0027,,,0.0,,,,high,"unit size in square feet; with Area2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",semi_elasticity,2.906255812511625,,100*b/0.092903 (X per sqft->m2),,,,,,167408.0,sqft,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Area,living_area,A_structural,level,sqft,0.0027,,,0.0,,,,high,"unit size in square feet; with Area2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,semi_elasticity,2.906255812511625,,100*b/0.092903 (X per sqft->m2),,,,,,167408.0,sqft,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Area,living_area,A_structural,level,sqft,0.0027,,,0.0,,,,high,"unit size in square feet; with Area2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,semi_elasticity,2.906255812511625,,100*b/0.092903 (X per sqft->m2),,,,,,167408.0,sqft,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Area,living_area,A_structural,level,sqft,0.0027,,,0.0,,,,high,"unit size in square feet; with Area2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,semi_elasticity,2.906255812511625,,100*b/0.092903 (X per sqft->m2),,,,,,167408.0,sqft,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Area2,living_area,A_structural,squared,sqft,-4.88e-07,,,0.0,,,,high,"size squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",other,-4.88e-07,,not harmonised,,,,,,167408.0,sqft,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Area2,living_area,A_structural,squared,sqft,-4.85e-07,,,0.0,,,,high,"size squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",other,-4.85e-07,,not harmonised,,,,,,167408.0,sqft,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Area2,living_area,A_structural,squared,sqft,-4.86e-07,,,0.0,,,,high,"size squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,other,-4.86e-07,,not harmonised,,,,,,167408.0,sqft,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Area2,living_area,A_structural,squared,sqft,-4.86e-07,,,0.0,,,,high,"size squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,other,-4.86e-07,,not harmonised,,,,,,167408.0,sqft,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Area2,living_area,A_structural,squared,sqft,-4.86e-07,,,0.0,,,,high,"size squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,other,-4.86e-07,,not harmonised,,,,,,167408.0,sqft,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Floor,floor_level,A_structural,level,count,0.0066,,,0.0,,,,high,"floor level (storey); with Floor2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",semi_elasticity,0.66,,100*b/1 (per unit count),,,,,,167408.0,count,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Floor,floor_level,A_structural,level,count,0.0069,,,0.0,,,,high,"floor level (storey); with Floor2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",semi_elasticity,0.69,,100*b/1 (per unit count),,,,,,167408.0,count,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Floor,floor_level,A_structural,level,count,0.0068,,,0.0,,,,high,"floor level (storey); with Floor2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,semi_elasticity,0.6799999999999999,,100*b/1 (per unit count),,,,,,167408.0,count,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Floor,floor_level,A_structural,level,count,0.007,,,0.0,,,,high,"floor level (storey); with Floor2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,167408.0,count,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Floor,floor_level,A_structural,level,count,0.0069,,,0.0,,,,high,"floor level (storey); with Floor2; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,semi_elasticity,0.69,,100*b/1 (per unit count),,,,,,167408.0,count,,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Floor2,floor_level,A_structural,squared,count,4.49e-06,,,0.0,,,,high,"floor level squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",other,4.49e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Floor2,floor_level,A_structural,squared,count,1.67e-06,,,0.0,,,,high,"floor level squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",other,1.67e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Floor2,floor_level,A_structural,squared,count,6.2e-06,,,0.0,,,,high,"floor level squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,other,6.2e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Floor2,floor_level,A_structural,squared,count,3.05e-06,,,0.0,,,,high,"floor level squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,other,3.05e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Floor2,floor_level,A_structural,squared,count,3.02e-06,,,0.0,,,,high,"floor level squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,other,3.02e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Scale of Dev.,other,other,level,count,0.0014,,,0.0,,,,high,"scale of development = number of housing units in the building; with squared term; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",semi_elasticity,0.13999999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,167408.0,count,unit_not_harmonisable,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Scale of Dev.,other,other,level,count,0.0014,,,0.0,,,,high,"scale of development = number of housing units in the building; with squared term; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",semi_elasticity,0.13999999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,167408.0,count,unit_not_harmonisable,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Scale of Dev.,other,other,level,count,0.0014,,,0.0,,,,high,"scale of development = number of housing units in the building; with squared term; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,semi_elasticity,0.13999999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,167408.0,count,unit_not_harmonisable,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Scale of Dev.,other,other,level,count,0.0014,,,0.0,,,,high,"scale of development = number of housing units in the building; with squared term; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,semi_elasticity,0.13999999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,167408.0,count,unit_not_harmonisable,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Scale of Dev.,other,other,level,count,0.0014,,,0.0,,,,high,"scale of development = number of housing units in the building; with squared term; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,semi_elasticity,0.13999999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,167408.0,count,unit_not_harmonisable,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c1,3,Equation (2),13,Scale of Dev.2,other,other,squared,count,-1.59e-06,,,0.0,,,,high,"scale of development squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,OLS,167418.0,0.896331,p,"OLS hedonic with S*N contextual interactions, 17 submarket dummies, monthly time dummies",other,-1.59e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,ols,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c2,3,Equation (3),13,Scale of Dev.2,other,other,squared,count,-1.59e-06,,,0.0,,,,high,"scale of development squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896594,p,"adds lagged-price spatial autoregressive terms WVP (vertical) and WHP (horizontal), OLS-estimated",other,-1.59e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c3,3,Equation (7),13,Scale of Dev.2,other,other,squared,count,-1.6e-06,,,0.0,,,,high,"scale of development squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896662,p,spatial lags interacted with liquidity L,other,-1.6e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c4,3,Equation (8),13,Scale of Dev.2,other,other,squared,count,-1.59e-06,,,0.0,,,,high,"scale of development squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896638,p,spatial lags interacted with volatility V,other,-1.59e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde,78763be000cac6c28c0629b4c2e5fffb2f7f7fde:T3-c5,3,Equation (9),13,Scale of Dev.2,other,other,squared,count,-1.6e-06,,,0.0,,,,high,"scale of development squared; p-value printed as (0.0000); no SE/t; neighbourhood (N), time (T) and S*N interaction coefficients not reported",log of transaction price (HKD),log,spatial_lag,167418.0,0.896711,p,spatial lags interacted with both L and V,other,-1.6e-06,,not harmonised,,,,,,167408.0,count,nonstandard_x_transform,2022.0,Hong Kong,Asia,High income,1998.0,2009.0,condo_apartment,transaction,journal,True,True,unknown,,2003.5,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,sizeland,lot_size,A_structural,level,ha,5.3e-05,,,0.01,,,,high,Land area in hectares; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,5.3e-07,2.0575897605861608e-07,100*b/10000 (X per ha->m2),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,ha,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,sizeland,lot_size,A_structural,level,ha,4.8e-05,,,0.01,,,,high,Land area in hectares; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,4.800000000000001e-07,1.863477519021429e-07,100*b/10000 (X per ha->m2),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,ha,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,sizehouse,living_area,A_structural,level,m2,0.002684,,,0.01,,,,high,Building size in m2; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,0.2684,0.10419945127194821,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,m2,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,sizehouse,living_area,A_structural,level,m2,0.002859,,,0.01,,,,high,Building size in m2; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,0.2859,0.11099337972671385,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,m2,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,bathrooms,bathrooms,A_structural,level,count,0.051512,,,0.01,,,,high,<0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,5.1512,1.9998219574964966,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,count,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,bathrooms,bathrooms,A_structural,level,count,0.055278,,,0.01,,,,high,<0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,5.5278,2.1460272978430526,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,count,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,age,age,A_structural,level,years,-0.010605,,,0.01,,,,high,<0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-1.0605,0.41171206435879687,100*b/1 (per year),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,years,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,age,age,A_structural,level,years,-0.010174,,,0.01,,,,high,<0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-1.0174,0.394979589135917,100*b/1 (per year),|b/z(p)| (two-sided normal),-0.013545063381575939,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5758293035489004,36157.0,years,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,agesquared,age,A_structural,squared,years,3.8e-05,,,0.01,,,,high,Age squared; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",other,3.8e-05,1.4752530358919644e-05,not harmonised,|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,years,nonstandard_x_transform,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,agesquared,age,A_structural,squared,years,3.6e-05,,,0.01,,,,high,Age squared; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",other,3.6e-05,1.3976081392660715e-05,not harmonised,|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,years,nonstandard_x_transform,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,garage,garage,A_structural,level,count,0.027471,,,0.01,,,,high,"Basement garage capacity, number of cars; <0.01 printed; recorded as 0.01 upper bound",ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,2.7471,1.0664914776049514,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,count,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,garage,garage,A_structural,level,count,0.017696,,,0.01,,,,high,"Basement garage capacity, number of cars; <0.01 printed; recorded as 0.01 upper bound",ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,1.7696,0.6870020453458999,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,count,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,True
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,condvpoor,condition,A_structural,dummy,binary,-0.923445,,,0.01,,,,high,Condition very poor (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,-62.75705085911289,14.2379310919052,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,True
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,condvpoor,condition,A_structural,dummy,binary,-1.072054,,,0.01,,,,high,Condition very poor (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,-68.60947924079028,14.246639845677679,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,True
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,condpoor,condition,A_structural,dummy,binary,-0.49363,,,0.01,,,,high,Condition poor (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,-40.06998980352606,11.697786241953958,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,condpoor,condition,A_structural,dummy,binary,-0.514455,,,0.01,,,,high,Condition poor (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,-41.397911105367534,11.940027999778447,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,True
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,condfair,condition,A_structural,dummy,binary,-0.219315,,,0.01,,,,high,Condition fair (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,-19.9836905328614,6.837604271980134,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,condfair,condition,A_structural,dummy,binary,-0.211597,,,0.01,,,,high,Condition fair (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,-19.343523105885964,6.648091940789928,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,condgood,condition,A_structural,dummy,binary,0.135936,,,0.01,,,,high,Condition good (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,14.401438728137572,6.04579840246442,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,condgood,condition,A_structural,dummy,binary,0.124734,,,0.01,,,,high,Condition good (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,13.151961317861826,5.485788477563271,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,condvgood,condition,A_structural,dummy,binary,0.29111,,,0.01,,,,high,Condition very good (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,32.939465042156435,15.120547308183168,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,condvgood,condition,A_structural,dummy,binary,0.262003,,,0.01,,,,high,Condition very good (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,29.28252369919837,13.21830113773887,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,condexcellent,condition,A_structural,dummy,binary,0.441086,,,0.01,,,,high,Condition excellent (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,53.1770667670773,26.617509014697585,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,condexcellent,condition,A_structural,dummy,binary,0.465527,,,0.01,,,,high,Condition excellent (reference: average); <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,56.705101405866706,28.787476887854666,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,poverty,income,F_neighbourhood,level,share (percent of tract population below poverty line),-0.001252,,,0.01,,,,high,"Census tract poverty rate in percent (proxy for neighbourhood income, inverse); <0.01 printed; recorded as 0.01 upper bound",ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-0.1252,0.04860570528780893,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,share,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,poverty,income,F_neighbourhood,level,share (percent of tract population below poverty line),-0.00114,,,0.01,,,,high,"Census tract poverty rate in percent (proxy for neighbourhood income, inverse); <0.01 printed; recorded as 0.01 upper bound",ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-0.11399999999999999,0.04425759107675893,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,share,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,bachelor,education_level,F_neighbourhood,level,share (percent with bachelor degree),0.011986,,,0.01,,,,high,Percent of tract population with a bachelor's degree; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,1.1986,0.4653258654789759,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,share,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,bachelor,education_level,F_neighbourhood,level,share (percent with bachelor degree),0.011502,,,0.01,,,,high,Percent of tract population with a bachelor's degree; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,1.1502000000000001,0.4465358004955098,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,share,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,crime,crime,F_neighbourhood,level,"property crimes per 1,000 inhabitants",-0.001909,,,0.01,,,,high,"Property-related crime rate per 1,000 population, 2012/2013; <0.01 printed; recorded as 0.01 upper bound",ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-0.19090000000000001,0.07411205382941474,100*b (unit as reported: property crimes per 1000 inhabitants; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,property crimes per 1000 inhabitants,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,crime,crime,F_neighbourhood,level,"property crimes per 1,000 inhabitants",-0.00107,,,0.01,,,,high,"Property-related crime rate per 1,000 population, 2012/2013; <0.01 printed; recorded as 0.01 upper bound",ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-0.107,0.04154001969485268,100*b (unit as reported: property crimes per 1000 inhabitants; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,property crimes per 1000 inhabitants,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,school,school_quality,F_neighbourhood,level,score (Ohio public school performance index),0.001646,,,0.01,,,,high,<0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,0.1646,0.06390174992310982,100*b (unit as reported: score; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,score,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,school,school_quality,F_neighbourhood,level,score (Ohio public school performance index),0.002347,,,0.01,,,,high,<0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,0.23470000000000002,0.09111628619048527,100*b (unit as reported: score; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,score,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,parkdist,green_space,E_environment,level,m,-5.1e-05,,,0.018,,,,high,Euclidean distance to closest park in metres,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-5.1000000000000005,2.155884731387404,100*b/0.001 (X per m->km),|b/z(p)| (two-sided normal),-0.01243984126469444,0.005258600753615238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.365618126864293,36157.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,parkdist,green_space,E_environment,level,m,-2.8e-05,,,0.01,,,,high,Euclidean distance to closest park in metres; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,-2.8,1.0870285527625,100*b/0.001 (X per m->km),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,parksize,green_space,E_environment,level,ha,0.000183,,,0.01,,,,high,Size of closest park in hectares; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",semi_elasticity,0.0183,0.007104508041269196,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,parksize,green_space,E_environment,level,ha,0.000181,,,0.01,,,,high,Size of closest park in hectares; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",semi_elasticity,0.0181,0.007026863144643304,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",|b/z(p)| (two-sided normal),0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,36157.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c1,3,SDEM,11,parkfacility,green_space,E_environment,binary,binary,-0.037543,,,0.01,,,,high,Closest park has built recreational facilities =1; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,spatial_error,36167.0,0.771,p,"Spatial Durbin error model (Bayesian), properties within 0-500 m of nearest park; p-values printed",pct_premium,-3.6949296481750715,1.403806264497365,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575942,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T3-c2,3,SLX,11,parkfacility,green_space,E_environment,binary,binary,-0.043141,,,0.01,,,,high,Closest park has built recreational facilities =1; <0.01 printed; recorded as 0.01 upper bound,ln(appraised market value),log,other,36167.0,0.6657,p,"Spatial lag of X model, properties within 0-500 m of nearest park; p-values printed",pct_premium,-4.2357988770098,1.6041213900211928,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.013545063381575944,0.0052585252302681535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5758293035489013,36157.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,True,True,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c1,4,SDEM 0-100 m,13,parkdist,green_space,E_environment,level,m,2.2e-05,,,,,,,high,"Distance to closest park (m); SDEM, 0-100 m buffer",ln(appraised market value),log,spatial_error,6340.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 0-100 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,2.2,,100*b/0.001 (X per m->km),,,,,,6330.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c1,4,SDEM 0-100 m,13,parksize,green_space,E_environment,level,ha,0.000289,,,,***,,,high,"Size of closest park (ha); SDEM, 0-100 m buffer",ln(appraised market value),log,spatial_error,6340.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 0-100 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0289,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,6330.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c1,4,SDEM 0-100 m,13,parkfacility,green_space,E_environment,binary,binary,-0.029609,,,,**,,,high,"Park facility dummy; SDEM, 0-100 m buffer",ln(appraised market value),log,spatial_error,6340.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 0-100 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-2.917494805686327,,100*(exp(b) - 1),,,,,,6330.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c2,4,SLX 0-100 m,13,parkdist,green_space,E_environment,level,m,-0.000131,,,,,,,high,"Distance to closest park (m); SLX, 0-100 m buffer",ln(appraised market value),log,other,6340.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 0-100 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-13.1,,100*b/0.001 (X per m->km),,,,,,6330.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c2,4,SLX 0-100 m,13,parksize,green_space,E_environment,level,ha,0.000335,,,,***,,,high,"Size of closest park (ha); SLX, 0-100 m buffer",ln(appraised market value),log,other,6340.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 0-100 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0335,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,6330.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c2,4,SLX 0-100 m,13,parkfacility,green_space,E_environment,binary,binary,-0.044068,,,,***,,,high,"Park facility dummy; SLX, 0-100 m buffer",ln(appraised market value),log,other,6340.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 0-100 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-4.311111318363414,,100*(exp(b) - 1),,,,,,6330.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c3,4,SDEM 0-250 m,13,parkdist,green_space,E_environment,level,m,-7.1e-05,,,,,,,high,"Distance to closest park (m); SDEM, 0-250 m buffer",ln(appraised market value),log,spatial_error,18415.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 0-250 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-7.1000000000000005,,100*b/0.001 (X per m->km),,,,,,18405.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c3,4,SDEM 0-250 m,13,parksize,green_space,E_environment,level,ha,0.000249,,,,***,,,high,"Size of closest park (ha); SDEM, 0-250 m buffer",ln(appraised market value),log,spatial_error,18415.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 0-250 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0249,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,18405.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c3,4,SDEM 0-250 m,13,parkfacility,green_space,E_environment,binary,binary,-0.035509,,,,***,,,high,"Park facility dummy; SDEM, 0-250 m buffer",ln(appraised market value),log,spatial_error,18415.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 0-250 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.4885951835520235,,100*(exp(b) - 1),,,,,,18405.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c4,4,SLX 0-250 m,13,parkdist,green_space,E_environment,level,m,-6.3e-05,,,,**,,,high,"Distance to closest park (m); SLX, 0-250 m buffer",ln(appraised market value),log,other,18415.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 0-250 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-6.3,,100*b/0.001 (X per m->km),,,,,,18405.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c4,4,SLX 0-250 m,13,parksize,green_space,E_environment,level,ha,0.000274,,,,***,,,high,"Size of closest park (ha); SLX, 0-250 m buffer",ln(appraised market value),log,other,18415.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 0-250 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0274,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,18405.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c4,4,SLX 0-250 m,13,parkfacility,green_space,E_environment,binary,binary,-0.041457,,,,***,,,high,"Park facility dummy; SLX, 0-250 m buffer",ln(appraised market value),log,other,18415.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 0-250 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-4.060941175017685,,100*(exp(b) - 1),,,,,,18405.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c5,4,SDEM 250-500 m,13,parkdist,green_space,E_environment,level,m,-4.3e-05,,,,,,,high,"Distance to closest park (m); SDEM, 250-500 m buffer",ln(appraised market value),log,spatial_error,17752.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 250-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-4.3,,100*b/0.001 (X per m->km),,,,,,17742.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c5,4,SDEM 250-500 m,13,parksize,green_space,E_environment,level,ha,0.000126,,,,***,,,high,"Size of closest park (ha); SDEM, 250-500 m buffer",ln(appraised market value),log,spatial_error,17752.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 250-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0126,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,17742.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c5,4,SDEM 250-500 m,13,parkfacility,green_space,E_environment,binary,binary,-0.037637,,,,***,,,high,"Park facility dummy; SDEM, 250-500 m buffer",ln(appraised market value),log,spatial_error,17752.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 250-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.6937530875720714,,100*(exp(b) - 1),,,,,,17742.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c6,4,SLX 250-500 m,13,parkdist,green_space,E_environment,level,m,-1.5e-05,,,,,,,high,"Distance to closest park (m); SLX, 250-500 m buffer",ln(appraised market value),log,other,17752.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 250-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-1.5,,100*b/0.001 (X per m->km),,,,,,17742.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c6,4,SLX 250-500 m,13,parksize,green_space,E_environment,level,ha,0.000119,,,,***,,,high,"Size of closest park (ha); SLX, 250-500 m buffer",ln(appraised market value),log,other,17752.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 250-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0119,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,17742.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c6,4,SLX 250-500 m,13,parkfacility,green_space,E_environment,binary,binary,-0.044139,,,,***,,,high,"Park facility dummy; SLX, 250-500 m buffer",ln(appraised market value),log,other,17752.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 250-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-4.317904988281674,,100*(exp(b) - 1),,,,,,17742.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c7,4,SDEM 100-500 m,13,parkdist,green_space,E_environment,level,m,-3.5e-05,,,,,,,high,"Distance to closest park (m); SDEM, 100-500 m buffer",ln(appraised market value),log,spatial_error,29827.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 100-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-3.4999999999999996,,100*b/0.001 (X per m->km),,,,,,29817.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c7,4,SDEM 100-500 m,13,parksize,green_space,E_environment,level,ha,0.000167,,,,***,,,high,"Size of closest park (ha); SDEM, 100-500 m buffer",ln(appraised market value),log,spatial_error,29827.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 100-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0167,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,29817.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c7,4,SDEM 100-500 m,13,parkfacility,green_space,E_environment,binary,binary,-0.037449,,,,***,,,high,"Park facility dummy; SDEM, 100-500 m buffer",ln(appraised market value),log,spatial_error,29827.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 100-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.675645811121886,,100*(exp(b) - 1),,,,,,29817.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c8,4,SLX 100-500 m,13,parkdist,green_space,E_environment,level,m,-9e-06,,,,,,,high,"Distance to closest park (m); SLX, 100-500 m buffer",ln(appraised market value),log,other,29827.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 100-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,-0.8999999999999999,,100*b/0.001 (X per m->km),,,,,,29817.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c8,4,SLX 100-500 m,13,parksize,green_space,E_environment,level,ha,0.000152,,,,***,,,high,"Size of closest park (ha); SLX, 100-500 m buffer",ln(appraised market value),log,other,29827.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 100-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.015200000000000002,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,29817.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c8,4,SLX 100-500 m,13,parkfacility,green_space,E_environment,binary,binary,-0.044089,,,,***,,,high,"Park facility dummy; SLX, 100-500 m buffer",ln(appraised market value),log,other,29827.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 100-500 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-4.31312076392647,,100*(exp(b) - 1),,,,,,29817.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c9,4,SDEM 500-750 m,13,parkdist,green_space,E_environment,level,m,1.5e-05,,,,,,,high,"Distance to closest park (m); SDEM, 500-750 m buffer",ln(appraised market value),log,spatial_error,9145.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 500-750 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,1.5,,100*b/0.001 (X per m->km),,,,,,9135.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c9,4,SDEM 500-750 m,13,parksize,green_space,E_environment,level,ha,0.000147,,,,***,,,high,"Size of closest park (ha); SDEM, 500-750 m buffer",ln(appraised market value),log,spatial_error,9145.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 500-750 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0147,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9135.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c9,4,SDEM 500-750 m,13,parkfacility,green_space,E_environment,binary,binary,-0.038445,,,,***,,,high,"Park facility dummy; SDEM, 500-750 m buffer",ln(appraised market value),log,spatial_error,9145.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 500-750 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.771537106101963,,100*(exp(b) - 1),,,,,,9135.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c10,4,SLX 500-750 m,13,parkdist,green_space,E_environment,level,m,0.00037,,,,,,,high,"Distance to closest park (m); SLX, 500-750 m buffer",ln(appraised market value),log,other,9145.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 500-750 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,37.0,,100*b/0.001 (X per m->km),,,,,,9135.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c10,4,SLX 500-750 m,13,parksize,green_space,E_environment,level,ha,9.1e-05,,,,***,,,high,"Size of closest park (ha); SLX, 500-750 m buffer",ln(appraised market value),log,other,9145.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 500-750 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0091,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9135.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c10,4,SLX 500-750 m,13,parkfacility,green_space,E_environment,binary,binary,-0.035325,,,,***,,,high,"Park facility dummy; SLX, 500-750 m buffer",ln(appraised market value),log,other,9145.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 500-750 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.470835451220533,,100*(exp(b) - 1),,,,,,9135.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c11,4,SDEM 750-1000 m,13,parkdist,green_space,E_environment,level,m,0.000346,,,,***,,,high,"Distance to closest park (m); SDEM, 750-1000 m buffer",ln(appraised market value),log,spatial_error,3989.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 750-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,34.6,,100*b/0.001 (X per m->km),,,,,,3979.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c11,4,SDEM 750-1000 m,13,parksize,green_space,E_environment,level,ha,5.3e-05,,,,,,,high,"Size of closest park (ha); SDEM, 750-1000 m buffer",ln(appraised market value),log,spatial_error,3989.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 750-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0053,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,3979.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c11,4,SDEM 750-1000 m,13,parkfacility,green_space,E_environment,binary,binary,-0.033261,,,,*,,,high,"Park facility dummy; SDEM, 750-1000 m buffer",ln(appraised market value),log,spatial_error,3989.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 750-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.271393502304054,,100*(exp(b) - 1),,,,,,3979.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c12,4,SLX 750-1000 m,13,parkdist,green_space,E_environment,level,m,0.000319,,,,***,,,high,"Distance to closest park (m); SLX, 750-1000 m buffer",ln(appraised market value),log,other,3989.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 750-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,31.9,,100*b/0.001 (X per m->km),,,,,,3979.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c12,4,SLX 750-1000 m,13,parksize,green_space,E_environment,level,ha,6.3e-05,,,,**,,,high,"Size of closest park (ha); SLX, 750-1000 m buffer",ln(appraised market value),log,other,3989.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 750-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0063,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,3979.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c12,4,SLX 750-1000 m,13,parkfacility,green_space,E_environment,binary,binary,-0.02655,,,,***,,,high,"Park facility dummy; SLX, 750-1000 m buffer",ln(appraised market value),log,other,3989.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 750-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-2.6200647349367,,100*(exp(b) - 1),,,,,,3979.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c13,4,SDEM 500-1000 m,13,parkdist,green_space,E_environment,level,m,0.000229,,,,***,,,high,"Distance to closest park (m); SDEM, 500-1000 m buffer",ln(appraised market value),log,spatial_error,13134.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 500-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,22.9,,100*b/0.001 (X per m->km),,,,,,13124.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c13,4,SDEM 500-1000 m,13,parksize,green_space,E_environment,level,ha,0.000146,,,,***,,,high,"Size of closest park (ha); SDEM, 500-1000 m buffer",ln(appraised market value),log,spatial_error,13134.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 500-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0146,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,13124.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c13,4,SDEM 500-1000 m,13,parkfacility,green_space,E_environment,binary,binary,-0.037838,,,,***,,,high,"Park facility dummy; SDEM, 500-1000 m buffer",ln(appraised market value),log,spatial_error,13134.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the 500-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.7131086978974714,,100*(exp(b) - 1),,,,,,13124.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c14,4,SLX 500-1000 m,13,parkdist,green_space,E_environment,level,m,0.000188,,,,***,,,high,"Distance to closest park (m); SLX, 500-1000 m buffer",ln(appraised market value),log,other,13134.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 500-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,18.799999999999997,,100*b/0.001 (X per m->km),,,,,,13124.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c14,4,SLX 500-1000 m,13,parksize,green_space,E_environment,level,ha,0.000106,,,,***,,,high,"Size of closest park (ha); SLX, 500-1000 m buffer",ln(appraised market value),log,other,13134.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 500-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.0106,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,13124.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c14,4,SLX 500-1000 m,13,parkfacility,green_space,E_environment,binary,binary,-0.035218,,,,***,,,high,"Park facility dummy; SLX, 500-1000 m buffer",ln(appraised market value),log,other,13134.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the 500-1000 m buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.460506278012898,,100*(exp(b) - 1),,,,,,13124.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c15,4,SDEM no buffer,13,parkdist,green_space,E_environment,level,m,6.1e-05,,,,***,,,high,"Distance to closest park (m); SDEM, no buffer buffer",ln(appraised market value),log,spatial_error,51786.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the no buffer buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,6.1,,100*b/0.001 (X per m->km),,,,,,51776.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c15,4,SDEM no buffer,13,parksize,green_space,E_environment,level,ha,0.000141,,,,***,,,high,"Size of closest park (ha); SDEM, no buffer buffer",ln(appraised market value),log,spatial_error,51786.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the no buffer buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.014100000000000001,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,51776.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c15,4,SDEM no buffer,13,parkfacility,green_space,E_environment,binary,binary,-0.034785,,,,***,,,high,"Park facility dummy; SDEM, no buffer buffer",ln(appraised market value),log,spatial_error,51786.0,,none,Sensitivity analysis: SDEM model re-estimated for properties in the no buffer buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.418695625878343,,100*(exp(b) - 1),,,,,,51776.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,spatial,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c16,4,SLX no buffer,13,parkdist,green_space,E_environment,level,m,4.8e-05,,,,***,,,high,"Distance to closest park (m); SLX, no buffer buffer",ln(appraised market value),log,other,51786.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the no buffer buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,4.800000000000001,,100*b/0.001 (X per m->km),,,,,,51776.0,m,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c16,4,SLX no buffer,13,parksize,green_space,E_environment,level,ha,0.000122,,,,***,,,high,"Size of closest park (ha); SLX, no buffer buffer",ln(appraised market value),log,other,51786.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the no buffer buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,semi_elasticity,0.012199999999999999,,"100*b (unit as reported: ha; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,51776.0,ha,unit_not_harmonisable,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831,79a14cd6c47cfe07a6fd907132f2ed86427b4831:T4-c16,4,SLX no buffer,13,parkfacility,green_space,E_environment,binary,binary,-0.037216,,,,***,,,high,"Park facility dummy; SLX, no buffer buffer",ln(appraised market value),log,other,51786.0,,none,Sensitivity analysis: SLX model re-estimated for properties in the no buffer buffer around the nearest park; only park coefficients printed with stars; same controls as Table 3,pct_premium,-3.653199621716363,,100*(exp(b) - 1),,,,,,51776.0,binary,,2018.0,United States,North America,High income,,,single_family,appraisal,journal,False,False,unknown,,2015.0,other,journal,False,False,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,AREA,living_area,A_structural,level,m2,0.012,0.001,,,***,,,high,floor area of apartment (m2); enters with AREA2,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,semi_elasticity,1.2,0.1,100*b/1 (X per m2->m2),reported,0.5168765907047376,0.0430730492253948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,395.0,m2,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,AREA,living_area,A_structural,level,m2,0.012,0.001,,,***,,,high,floor area of apartment (m2); enters with AREA2,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,semi_elasticity,1.2,0.1,100*b/1 (X per m2->m2),reported,0.5168765907047376,0.0430730492253948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,395.0,m2,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,AREA,living_area,A_structural,level,m2,0.012,0.001,,,***,,,high,floor area of apartment (m2); enters with AREA2,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,semi_elasticity,1.2,0.1,100*b/1 (X per m2->m2),reported,0.5168765907047376,0.0430730492253948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,395.0,m2,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,AREA,living_area,A_structural,level,m2,0.012,0.001,,,***,,,high,floor area of apartment (m2); enters with AREA2,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,semi_elasticity,1.2,0.1,100*b/1 (X per m2->m2),reported,0.5168765907047376,0.0430730492253948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,395.0,m2,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,AREA2,living_area,A_structural,squared,m2,-2.39e-05,4.7e-06,,,***,,,high,square of floor area; printed in E-05 notation,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,other,-2.39e-05,4.7e-06,not harmonised,reported,-0.24787463696447357,0.04874522149510568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.085106382978724,395.0,m2,nonstandard_x_transform,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,AREA2,living_area,A_structural,squared,m2,-2.45e-05,4.9e-06,,,***,,,high,square of floor area; printed in E-05 notation,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,other,-2.45e-05,4.9e-06,not harmonised,reported,-0.2439750182371333,0.04879500364742666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,395.0,m2,nonstandard_x_transform,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,AREA2,living_area,A_structural,squared,m2,-2.1e-05,3.8e-06,,,***,,,high,square of floor area; printed in E-05 notation,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,other,-2.1e-05,3.8e-06,not harmonised,reported,-0.26789550007133184,0.04847632858433623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.526315789473684,395.0,m2,nonstandard_x_transform,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,AREA2,living_area,A_structural,squared,m2,-2.42e-05,3.7e-06,,,***,,,high,square of floor area; printed in E-05 notation,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,other,-2.42e-05,3.7e-06,not harmonised,reported,-0.312598106385112,0.04779392535640143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.54054054054054,395.0,m2,nonstandard_x_transform,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,CNSTR_LQ,condition,A_structural,binary,binary,-0.14,0.021,,,***,,,high,1 = construction quality below average,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,-13.08334369594455,1.8256522943374924,100*(exp(b - se^2/2) - 1),reported,-0.31802169777052086,0.04770325466557813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.666666666666667,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,CNSTR_LQ,condition,A_structural,binary,binary,-0.104,0.023,,,***,,,high,1 = construction quality below average,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,-9.901304514776887,2.0728181840687707,100*(exp(b - se^2/2) - 1),reported,-0.22184423491172459,0.049061705797785245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.521739130434782,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,CNSTR_LQ,condition,A_structural,binary,binary,-0.104,0.025,,,***,,,high,1 = construction quality below average,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,-9.905629148368133,2.253063243553012,100*(exp(b - se^2/2) - 1),reported,-0.20487252209478746,0.0492482024266316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.159999999999999,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,CNSTR_LQ,condition,A_structural,binary,binary,-0.102,0.021,,,***,,,high,1 = construction quality below average,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,-9.716954439611836,1.8963620585046412,100*(exp(b - se^2/2) - 1),reported,-0.23740261571889446,0.04887700911859592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.857142857142857,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,AGE,age,A_structural,level,years,-0.001,0.001,,,,,,high,age of building in years,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,semi_elasticity,-0.1,0.1,100*b/1 (per year),reported,-0.050251890762960605,0.050251890762960605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,395.0,years,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,AGE,age,A_structural,level,years,-0.009,0.002,,,***,,,high,age of building in years,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,semi_elasticity,-0.8999999999999999,0.2,100*b/1 (per year),reported,-0.2208298071653624,0.04907329048119165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,395.0,years,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,AGE,age,A_structural,level,years,-0.018,0.002,,,***,,,high,age of building in years,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,semi_elasticity,-1.7999999999999998,0.2,100*b/1 (per year),reported,-0.4125143236626951,0.045834924851410566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,395.0,years,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,AGE,age,A_structural,level,years,-0.018,0.002,,,***,,,high,age of building in years,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,semi_elasticity,-1.7999999999999998,0.2,100*b/1 (per year),reported,-0.4125143236626951,0.045834924851410566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,395.0,years,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,BATHROOM,bathrooms,A_structural,binary,binary,0.135,0.03,,,***,,,high,1 = more than one bathroom,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,14.402185866532502,3.4336103530539432,100*(exp(b - se^2/2) - 1),reported,0.22082980716536244,0.04907329048119165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.500000000000001,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,BATHROOM,bathrooms,A_structural,binary,binary,0.144,0.03,,,***,,,high,1 = more than one bathroom,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,15.436452759055562,3.4646523255747406,100*(exp(b - se^2/2) - 1),reported,0.23476444178167144,0.04890925870451489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,BATHROOM,bathrooms,A_structural,binary,binary,0.123,0.033,,,***,,,high,1 = more than one bathroom,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,13.026882199230826,3.731918589126715,100*(exp(b - se^2/2) - 1),reported,0.18432598624521515,0.0494533133828626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.727272727272727,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,BATHROOM,bathrooms,A_structural,binary,binary,0.151,0.03,,,***,,,high,1 = more than one bathroom,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,16.24734273214341,3.488989974245461,100*(exp(b - se^2/2) - 1),reported,0.24550379093011793,0.04877558760201018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.033333333333333,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,BLW_GRND,floor_level,A_structural,binary,binary,-0.205,0.05,,,***,,,high,1 = apartment below ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,-18.637035655598556,4.073236582057073,100*(exp(b - se^2/2) - 1),reported,-0.20203909408027115,0.04927782782445639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.1,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,BLW_GRND,floor_level,A_structural,binary,binary,-0.191,0.045,,,***,,,high,1 = apartment below ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,-17.470344777969693,3.717596646680372,100*(exp(b - se^2/2) - 1),reported,-0.20885158657961292,0.04920587118367843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.2444444444444445,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,BLW_GRND,floor_level,A_structural,binary,binary,-0.237,0.057,,,***,,,high,1 = apartment below ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,-21.22893878191845,4.497250341700376,100*(exp(b - se^2/2) - 1),reported,-0.20477319025688645,0.04924924828963092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.157894736842105,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,BLW_GRND,floor_level,A_structural,binary,binary,-0.266,0.051,,,***,,,high,1 = apartment below ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,-23.455697872582903,3.908839550255198,100*(exp(b - se^2/2) - 1),reported,-0.2538344748026419,0.048667512086220804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.215686274509805,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,True
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,GROUND,floor_level,A_structural,binary,binary,-0.075,0.016,,,***,,,high,1 = apartment on ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,-7.23752572379468,1.4843895781256844,100*(exp(b - se^2/2) - 1),reported,-0.22955538898594324,0.04897181631700122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.6875,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,GROUND,floor_level,A_structural,binary,binary,-0.078,0.016,,,***,,,high,1 = apartment on ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,-7.515396132607299,1.4799430824696629,100*(exp(b - se^2/2) - 1),reported,-0.23822597202185103,0.04886686605576431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.875,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,GROUND,floor_level,A_structural,binary,binary,-0.086,0.015,,,***,,,high,1 = apartment on ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,-8.250899232442798,1.3763913468302262,100*(exp(b - se^2/2) - 1),reported,-0.27717286423410936,0.04834410422687954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.733333333333333,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,GROUND,floor_level,A_structural,binary,binary,-0.088,0.014,,,***,,,high,1 = apartment on ground floor,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,-8.432886344525315,1.2820652274126558,100*(exp(b - se^2/2) - 1),reported,-0.3015467461141932,0.04797334597271255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.285714285714286,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,NRTH_WST,sunlight,E_environment,binary,binary,-0.011,0.014,,,,,,high,1 = faces north and/or west (less sunlight),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,-1.103664438263896,1.3846843902855162,100*(exp(b - se^2/2) - 1),reported,-0.03950271872126122,0.05027618746342336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7857142857142857,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,NRTH_WST,sunlight,E_environment,binary,binary,-0.013,0.014,,,,,,high,1 = faces north and/or west (less sunlight),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,-1.3012594485121265,1.3819177890284027,100*(exp(b - se^2/2) - 1),reported,-0.046670588265470755,0.05026063351666081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9285714285714285,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,NRTH_WST,sunlight,E_environment,binary,binary,-0.014,0.012,,,,,,high,1 = faces north and/or west (less sunlight),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,-1.3973452204421477,1.1833170531154342,100*(exp(b - se^2/2) - 1),reported,-0.058600493179042173,0.05022899415346472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1666666666666667,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,NRTH_WST,sunlight,E_environment,binary,binary,-0.019,0.011,,,*,,,high,1 = faces north and/or west (less sunlight),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,-1.887999731296497,1.0792972984670866,100*(exp(b - se^2/2) - 1),reported,-0.08658215639762232,0.05012651159862344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7272727272727273,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,POOL,pool,C_exterior,binary,binary,0.233,0.045,,,***,,,high,"swimming pool in apartment complex (shared, not private)","log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,26.110396493024357,5.680716656972676,100*(exp(b - se^2/2) - 1),reported,0.25210722338654795,0.04869023627637192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.177777777777778,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,POOL,pool,C_exterior,binary,binary,0.234,0.046,,,***,,,high,"swimming pool in apartment complex (shared, not private)","log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,26.23082633247449,5.812764664155778,100*(exp(b - se^2/2) - 1),reported,0.2479592783916354,0.04874413164963772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.086956521739131,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,True
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,POOL,pool,C_exterior,binary,binary,0.217,0.039,,,***,,,high,"swimming pool in apartment complex (shared, not private)","log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,24.139965861825562,4.845141998337279,100*(exp(b - se^2/2) - 1),reported,0.26959454874375655,0.048452476502334134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.564102564102564,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,POOL,pool,C_exterior,binary,binary,0.19,0.041,,,***,,,high,"swimming pool in apartment complex (shared, not private)","log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,20.823365038207186,4.957923350394731,100*(exp(b - se^2/2) - 1),reported,0.2270780274301531,0.04900104802440146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.634146341463414,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,SECURITY,other,other,binary,binary,0.196,0.023,,,***,,,high,security guards present in complex; no taxonomy leaf,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,pct_premium,21.6205176518367,2.7980118822689275,100*(exp(b - se^2/2) - 1),reported,0.39407753596131007,0.04624379248525577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.521739130434783,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,False,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,SECURITY,other,other,binary,binary,0.174,0.024,,,***,,,high,security guards present in complex; no taxonomy leaf,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,pct_premium,18.971287916665315,2.8561333579688704,100*(exp(b - se^2/2) - 1),reported,0.34269767738760554,0.047268645156911114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.249999999999999,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,False,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,SECURITY,other,other,binary,binary,0.134,0.024,,,***,,,high,security guards present in complex; no taxonomy leaf,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,14.306356992682435,2.744142767147153,100*(exp(b - se^2/2) - 1),reported,0.2704582785291851,0.04844028869179435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.583333333333334,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,False,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,SECURITY,other,other,binary,binary,0.148,0.024,,,***,,,high,security guards present in complex; no taxonomy leaf,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,15.917900473152425,2.7828309512711376,100*(exp(b - se^2/2) - 1),reported,0.2963415823677911,0.048055391735317476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.166666666666666,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,False,False,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c1,2,1,8,DISTANCE,cbd_distance,D_location,level,km,-0.018,0.003,,,***,,,high,km from CBD (Anitpark); Table 1 mislabels it 'Binary' but it is continuous (mean 5.77 km),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.851,se,,semi_elasticity,-1.7999999999999998,0.3,100*b/1 (X per km->km),reported,-0.28900983051545276,0.04816830508590881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,395.0,km,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c2,2,2,8,DISTANCE,cbd_distance,D_location,level,km,-0.018,0.003,,,***,,,high,km from CBD (Anitpark); Table 1 mislabels it 'Binary' but it is continuous (mean 5.77 km),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.861,se,,semi_elasticity,-1.7999999999999998,0.3,100*b/1 (X per km->km),reported,-0.28900983051545276,0.04816830508590881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,395.0,km,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,DISTANCE,cbd_distance,D_location,level,km,-0.03,0.003,,,***,,,high,km from CBD (Anitpark); Table 1 mislabels it 'Binary' but it is continuous (mean 5.77 km),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,semi_elasticity,-3.0,0.3,100*b/1 (X per km->km),reported,-0.44946657497549475,0.044946657497549475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,395.0,km,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,DISTANCE,cbd_distance,D_location,level,km,-0.01,0.005,,,**,,,high,km from CBD (Anitpark); Table 1 mislabels it 'Binary' but it is continuous (mean 5.77 km),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,semi_elasticity,-1.0,0.5,100*b/1 (X per km->km),reported,-0.10012523486435178,0.05006261743217589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,395.0,km,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,SCHOOL,school_quality,F_neighbourhood,binary,binary,0.2,0.029,,,***,,,high,1 = high-quality public primary school (top-100 SBS) within 1 km; mixes proximity and quality,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,22.08892662695998,3.5420679986644927,100*(exp(b - se^2/2) - 1),reported,0.32782700674805615,0.04753491597846814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.8965517241379315,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,True
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,SCHOOL,school_quality,F_neighbourhood,binary,binary,0.151,0.028,,,***,,,high,1 = high-quality public primary school (top-100 SBS) within 1 km; mixes proximity and quality,"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,16.254085273553677,3.2563906426290967,100*(exp(b - se^2/2) - 1),reported,0.26187469303726163,0.04855954572876376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.392857142857142,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c3,2,3,8,AIR,air_pollution,E_environment,binary,binary,0.141,0.02,,,***,,,high,1 = air perceived clean by agents (sign reversed relative to pollution),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.894,se,,pct_premium,15.119438608210501,2.302849295969488,100*(exp(b - se^2/2) - 1),reported,0.33431382116572006,0.04742040016535038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.049999999999999,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa,7a87d94cbb2a1bb3af93865c70eaef38f5783daa:T2-c4,2,4,8,AIR,air_pollution,E_environment,binary,binary,0.091,0.022,,,***,,,high,1 = air perceived clean by agents (sign reversed relative to pollution),"log(PRICE), sale price of apartment (TL)",log,OLS,405.0,0.908,se,,pct_premium,9.50039822282729,2.4095918115686237,100*(exp(b - se^2/2) - 1),reported,0.20375693365673614,0.049259918026903236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.136363636363637,395.0,binary,,2014.0,Turkey,Asia,Upper middle income,,,condo_apartment,transaction,journal,False,False,robust,,2011.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,log size in sq.m.,living_area,A_structural,log,m2,0.939,0.002,,,***,,,high,"apartment size in m2, log; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01",log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,elasticity,0.939,0.002,b (log-log),reported,0.8979398850703288,0.0019125450161242353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),469.49999999999994,52956.0,m2,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Floors,floor_level,A_structural,level,count,0.002,0.0004,,,***,,,high,floor level of apartment (integer); all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,0.2,0.04,100*b/1 (per unit count),reported,0.021722506143937486,0.004344501228787497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,52956.0,count,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Age (relative to 2017),age,A_structural,level,years,-0.0002,3e-05,,,***,,,high,building age relative to 2017; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-0.02,0.003,100*b/1 (per year),reported,-0.028958028100483903,0.004343704215072586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.666666666666667,52956.0,years,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Bathtub,bathroom_quality,B_interior,binary,binary,-0.032,0.002,,,***,,,high,dwelling has a bathtub; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,-3.1495354932029596,0.19370131641583954,100*(exp(b - se^2/2) - 1),reported,-0.06936097533661631,0.004335060958538519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Refurbished,renovation,A_structural,binary,binary,-0.015,0.002,,,***,,,high,refurbished dwelling; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,-1.4890030618846306,0.19702238792061252,100*(exp(b - se^2/2) - 1),reported,-0.032574153958821656,0.0043432205278428875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Built-in kitchen,kitchen_quality,B_interior,binary,binary,0.084,0.002,,,***,,,high,built-in kitchen; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,8.76267185532138,0.21752577876176524,100*(exp(b - se^2/2) - 1),reported,0.17954621161490197,0.004274909800354809,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Balcony,terrace_balcony,C_exterior,binary,binary,0.011,0.002,,,***,,,high,all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,1.1058700325296877,0.2022121444889439,100*(exp(b - se^2/2) - 1),reported,0.023893572955169647,0.004344285991849026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Parking,parking,A_structural,binary,binary,0.053,0.002,,,***,,,high,parking space included; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,5.442753626217445,0.2108859290238712,100*(exp(b - se^2/2) - 1),reported,0.11440042072715684,0.004316997008571956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Elevator,accessibility,B_interior,binary,binary,0.053,0.002,,,***,,,high,all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,5.442753626217445,0.2108859290238712,100*(exp(b - se^2/2) - 1),reported,0.11440042072715684,0.004316997008571956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Terrace,terrace_balcony,C_exterior,binary,binary,0.041,0.002,,,***,,,high,all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,pct_premium,4.185002184335218,0.2083704211090959,100*(exp(b - se^2/2) - 1),reported,0.08873191033744966,0.004328385870119495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,log Purchasing Power,income,F_neighbourhood,log,currency,0.406,0.011,,,***,,,high,"household purchasing power EUR per household at ZIP level, log; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01",log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,elasticity,0.406,0.011,b (log-log),reported,0.15836541373350263,0.004290688549429874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.909090909090914,52956.0,currency,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,CBD_distance,cbd_distance,D_location,level,km,-0.019,0.001,,,***,,,high,all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-1.9,0.1,100*b/1 (X per km->km),reported,-0.08228501421715072,0.004330790221955301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Bar_distance,neighbourhood_amenities,F_neighbourhood,level,km,-0.031,0.002,,,***,,,high,distance to nearest bar; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-3.1,0.2,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.06720339122043364,0.004335702659382816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.5,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,False,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Beergarden_distance,neighbourhood_amenities,F_neighbourhood,level,km,-0.02,0.002,,,***,,,high,distance to nearest beer garden; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-2.0,0.2,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.04341429447794924,0.004341429447794924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,False,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Cafe_distance,neighbourhood_amenities,F_neighbourhood,level,km,-0.014,0.003,,,***,,,high,distance to nearest cafe; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-1.4000000000000001,0.3,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.02027495562272223,0.004344633347726192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.666666666666667,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,False,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Bakery_distance,commercial_distance,D_location,level,km,-0.011,0.003,,,***,,,high,distance to nearest bakery; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-1.0999999999999999,0.3,100*b/1 (X per km->km),reported,-0.015931575331981546,0.004344975090540422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6666666666666665,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Convenience store_distance,commercial_distance,D_location,level,km,-0.036,0.002,,,***,,,high,distance to nearest convenience store; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-3.5999999999999996,0.2,100*b/1 (X per km->km),reported,-0.07798128673650545,0.004332293707583636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Department store_distance,commercial_distance,D_location,level,km,-0.006,0.001,,,***,,,high,distance to nearest department store; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-0.6,0.1,100*b/1 (X per km->km),reported,-0.026064301757134343,0.004344050292855724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Supermarket_distance,commercial_distance,D_location,level,km,-0.018,0.006,,,***,,,high,distance to nearest supermarket; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-1.7999999999999998,0.6,100*b/1 (X per km->km),reported,-0.013035472159055678,0.004345157386351893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Bus station_distance,transit_distance,D_location,level,km,-0.028,0.001,,,***,,,high,distance to nearest bus station; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-2.8000000000000003,0.1,100*b/1 (X per km->km),reported,-0.12078394222604698,0.0043137122223588205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Railway station_distance,transit_distance,D_location,level,km,-0.02,0.002,,,***,,,high,distance to nearest railway station; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-2.0,0.2,100*b/1 (X per km->km),reported,-0.04341429447794924,0.004341429447794924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Traffic signals_distance,other,other,level,km,0.086,0.007,,,***,,,high,distance to nearest traffic signal (traffic nuisance proxy); no taxonomy leaf; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,8.6,0.7000000000000001,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.05331197565882659,0.004339346855951002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.285714285714285,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,False,False,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Car wash_distance,other,other,level,km,0.012,0.002,,,***,,,high,distance to nearest car wash (nuisance proxy); no taxonomy leaf; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,1.2,0.2,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.026064301757134343,0.004344050292855724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,False,False,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,Park_distance,green_space,E_environment,level,km,-0.024,0.006,,,***,,,high,distance to nearest park; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-2.4,0.6,100*b/1 (X per km->km),reported,-0.01737948112335037,0.0043448702808375934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c1,2,OLS,11,School_distance,school_distance,D_location,level,km,-0.003,0.005,,,***,,,medium,distance to nearest school; all coefficients printed ***; stars * p<0.1 ** p<0.05 *** p<0.01; printed *** inconsistent with beta/SE = -0.6,log monthly asking rent (EUR/month),log,OLS,52966.0,0.88,se,OLS; year and month controls; apartment coordinates as location controls; adj R2 0.880,semi_elasticity,-0.3,0.5,100*b/1 (X per km->km),reported,-0.0026073071010466837,0.004345511835077806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,ols,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,log size in sq.m.,living_area,A_structural,log,m2,0.928,0.008,,,***,,,high,"apartment size in m2, log; SAR; all printed ***; SE decimal comma in source",log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",elasticity,0.928,0.008,b (log-log),reported,0.4501266326538799,0.0038804020056368953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),116.0,52956.0,m2,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Floors,floor_level,A_structural,level,count,0.003,0.002,,,***,,,medium,floor level of apartment (integer); SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,0.3,0.2,100*b/1 (per unit count),reported,0.006518151438010669,0.0043454342920071125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,52956.0,count,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Age (relative to 2017),age,A_structural,level,years,-0.0,0.0001,,,***,,,medium,building age relative to 2017; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-0.0,0.01,100*b/1 (per year),reported,-0.0,0.0043455266056571734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,52956.0,years,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Bathtub,bathroom_quality,B_interior,binary,binary,-0.032,0.006,,,***,,,high,dwelling has a bathtub; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,-3.15108508823827,0.5811039492475186,100*(exp(b - se^2/2) - 1),reported,-0.023169920061469982,0.004344360011525622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.333333333333333,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Refurbished,renovation,A_structural,binary,binary,-0.013,0.007,,,***,,,medium,refurbished dwelling; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,-1.2940048244774238,0.6909588945142013,100*(exp(b - se^2/2) - 1),reported,-0.00807000090432483,0.004345385102328754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.857142857142857,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Built-in kitchen,kitchen_quality,B_interior,binary,binary,0.077,0.007,,,***,,,high,built-in kitchen; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,8.001561568587379,0.7560294534748203,100*(exp(b - se^2/2) - 1),reported,0.047746275676795756,0.004340570516072341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Balcony,terrace_balcony,C_exterior,binary,binary,0.012,0.007,,,***,,,medium,SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,1.2047493398746223,0.7084506022062544,100*(exp(b - se^2/2) - 1),reported,0.007449267486690175,0.0043454060339026025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7142857142857142,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Parking,parking,A_structural,binary,binary,0.048,0.008,,,***,,,high,parking space included; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,4.913708240066983,0.8393365242595766,100*(exp(b - se^2/2) - 1),reported,0.026064301757134343,0.004344050292855724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Elevator,accessibility,B_interior,binary,binary,0.048,0.009,,,***,,,high,SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,4.912816477336945,0.9442535897920236,100*(exp(b - se^2/2) - 1),reported,0.023169920061469985,0.004344360011525622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333334,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Terrace,terrace_balcony,C_exterior,binary,binary,0.041,0.009,,,***,,,high,SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",pct_premium,4.180991138964241,0.9376668949909315,100*(exp(b - se^2/2) - 1),reported,0.019792409996508314,0.00434467536508719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.555555555555556,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,log Purchasing Power,income,F_neighbourhood,log,currency,0.313,0.04,,,***,,,high,"household purchasing power EUR per household at ZIP level, log; SAR; all printed ***; SE decimal comma in source",log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",elasticity,0.313,0.04,b (log-log),reported,0.033984104224788404,0.004343016514349956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.825,52956.0,currency,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,CBD_distance,cbd_distance,D_location,level,km,-0.014,0.002,,,***,,,high,SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-1.4000000000000001,0.2,100*b/1 (X per km->km),reported,-0.0304046228469153,0.0043435175495593285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Bar_distance,neighbourhood_amenities,F_neighbourhood,level,km,-0.024,0.008,,,***,,,high,distance to nearest bar; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-2.4,0.8,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.01303547215905568,0.004345157386351893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,False,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Beergarden_distance,neighbourhood_amenities,F_neighbourhood,level,km,-0.015,0.005,,,***,,,high,distance to nearest beer garden; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-1.5,0.5,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.01303547215905568,0.004345157386351893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,False,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Cafe_distance,neighbourhood_amenities,F_neighbourhood,level,km,-0.011,0.01,,,***,,,medium,distance to nearest cafe; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-1.0999999999999999,1.0,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.004780024656766019,0.004345476960696382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0999999999999999,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,False,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Bakery_distance,commercial_distance,D_location,level,km,-0.016,0.009,,,***,,,medium,distance to nearest bakery; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-1.6,0.8999999999999999,100*b/1 (X per km->km),reported,-0.007725150111422797,0.004345396937675323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.777777777777778,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Convenience store_distance,commercial_distance,D_location,level,km,-0.035,0.007,,,***,,,high,distance to nearest convenience store; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-3.5000000000000004,0.7000000000000001,100*b/1 (X per km->km),reported,-0.021722506143937486,0.004344501228787497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Department store_distance,commercial_distance,D_location,level,km,-0.008,0.005,,,***,,,medium,distance to nearest department store; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-0.8,0.5,100*b/1 (X per km->km),reported,-0.006952674517917867,0.004345421573698667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Supermarket_distance,commercial_distance,D_location,level,km,-0.029,0.02,,,***,,,medium,distance to nearest supermarket; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-2.9000000000000004,2.0,100*b/1 (X per km->km),reported,-0.006300888498074322,0.0043454403434995325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.45,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Bus station_distance,transit_distance,D_location,level,km,-0.017,0.004,,,***,,,high,distance to nearest bus station; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-1.7000000000000002,0.4,100*b/1 (X per km->km),reported,-0.018465339216936005,0.004344785698102589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.25,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Railway station_distance,transit_distance,D_location,level,km,-0.02,0.007,,,***,,,high,distance to nearest railway station; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-2.0,0.7000000000000001,100*b/1 (X per km->km),reported,-0.012414833453986323,0.004345191708895213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.857142857142857,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Traffic signals_distance,other,other,level,km,0.075,0.024,,,***,,,high,distance to nearest traffic signal (traffic nuisance proxy); no taxonomy leaf; SAR; all printed ***; SE decimal comma in source,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,7.5,2.4,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.013578518691919153,0.004345125981414129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.125,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,False,False,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Car wash_distance,other,other,level,km,0.007,0.006,,,***,,,medium,distance to nearest car wash (nuisance proxy); no taxonomy leaf; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,0.7000000000000001,0.6,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.005069715887709963,0.004345470760894253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1666666666666667,52956.0,km,unit_not_harmonisable,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,False,False,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,Park_distance,green_space,E_environment,level,km,-0.019,0.02,,,***,,,medium,distance to nearest park; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,-1.9,2.0,100*b/1 (X per km->km),reported,-0.004128215098073798,0.004345489576919787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.95,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c3,2,SAR,11,School_distance,school_distance,D_location,level,km,0.008,0.006,,,***,,,medium,distance to nearest school; SAR; all printed ***; SE decimal comma in source; printed *** inconsistent with beta/SE ratio,log monthly asking rent (EUR/month),log,spatial_lag,52966.0,0.885,se,"spatial autoregressive model, rho 0.131; SEs printed with comma decimal separator; R2 0.885",semi_elasticity,0.8,0.6,100*b/1 (X per km->km),reported,0.005793938221318773,0.004345453665989081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,52956.0,km,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,spatial,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,log size in sq.m.,living_area,A_structural,log,m2,0.9,,,,***,,,medium,"apartment size in m2, log; GAM parametric term; no SE printed for this entry",log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",elasticity,0.9,,b (log-log),,,,,,52956.0,m2,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,False,False,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Floors,floor_level,A_structural,level,count,0.003,0.0003,,,***,,,high,floor level of apartment (integer); GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",semi_elasticity,0.3,0.03,100*b/1 (per unit count),reported,0.04341429447794925,0.004341429447794924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.000000000000002,52956.0,count,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Bathtub,bathroom_quality,B_interior,binary,binary,-0.016,0.001,,,***,,,high,dwelling has a bathtub; GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,-1.587317200825189,0.09841273200552851,100*(exp(b - se^2/2) - 1),reported,-0.06936097533661631,0.004335060958538519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Refurbished,renovation,A_structural,binary,binary,0.005,0.002,,,***,,,high,refurbished dwelling; GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,0.5010510836369386,0.2010025041718802,100*(exp(b - se^2/2) - 1),reported,0.010863175483441883,0.004345270193376753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Built-in kitchen,kitchen_quality,B_interior,binary,binary,0.077,0.002,,,***,,,high,built-in kitchen; GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,8.003991631060758,0.2160084152785201,100*(exp(b - se^2/2) - 1),reported,0.16500938728063141,0.0042859581111852315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Balcony,terrace_balcony,C_exterior,binary,binary,0.025,0.002,,,***,,,high,GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,2.5313069896238494,0.20506302410488578,100*(exp(b - se^2/2) - 1),reported,0.05423912354005741,0.0043391298832045925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Parking,parking,A_structural,binary,binary,0.032,0.002,,,***,,,high,parking space included; GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,3.251544027217279,0.20650350106102366,100*(exp(b - se^2/2) - 1),reported,0.06936097533661631,0.004335060958538519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Elevator,accessibility,B_interior,binary,binary,0.02,0.002,,,***,,,high,GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,2.019929962611622,0.20404026800535116,100*(exp(b - se^2/2) - 1),reported,0.04341429447794924,0.004341429447794924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,Terrace,terrace_balcony,C_exterior,binary,binary,0.02,0.002,,,***,,,high,GAM parametric term,log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",pct_premium,2.019929962611622,0.20404026800535116,100*(exp(b - se^2/2) - 1),reported,0.04341429447794924,0.004341429447794924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,52956.0,binary,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678,7b29c36e620db272e042f7b9f343c676a2038678:T2-c2,2,GAM,11,log Purchasing Power,income,F_neighbourhood,log,currency,0.069,0.002,,,***,,,high,"household purchasing power EUR per household at ZIP level, log; GAM parametric term",log monthly asking rent (EUR/month),log,other,52966.0,0.898,se,"generalised additive model; only parametric (linear) terms extracted - size, age and all distance variables enter as smooth splines (edf reported, not coefficients); adj R2 0.898",elasticity,0.069,0.002,b (log-log),reported,0.14826372356909787,0.004297499233886895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.5,52956.0,currency,,2022.0,Germany,Europe,High income,2013.0,2019.0,rental,rent,journal,True,False,OLS,XGBoost;permutation feature importance;partial dependence plots;accumulated local effects;H-statistic interactions,2016.0,other,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,AREA,lot_size,A_structural,level,m2 (land area),0.0142,,9.426,,***,,,high,Land area of the property in m2,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,1.4200000000000002,0.15064714619138553,100*b/1 (X per m2->m2),|b/t|,0.5278792386170951,0.05600246537418788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.426,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,AREA,lot_size,A_structural,level,m2 (land area),0.0143,,9.426,,***,,,high,Land area of the property in m2,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,1.43,0.1517080415870995,100*b/1 (X per m2->m2),|b/t|,0.5278792386170951,0.05600246537418788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.426,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,AREA,lot_size,A_structural,level,m2 (land area),0.0145,,9.864,,***,,,high,Land area of the property in m2,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,1.4500000000000002,0.1469991889699919,100*b/1 (X per m2->m2),|b/t|,0.5452316507926622,0.055274903770545634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.864,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,AREA,lot_size,A_structural,level,m2 (land area),0.0141,,10.124,,***,,,high,Land area of the property in m2,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,1.41,0.13927301461872776,100*b/1 (X per m2->m2),|b/t|,0.5552126230909014,0.05484123104414275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.124,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m5,3,Model 5,3,AREA,lot_size,A_structural,level,m2 (land area),0.0148,,10.012,,***,,,high,"Land area of the property in m2; printed with typo '0,.0148'-style decimal separator","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7689,t,"USE, TERRACE, NOF, SHAPE dropped; adjusted R2 0.764; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,1.48,0.14782261286456252,100*b/1 (X per m2->m2),|b/t|,0.5509420345150962,0.055028169647932085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.012,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,True
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m6,3,Model 6,3,AREA,lot_size,A_structural,level,m2 (land area),0.0146,,9.942,,***,,,high,"Land area of the property in m2; printed with typo '0,.0148'-style decimal separator","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7846,t,"Final model: AREA, HAREA, DIS1, DIS2, LOCATION only; adjusted R2 0.7809; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,1.46,0.1468517400925367,100*b/1 (X per m2->m2),|b/t|,0.5482507287844379,0.05514491337602474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.942,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,True
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,HAREA,living_area,A_structural,level,m2 (house/floor area),0.0008,,1.432,,,,,medium,House area in m2 (descriptive stats garbled: mean 2.780),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,0.08,0.0558659217877095,100*b/1 (X per m2->m2),|b/t|,0.09400515020095578,0.065646054609606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.432,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,HAREA,living_area,A_structural,level,m2 (house/floor area),0.0008,,1.42,,,,,medium,House area in m2 (descriptive stats garbled: mean 2.780),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,0.08,0.056338028169014086,100*b/1 (X per m2->m2),|b/t|,0.0932242723927969,0.06565089605126544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.42,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,HAREA,living_area,A_structural,level,m2 (house/floor area),0.0008,,1.274,,,,,medium,House area in m2 (descriptive stats garbled: mean 2.780),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,0.08,0.06279434850863422,100*b/1 (X per m2->m2),|b/t|,0.08371022622152519,0.0657066139886383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.274,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,HAREA,living_area,A_structural,level,m2 (house/floor area),0.0009,,2.524,,*,,,medium,House area in m2 (descriptive stats garbled: mean 2.780),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,0.09,0.03565768621236133,100*b/1 (X per m2->m2),|b/t|,0.16416956363090401,0.06504340872856736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.524,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m5,3,Model 5,3,HAREA,living_area,A_structural,level,m2 (house/floor area),0.0012,,2.788,,*,,,medium,"House area in m2 (descriptive stats garbled: mean 2.780); printed with typo '0,.0148'-style decimal separator","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7689,t,"USE, TERRACE, NOF, SHAPE dropped; adjusted R2 0.764; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,0.12,0.0430416068866571,100*b/1 (X per m2->m2),|b/t|,0.18080546443203704,0.06485131435869336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.788,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m6,3,Model 6,3,HAREA,living_area,A_structural,level,m2 (house/floor area),0.0012,,2.982,,*,,,medium,"House area in m2 (descriptive stats garbled: mean 2.780); printed with typo '0,.0148'-style decimal separator","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7846,t,"Final model: AREA, HAREA, DIS1, DIS2, LOCATION only; adjusted R2 0.7809; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,0.12,0.04024144869215291,100*b/1 (X per m2->m2),|b/t|,0.1929330106771,0.06469919875154258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.982,230.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,DIS1,cbd_distance,D_location,level,km,-0.045,,-1.984,,*,,,high,"Distance from property to Hanoi city centre, km","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,-4.5,2.2681451612903225,100*b/1 (X per km->km),|b/t|,-0.1297158065328419,0.06538095087340821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.984,230.0,km,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,DIS1,cbd_distance,D_location,level,km,-0.065,,-1.928,,*,,,high,"Distance from property to Hanoi city centre, km","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,-6.5,3.3713692946058096,100*b/1 (X per km->km),|b/t|,-0.1261135372709713,0.06541158572145814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.928,230.0,km,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,DIS1,cbd_distance,D_location,level,km,-0.0665,,-1.948,,*,,,high,"Distance from property to Hanoi city centre, km","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-6.65,3.4137577002053385,100*b/1 (X per km->km),|b/t|,-0.12740064242800633,0.06540074046612233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.948,230.0,km,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,DIS1,cbd_distance,D_location,level,km,-0.0648,,-2.114,,*,,,high,"Distance from property to Hanoi city centre, km","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-6.4799999999999995,3.0652790917691584,100*b/1 (X per km->km),|b/t|,-0.13805822044457158,0.06530663218759299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.114,230.0,km,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m5,3,Model 5,3,DIS1,cbd_distance,D_location,level,km,-0.0684,,-2.682,,*,,,high,"Distance from property to Hanoi city centre, km","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7689,t,"USE, TERRACE, NOF, SHAPE dropped; adjusted R2 0.764; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-6.84,2.550335570469799,100*b/1 (X per km->km),|b/t|,-0.17414368330670243,0.06493053068855423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.682,230.0,km,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m6,3,Model 6,3,DIS1,cbd_distance,D_location,level,km,-0.084,,-2.683,,*,,,high,"Distance from property to Hanoi city centre, km","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7846,t,"Final model: AREA, HAREA, DIS1, DIS2, LOCATION only; adjusted R2 0.7809; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-8.4,3.130823704808051,100*b/1 (X per km->km),|b/t|,-0.17420664368466754,0.06492979637892939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.683,230.0,km,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,DIS2,other,other,level,"km (distance from property to street frontage, i.e. alley depth)",-0.0016,,-1.914,,*,,,medium,"Distance to street frontage (alley depth); text interprets as 0.12-0.16% per metre, suggesting the unit may actually be metres; taxonomy other","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,-0.16,0.08359456635318706,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.1252121841536481,0.06541911397787258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.914,230.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,DIS2,other,other,level,"km (distance from property to street frontage, i.e. alley depth)",-0.0016,,-1.976,,*,,,medium,"Distance to street frontage (alley depth); text interprets as 0.12-0.16% per metre, suggesting the unit may actually be metres; taxonomy other","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,-0.16,0.08097165991902834,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.12920150755339155,0.0653853783164937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.976,230.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,DIS2,other,other,level,"km (distance from property to street frontage, i.e. alley depth)",-0.0016,,-1.978,,*,,,medium,"Distance to street frontage (alley depth); text interprets as 0.12-0.16% per metre, suggesting the unit may actually be metres; taxonomy other","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.16,0.08088978766430738,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.12933009209002513,0.06538427304854658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.978,230.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,DIS2,other,other,level,"km (distance from property to street frontage, i.e. alley depth)",-0.0016,,-1.928,,*,,,medium,"Distance to street frontage (alley depth); text interprets as 0.12-0.16% per metre, suggesting the unit may actually be metres; taxonomy other","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.16,0.08298755186721993,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.1261135372709713,0.06541158572145814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.928,230.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m5,3,Model 5,3,DIS2,other,other,level,"km (distance from property to street frontage, i.e. alley depth)",-0.0017,,-1.926,,*,,,medium,"Distance to street frontage (alley depth); text interprets as 0.12-0.16% per metre, suggesting the unit may actually be metres; taxonomy other","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7689,t,"USE, TERRACE, NOF, SHAPE dropped; adjusted R2 0.764; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.16999999999999998,0.08826583592938733,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.1259847916078311,0.06541266438620515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.926,230.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m6,3,Model 6,3,DIS2,other,other,level,"km (distance from property to street frontage, i.e. alley depth)",-0.0015,,-1.968,,*,,,medium,"Distance to street frontage (alley depth); text interprets as 0.12-0.16% per metre, suggesting the unit may actually be metres; taxonomy other","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7846,t,"Final model: AREA, HAREA, DIS1, DIS2, LOCATION only; adjusted R2 0.7809; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.15,0.07621951219512195,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,-0.12868710428960844,0.06538978876504493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.968,230.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,LOCATION,other,other,binary,"binary (1 = property on street frontage, 0 = in alley)",0.9284,,7.192,,***,,,high,Frontage vs alley location dummy; taxonomy other (street-frontage access),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,150.94614295255732,32.6651344475941,100*(exp(b - se^2/2) - 1),|b/t|,0.42848644700127075,0.05957820453299093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.192,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,LOCATION,other,other,binary,"binary (1 = property on street frontage, 0 = in alley)",0.8294,,7.899,,***,,,high,Frontage vs alley location dummy; taxonomy other (street-frontage access),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,127.9343433051372,24.065548276274026,100*(exp(b - se^2/2) - 1),|b/t|,0.46194230417059534,0.05848111206109576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.899,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,LOCATION,other,other,binary,"binary (1 = property on street frontage, 0 = in alley)",0.8368,,9.827,,***,,,high,Frontage vs alley location dummy; taxonomy other (street-frontage access),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,130.06103805296792,19.66157652423908,100*(exp(b - se^2/2) - 1),|b/t|,0.5437920575606542,0.055336527685016214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.827,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,LOCATION,other,other,binary,"binary (1 = property on street frontage, 0 = in alley)",0.8426,,9.248,,***,,,high,Frontage vs alley location dummy; taxonomy other (street-frontage access),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,131.27778601557708,21.159732065963023,100*(exp(b - se^2/2) - 1),|b/t|,0.520631674585679,0.056296677615233465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.248,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m5,3,Model 5,3,LOCATION,other,other,binary,"binary (1 = property on street frontage, 0 = in alley)",0.8424,,9.842,,***,,,high,Frontage vs alley location dummy; taxonomy other (street-frontage access),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7689,t,"USE, TERRACE, NOF, SHAPE dropped; adjusted R2 0.764; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,131.34431918994554,19.873971790323093,100*(exp(b - se^2/2) - 1),|b/t|,0.5443762564621017,0.05531154810628954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.842,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m6,3,Model 6,3,LOCATION,other,other,binary,"binary (1 = property on street frontage, 0 = in alley)",0.8468,,9.468,,***,,,high,Frontage vs alley location dummy; taxonomy other (street-frontage access),"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7846,t,"Final model: AREA, HAREA, DIS1, DIS2, LOCATION only; adjusted R2 0.7809; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,132.28628510908274,20.85850449679305,100*(exp(b - se^2/2) - 1),|b/t|,0.5295727551421516,0.05593290611978787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.468,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,WIDTH,other,other,level,m (front pavement width),-0.0085,,-1.826,,*,,,high,Width of the front pavement/facade in metres; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,-0.8500000000000001,0.46549835706462217,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,-0.11953951880169254,0.06546523483115692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.826,230.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,WIDTH,other,other,level,m (front pavement width),-0.0088,,-1.824,,*,,,high,Width of the front pavement/facade in metres; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),semi_elasticity,-0.88,0.4824561403508772,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,-0.1194104562598024,0.06546625891436535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.824,230.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,WIDTH,other,other,level,m (front pavement width),-0.0086,,-1.946,,*,,,high,Width of the front pavement/facade in metres; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.86,0.4419321685508736,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,-0.1272719607604575,0.06540182978440776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.946,230.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,WIDTH,other,other,level,m (front pavement width),-0.0084,,-1.964,,*,,,high,Width of the front pavement/facade in metres; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.84,0.4276985743380855,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,-0.12842986367206669,0.06539198761306858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.964,230.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m5,3,Model 5,3,WIDTH,other,other,level,m (front pavement width),-0.0094,,-1.848,,*,,,high,Width of the front pavement/facade in metres; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7689,t,"USE, TERRACE, NOF, SHAPE dropped; adjusted R2 0.764; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",semi_elasticity,-0.9400000000000001,0.5086580086580086,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,-0.12095880545592241,0.06545389905623507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.848,230.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,SHAPE,other,other,binary,binary (1 = back of lot wider than or equal to front),0.1283,,0.984,,,,,high,Lot shape dummy; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,12.727106158003053,14.823526700574353,100*(exp(b - se^2/2) - 1),|b/t|,0.0647468956932889,0.06579969074521229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.984,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,SHAPE,other,other,binary,binary (1 = back of lot wider than or equal to front),0.1078,,0.872,,,,,high,Lot shape dummy; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,10.534618053560862,13.769533312499716,100*(exp(b - se^2/2) - 1),|b/t|,0.05740316764076508,0.06582932068895077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.872,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,SHAPE,other,other,binary,binary (1 = back of lot wider than or equal to front),0.1268,,0.924,,,,,high,Lot shape dummy; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,12.455122029912502,15.578147895925015,100*(exp(b - se^2/2) - 1),|b/t|,0.060813987419876134,0.06581600370116464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.924,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m4,3,Model 4,3,SHAPE,other,other,binary,binary (1 = back of lot wider than or equal to front),0.1046,,0.924,,,,,high,Lot shape dummy; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7784,t,"USE, TERRACE, NOF dropped; adjusted R2 0.7727; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,10.31751125988627,12.568600325157654,100*(exp(b - se^2/2) - 1),|b/t|,0.060813987419876134,0.06581600370116464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.924,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,NOF,storeys,A_structural,dummy,"binary (1 = number of floors <= 4, 0 = > 4)",-0.1193,,-0.742,,,,,high,"Number of floors entered as a dummy (<=4 floors), not a count","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,-12.385648616931988,14.270040533076951,100*(exp(b - se^2/2) - 1),|b/t|,-0.04886757754474413,0.06585926892822658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.742,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,NOF,storeys,A_structural,dummy,"binary (1 = number of floors <= 4, 0 = > 4)",-0.1428,,-0.846,,,,,high,"Number of floors entered as a dummy (<=4 floors), not a count","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,-14.533505349861608,14.633243263604982,100*(exp(b - se^2/2) - 1),|b/t|,-0.05569699616281447,0.06583569286384688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.846,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m3,3,Model 3,3,NOF,storeys,A_structural,dummy,"binary (1 = number of floors <= 4, 0 = > 4)",-0.1464,,-0.846,,,,,high,"Number of floors entered as a dummy (<=4 floors), not a count","LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,"USE, TERRACE dropped; adjusted R2 0.7758; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426))",pct_premium,-14.902547811271749,14.948237918534149,100*(exp(b - se^2/2) - 1),|b/t|,-0.05569699616281447,0.06583569286384688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.846,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,TERRACE,terrace_balcony,C_exterior,binary,binary,-0.0426,,-0.492,,,,,high,Terrace dummy,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,-4.529082415458396,8.297429124377965,100*(exp(b - se^2/2) - 1),|b/t|,-0.03242446118077492,0.06590337638368886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.492,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m2,3,Model 2,3,TERRACE,terrace_balcony,C_exterior,binary,binary,-0.0428,,-0.548,,,,,high,Terrace dummy,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7918,t,USE dropped; adjusted R2 0.7846; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,-4.481474786895823,7.48299416722531,100*(exp(b - se^2/2) - 1),|b/t|,-0.03611048345247726,0.06589504279649136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.548,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,True,True,False
7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f,7de41ea5f998e11b3cc730af7771c0a4b73baa2f:T3-m1,3,Model 1,3,USE,other,other,binary,"binary (1 = business/office or mixed use allowed, 0 = residential only)",0.0602,,0.62,,,,,high,Intended use dummy; taxonomy other,"LogPRICE: natural log of real estate price (VND; scale not stated, mean 8.20)",log,OLS,240.0,0.7824,t,All 10 variables; adjusted R2 0.7739; table note says 'Robust standard errors in parentheses' but the parenthesised values are t-statistics (e.g. 0.0142 with (9.426)),pct_premium,5.705433260482784,10.312152563320563,100*(exp(b - se^2/2) - 1),|b/t|,0.04084746932428451,0.06588301503916857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.62,230.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2021.0,2021.0,unknown,transaction,journal,False,False,robust,,2021.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,House age,age,A_structural,level,years,66.18976,33.64222,1.97,,*,,,high,,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,66.18976,33.64222,"b (currency per unit, as reported)",reported,0.1572827848184969,0.07983897706522686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.97,153.0,years,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,House age,age,A_structural,level,years,0.0018017,0.0008823,2.04,,*,,,high,,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,0.18017,0.08823,100*b/1 (per year),reported,0.16272599908685828,0.07976764661120504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.04,153.0,years,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,House age,age,A_structural,level,years,166.016,45.90104,3.62,,**,,,high,,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,166.016,45.90104,"b (currency per unit, as reported)",reported,0.27278687273075547,0.07535548970462859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.62,163.0,years,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,House age,age,A_structural,level,years,0.0045487,0.0010464,-4.35,,**,,,medium,printed coefficient sign disagrees with printed t-value sign,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,0.45487,0.10464000000000001,100*b/1 (per year),reported,-0.322512126789416,0.07414071880216459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.35,163.0,years,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,House age,age,A_structural,level,years,71.68437,38.28094,1.87,,,,,high,,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,71.68437,38.28094,"b (currency per unit, as reported)",reported,0.14714974417305024,0.07868970276633702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.87,158.0,years,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,House age,age,A_structural,level,years,0.0013215,0.0010493,-1.26,,,,,medium,printed coefficient sign disagrees with printed t-value sign,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,0.13215,0.10493,100*b/1 (per year),reported,-0.0997403696318052,0.07915902351730572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.26,158.0,years,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,House unit size,living_area,A_structural,level,sq_yard,79.99556,4.551541,17.58,,**,,,high,Area covered by housing unit in square yards (1 sq yard = 0.836 m2),monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,79.99556,4.551541,"b (currency per unit, as reported)",reported,0.8178457020117681,0.046521370990430515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.58,153.0,sq_yard,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,House unit size,living_area,A_structural,level,sq_yard,0.0013917,0.0001194,11.66,,**,,,high,Area covered by housing unit in square yards (1 sq yard = 0.836 m2),log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,0.13917000000000002,0.011940000000000001,100*b (unit as reported: sq_yard; area unit not harmonisable),reported,0.6859350461693504,0.05882804855654806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.66,153.0,sq_yard,unit_not_harmonisable,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,House unit size,living_area,A_structural,level,sq_yard,65.67229,4.548889,14.44,,**,,,high,Area covered by housing unit in square yards (1 sq yard = 0.836 m2),monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,65.67229,4.548889,"b (currency per unit, as reported)",reported,0.7491690631445422,0.0518815140681816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.44,163.0,sq_yard,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,House unit size,living_area,A_structural,level,sq_yard,0.0009076,0.0001037,8.75,,**,,,high,Area covered by housing unit in square yards (1 sq yard = 0.836 m2),log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,0.09076000000000001,0.01037,100*b (unit as reported: sq_yard; area unit not harmonisable),reported,0.5653255770683472,0.06460863737923968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.75,163.0,sq_yard,unit_not_harmonisable,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,House unit size,living_area,A_structural,level,sq_yard,77.49149,5.64242,13.73,,**,,,high,Area covered by housing unit in square yards (1 sq yard = 0.836 m2),monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,77.49149,5.64242,"b (currency per unit, as reported)",reported,0.737582889967733,0.05372053095176497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.73,158.0,sq_yard,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,House unit size,living_area,A_structural,level,sq_yard,0.001857,0.0001547,12.01,,**,,,high,Area covered by housing unit in square yards (1 sq yard = 0.836 m2),log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,0.1857,0.01547,100*b (unit as reported: sq_yard; area unit not harmonisable),reported,0.6908232813840082,0.0575206728879274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.01,158.0,sq_yard,unit_not_harmonisable,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Bedrooms,bedrooms,A_structural,level,count,1690.173,554.19,3.05,,**,,,high,,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,1690.173,554.19,"b (currency per unit, as reported)",reported,0.2394072150237823,0.0784941688602565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.05,153.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Bedrooms,bedrooms,A_structural,level,count,0.0420453,0.0145349,2.89,,**,,,high,,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,4.20453,1.45349,100*b/1 (per unit count),reported,0.22751526028282973,0.07872500355807256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.89,153.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Bedrooms,bedrooms,A_structural,level,count,2041.083,718.7292,2.84,,**,,,high,,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,2041.083,718.7292,"b (currency per unit, as reported)",reported,0.21713858208422665,0.07645724721275586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.84,163.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Bedrooms,bedrooms,A_structural,level,count,0.0488116,0.0163849,2.98,,**,,,high,,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,4.8811599999999995,1.63849,100*b/1 (per unit count),reported,0.22730191059781765,0.07627580892544217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.98,163.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Bedrooms,bedrooms,A_structural,level,count,1596.682,413.7285,3.86,,**,,,high,,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,1596.682,413.7285,"b (currency per unit, as reported)",reported,0.2935555599904578,0.07605066320996316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.86,158.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Bedrooms,bedrooms,A_structural,level,count,0.0443812,0.0113403,3.91,,**,,,high,,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,4.4381200000000005,1.1340299999999999,100*b/1 (per unit count),reported,0.29702457792147885,0.07596536519730918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.91,158.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Drawing room,other,other,dummy,binary,2549.089,852.2414,2.99,,**,,,high,Dummy: presence of a drawing room (guest room),monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,2549.089,852.2414,"b (currency per unit, as reported)",reported,0.23496002880824746,0.07858194943419647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.99,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Drawing room,other,other,dummy,binary,0.0834143,0.0223519,3.73,,**,,,high,Dummy: presence of a drawing room (guest room),log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,pct_premium,8.672055533846844,2.429633773833604,100*(exp(b - se^2/2) - 1),reported,0.2887113652550998,0.07740251079225197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.73,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Drawing room,other,other,dummy,binary,3378.79,1378.357,2.45,,*,,,high,Dummy: presence of a drawing room (guest room),monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,3378.79,1378.357,"b (currency per unit, as reported)",reported,0.18846014453071327,0.07692250797171969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Drawing room,other,other,dummy,binary,0.067677,0.0314224,2.15,,*,,,high,Dummy: presence of a drawing room (guest room),log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,pct_premium,6.949151552844235,3.3622585020087765,100*(exp(b - se^2/2) - 1),reported,0.166062779645818,0.07723850216084559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.15,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Drawing room,other,other,dummy,binary,950.7497,585.3236,1.62,,,,,high,Dummy: presence of a drawing room (guest room),monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,950.7497,585.3236,"b (currency per unit, as reported)",reported,0.12782307332662451,0.07890313168310155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.62,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Drawing room,other,other,dummy,binary,0.033505,0.0160437,2.09,,*,,,high,Dummy: presence of a drawing room (guest room),log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,pct_premium,3.3939537334552794,1.659035079845791,100*(exp(b - se^2/2) - 1),reported,0.1640196692408534,0.07847831064155665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.09,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Bathrooms,bathrooms,A_structural,level,count,734.8915,620.7361,1.18,,,,,high,,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,734.8915,620.7361,"b (currency per unit, as reported)",reported,0.09496619732673113,0.08047982824299249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.18,153.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Bathrooms,bathrooms,A_structural,level,count,0.0181466,0.0162802,1.11,,,,,high,,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,1.81466,1.6280200000000002,100*b/1 (per unit count),reported,0.08937902088908324,0.08052164044061552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.11,153.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Bathrooms,bathrooms,A_structural,level,count,894.3884,678.3305,1.32,,,,,high,,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,894.3884,678.3305,"b (currency per unit, as reported)",reported,0.10284217120915926,0.07791073576451459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.32,163.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Bathrooms,bathrooms,A_structural,level,count,0.0122106,0.0154639,0.79,,,,,high,,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,1.22106,1.54639,100*b/1 (per unit count),reported,0.06175945514506461,0.07817652550008178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.79,163.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Bathrooms,bathrooms,A_structural,level,count,951.5119,460.7668,2.07,,*,,,high,,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,951.5119,460.7668,"b (currency per unit, as reported)",reported,0.16249174004013545,0.07849842514016206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.07,158.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Bathrooms,bathrooms,A_structural,level,count,0.0222714,0.0126296,1.76,,,,,high,,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,2.22714,1.2629599999999999,100*b/1 (per unit count),reported,0.13866540760576157,0.07878716341236454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.76,158.0,count,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,TV lounge,other,other,dummy,binary,2003.42,953.2602,2.1,,*,,,high,Dummy: presence of a TV lounge / living area,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,2003.42,953.2602,"b (currency per unit, as reported)",reported,0.16737983495058364,0.07970468330980174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,TV lounge,other,other,dummy,binary,0.087841,0.0250014,3.51,,**,,,high,Dummy: presence of a TV lounge / living area,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,pct_premium,9.147333278811765,2.7296891283297087,100*(exp(b - se^2/2) - 1),reported,0.2729884588985597,0.07777448971468938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.51,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,TV lounge,other,other,dummy,binary,1652.742,1157.746,1.43,,,,,high,Dummy: presence of a TV lounge / living area,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,1652.742,1157.746,"b (currency per unit, as reported)",reported,0.11131020511130388,0.0778393042736391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.43,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,TV lounge,other,other,dummy,binary,0.0591468,0.0263932,2.24,,*,,,high,Dummy: presence of a TV lounge / living area,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,pct_premium,6.056151552904465,2.8001363382344406,100*(exp(b - se^2/2) - 1),reported,0.17281070020554806,0.07714763402033395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.24,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,TV lounge,other,other,dummy,binary,1137.778,511.8009,2.22,,*,,,high,Dummy: presence of a TV lounge / living area,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,1137.778,511.8009,"b (currency per unit, as reported)",reported,0.17392202291775374,0.07834325356655573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.22,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,TV lounge,other,other,dummy,binary,0.0327429,0.0140285,2.33,,*,,,high,Dummy: presence of a TV lounge / living area,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,pct_premium,3.3183177940635966,1.4495436483908435,100*(exp(b - se^2/2) - 1),reported,0.18226005300227388,0.07822319871342226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.33,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Garage,garage,A_structural,dummy,binary,977.9888,707.9799,1.38,,,,,high,Garage/parking space dummy,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,977.9888,707.9799,"b (currency per unit, as reported)",reported,0.11087846615085772,0.08034671460207082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.38,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Garage,garage,A_structural,dummy,binary,0.0537868,0.0185684,2.9,,**,,,high,Garage/parking space dummy,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,pct_premium,5.5077693411637485,1.9594482297672722,100*(exp(b - se^2/2) - 1),reported,0.2282615590111986,0.0787108824176547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Garage,garage,A_structural,dummy,binary,1545.144,1140.948,1.35,,,,,high,Garage/parking space dummy,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,1545.144,1140.948,"b (currency per unit, as reported)",reported,0.10515393275111268,0.07789180203786124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.35,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Garage,garage,A_structural,dummy,binary,0.0643446,0.0260102,2.47,,*,,,high,Garage/parking space dummy,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,pct_premium,6.609915220956553,2.773883366555522,100*(exp(b - se^2/2) - 1),reported,0.18994330645012425,0.0769001240688762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.47,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Garage,garage,A_structural,dummy,binary,1375.326,527.7794,2.61,,*,,,high,Garage/parking space dummy,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,1375.326,527.7794,"b (currency per unit, as reported)",reported,0.20330401747688456,0.07789425956968758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.61,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Garage,garage,A_structural,dummy,binary,0.0337009,0.0144664,2.33,,*,,,high,Garage/parking space dummy,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,pct_premium,3.4166989540794113,1.4962238879788965,100*(exp(b - se^2/2) - 1),reported,0.18226005300227388,0.07822319871342226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.33,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Servant quarter,other,other,dummy,binary,2646.275,671.7168,3.94,,**,,,high,Dummy: servant quarter in home,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,2646.275,671.7168,"b (currency per unit, as reported)",reported,0.30350500540161446,0.077031727259293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.94,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Servant quarter,other,other,dummy,binary,0.0585302,0.0176173,3.32,,**,,,high,Dummy: servant quarter in home,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,pct_premium,6.01124796485093,1.8679218090345746,100*(exp(b - se^2/2) - 1),reported,0.25923074177670363,0.07808154872792279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.32,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Servant quarter,other,other,dummy,binary,3936.82,1136.299,3.46,,**,,,high,Dummy: servant quarter in home,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,3936.82,1136.299,"b (currency per unit, as reported)",reported,0.2615726409350095,0.07559902917196806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.46,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Servant quarter,other,other,dummy,binary,0.088549,0.0259043,3.42,,**,,,high,Dummy: servant quarter in home,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,pct_premium,9.222126849189971,2.830272183929091,100*(exp(b - se^2/2) - 1),reported,0.2587522460607761,0.07565855147975911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.42,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Servant quarter,other,other,dummy,binary,2524.987,631.1266,4.0,,**,,,high,Dummy: servant quarter in home,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,2524.987,631.1266,"b (currency per unit, as reported)",reported,0.30323921743156135,0.07580980435789034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Servant quarter,other,other,dummy,binary,0.0794629,0.0172992,4.59,,**,,,high,Dummy: servant quarter in home,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,pct_premium,8.254339607047267,1.872993708985544,100*(exp(b - se^2/2) - 1),reported,0.34300746570961727,0.07472929536157238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.59,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Lawn,garden,C_exterior,dummy,binary,1815.562,679.3289,2.67,,**,,,high,Dummy: presence of a lawn,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,1815.562,679.3289,"b (currency per unit, as reported)",reported,0.21099705867462096,0.07902511560847228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.67,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Lawn,garden,C_exterior,dummy,binary,0.0913645,0.0178169,5.13,,**,,,high,Dummy: presence of a lawn,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,pct_premium,9.549441185101749,1.9521412598029204,100*(exp(b - se^2/2) - 1),reported,0.3830952383473862,0.07467743437570881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.13,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Lawn,garden,C_exterior,dummy,binary,2333.943,1089.434,2.14,,*,,,high,Dummy: presence of a lawn,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,2333.943,1089.434,"b (currency per unit, as reported)",reported,0.16531155025463876,0.07724838796945735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.14,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Lawn,garden,C_exterior,dummy,binary,0.0558746,0.0248359,2.25,,*,,,high,Dummy: presence of a lawn,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,pct_premium,5.713898576677501,2.626309671468175,100*(exp(b - se^2/2) - 1),reported,0.17355898767151737,0.07713732785400773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Lawn,garden,C_exterior,dummy,binary,1034.311,502.4433,2.06,,*,,,high,Dummy: presence of a lawn,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,1034.311,502.4433,"b (currency per unit, as reported)",reported,0.16172733615078402,0.0785084156071767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.06,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Lawn,garden,C_exterior,dummy,binary,0.0295001,0.013772,2.14,,*,,,high,Dummy: presence of a lawn,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,pct_premium,2.9841869819116074,1.4184327323751555,100*(exp(b - se^2/2) - 1),reported,0.16783431021534648,0.0784272477641806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.14,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Distance to CBD,cbd_distance,D_location,level,km,1113.781,142.3801,7.82,,**,,,high,Distance from home to city centre (km); printed sign positive in all tables although expected negative and t-values negative in Tables 5-6,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,1113.781,142.3801,"b (currency per unit, as reported)",reported,0.5343739517176588,0.06833426492553181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.82,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Distance to CBD,cbd_distance,D_location,level,km,0.0122385,0.0037342,3.28,,**,,,high,Distance from home to city centre (km); printed sign positive in all tables although expected negative and t-values negative in Tables 5-6,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,1.2238499999999999,0.37342,100*b/1 (X per km->km),reported,0.2563138361018776,0.07814446222618218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.28,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Distance to CBD,cbd_distance,D_location,level,km,4085.165,808.8975,5.05,,**,,,high,Distance from home to city centre (km); printed sign positive in all tables although expected negative and t-values negative in Tables 5-6,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,4085.165,808.8975,"b (currency per unit, as reported)",reported,0.36781786646582787,0.07283522108234215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.05,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Distance to CBD,cbd_distance,D_location,level,km,0.1300239,0.0184405,-7.05,,**,,,medium,Distance from home to city centre (km); printed sign positive in all tables although expected negative and t-values negative in Tables 5-6; printed coefficient sign disagrees with printed t-value sign,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,13.00239,1.8440499999999997,100*b/1 (X per km->km),reported,-0.48339574109390737,0.06856677178636984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.05,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,True
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Distance to CBD,cbd_distance,D_location,level,km,809.7306,330.3623,2.45,,*,,,high,Distance from home to city centre (km); printed sign positive in all tables although expected negative and t-values negative in Tables 5-6,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,809.7306,330.3623,"b (currency per unit, as reported)",reported,0.19131140015293974,0.07808628577671009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Distance to CBD,cbd_distance,D_location,level,km,0.0184054,0.0090552,-2.03,,*,,,medium,Distance from home to city centre (km); printed sign positive in all tables although expected negative and t-values negative in Tables 5-6; printed coefficient sign disagrees with printed t-value sign,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,1.8405399999999998,0.9055199999999999,100*b/1 (X per km->km),reported,-0.15943238206501587,0.0785381192438502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.03,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Distance to school,school_distance,D_location,level,km,1324.727,1594.824,0.83,,,,,high,,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,1324.727,1594.824,"b (currency per unit, as reported)",reported,0.06695096502351645,0.08066381328134514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.83,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Distance to school,school_distance,D_location,level,km,0.0751735,0.0418278,1.8,,,,,high,,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,7.51735,4.18278,100*b/1 (X per km->km),reported,0.1440046082211958,0.08000256012288656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Distance to school,school_distance,D_location,level,km,2238.825,1076.843,2.08,,*,,,high,,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,2238.825,1076.843,"b (currency per unit, as reported)",reported,0.160798169784715,0.0773068123964976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.08,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Distance to school,school_distance,D_location,level,km,0.0511055,0.0245488,-2.08,,*,,,medium,printed coefficient sign disagrees with printed t-value sign,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,5.11055,2.4548799999999997,100*b/1 (X per km->km),reported,-0.160798169784715,0.0773068123964976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Distance to school,school_distance,D_location,level,km,1809.456,688.3523,2.63,,**,,,high,,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,1809.456,688.3523,"b (currency per unit, as reported)",reported,0.20479680036735484,0.0778695058431007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.63,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Distance to school,school_distance,D_location,level,km,-0.0361071,0.0188677,-1.91,,,,,high,,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,-3.61071,1.88677,100*b/1 (X per km->km),reported,-0.15022702439526833,0.07865289235354363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.91,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Distance to park,green_space,E_environment,level,km,3562.901,999.7477,3.56,,**,,,high,Distance from housing unit to park (km),monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,3562.901,999.7477,"b (currency per unit, as reported)",reported,0.2765816373528737,0.07769147116653756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.56,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Distance to park,green_space,E_environment,level,km,0.1086817,0.0262206,4.14,,**,,,high,Distance from housing unit to park (km),log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,10.868170000000001,2.62206,100*b/1 (X per km->km),reported,0.3173932142056196,0.07666502758589847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.14,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Distance to park,green_space,E_environment,level,km,2816.003,1211.215,2.32,,*,,,high,Distance from housing unit to park (km),monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,2816.003,1211.215,"b (currency per unit, as reported)",reported,0.17878852324638245,0.07706401864068209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.32,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Distance to park,green_space,E_environment,level,km,0.086923,0.0276121,-3.15,,**,,,medium,Distance from housing unit to park (km); printed coefficient sign disagrees with printed t-value sign,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,8.6923,2.76121,100*b/1 (X per km->km),reported,-0.23954372623574144,0.07604562737642585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.15,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Distance to park,green_space,E_environment,level,km,2949.794,1098.344,-2.69,,**,,,medium,Distance from housing unit to park (km); printed coefficient sign disagrees with printed t-value sign,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,2949.794,1098.344,"b (currency per unit, as reported)",reported,-0.20926654879679546,0.07779425605828827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.69,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Distance to park,green_space,E_environment,level,km,-0.0778371,0.0301056,-2.59,,*,,,high,Distance from housing unit to park (km),log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,-7.783710000000001,3.01056,100*b/1 (X per km->km),reported,-0.20180981551282587,0.07791884768834975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.59,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Distance to slums,hazard_site,E_environment,level,km,5894.832,1632.232,3.61,,**,,,high,Distance to existing (unimproved) slum site (km),monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,5894.832,1632.232,"b (currency per unit, as reported)",reported,0.28016325301582873,0.07760754931186392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.61,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Distance to slums,hazard_site,E_environment,level,km,0.1864864,0.0428089,4.36,,**,,,high,Distance to existing (unimproved) slum site (km),log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,semi_elasticity,18.64864,4.280889999999999,100*b/1 (X per km->km),reported,0.3324376061259826,0.07624715736834464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.36,153.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,True
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Distance to upgraded slums,hazard_site,E_environment,level,km,6568.099,2070.675,3.17,,**,,,high,Distance to government-upgraded slum site (km),monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,6568.099,2070.675,"b (currency per unit, as reported)",reported,0.2409765823537718,0.07601784932295641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.17,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Distance to upgraded slums,hazard_site,E_environment,level,km,0.1258162,0.0472052,2.67,,**,,,high,Distance to government-upgraded slum site (km),log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,semi_elasticity,12.58162,4.7205200000000005,100*b/1 (X per km->km),reported,0.20470206056405732,0.07666743841350461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.67,163.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Distance to evicted slums,hazard_site,E_environment,level,km,2930.03,1490.882,1.97,,*,,,high,Distance to evicted/demolished slum site (km),monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,2930.03,1490.882,"b (currency per unit, as reported)",reported,0.15483474422959329,0.07859631686781385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.97,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Distance to evicted slums,hazard_site,E_environment,level,km,0.0792828,0.0408651,1.94,,*,,,high,Distance to evicted/demolished slum site (km),log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,semi_elasticity,7.92828,4.0865100000000005,100*b/1 (X per km->km),reported,0.1525321276461744,0.07862480806503837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.94,158.0,km,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-lin,4,Linear model,7,Visibility,hazard_site,E_environment,dummy,binary,5985.225,1228.731,4.87,,**,,,high,Dummy: slum site visible from the house; Table 4 printed positive although text reports a ~15% rent discount,monthly house rent (PKR),level,OLS,163.0,0.9547,none,Houses around existing (unimproved) slum sites,monetary,5985.225,1228.731,"b (currency per unit, as reported)",reported,0.36634470515560646,0.07522478545289661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.87,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T4-log,4,Log-linear model,7,Visibility,hazard_site,E_environment,dummy,binary,0.151787,0.0322262,4.71,,**,,,high,Dummy: slum site visible from the house; Table 4 printed positive although text reports a ~15% rent discount,log monthly house rent (PKR),log,OLS,163.0,0.9466,none,Houses around existing (unimproved) slum sites,pct_premium,16.330807586160123,3.750847045618006,100*(exp(b - se^2/2) - 1),reported,0.3558554028352483,0.07555316408391685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.71,153.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-lin,5,Linear model,8,Visibility,hazard_site,E_environment,dummy,binary,4921.391,1451.89,3.39,,**,,,high,Dummy: slum site visible from the house; Table 4 printed positive although text reports a ~15% rent discount,monthly house rent (PKR),level,OLS,173.0,0.9441,none,Houses around upgraded slum sites,monetary,4921.391,1451.89,"b (currency per unit, as reported)",reported,0.2566325938590301,0.07570283004691153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.39,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T5-log,5,Log-linear model,8,Visibility,hazard_site,E_environment,dummy,binary,-0.1080642,0.0330988,-3.26,,**,,,high,Dummy: slum site visible from the house; Table 4 printed positive although text reports a ~15% rent discount,log monthly house rent (PKR),log,OLS,173.0,0.9193,none,Houses around upgraded slum sites,pct_premium,-10.292155244643531,2.970848894251641,100*(exp(b - se^2/2) - 1),reported,-0.2474048556487383,0.0758910600149504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.26,163.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-lin,6,Linear model,9,Visibility,hazard_site,E_environment,dummy,binary,2237.263,1077.658,-2.08,,*,,,medium,Dummy: slum site visible from the house; Table 4 printed positive although text reports a ~15% rent discount; printed coefficient sign disagrees with printed t-value sign,monthly house rent (PKR),level,OLS,168.0,0.8817,none,Houses around evicted/demolished slum sites,monetary,2237.263,1077.658,"b (currency per unit, as reported)",reported,-0.1632558514784991,0.0784883901338938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,False,True,False
7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8,7e1fb30ea0df3365f3fc36da9a0ba3d2741cb1a8:T6-log,6,Log-linear model,9,Visibility,hazard_site,E_environment,dummy,binary,-0.0676483,0.0295386,-2.29,,*,,,high,Dummy: slum site visible from the house; Table 4 printed positive although text reports a ~15% rent discount,log monthly house rent (PKR),log,OLS,168.0,0.8683,none,Houses around evicted/demolished slum sites,pct_premium,-6.581852830077917,2.7606453907486252,100*(exp(b - se^2/2) - 1),reported,-0.17923248880455112,0.07826746236006597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.29,158.0,binary,,2021.0,Pakistan,Asia,Lower middle income,,,single_family,rent,journal,False,False,unknown,,2018.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Rooms,bedrooms,A_structural,level,count,0.012,0.004,,,***,,,high,"Total rooms, mapped to bedrooms (closest leaf, synonym 'rooms'); bedrooms entered separately",LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.022110286009454527,0.007370095336484842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Rooms,bedrooms,A_structural,level,count,0.012,0.004,,,***,,,high,"Total rooms, mapped to bedrooms (closest leaf, synonym 'rooms'); bedrooms entered separately",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.022110286009454527,0.007370095336484842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Rooms,bedrooms,A_structural,level,count,0.012,0.004,,,***,,,high,"Total rooms, mapped to bedrooms (closest leaf, synonym 'rooms'); bedrooms entered separately",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.022110286009454527,0.007370095336484842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Rooms,bedrooms,A_structural,level,count,0.012,0.004,,,***,,,high,"Total rooms, mapped to bedrooms (closest leaf, synonym 'rooms'); bedrooms entered separately",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.022110286009454527,0.007370095336484842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Rooms,bedrooms,A_structural,level,count,0.012,0.004,,,***,,,high,"Total rooms, mapped to bedrooms (closest leaf, synonym 'rooms'); bedrooms entered separately",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.022110286009454527,0.007370095336484842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Rooms,bedrooms,A_structural,level,count,0.012,0.004,,,***,,,high,"Total rooms, mapped to bedrooms (closest leaf, synonym 'rooms'); bedrooms entered separately",LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,1.2,0.4,100*b/1 (per unit count),reported,0.022110286009454527,0.007370095336484842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Bedrooms,bedrooms,A_structural,level,count,0.006,0.011,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.6,1.0999999999999999,100*b/1 (per unit count),reported,0.004021002485894134,0.007371837890805911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5454545454545455,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Bedrooms,bedrooms,A_structural,level,count,0.006,0.011,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.6,1.0999999999999999,100*b/1 (per unit count),reported,0.004021002485894134,0.007371837890805911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5454545454545455,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Bedrooms,bedrooms,A_structural,level,count,0.006,0.011,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.6,1.0999999999999999,100*b/1 (per unit count),reported,0.004021002485894134,0.007371837890805911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5454545454545455,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Bedrooms,bedrooms,A_structural,level,count,0.006,0.011,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.6,1.0999999999999999,100*b/1 (per unit count),reported,0.004021002485894134,0.007371837890805911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5454545454545455,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Bedrooms,bedrooms,A_structural,level,count,0.006,0.011,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.6,1.0999999999999999,100*b/1 (per unit count),reported,0.004021002485894134,0.007371837890805911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5454545454545455,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Bedrooms,bedrooms,A_structural,level,count,0.007,0.011,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.7000000000000001,1.0999999999999999,100*b/1 (per unit count),reported,0.004691155871979544,0.007371816370253567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6363636363636365,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Full baths,bathrooms,A_structural,level,count,0.125,0.021,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,12.5,2.1,100*b/1 (per unit count),reported,0.04383815763717279,0.00736481048304503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.952380952380952,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Full baths,bathrooms,A_structural,level,count,0.125,0.021,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,12.5,2.1,100*b/1 (per unit count),reported,0.04383815763717279,0.00736481048304503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.952380952380952,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Full baths,bathrooms,A_structural,level,count,0.125,0.021,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,12.5,2.1,100*b/1 (per unit count),reported,0.04383815763717279,0.00736481048304503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.952380952380952,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Full baths,bathrooms,A_structural,level,count,0.125,0.021,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,12.5,2.1,100*b/1 (per unit count),reported,0.04383815763717279,0.00736481048304503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.952380952380952,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Full baths,bathrooms,A_structural,level,count,0.124,0.021,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,12.4,2.1,100*b/1 (per unit count),reported,0.04348811830496353,0.0073649232613244705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.904761904761904,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Full baths,bathrooms,A_structural,level,count,0.124,0.021,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,12.4,2.1,100*b/1 (per unit count),reported,0.04348811830496353,0.0073649232613244705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.904761904761904,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Half baths,bathrooms,A_structural,level,count,0.034,0.015,,,**,,,high,half bathrooms,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.4000000000000004,1.5,100*b/1 (per unit count),reported,0.016707302028437038,0.0073708685419575145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.266666666666667,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Half baths,bathrooms,A_structural,level,count,0.033,0.015,,,**,,,high,half bathrooms,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.3000000000000003,1.5,100*b/1 (per unit count),reported,0.01621604196584575,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Half baths,bathrooms,A_structural,level,count,0.033,0.015,,,**,,,high,half bathrooms,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.3000000000000003,1.5,100*b/1 (per unit count),reported,0.01621604196584575,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Half baths,bathrooms,A_structural,level,count,0.033,0.015,,,**,,,high,half bathrooms,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.3000000000000003,1.5,100*b/1 (per unit count),reported,0.01621604196584575,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Half baths,bathrooms,A_structural,level,count,0.033,0.015,,,**,,,high,half bathrooms,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.3000000000000003,1.5,100*b/1 (per unit count),reported,0.01621604196584575,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Half baths,bathrooms,A_structural,level,count,0.033,0.015,,,**,,,high,half bathrooms,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.3000000000000003,1.5,100*b/1 (per unit count),reported,0.01621604196584575,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Fireplaces,fireplace,B_interior,level,count,0.086,0.012,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,8.6,1.2,100*b/1 (per unit count),reported,0.05275835339702551,0.007361630706561699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Fireplaces,fireplace,B_interior,level,count,0.086,0.012,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,8.6,1.2,100*b/1 (per unit count),reported,0.05275835339702551,0.007361630706561699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Fireplaces,fireplace,B_interior,level,count,0.086,0.012,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,8.6,1.2,100*b/1 (per unit count),reported,0.05275835339702551,0.007361630706561699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Fireplaces,fireplace,B_interior,level,count,0.086,0.012,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,8.6,1.2,100*b/1 (per unit count),reported,0.05275835339702551,0.007361630706561699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Fireplaces,fireplace,B_interior,level,count,0.086,0.012,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,8.6,1.2,100*b/1 (per unit count),reported,0.05275835339702551,0.007361630706561699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Fireplaces,fireplace,B_interior,level,count,0.086,0.012,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,8.6,1.2,100*b/1 (per unit count),reported,0.05275835339702551,0.007361630706561699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,18401.0,count,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Acreage,lot_size,A_structural,level,acres,0.007,0.019,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.00017297376702701575,0.0004695002247876141,100*b/4046.86 (X per acre->m2),reported,0.0027159522150879915,0.007371870298095975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,18401.0,acre,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Acreage,lot_size,A_structural,level,acres,0.006,0.019,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0001482632288802992,0.0004695002247876141,100*b/4046.86 (X per acre->m2),reported,0.002327961319418908,0.007371877511493207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31578947368421056,18401.0,acre,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Acreage,lot_size,A_structural,level,acres,0.007,0.019,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.00017297376702701575,0.0004695002247876141,100*b/4046.86 (X per acre->m2),reported,0.0027159522150879915,0.007371870298095975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,18401.0,acre,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Acreage,lot_size,A_structural,level,acres,0.007,0.019,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.00017297376702701575,0.0004695002247876141,100*b/4046.86 (X per acre->m2),reported,0.0027159522150879915,0.007371870298095975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,18401.0,acre,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Acreage,lot_size,A_structural,level,acres,0.007,0.019,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.00017297376702701575,0.0004695002247876141,100*b/4046.86 (X per acre->m2),reported,0.0027159522150879915,0.007371870298095975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,18401.0,acre,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Acreage,lot_size,A_structural,level,acres,0.007,0.019,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.00017297376702701575,0.0004695002247876141,100*b/4046.86 (X per acre->m2),reported,0.0027159522150879915,0.007371870298095975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,18401.0,acre,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Square feet,living_area,A_structural,level,sqft,0.0,0.0,,,***,,,low,coefficient and SE printed as 0.000 (rounded to 3 decimals); not usable numerically,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0,,100*b/0.092903 (X per sqft->m2),,,,,,18401.0,sqft,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Square feet,living_area,A_structural,level,sqft,0.0,0.0,,,***,,,low,coefficient and SE printed as 0.000 (rounded to 3 decimals); not usable numerically,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0,,100*b/0.092903 (X per sqft->m2),,,,,,18401.0,sqft,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Square feet,living_area,A_structural,level,sqft,0.0,0.0,,,***,,,low,coefficient and SE printed as 0.000 (rounded to 3 decimals); not usable numerically,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0,,100*b/0.092903 (X per sqft->m2),,,,,,18401.0,sqft,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Square feet,living_area,A_structural,level,sqft,0.0,0.0,,,***,,,low,coefficient and SE printed as 0.000 (rounded to 3 decimals); not usable numerically,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0,,100*b/0.092903 (X per sqft->m2),,,,,,18401.0,sqft,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Square feet,living_area,A_structural,level,sqft,0.0,0.0,,,***,,,low,coefficient and SE printed as 0.000 (rounded to 3 decimals); not usable numerically,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0,,100*b/0.092903 (X per sqft->m2),,,,,,18401.0,sqft,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Square feet,living_area,A_structural,level,sqft,0.0,0.0,,,***,,,low,coefficient and SE printed as 0.000 (rounded to 3 decimals); not usable numerically,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,0.0,,100*b/0.092903 (X per sqft->m2),,,,,,18401.0,sqft,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Property age,age,A_structural,level,years,0.039,0.001,,,***,,,high,positive sign as printed; year built also included in the same model,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.9,0.1,100*b/1 (per year),reported,0.2763109774319011,0.007084896857228233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.0,18401.0,years,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Property age,age,A_structural,level,years,0.039,0.001,,,***,,,high,positive sign as printed; year built also included in the same model,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.9,0.1,100*b/1 (per year),reported,0.2763109774319011,0.007084896857228233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.0,18401.0,years,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Property age,age,A_structural,level,years,0.039,0.001,,,***,,,high,positive sign as printed; year built also included in the same model,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.9,0.1,100*b/1 (per year),reported,0.2763109774319011,0.007084896857228233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.0,18401.0,years,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Property age,age,A_structural,level,years,0.039,0.001,,,***,,,high,positive sign as printed; year built also included in the same model,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,3.9,0.1,100*b/1 (per year),reported,0.2763109774319011,0.007084896857228233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.0,18401.0,years,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Property age,age,A_structural,level,years,0.042,0.001,,,***,,,high,positive sign as printed; year built also included in the same model,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,4.2,0.1,100*b/1 (per year),reported,0.2957673139345615,0.007042078903203845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,18401.0,years,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Property age,age,A_structural,level,years,0.042,0.001,,,***,,,high,positive sign as printed; year built also included in the same model,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",semi_elasticity,4.2,0.1,100*b/1 (per year),reported,0.2957673139345615,0.007042078903203845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,18401.0,years,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Year built,age,A_structural,other,years,0.042,0.001,,,***,,,high,"year of construction (calendar year), not age; entered alongside property age",LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",other,0.042,0.001,not harmonised,reported,0.2957673139345615,0.007042078903203845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,18401.0,years,year_built_not_age;nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Year built,age,A_structural,other,years,0.042,0.001,,,***,,,high,"year of construction (calendar year), not age; entered alongside property age",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",other,0.042,0.001,not harmonised,reported,0.2957673139345615,0.007042078903203845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,18401.0,years,year_built_not_age;nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Year built,age,A_structural,other,years,0.042,0.001,,,***,,,high,"year of construction (calendar year), not age; entered alongside property age",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",other,0.042,0.001,not harmonised,reported,0.2957673139345615,0.007042078903203845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,18401.0,years,year_built_not_age;nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Year built,age,A_structural,other,years,0.042,0.001,,,***,,,high,"year of construction (calendar year), not age; entered alongside property age",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",other,0.042,0.001,not harmonised,reported,0.2957673139345615,0.007042078903203845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,18401.0,years,year_built_not_age;nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Year built,age,A_structural,other,years,0.044,0.002,,,***,,,high,"year of construction (calendar year), not age; entered alongside property age",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",other,0.044,0.002,not harmonised,reported,0.16008999322453848,0.0072768178738426585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,18401.0,years,year_built_not_age;nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Year built,age,A_structural,other,years,0.044,0.001,,,***,,,high,"year of construction (calendar year), not age; entered alongside property age",LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",other,0.044,0.001,not harmonised,reported,0.3085384064774159,0.007012236510850363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.0,18401.0,years,year_built_not_age;nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Carport,garage,A_structural,binary,binary,0.066,0.017,,,***,,,high,carport indicator,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,6.807236955724605,1.8159854191821887,100*(exp(b - se^2/2) - 1),reported,0.028608593325339986,0.007368880098951208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8823529411764706,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Carport,garage,A_structural,binary,binary,0.066,0.017,,,***,,,high,carport indicator,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,6.807236955724605,1.8159854191821887,100*(exp(b - se^2/2) - 1),reported,0.028608593325339986,0.007368880098951208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8823529411764706,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Carport,garage,A_structural,binary,binary,0.066,0.017,,,***,,,high,carport indicator,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,6.807236955724605,1.8159854191821887,100*(exp(b - se^2/2) - 1),reported,0.028608593325339986,0.007368880098951208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8823529411764706,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Carport,garage,A_structural,binary,binary,0.066,0.017,,,***,,,high,carport indicator,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,6.807236955724605,1.8159854191821887,100*(exp(b - se^2/2) - 1),reported,0.028608593325339986,0.007368880098951208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8823529411764706,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Carport,garage,A_structural,binary,binary,0.066,0.017,,,***,,,high,carport indicator,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,6.807236955724605,1.8159854191821887,100*(exp(b - se^2/2) - 1),reported,0.028608593325339986,0.007368880098951208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8823529411764706,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Carport,garage,A_structural,binary,binary,0.065,0.017,,,***,,,high,carport indicator,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,6.7004831045906155,1.8141703414531276,100*(exp(b - se^2/2) - 1),reported,0.028175476543229556,0.00736897078822927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8235294117647056,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Covered garage,garage,A_structural,binary,binary,0.074,0.008,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,7.677234822976331,0.8614454443969759,100*(exp(b - se^2/2) - 1),reported,0.06803206462294246,0.007354817797074861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.25,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Covered garage,garage,A_structural,binary,binary,0.073,0.008,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,7.569611408829036,0.8605844295317628,100*(exp(b - se^2/2) - 1),reported,0.06711688201818963,0.007355274741719412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.125,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Covered garage,garage,A_structural,binary,binary,0.073,0.008,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,7.569611408829036,0.8605844295317628,100*(exp(b - se^2/2) - 1),reported,0.06711688201818963,0.007355274741719412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.125,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Covered garage,garage,A_structural,binary,binary,0.073,0.008,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,7.569611408829036,0.8605844295317628,100*(exp(b - se^2/2) - 1),reported,0.06711688201818963,0.007355274741719412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.125,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Covered garage,garage,A_structural,binary,binary,0.073,0.008,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,7.569611408829036,0.8605844295317628,100*(exp(b - se^2/2) - 1),reported,0.06711688201818963,0.007355274741719412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.125,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Covered garage,garage,A_structural,binary,binary,0.073,0.008,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,7.569611408829036,0.8605844295317628,100*(exp(b - se^2/2) - 1),reported,0.06711688201818963,0.007355274741719412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.125,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,No pool,pool,C_exterior,binary,binary,0.088,0.04,,,**,,,high,"indicator for no pool, printed together with in-ground and above-ground pool indicators (reference category unclear)",LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,9.111488087360954,4.36795248811279,100*(exp(b - se^2/2) - 1),reported,0.016216041965845748,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1999999999999997,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,No pool,pool,C_exterior,binary,binary,0.089,0.04,,,**,,,high,"indicator for no pool, printed together with in-ground and above-ground pool indicators (reference category unclear)",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,9.220654149382156,4.372322625305321,100*(exp(b - se^2/2) - 1),reported,0.01640026588464889,0.0073709060155725354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2249999999999996,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,No pool,pool,C_exterior,binary,binary,0.089,0.04,,,**,,,high,"indicator for no pool, printed together with in-ground and above-ground pool indicators (reference category unclear)",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,9.220654149382156,4.372322625305321,100*(exp(b - se^2/2) - 1),reported,0.01640026588464889,0.0073709060155725354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2249999999999996,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,No pool,pool,C_exterior,binary,binary,0.089,0.04,,,**,,,high,"indicator for no pool, printed together with in-ground and above-ground pool indicators (reference category unclear)",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,9.220654149382156,4.372322625305321,100*(exp(b - se^2/2) - 1),reported,0.01640026588464889,0.0073709060155725354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2249999999999996,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,No pool,pool,C_exterior,binary,binary,0.088,0.04,,,**,,,high,"indicator for no pool, printed together with in-ground and above-ground pool indicators (reference category unclear)",LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,9.111488087360954,4.36795248811279,100*(exp(b - se^2/2) - 1),reported,0.016216041965845748,0.007370928166293523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1999999999999997,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,No pool,pool,C_exterior,binary,binary,0.089,0.04,,,**,,,high,"indicator for no pool, printed together with in-ground and above-ground pool indicators (reference category unclear)",LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,9.220654149382156,4.372322625305321,100*(exp(b - se^2/2) - 1),reported,0.01640026588464889,0.0073709060155725354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2249999999999996,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,In-ground pool,pool,C_exterior,binary,binary,0.105,0.04,,,**,,,high,reference category unclear (no-pool indicator also reported),LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,10.982239720005428,4.442842441422821,100*(exp(b - se^2/2) - 1),reported,0.01934760869203614,0.007370517596966148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.625,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,In-ground pool,pool,C_exterior,binary,binary,0.106,0.039,,,**,,,high,reference category unclear (no-pool indicator also reported),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,11.097665740474794,4.336105318375471,100*(exp(b - se^2/2) - 1),reported,0.0200324186312036,0.007370418175631514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.717948717948718,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,In-ground pool,pool,C_exterior,binary,binary,0.106,0.04,,,**,,,high,reference category unclear (no-pool indicator also reported),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,11.093277469346962,4.447287506026123,100*(exp(b - se^2/2) - 1),reported,0.019531801668709634,0.0073704911957394835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,In-ground pool,pool,C_exterior,binary,binary,0.106,0.04,,,**,,,high,reference category unclear (no-pool indicator also reported),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,11.093277469346962,4.447287506026123,100*(exp(b - se^2/2) - 1),reported,0.019531801668709634,0.0073704911957394835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,In-ground pool,pool,C_exterior,binary,binary,0.106,0.039,,,**,,,high,reference category unclear (no-pool indicator also reported),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,11.097665740474794,4.336105318375471,100*(exp(b - se^2/2) - 1),reported,0.0200324186312036,0.007370418175631514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.717948717948718,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,In-ground pool,pool,C_exterior,binary,binary,0.107,0.04,,,**,,,high,reference category unclear (no-pool indicator also reported),LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,11.204426311975224,4.451737017917303,100*(exp(b - se^2/2) - 1),reported,0.019715992656676538,0.0073704645445519775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.675,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Above ground pool,pool,C_exterior,binary,binary,0.124,0.044,,,***,,,high,reference category unclear,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,13.092060982797872,4.980869832396371,100*(exp(b - se^2/2) - 1),reported,0.020770865438215227,0.0073703070909795975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8181818181818183,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Above ground pool,pool,C_exterior,binary,binary,0.122,0.044,,,***,,,high,reference category unclear,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,12.866102894240194,4.970918047833402,100*(exp(b - se^2/2) - 1),reported,0.020435992537675172,0.007370357964407439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.772727272727273,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Above ground pool,pool,C_exterior,binary,binary,0.122,0.044,,,***,,,high,reference category unclear,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,12.866102894240194,4.970918047833402,100*(exp(b - se^2/2) - 1),reported,0.020435992537675172,0.007370357964407439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.772727272727273,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Above ground pool,pool,C_exterior,binary,binary,0.123,0.044,,,***,,,high,reference category unclear,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,12.979025449001602,4.975891452168953,100*(exp(b - se^2/2) - 1),reported,0.020603429854737797,0.0073703326309631145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7954545454545454,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Above ground pool,pool,C_exterior,binary,binary,0.122,0.044,,,***,,,high,reference category unclear,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,12.866102894240194,4.970918047833402,100*(exp(b - se^2/2) - 1),reported,0.020435992537675172,0.007370357964407439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.772727272727273,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Above ground pool,pool,C_exterior,binary,binary,0.122,0.044,,,***,,,high,reference category unclear,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,12.866102894240194,4.970918047833402,100*(exp(b - se^2/2) - 1),reported,0.020435992537675172,0.007370357964407439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.772727272727273,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Townhouse,dwelling_type,A_structural,binary,binary,-0.213,0.022,,,***,,,high,townhouse vs other,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-19.203941290619888,1.7779435018762744,100*(exp(b - se^2/2) - 1),reported,-0.0711922691358488,0.007353192117317717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.681818181818182,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Townhouse,dwelling_type,A_structural,binary,binary,-0.21,0.022,,,***,,,high,townhouse vs other,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-18.96118916837244,1.7832853411344116,100*(exp(b - se^2/2) - 1),reported,-0.07019453684973942,0.007353713384258415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.545454545454545,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Townhouse,dwelling_type,A_structural,binary,binary,-0.21,0.022,,,***,,,high,townhouse vs other,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-18.96118916837244,1.7832853411344116,100*(exp(b - se^2/2) - 1),reported,-0.07019453684973942,0.007353713384258415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.545454545454545,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Townhouse,dwelling_type,A_structural,binary,binary,-0.21,0.022,,,***,,,high,townhouse vs other,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-18.96118916837244,1.7832853411344116,100*(exp(b - se^2/2) - 1),reported,-0.07019453684973942,0.007353713384258415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.545454545454545,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Townhouse,dwelling_type,A_structural,binary,binary,-0.21,0.022,,,***,,,high,townhouse vs other,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-18.96118916837244,1.7832853411344116,100*(exp(b - se^2/2) - 1),reported,-0.07019453684973942,0.007353713384258415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.545454545454545,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Townhouse,dwelling_type,A_structural,binary,binary,-0.21,0.022,,,***,,,high,townhouse vs other,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-18.96118916837244,1.7832853411344116,100*(exp(b - se^2/2) - 1),reported,-0.07019453684973942,0.007353713384258415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.545454545454545,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Condominium,dwelling_type,A_structural,binary,binary,-0.458,0.181,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-37.77295663842525,11.449109319353276,100*(exp(b - se^2/2) - 1),reported,-0.018650507096749182,0.007370615250025332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5303867403314917,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,True
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Condominium,dwelling_type,A_structural,binary,binary,-0.457,0.181,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-37.71069847116822,11.460564155135952,100*(exp(b - se^2/2) - 1),reported,-0.018609799583862385,0.007370620841748559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5248618784530388,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Condominium,dwelling_type,A_structural,binary,binary,-0.456,0.18,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-37.63712253105928,11.408649067773881,100*(exp(b - se^2/2) - 1),reported,-0.018672217732377466,0.007370612262780578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5333333333333337,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Condominium,dwelling_type,A_structural,binary,binary,-0.457,0.18,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-37.6994542374807,11.397246121129676,100*(exp(b - se^2/2) - 1),reported,-0.01871315125476493,0.007370606621132793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.538888888888889,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Condominium,dwelling_type,A_structural,binary,binary,-0.458,0.18,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-37.761723643351175,11.38585457173254,100*(exp(b - se^2/2) - 1),reported,-0.018754084683055006,0.007370600967139521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5444444444444447,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,True
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Condominium,dwelling_type,A_structural,binary,binary,-0.458,0.18,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-37.761723643351175,11.38585457173254,100*(exp(b - se^2/2) - 1),reported,-0.018754084683055006,0.007370600967139521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5444444444444447,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,True
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Subdivision,other,other,binary,binary,0.01,0.014,,,,,,high,located in a subdivision,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.9951187017896324,1.414070233917835,100*(exp(b - se^2/2) - 1),reported,0.005265568063627795,0.007371795289078912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7142857142857143,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Subdivision,other,other,binary,binary,0.01,0.014,,,,,,high,located in a subdivision,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.9951187017896324,1.414070233917835,100*(exp(b - se^2/2) - 1),reported,0.005265568063627795,0.007371795289078912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7142857142857143,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Subdivision,other,other,binary,binary,0.01,0.014,,,,,,high,located in a subdivision,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.9951187017896324,1.414070233917835,100*(exp(b - se^2/2) - 1),reported,0.005265568063627795,0.007371795289078912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7142857142857143,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Subdivision,other,other,binary,binary,0.009,0.014,,,,,,high,located in a subdivision,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.8941740638188866,1.412656870483415,100*(exp(b - se^2/2) - 1),reported,0.0047390237398210215,0.007371814706388257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6428571428571428,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Subdivision,other,other,binary,binary,0.009,0.014,,,,,,high,located in a subdivision,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.8941740638188866,1.412656870483415,100*(exp(b - se^2/2) - 1),reported,0.0047390237398210215,0.007371814706388257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6428571428571428,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Subdivision,other,other,binary,binary,0.009,0.014,,,,,,high,located in a subdivision,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.8941740638188866,1.412656870483415,100*(exp(b - se^2/2) - 1),reported,0.0047390237398210215,0.007371814706388257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6428571428571428,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,Neighborhood,other,other,binary,binary,-0.041,0.058,,,*,,,medium,neighborhood association indicator; col1 SE printed 0.058 (inconsistent with star),LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-4.178394567187793,5.5670089536972345,100*(exp(b - se^2/2) - 1),reported,-0.005211098156797257,0.007371797392542462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7068965517241379,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,Neighborhood,other,other,binary,binary,-0.04,0.021,,,*,,,high,neighborhood association indicator; col1 SE printed 0.058 (inconsistent with star),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-3.9422391563815107,2.0176578222198787,100*(exp(b - se^2/2) - 1),reported,-0.014040325404872685,0.007371170837558161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9047619047619047,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,Neighborhood,other,other,binary,binary,-0.04,0.021,,,*,,,high,neighborhood association indicator; col1 SE printed 0.058 (inconsistent with star),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-3.9422391563815107,2.0176578222198787,100*(exp(b - se^2/2) - 1),reported,-0.014040325404872685,0.007371170837558161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9047619047619047,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,Neighborhood,other,other,binary,binary,-0.04,0.021,,,*,,,high,neighborhood association indicator; col1 SE printed 0.058 (inconsistent with star),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-3.9422391563815107,2.0176578222198787,100*(exp(b - se^2/2) - 1),reported,-0.014040325404872685,0.007371170837558161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9047619047619047,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,Neighborhood,other,other,binary,binary,-0.04,0.021,,,*,,,high,neighborhood association indicator; col1 SE printed 0.058 (inconsistent with star),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-3.9422391563815107,2.0176578222198787,100*(exp(b - se^2/2) - 1),reported,-0.014040325404872685,0.007371170837558161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9047619047619047,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,Neighborhood,other,other,binary,binary,-0.04,0.021,,,*,,,high,neighborhood association indicator; col1 SE printed 0.058 (inconsistent with star),LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,-3.9422391563815107,2.0176578222198787,100*(exp(b - se^2/2) - 1),reported,-0.014040325404872685,0.007371170837558161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9047619047619047,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,RING1,green_space,E_environment,dummy,binary (<=0.25 miles of greenline),0.002,0.021,,,,,,high,within 0.25 miles of a greenline; reference RING3 (0.51-1 mile),LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.17810842497096857,2.1042042028014007,100*(exp(b - se^2/2) - 1),reported,0.0007020853019307911,0.007371895670273306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09523809523809523,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,RING1,green_space,E_environment,dummy,binary (<=0.25 miles of greenline),0.002,0.021,,,,,,high,within 0.25 miles of a greenline; reference RING3 (0.51-1 mile),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.17810842497096857,2.1042042028014007,100*(exp(b - se^2/2) - 1),reported,0.0007020853019307911,0.007371895670273306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09523809523809523,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,RING1,green_space,E_environment,dummy,binary (<=0.25 miles of greenline),0.002,0.021,,,,,,high,within 0.25 miles of a greenline; reference RING3 (0.51-1 mile),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.17810842497096857,2.1042042028014007,100*(exp(b - se^2/2) - 1),reported,0.0007020853019307911,0.007371895670273306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09523809523809523,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,RING1,green_space,E_environment,dummy,binary (<=0.25 miles of greenline),0.003,0.021,,,,,,high,within 0.25 miles of a greenline; reference RING3 (0.51-1 mile),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.2783366391506892,2.106309459457092,100*(exp(b - se^2/2) - 1),reported,0.00105312762845146,0.007371893399160221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.14285714285714285,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,RING1,green_space,E_environment,dummy,binary (<=0.25 miles of greenline),0.002,0.021,,,,,,high,within 0.25 miles of a greenline; reference RING3 (0.51-1 mile),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.17810842497096857,2.1042042028014007,100*(exp(b - se^2/2) - 1),reported,0.0007020853019307911,0.007371895670273306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09523809523809523,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,RING1,green_space,E_environment,dummy,binary (<=0.25 miles of greenline),0.002,0.021,,,,,,high,within 0.25 miles of a greenline; reference RING3 (0.51-1 mile),LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.17810842497096857,2.1042042028014007,100*(exp(b - se^2/2) - 1),reported,0.0007020853019307911,0.007371895670273306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09523809523809523,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c1,8,1,30,RING2,green_space,E_environment,dummy,binary (0.26-0.50 miles of greenline),0.021,0.014,,,,,,high,0.26-0.50 miles of a greenline; reference RING3,LNSP (natural log of sale price),log,OLS,18411.0,0.84,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,2.112197678021621,1.42971087229254,100*(exp(b - se^2/2) - 1),reported,0.011057170238340774,0.007371446825560516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,RING2,green_space,E_environment,dummy,binary (0.26-0.50 miles of greenline),0.021,0.014,,,,,,high,0.26-0.50 miles of a greenline; reference RING3,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,2.112197678021621,1.42971087229254,100*(exp(b - se^2/2) - 1),reported,0.011057170238340774,0.007371446825560516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,RING2,green_space,E_environment,dummy,binary (0.26-0.50 miles of greenline),0.021,0.014,,,,,,high,0.26-0.50 miles of a greenline; reference RING3,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,2.112197678021621,1.42971087229254,100*(exp(b - se^2/2) - 1),reported,0.011057170238340774,0.007371446825560516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,RING2,green_space,E_environment,dummy,binary (0.26-0.50 miles of greenline),0.02,0.014,,,,,,high,0.26-0.50 miles of a greenline; reference RING3,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,2.0101365194280074,1.428281876037458,100*(exp(b - se^2/2) - 1),reported,0.010530698171885962,0.007371488720320173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4285714285714286,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,RING2,green_space,E_environment,dummy,binary (0.26-0.50 miles of greenline),0.02,0.014,,,,,,high,0.26-0.50 miles of a greenline; reference RING3,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,2.0101365194280074,1.428281876037458,100*(exp(b - se^2/2) - 1),reported,0.010530698171885962,0.007371488720320173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4285714285714286,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,RING2,green_space,E_environment,dummy,binary (0.26-0.50 miles of greenline),0.02,0.014,,,,,,high,0.26-0.50 miles of a greenline; reference RING3,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,2.0101365194280074,1.428281876037458,100*(exp(b - se^2/2) - 1),reported,0.010530698171885962,0.007371488720320173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4285714285714286,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,30,SFG,other,other,dummy,binary,0.006,0.017,,,,,,high,Shelby Farms Greenline area indicator (SFGE base); column alignment verified in PDF (cols 2-6),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.5872676950303246,1.7102306612919105,100*(exp(b - se^2/2) - 1),reported,0.0026018373652519225,0.007371872534880449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3529411764705882,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,30,SFG,other,other,dummy,binary,0.007,0.017,,,,,,high,Shelby Farms Greenline area indicator (SFGE base); column alignment verified in PDF (cols 2-6),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.6879052731279423,1.7119417473536427,100*(exp(b - se^2/2) - 1),reported,0.0030354732159275236,0.0073718635243954144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4117647058823529,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,30,SFG,other,other,dummy,binary,0.006,0.017,,,,,,high,Shelby Farms Greenline area indicator (SFGE base); column alignment verified in PDF (cols 2-6),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.5872676950303246,1.7102306612919105,100*(exp(b - se^2/2) - 1),reported,0.0026018373652519225,0.007371872534880449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3529411764705882,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,30,SFG,other,other,dummy,binary,0.005,0.017,,,,,,high,Shelby Farms Greenline area indicator (SFGE base); column alignment verified in PDF (cols 2-6),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.48673070420879316,1.7085212854609817,100*(exp(b - se^2/2) - 1),reported,0.002168200046812595,0.007371880159162822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.29411764705882354,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,30,SFG,other,other,dummy,binary,0.005,0.018,,,,,,high,Shelby Farms Greenline area indicator (SFGE base); column alignment verified in PDF (cols 2-6),LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,0.48497220180840905,1.8090225375469215,100*(exp(b - se^2/2) - 1),reported,0.002047745008613626,0.007371882031009054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2777777777777778,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c2,8,2,31,WR,other,other,dummy,binary,0.048,0.037,,,,,,high,Wolf River Greenline area indicator (SFGE base),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,4.845274374417019,3.881931424700541,100*(exp(b - se^2/2) - 1),reported,0.009563105368794006,0.00737156038844538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2972972972972974,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,31,WR,other,other,dummy,binary,0.048,0.038,,,,,,high,Wolf River Greenline area indicator (SFGE base),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,4.841342750346378,3.9868484902329886,100*(exp(b - se^2/2) - 1),reported,0.009311466815869711,0.007371577895896854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2631578947368423,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,31,WR,other,other,dummy,binary,0.048,0.037,,,,,,high,Wolf River Greenline area indicator (SFGE base),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,4.845274374417019,3.881931424700541,100*(exp(b - se^2/2) - 1),reported,0.009563105368794006,0.00737156038844538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2972972972972974,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,31,WR,other,other,dummy,binary,0.047,0.037,,,,,,high,Wolf River Greenline area indicator (SFGE base),LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,4.740481505209937,3.878051433594726,100*(exp(b - se^2/2) - 1),reported,0.009363891661866273,0.007371574287001107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2702702702702704,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,31,WR,other,other,dummy,binary,0.048,0.038,,,,,,high,Wolf River Greenline area indicator (SFGE base),LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",pct_premium,4.841342750346378,3.9868484902329886,100*(exp(b - se^2/2) - 1),reported,0.009311466815869711,0.007371577895896854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2631578947368423,18401.0,binary,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,False,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c3,8,3,31,Log Violent crime density,crime,F_neighbourhood,log,"crimes per square mile at census-tract level, natural log",0.007,0.028,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",elasticity,0.007,0.028,b (log-log),reported,0.0018429712419178142,0.007371884967671256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,18401.0,crimes per sq mile at census-tract level natural log,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,31,Log Violent crime density,crime,F_neighbourhood,log,"crimes per square mile at census-tract level, natural log",0.026,0.027,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",elasticity,0.026,0.027,b (log-log),reported,0.007098685384026303,0.007371711744950393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9629629629629629,18401.0,crimes per sq mile at census-tract level natural log,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,True
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c4,8,4,31,Log Property crime density,crime,F_neighbourhood,log,"crimes per square mile at census-tract level, natural log",-0.006,0.015,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",elasticity,-0.006,0.015,b (log-log),reported,-0.0029487461749551744,0.007371865437387936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,18401.0,crimes per sq mile at census-tract level natural log,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,31,Log Property crime density,crime,F_neighbourhood,log,"crimes per square mile at census-tract level, natural log",-0.004,0.013,,,,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",elasticity,-0.004,0.013,b (log-log),reported,-0.0022682703146921527,0.007371878522749496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3076923076923077,18401.0,crimes per sq mile at census-tract level natural log,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c5,8,5,31,Log Other crime density,crime,F_neighbourhood,log,"crimes per square mile at census-tract level, natural log",-0.04,0.017,,,**,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",elasticity,-0.04,0.017,b (log-log),reported,-0.01734303233243199,0.007370788741283596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3529411764705883,18401.0,crimes per sq mile at census-tract level natural log,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T8-c6,8,6,31,Log Other crime density,crime,F_neighbourhood,log,"crimes per square mile at census-tract level, natural log",-0.047,0.017,,,***,,,high,,LNSP (natural log of sale price),log,OLS,18411.0,0.842,se,"Full sample; adj. R2; year and census-tract FE; col1 no greenline dummies, col2 + SFG/WR, col3 + violent crime, col4 + property crime, col5 + other crime, col6 all three crime densities",elasticity,-0.047,0.017,b (log-log),reported,-0.020376896602738743,0.007370366856309758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.764705882352941,18401.0,crimes per sq mile at census-tract level natural log,,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,True,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c1,6,1,22,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.006,0.035,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,0.006,0.035,not harmonised,reported,0.0012637528457897394,0.007371891600440148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1714285714285714,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c1,6,1,22,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.031,0.027,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,18411.0,0.841,se,"Full sample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,0.031,0.027,not harmonised,reported,0.008463727283743078,0.007371633440679455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1481481481481481,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c2,6,2,22,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.008,0.043,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,1806.0,0.904,se,"WR (Wolf River Greenline) subsample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,-0.008,0.043,not harmonised,reported,-0.004389996564047065,0.023596231531752974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.186046511627907,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c2,6,2,22,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.029,0.039,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,1806.0,0.904,se,"WR (Wolf River Greenline) subsample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,-0.029,0.039,not harmonised,reported,-0.017543384539841163,0.02359282748461398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7435897435897436,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c3,6,3,22,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.11,0.04,,,**,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,11438.0,0.83,se,"SFG (Shelby Farms Greenline) subsample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,0.11,0.04,not harmonised,reported,0.025716030247630826,0.009351283726411209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c3,6,3,22,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.126,0.025,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,11438.0,0.83,se,"SFG (Shelby Farms Greenline) subsample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,0.126,0.025,not harmonised,reported,0.04709375194605225,0.009343998401994494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.04,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c4,6,4,22,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.03,0.028,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,5167.0,0.855,se,"SFGE (Shelby Farms Greenway Extension) subsample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,-0.03,0.028,not harmonised,reported,-0.014918196579239143,0.013923650140623202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0714285714285714,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T6-c4,6,4,22,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.018,0.01,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect,LNSP (natural log of sale price),log,OLS,5167.0,0.855,se,"SFGE (Shelby Farms Greenway Extension) subsample; baseline DiD, property/neighborhood controls not shown; year and tract FE",other,-0.018,0.01,not harmonised,reported,-0.025057489362001757,0.01392082742333431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7999999999999998,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c1,7,1,23,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.243,0.046,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.243,0.046,not harmonised,reported,-0.03891335401094,0.007366313927996873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.282608695652174,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c1,7,1,23,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.287,0.038,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.287,0.038,not harmonised,reported,-0.05559112747765082,0.007360497714810909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.552631578947368,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c1,7,1,23,Log Property crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.057,0.014,,,***,,,high,log crime density main effect; model also includes crime x Post and RING x crime x Post interactions (not extracted),LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.057,0.014,not harmonised,reported,-0.030000644065061723,0.007368579244050248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.071428571428571,18401.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c2,7,2,23,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.164,0.065,,,*,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,0.164,0.065,not harmonised,reported,0.059430448574083014,0.02355475095924022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.523076923076923,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c2,7,2,23,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.08,0.038,,,*,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,0.08,0.038,not harmonised,reported,0.04961557316042556,0.02356739725120214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1052631578947367,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c2,7,2,23,Log Property crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",0.024,0.015,,,,,,high,log crime density main effect; model also includes crime x Post and RING x crime x Post interactions (not extracted),LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,0.024,0.015,not harmonised,reported,0.03772745566495286,0.023579659790595533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,1796.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c3,7,3,23,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.235,0.138,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.235,0.138,not harmonised,reported,-0.015927534913263708,0.009353190715022944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7028985507246375,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c3,7,3,23,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.304,0.142,,,**,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.304,0.142,not harmonised,reported,-0.02002225801551287,0.009352502099351406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1408450704225355,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c3,7,3,23,Log Property crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.108,0.031,,,***,,,high,log crime density main effect; model also includes crime x Post and RING x crime x Post interactions (not extracted),LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.108,0.031,not harmonised,reported,-0.03257215120685769,0.009349413772338782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4838709677419355,11428.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c4,7,4,23,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.153,0.078,,,*,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.153,0.078,not harmonised,reported,-0.02730463085513279,0.013920007886930444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9615384615384615,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c4,7,4,23,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.181,0.047,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log property crime density,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.181,0.047,not harmonised,reported,-0.05354988806217199,0.01390521955205571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.851063829787234,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T7-c4,7,4,23,Log Property crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",0.002,0.016,,,,,,high,log crime density main effect; model also includes crime x Post and RING x crime x Post interactions (not extracted),LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with property crime interactions; adj. R2; controls not shown; year and tract FE,other,0.002,0.016,not harmonised,reported,0.0017406473344759293,0.013925178675807434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.125,5157.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c1,9,1,32,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.204,0.034,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.204,0.034,not harmonised,reported,-0.044188180824955584,0.007364696804159265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c1,9,1,32,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.234,0.027,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.234,0.027,not harmonised,reported,-0.06375978010383865,0.0073568977042890735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.666666666666668,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c1,9,1,32,Log Violent crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.044,0.025,,,*,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.044,0.025,not harmonised,reported,-0.012973447656853144,0.00737127707775747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7599999999999998,18401.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c2,9,2,32,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.151,0.094,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,0.151,0.094,not harmonised,reported,0.03787774862201885,0.023579525632250145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6063829787234043,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c2,9,2,32,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.082,0.047,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,0.082,0.047,not harmonised,reported,0.0411334477068259,0.023576488319766064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7446808510638299,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c2,9,2,32,Log Violent crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",0.025,0.014,,,,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,0.025,0.014,not harmonised,reported,0.04209917705972919,0.023575539153448344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7857142857142858,1796.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c3,9,3,32,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.156,0.147,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.156,0.147,not harmonised,reported,-0.009926605201104473,0.009353916439502293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0612244897959184,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c3,9,3,32,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.218,0.143,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.218,0.143,not harmonised,reported,-0.014259069479549831,0.009353426309979934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5244755244755246,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c3,9,3,32,Log Violent crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.014,0.038,,,,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.014,0.038,not harmonised,reported,-0.0034463290758457534,0.009354321777295616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.368421052631579,11428.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c4,9,4,32,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.146,0.075,,,*,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.146,0.075,not harmonised,reported,-0.027097767942652895,0.013920086271910733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9466666666666665,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c4,9,4,32,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.145,0.047,,,**,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log violent crime density,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.145,0.047,not harmonised,reported,-0.04292113282794443,0.013912367192506127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.085106382978723,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T9-c4,9,4,32,Log Violent crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",0.008,0.026,,,,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with violent crime interactions; adj. R2; controls not shown; year and tract FE,other,0.008,0.026,not harmonised,reported,0.0042846375232789904,0.013925071950656717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3076923076923077,5157.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c1,10,1,33,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.123,0.021,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.123,0.021,not harmonised,reported,-0.04313806295625699,0.007365035138873144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.857142857142857,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c1,10,1,33,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.145,0.019,,,***,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.145,0.019,not harmonised,reported,-0.05617039544517648,0.0073602587135058834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.63157894736842,18401.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c1,10,1,33,Log Other crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.075,0.014,,,***,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,18411.0,0.843,se,Full sample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.075,0.014,not harmonised,reported,-0.03946154700559475,0.0073661554410443565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.357142857142857,18401.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c2,10,2,33,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.054,0.039,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,0.054,0.039,not harmonised,reported,0.032654595924643,0.02358387483446439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3846153846153846,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c2,10,2,33,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),0.006,0.057,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,0.006,0.057,not harmonised,reported,0.0024838301179893833,0.023596386120899145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.10526315789473684,1796.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c2,10,2,33,Log Other crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",0.016,0.021,,,,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,1806.0,0.905,se,WR (Wolf River Greenline) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,0.016,0.021,not harmonised,reported,0.01797534966669609,0.023592646437538622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7619047619047619,1796.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c3,10,3,33,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.144,0.113,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.144,0.113,not harmonised,reported,-0.01191977555997202,0.00935371276581138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2743362831858405,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c3,10,3,33,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.193,0.112,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.193,0.112,not harmonised,reported,-0.016117502786593154,0.009353162238851985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7232142857142858,11428.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c3,10,3,33,Log Other crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.092,0.032,,,***,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,11438.0,0.831,se,SFG (Shelby Farms Greenline) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.092,0.032,not harmonised,reported,-0.026884114230158577,0.009350996253968201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.875,11428.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c4,10,4,33,RING1,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.07,0.05,,,,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.07,0.05,not harmonised,reported,-0.019491575989972174,0.013922554278551552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4000000000000001,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c4,10,4,33,RING2,green_space,E_environment,other,binary (RING1 <=0.25 mi; RING2 0.26-0.50 mi),-0.086,0.039,,,*,,,high,DiD specification with RING x Post interactions (not extracted); coefficient is the pre-opening proximity effect; also interacted with log other crime density,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.086,0.039,not harmonised,reported,-0.03069238409907215,0.013918639300742023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.205128205128205,5157.0,binary,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384,7f45f70c2c2271b7539c0915321311343666b384:T10-c4,10,4,33,Log Other crime density,crime,F_neighbourhood,other,"crimes per square mile at census-tract level, natural log",-0.039,0.018,,,*,,,high,log crime density main effect; interactions with Post and RINGs not extracted,LNSP (natural log of sale price),log,OLS,5167.0,0.856,se,SFGE (Shelby Farms Greenway Extension) subsample; extended DiD with other crime interactions; adj. R2; controls not shown; year and tract FE,other,-0.039,0.018,not harmonised,reported,-0.03015754300823977,0.013918866003802969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.166666666666667,5157.0,crimes per sq mile at census-tract level natural log,nonstandard_x_transform,2026.0,United States,North America,High income,2006.0,2022.0,mixed,transaction,journal,True,True,unknown,,2014.0,ols,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,D_TR,flood_risk,E_environment,binary,binary,0.214,,5.01,,***,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,23.749321512474133,5.290723515485006,100*(exp(b - se^2/2) - 1),|b/t|,0.10264970892022152,0.020488963856331643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.01,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.139,,-2.62,,***,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",other,-0.139,0.05305343511450382,not harmonised,|b/t|,-0.053887745104861835,0.020567841643077036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.62,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,CBD (km),cbd_distance,D_location,level,km,-0.129,,-6.31,,***,,,high,"Geodesic distance to District 1 (city centre); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,-12.9,2.0443740095087164,100*b/1 (X per km->km),|b/t|,-0.12888785694104277,0.02042596781949965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.31,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Traffic_congestion (km),other,other,level,km,0.067,,2.52,,**,,,high,"Distance to nearest traffic congestion point; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,6.7,2.658730158730159,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.051836596936289524,0.02057007814932124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.52,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Nearest_river (km),water_proximity,E_environment,level,km,-0.041,,-0.81,,,,,high,"Distance to nearest river; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,-4.1000000000000005,5.061728395061729,100*b/1 (X per km->km),|b/t|,-0.01668187222611673,0.020594903982860163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.81,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Large_parks (km),green_space,E_environment,level,km,-0.144,,-4.58,,***,,,high,"Distance to large parks; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,-14.399999999999999,3.144104803493449,100*b/1 (X per km->km),|b/t|,-0.0939207837262962,0.020506721337619258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.58,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Hospital (1 km),hospital_distance,D_location,binary,binary,0.02,,0.49,,,,,high,"1 if a hospital within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,1.9351880175747516,4.164087102150024,100*(exp(b - se^2/2) - 1),|b/t|,0.010092393374267402,0.020596721171974287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,School (1 km),school_distance,D_location,binary,binary,0.314,,11.73,,***,,,high,"1 if a school within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,36.83993664891898,3.664376617867655,100*(exp(b - se^2/2) - 1),|b/t|,0.2348541134113629,0.020021663547430766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.73,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Area (m2),living_area,A_structural,level,m2,0.006,,33.09,,***,,,high,"Floor area in square metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,0.6,0.01813236627379873,100*b/1 (X per m2->m2),|b/t|,0.5632025836340586,0.01702032588800419,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.09,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.017,,1.05,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,1.7000000000000002,1.6190476190476193,100*b/1 (per unit count),|b/t|,0.021622602260741416,0.02059295453403944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.05,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.091,,5.76,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,9.1,1.5798611111111112,100*b/1 (per unit count),|b/t|,0.11781684737954615,0.02045431378117121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.76,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Floor_level,floor_level,A_structural,level,count,-0.01,,-7.01,,***,,,high,"Floor level of the unit (0-38); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,-1.0,0.14265335235378032,100*b/1 (per unit count),|b/t|,-0.14290833602310246,0.02038635321299607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.01,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,FRW (m),other,other,level,m,0.003,,3.13,,***,,,high,"Front road width in metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",semi_elasticity,0.3,0.09584664536741214,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.0643374496911293,0.020555095747964634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.13,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Secured_area,other,other,binary,binary,0.172,,5.26,,***,,,high,"1 if inside a secured (gated/guarded) area; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,18.70430316735352,3.883661355756498,100*(exp(b - se^2/2) - 1),|b/t|,0.10771391684831617,0.02047793095975593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.26,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,-0.02,,-0.47,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,-2.068838425557884,4.171058184284066,100*(exp(b - se^2/2) - 1),|b/t|,-0.009680498375311505,0.020596805053854268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.47,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Fiber_Internet,other,other,binary,binary,-0.147,,-4.12,,***,,,high,"1 if fibre internet installed; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,-13.725534932652728,3.080199385441722,100*(exp(b - se^2/2) - 1),|b/t|,-0.08455887577971505,0.020523998975658993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.12,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Fully_furnished,other,other,binary,binary,-0.014,,-0.38,,,,,high,"1 if fully furnished; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,-1.4571463868934043,3.6329909525473862,100*(exp(b - se^2/2) - 1),|b/t|,-0.007826912926740724,0.020597139280896647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.38,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Garden,garden,C_exterior,binary,binary,0.05,,1.2,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,5.035893062297259,4.380296234900102,100*(exp(b - se^2/2) - 1),|b/t|,0.02470977722860051,0.02059148102383376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Swimming_pool,pool,C_exterior,binary,binary,-0.314,,-7.88,,***,,,high,"Private or shared (apartment) pool; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,-27.006071550598985,2.9109514573865414,100*(exp(b - se^2/2) - 1),|b/t|,-0.16021375874273858,0.020331695271921135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.88,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Washing_machine,appliances,B_interior,binary,binary,0.084,,2.25,,**,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,8.687120136188465,4.060481203552952,100*(exp(b - se^2/2) - 1),|b/t|,0.0462952917881912,0.02057568523919609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Legal status: 1(building permit),other,other,binary,binary,0.001,,0.01,,,,,high,"Legal status dummy (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,-0.39920106560085156,10.010005001667084,100*(exp(b - se^2/2) - 1),|b/t|,0.00020597769769140835,0.020597769769140833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Legal status: 2(red book),other,other,binary,binary,0.176,,3.61,,***,,,high,"Land-use-rights certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,19.102174734511145,5.8135484299866445,100*(exp(b - se^2/2) - 1),|b/t|,0.07415323253384883,0.020541061643725437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.61,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,17,Legal status: 3 (pink book),other,other,binary,binary,0.186,,6.42,,***,,,high,"Land-use rights + house ownership certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,20.39168856974378,3.489447670252946,100*(exp(b - se^2/2) - 1),|b/t|,0.13109642353882642,0.020420003666483864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.42,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.165,,3.1,,***,,,high,"Orientation the house faces (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,17.772369897591677,6.277414986689661,100*(exp(b - se^2/2) - 1),|b/t|,0.063723312903259,0.020555907388148068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.094,,1.44,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,9.6221652613103,7.1711538969593285,100*(exp(b - se^2/2) - 1),|b/t|,0.029647750475658454,0.02058871560809615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.44,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 3 (east),sunlight,E_environment,binary,binary,-0.084,,-1.45,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,-8.211025804522286,5.326360380137274,100*(exp(b - se^2/2) - 1),|b/t|,-0.029853454771357153,0.02058858949748769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.45,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.076,,1.64,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,7.780464302909196,5.0000704656069255,100*(exp(b - se^2/2) - 1),|b/t|,0.03376108604687994,0.020586028077365818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.64,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.108,,2.5,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,11.30086900113616,4.81268626006954,100*(exp(b - se^2/2) - 1),|b/t|,0.05142628773229594,0.020570515092918375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.281,,3.25,,***,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,31.951230336853964,11.451429621717148,100*(exp(b - se^2/2) - 1),|b/t|,0.06679325907436817,0.020551772022882515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.255,,3.7,,***,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,28.740052869814114,8.89372198169154,100*(exp(b - se^2/2) - 1),|b/t|,0.0759913815451199,0.020538211228410784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c1,6,1,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.119,,1.68,,*,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,OLS,2367.0,0.647,t,"OLS model, Eq. (2); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates",pct_premium,12.354775740614565,7.978453587875833,100*(exp(b - se^2/2) - 1),|b/t|,0.034583554026863084,0.020585448825513743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.68,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,ols,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,D_TR,flood_risk,E_environment,binary,binary,0.19,,4.51,,***,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,20.817697395446523,5.0943996353631436,100*(exp(b - se^2/2) - 1),|b/t|,0.09249768934738291,0.020509465487224595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.51,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.155,,-3.43,,***,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",other,-0.155,0.04518950437317784,not harmonised,|b/t|,-0.07047468452892496,0.020546555256246338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.43,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,CBD (km),cbd_distance,D_location,level,km,-0.195,,-5.95,,***,,,high,"Geodesic distance to District 1 (city centre); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-19.5,3.2773109243697482,100*b/1 (X per km->km),|b/t|,-0.1216465627350336,0.020444800459669513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.95,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Traffic_congestion (km),other,other,level,km,0.07,,1.73,,*,,,high,"Distance to nearest traffic congestion point; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,7.000000000000001,4.046242774566474,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.03561154000312739,0.02058470520411988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.73,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Nearest_river (km),water_proximity,E_environment,level,km,-0.274,,-3.79,,***,,,high,"Distance to nearest river; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-27.400000000000002,7.229551451187335,100*b/1 (X per km->km),|b/t|,-0.07782875613234232,0.020535291855499294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.79,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Large_parks (km),green_space,E_environment,level,km,-0.372,,-8.4,,***,,,high,"Distance to large parks; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-37.2,4.428571428571428,100*b/1 (X per km->km),|b/t|,-0.17048818983729322,0.020296213075868243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.4,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Hospital (1 km),hospital_distance,D_location,binary,binary,0.032,,0.7,,,,,high,"1 if a hospital within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.143919333210321,4.72008002425197,100*(exp(b - se^2/2) - 1),|b/t|,0.014416940643244223,0.02059562949034889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,School (1 km),school_distance,D_location,binary,binary,0.37,,10.74,,***,,,high,"1 if a school within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,44.6875749487059,4.98754010638203,100*(exp(b - se^2/2) - 1),|b/t|,0.2159978937055385,0.020111535726772673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.74,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Area (m2),living_area,A_structural,level,m2,0.005,,29.35,,***,,,high,"Floor area in square metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.5,0.017035775127768313,100*b/1 (X per m2->m2),|b/t|,0.5173525305585203,0.017627002744753668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.35,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.039,,2.77,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,3.9,1.407942238267148,100*b/1 (per unit count),|b/t|,0.05696318077687092,0.020564325190206106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.77,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.049,,3.63,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,4.9,1.3498622589531681,100*b/1 (per unit count),|b/t|,0.07456177608021554,0.02054043418187756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.63,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Floor_level,floor_level,A_structural,level,count,-0.006,,-4.86,,***,,,high,"Floor level of the unit (0-38); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-0.6,0.12345679012345678,100*b/1 (per unit count),|b/t|,-0.09960732264650994,0.0204953338778827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.86,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,FRW (m),other,other,level,m,0.002,,3.02,,***,,,high,"Front road width in metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.2,0.06622516556291391,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.0620852626878607,0.020558034002602883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.02,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Secured_area,other,other,binary,binary,0.143,,5.24,,***,,,high,"1 if inside a secured (gated/guarded) area; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,15.330026251075623,3.1485374358442653,100*(exp(b - se^2/2) - 1),|b/t|,0.10730908329093544,0.020478832689109817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.24,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,0.022,,0.62,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,2.160043082350138,3.627316654572523,100*(exp(b - se^2/2) - 1),|b/t|,0.012769576283608359,0.02059609078001348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.62,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Fiber_Internet,other,other,binary,binary,-0.112,,-3.79,,***,,,high,"1 if fibre internet installed; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-10.634603646390762,2.642030523483905,100*(exp(b - se^2/2) - 1),|b/t|,-0.07782875613234232,0.020535291855499294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.79,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Fully_furnished,other,other,binary,binary,-0.012,,-0.37,,,,,high,"1 if fully furnished; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.2447808386584547,3.2045569065792345,100*(exp(b - se^2/2) - 1),|b/t|,-0.007620953658177923,0.020597172049129524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Garden,garden,C_exterior,binary,binary,0.037,,1.06,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.706104810930677,3.62213601613319,100*(exp(b - se^2/2) - 1),|b/t|,0.021828434147279824,0.020592862403094176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.06,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Swimming_pool,pool,C_exterior,binary,binary,-0.286,,-8.38,,***,,,high,"Private or shared (apartment) pool; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-24.917478463175,2.563975037718489,100*(exp(b - se^2/2) - 1),|b/t|,-0.1700940233752271,0.020297616154561705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.38,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Washing_machine,appliances,B_interior,binary,binary,0.094,,3.0,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,9.802060748348218,3.4421538705404773,100*(exp(b - se^2/2) - 1),|b/t|,0.061675671210559474,0.02055855707018649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Legal status: 1(building permit),other,other,binary,binary,-0.012,,-0.24,,,,,high,"Legal status dummy (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.3162605169658614,4.940358564309653,100*(exp(b - se^2/2) - 1),|b/t|,-0.00494340444675543,0.020597518528147625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Legal status: 2(red book),other,other,binary,binary,0.162,,3.95,,***,,,high,"Land-use-rights certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,17.487173617148866,4.822515420101316,100*(exp(b - se^2/2) - 1),|b/t|,0.08109323089084672,0.020529931871100436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.95,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,17,Legal status: 3 (pink book),other,other,binary,binary,0.114,,4.65,,***,,,high,"Land-use rights + house ownership certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,12.041536673290022,2.7476503706837296,100*(exp(b - se^2/2) - 1),|b/t|,0.09534330263552618,0.02050393605065079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.65,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.084,,1.88,,*,,,high,"Orientation the house faces (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.654377603179132,4.859618461699011,100*(exp(b - se^2/2) - 1),|b/t|,0.03869480677969587,0.02058234403175312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.88,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.111,,2.05,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,11.575810296945832,6.0502846176021885,100*(exp(b - se^2/2) - 1),|b/t|,0.04218783549425878,0.020579431948418915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.05,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 3 (east),sunlight,E_environment,binary,binary,0.032,,0.67,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.134052246298924,4.9314268910095205,100*(exp(b - se^2/2) - 1),|b/t|,0.01379919204520835,0.020595809022699026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.67,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.086,,2.2,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.89739802484064,4.260152010647321,100*(exp(b - se^2/2) - 1),|b/t|,0.04526863966912196,0.020576654395055436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.039,,1.07,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.908004528474418,3.7898176506874908,100*(exp(b - se^2/2) - 1),|b/t|,0.022034263258132666,0.020592769400123986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.07,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.195,,2.71,,***,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,21.21688428316224,8.744857651863375,100*(exp(b - se^2/2) - 1),|b/t|,0.055733196159096436,0.020565755040256987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.71,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.241,,4.21,,***,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,27.04377482010898,7.284502838700314,100*(exp(b - se^2/2) - 1),|b/t|,0.08639239542539648,0.020520759008407715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.21,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c2,6,2,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.14,,2.36,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.598,t,"SEM model, Eq. (3); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,14.825161205608195,6.823658128814062,100*(exp(b - se^2/2) - 1),|b/t|,0.04855340559396772,0.020573476946596492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.36,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,D_TR,flood_risk,E_environment,binary,binary,0.213,,5.02,,***,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,23.627130294944987,5.250257583961891,100*(exp(b - se^2/2) - 1),|b/t|,0.10285243324455957,0.02048853251883657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.02,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.119,,-2.23,,**,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",other,-0.119,0.05336322869955157,not harmonised,|b/t|,-0.04588464836170898,0.02057607549852421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.23,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,CBD (km),cbd_distance,D_location,level,km,-0.139,,-6.79,,***,,,high,"Geodesic distance to District 1 (city centre); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-13.900000000000002,2.0471281296023562,100*b/1 (X per km->km),|b/t|,-0.13851075038580152,0.020399226860942785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.79,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Traffic_congestion (km),other,other,level,km,0.094,,3.42,,***,,,high,"Distance to nearest traffic congestion point; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,9.4,2.7485380116959064,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.07027023502187607,0.0205468523455778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.42,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Nearest_river (km),water_proximity,E_environment,level,km,0.013,,0.24,,,,,high,"Distance to nearest river; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,1.3,5.416666666666667,100*b/1 (X per km->km),|b/t|,0.00494340444675543,0.020597518528147625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Large_parks (km),green_space,E_environment,level,km,-0.177,,-5.46,,***,,,high,"Distance to large parks; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-17.7,3.2417582417582413,100*b/1 (X per km->km),|b/t|,-0.11175927422624948,0.020468731543269135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.46,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Hospital (1 km),hospital_distance,D_location,binary,binary,0.026,,0.62,,,,,high,"1 if a hospital within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,2.5438891317917323,4.304010429082176,100*(exp(b - se^2/2) - 1),|b/t|,0.012769576283608359,0.02059609078001348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.62,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,School (1 km),school_distance,D_location,binary,binary,0.332,,12.24,,***,,,high,"1 if a school within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,39.32402359718672,3.7804407328934273,100*(exp(b - se^2/2) - 1),|b/t|,0.2444668914496389,0.019972785249153503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.24,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Area (m2),living_area,A_structural,level,m2,0.006,,32.93,,***,,,high,"Floor area in square metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.6,0.018220467658669907,100*b/1 (X per m2->m2),|b/t|,0.5613388574172153,0.01704642749520848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.93,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.018,,1.09,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,1.7999999999999998,1.6513761467889905,100*b/1 (per unit count),|b/t|,0.02244591304829364,0.020592580778251046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.09,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.084,,5.3,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,8.4,1.5849056603773586,100*b/1 (per unit count),|b/t|,0.1085234230482258,0.02047611755626902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Floor_level,floor_level,A_structural,level,count,-0.01,,-6.63,,***,,,high,"Floor level of the unit (0-38); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-1.0,0.1508295625942685,100*b/1 (per unit count),|b/t|,-0.13530733689554178,0.02040834643974989,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.63,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,FRW (m),other,other,level,m,0.002,,2.95,,***,,,high,"Front road width in metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.2,0.06779661016949153,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.060651556629522554,0.0205598497049229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.95,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Secured_area,other,other,binary,binary,0.166,,5.08,,***,,,high,"1 if inside a secured (gated/guarded) area; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,17.99429636649579,3.857778245788659,100*(exp(b - se^2/2) - 1),|b/t|,0.1040685092172349,0.02048592701126671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.08,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,-0.023,,-0.56,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-2.3561422317477376,4.013756629783102,100*(exp(b - se^2/2) - 1),|b/t|,-0.011533984039828969,0.020596400071123157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.56,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Fiber_Internet,other,other,binary,binary,-0.146,,-4.12,,***,,,high,"1 if fibre internet installed; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-13.638471963600708,3.0623064239067435,100*(exp(b - se^2/2) - 1),|b/t|,-0.08455887577971505,0.020523998975658993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.12,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Fully_furnished,other,other,binary,binary,-0.008,,-0.21,,,,,high,"1 if fully furnished; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-0.8687665811284662,3.779169199379279,100*(exp(b - se^2/2) - 1),|b/t|,-0.004325491278010889,0.020597577514337568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Garden,garden,C_exterior,binary,binary,0.053,,1.27,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.351185615182685,4.4003756843563675,100*(exp(b - se^2/2) - 1),|b/t|,0.026150222366114233,0.020590726272530894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.27,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Swimming_pool,pool,C_exterior,binary,binary,-0.294,,-7.33,,***,,,high,"Private or shared (apartment) pool; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-25.532274674571596,2.9892399520372015,100*(exp(b - se^2/2) - 1),|b/t|,-0.14928968012719454,0.020366941354323945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.33,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Washing_machine,appliances,B_interior,binary,binary,0.081,,2.17,,**,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.361572234181569,4.047651733728461,100*(exp(b - se^2/2) - 1),|b/t|,0.04465257933256283,0.02057722549887688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.17,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Legal status: 1(building permit),other,other,binary,binary,-0.009,,-0.16,,,,,high,"Legal status dummy (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.052623853083412,5.57460213059747,100*(exp(b - se^2/2) - 1),|b/t|,-0.003295625335694015,0.020597658348087595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Legal status: 2(red book),other,other,binary,binary,0.136,,2.74,,***,,,high,"Land-use-rights certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,14.42714903889617,5.686596260178876,100*(exp(b - se^2/2) - 1),|b/t|,0.05634822053746406,0.0205650439917752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.74,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,17,Legal status: 3 (pink book),other,other,binary,binary,0.174,,6.01,,***,,,high,"Land-use rights + house ownership certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,18.955691585510316,3.4454187762519646,100*(exp(b - se^2/2) - 1),|b/t|,0.12285482702038672,0.020441734945155863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.01,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.142,,2.66,,***,,,high,"Orientation the house faces (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,15.093551522635341,6.152852785423109,100*(exp(b - se^2/2) - 1),|b/t|,0.0547080148751838,0.020566922885407443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.66,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.073,,1.13,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,7.348815648920537,6.94940966325428,100*(exp(b - se^2/2) - 1),|b/t|,0.023269178171121428,0.020592193071788875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.13,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 3 (east),sunlight,E_environment,binary,binary,-0.12,,-2.04,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-11.461270108003617,5.217179039512692,100*(exp(b - se^2/2) - 1),|b/t|,-0.041982404779292035,0.02057961018592747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.04,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.062,,1.33,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,6.280692324267956,4.959824464145721,100*(exp(b - se^2/2) - 1),|b/t|,0.027384760335620602,0.02059004536512827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.33,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.1,,2.32,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,10.41447422519215,4.763667750326068,100*(exp(b - se^2/2) - 1),|b/t|,0.047732357608403116,0.02057429207258755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.32,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.26,,3.01,,***,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,29.21007172505017,11.20271835658142,100*(exp(b - se^2/2) - 1),|b/t|,0.06188047085719505,0.020558295965845533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.01,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.211,,3.02,,***,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,23.190193379605905,8.628030030395834,100*(exp(b - se^2/2) - 1),|b/t|,0.0620852626878607,0.020558034002602883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.02,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c3,6,3,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.089,,1.26,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_lag,2367.0,0.647,t,"SAR model, Eq. (4); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,9.035720546748705,7.720966540717729,100*(exp(b - se^2/2) - 1),|b/t|,0.025944454252219327,0.02059083670811058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.26,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,D_TR,flood_risk,E_environment,binary,binary,0.082,,1.83,,*,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.43666548064923,4.863791060438417,100*(exp(b - se^2/2) - 1),|b/t|,0.03766716962373732,0.020583152799856456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.83,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.116,,-2.19,,**,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",other,-0.116,0.05296803652968037,not harmonised,|b/t|,-0.045063291927399406,0.0205768456289495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.19,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,CBD (km),cbd_distance,D_location,level,km,-0.112,,-5.56,,***,,,high,"Geodesic distance to District 1 (city centre); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-11.200000000000001,2.0143884892086334,100*b/1 (X per km->km),|b/t|,-0.11377988410891823,0.020464007933258675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.56,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Traffic_congestion (km),other,other,level,km,0.052,,1.97,,**,,,high,"Distance to nearest traffic congestion point; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,5.2,2.6395939086294415,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.04054424213152403,0.020580833569301536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.97,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Nearest_river (km),water_proximity,E_environment,level,km,-0.03,,-0.56,,,,,high,"Distance to nearest river; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-3.0,5.357142857142856,100*b/1 (X per km->km),|b/t|,-0.011533984039828969,0.020596400071123157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.56,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Large_parks (km),green_space,E_environment,level,km,-0.105,,-3.25,,***,,,high,"Distance to large parks; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-10.5,3.230769230769231,100*b/1 (X per km->km),|b/t|,-0.06679325907436817,0.020551772022882515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.25,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Hospital (1 km),hospital_distance,D_location,binary,binary,-0.16,,-3.21,,***,,,high,"1 if a hospital within 1 km; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-14.891410726549559,4.247445677090149,100*(exp(b - se^2/2) - 1),|b/t|,-0.06597478859482717,0.02055289364324834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.21,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,School (1 km),school_distance,D_location,binary,binary,0.355,,12.45,,***,,,high,"1 if a school within 1 km; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,42.56009930225457,4.066619536304622,100*(exp(b - se^2/2) - 1),|b/t|,0.24840441918169118,0.01995216218326837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.45,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Area (m2),living_area,A_structural,level,m2,0.006,,32.98,,***,,,high,"Floor area in square metres; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.6,0.01819284414796847,100*b/1 (X per m2->m2),|b/t|,0.5619221923922351,0.017038271449127805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.98,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.022,,1.32,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,2.1999999999999997,1.6666666666666667,100*b/1 (per unit count),|b/t|,0.0271790125560731,0.020590161027328104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.32,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.074,,4.68,,***,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,7.3999999999999995,1.5811965811965811,100*b/1 (per unit count),|b/t|,0.09595277535049955,0.02050272977574777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.68,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Floor_level,floor_level,A_structural,level,count,-0.009,,-6.52,,***,,,high,"Floor level of the unit (0-38); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-0.8999999999999999,0.13803680981595093,100*b/1 (per unit count),|b/t|,-0.13310251984071303,0.02041449690808482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.52,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,FRW (m),other,other,level,m,0.002,,2.52,,**,,,high,"Front road width in metres; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.2,0.07936507936507936,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.051836596936289524,0.02057007814932124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.52,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Secured_area,other,other,binary,binary,0.156,,4.83,,***,,,high,"1 if inside a secured (gated/guarded) area; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,16.821672005286125,3.7750908422778378,100*(exp(b - se^2/2) - 1),|b/t|,0.09899850722139193,0.020496585346043875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.83,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,-0.046,,-1.12,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-4.5763210321931975,3.922493773283292,100*(exp(b - se^2/2) - 1),|b/t|,-0.02306336626289044,0.02059229130615218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.12,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Fiber_Internet,other,other,binary,binary,-0.127,,-3.62,,***,,,high,"1 if fibre internet installed; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-11.980816767352586,3.089866751929252,100*(exp(b - se^2/2) - 1),|b/t|,-0.07435750898697663,0.020540748338943822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.62,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Fully_furnished,other,other,binary,binary,-0.038,,-1.01,,,,,high,"1 if fully furnished; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-3.7968200979305533,3.6220882924619384,100*(exp(b - se^2/2) - 1),|b/t|,-0.02079924748024231,0.020593314336873576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.01,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Garden,garden,C_exterior,binary,binary,0.054,,1.32,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.460176648417692,4.3178915542707825,100*(exp(b - se^2/2) - 1),|b/t|,0.0271790125560731,0.020590161027328104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.32,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Swimming_pool,pool,C_exterior,binary,binary,-0.226,,-5.57,,***,,,high,"Private or shared (apartment) pool; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-20.293826604449492,3.23670181286252,100*(exp(b - se^2/2) - 1),|b/t|,-0.11398186788735401,0.02046353103902226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.57,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Washing_machine,appliances,B_interior,binary,binary,0.074,,1.99,,**,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,7.606256215718532,4.004206211392978,100*(exp(b - se^2/2) - 1),|b/t|,0.040955171851186335,0.020580488367430316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.99,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Legal status: 1(building permit),other,other,binary,binary,-0.015,,-0.27,,,,,high,"Legal status dummy (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.6407122454378253,5.472844108905903,100*(exp(b - se^2/2) - 1),|b/t|,-0.005561311952991143,0.020597451677744974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.27,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Legal status: 2(red book),other,other,binary,binary,0.067,,1.33,,,,,high,"Land-use-rights certificate (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,6.793954415797554,5.386676468999865,100*(exp(b - se^2/2) - 1),|b/t|,0.027384760335620602,0.02059004536512827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.33,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,17,Legal status: 3 (pink book),other,other,binary,binary,0.155,,5.38,,***,,,high,"Land-use rights + house ownership certificate (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,16.717346044094228,3.364070334038929,100*(exp(b - se^2/2) - 1),|b/t|,0.11014178642223929,0.020472451007851167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.38,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.127,,2.4,,**,,,high,"Orientation the house faces (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,13.382844772357494,6.008248385760365,100*(exp(b - se^2/2) - 1),|b/t|,0.049374355166881816,0.020572647986200756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.087,,1.35,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.863373598870684,7.030223047073344,100*(exp(b - se^2/2) - 1),|b/t|,0.027796245427691938,0.020589811427919956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.35,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 3 (east),sunlight,E_environment,binary,binary,-0.133,,-2.27,,**,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-12.603627793026838,5.1293769713861925,100*(exp(b - se^2/2) - 1),|b/t|,-0.04670591174566217,0.02057529151791285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.27,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.052,,1.13,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.226100345212892,4.847392797406634,100*(exp(b - se^2/2) - 1),|b/t|,0.023269178171121428,0.020592193071788875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.13,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.086,,2.0,,**,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.879926793999227,4.686167211712053,100*(exp(b - se^2/2) - 1),|b/t|,0.04116062893098295,0.020580314465491474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.23,,2.69,,***,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,25.40078674521553,10.76126402541933,100*(exp(b - se^2/2) - 1),|b/t|,0.05532314461545527,0.020566224764109766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.69,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.196,,2.83,,***,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,21.36127660371998,8.425416022810106,100*(exp(b - se^2/2) - 1),|b/t|,0.0581929060754883,0.020562864337628373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.83,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c4,6,4,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.041,,0.58,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.658,t,"SLX model, Eq. (5); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.9252273024305007,7.364816608166321,100*(exp(b - se^2/2) - 1),|b/t|,0.011945854271080587,0.020596300467380323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.58,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,D_TR,flood_risk,E_environment,binary,binary,0.104,,2.21,,**,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,10.837251153020633,5.221649199602741,100*(exp(b - se^2/2) - 1),|b/t|,0.04547398167262297,0.020576462295304514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.21,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.123,,-2.31,,**,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",other,-0.123,0.053246753246753244,not harmonised,|b/t|,-0.04752708042823765,0.020574493691877773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.31,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,CBD (km),cbd_distance,D_location,level,km,-0.127,,-5.76,,***,,,high,"Geodesic distance to District 1 (city centre); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-12.7,2.204861111111111,100*b/1 (X per km->km),|b/t|,-0.11781684737954615,0.02045431378117121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.76,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Traffic_congestion (km),other,other,level,km,0.076,,2.5,,**,,,high,"Distance to nearest traffic congestion point; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,7.6,3.04,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.05142628773229594,0.020570515092918375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Nearest_river (km),water_proximity,E_environment,level,km,-0.044,,-0.81,,,,,high,"Distance to nearest river; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-4.3999999999999995,5.432098765432098,100*b/1 (X per km->km),|b/t|,-0.01668187222611673,0.020594903982860163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.81,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Large_parks (km),green_space,E_environment,level,km,-0.128,,-3.65,,***,,,high,"Distance to large parks; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-12.8,3.5068493150684934,100*b/1 (X per km->km),|b/t|,-0.07497028208647921,0.020539803311364166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.65,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Hospital (1 km),hospital_distance,D_location,binary,binary,-0.152,,-3.03,,***,,,high,"1 if a hospital within 1 km; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-14.209187396453304,4.309116127810256,100*(exp(b - se^2/2) - 1),|b/t|,-0.06229004667708934,0.020557771180557537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.03,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,School (1 km),school_distance,D_location,binary,binary,0.365,,12.49,,***,,,high,"1 if a school within 1 km; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,43.98990357518549,4.209668638706679,100*(exp(b - se^2/2) - 1),|b/t|,0.24915303708821432,0.01994820152827977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.49,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Area (m2),living_area,A_structural,level,m2,0.006,,32.96,,***,,,high,"Floor area in square metres; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.6,0.01820388349514563,100*b/1 (X per m2->m2),|b/t|,0.5616889589003728,0.01704153394722005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.96,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.022,,1.38,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,2.1999999999999997,1.5942028985507246,100*b/1 (per unit count),|b/t|,0.028413446507244575,0.02058945399075694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.38,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.072,,4.56,,***,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,7.199999999999999,1.5789473684210527,100*b/1 (per unit count),|b/t|,0.09351424368598915,0.02050750958026078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.56,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Floor_level,floor_level,A_structural,level,count,-0.009,,-6.34,,***,,,high,"Floor level of the unit (0-38); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-0.8999999999999999,0.14195583596214512,100*b/1 (per unit count),|b/t|,-0.1294903849480182,0.020424350938173217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.34,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,FRW (m),other,other,level,m,0.002,,2.53,,**,,,high,"Front road width in metres; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.2,0.0790513833992095,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.052041741707570054,0.02056985838243876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.53,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Secured_area,other,other,binary,binary,0.156,,4.84,,***,,,high,"1 if inside a secured (gated/guarded) area; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,16.821923532265682,3.7672910678103215,100*(exp(b - se^2/2) - 1),|b/t|,0.09920145808365242,0.020496169025548024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.84,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,-0.046,,-1.1,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-4.5792742580215595,3.9938118418884434,100*(exp(b - se^2/2) - 1),|b/t|,-0.022651733675362246,0.02059248515942022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Fiber_Internet,other,other,binary,binary,-0.126,,-3.58,,***,,,high,"1 if fibre internet installed; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-11.8931023497706,3.102890242869006,100*(exp(b - se^2/2) - 1),|b/t|,-0.07354034726660287,0.02054199644318516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.58,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Fully_furnished,other,other,binary,binary,-0.034,,-0.92,,,,,high,"1 if fully furnished; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-3.408833422655866,3.572112082356003,100*(exp(b - se^2/2) - 1),|b/t|,-0.01894654703720445,0.020594072866526574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.92,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Garden,garden,C_exterior,binary,binary,0.052,,1.28,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.250686033639651,4.2793389539605435,100*(exp(b - se^2/2) - 1),|b/t|,0.02635598715614808,0.020590614965740685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.28,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Swimming_pool,pool,C_exterior,binary,binary,-0.228,,-5.61,,***,,,high,"Private or shared (apartment) pool; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-20.453296733596627,3.235585227138743,100*(exp(b - se^2/2) - 1),|b/t|,-0.11478966143245657,0.020461615228601883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.61,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Washing_machine,appliances,B_interior,binary,binary,0.073,,1.96,,*,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,7.498467835819556,4.0065474078965995,100*(exp(b - se^2/2) - 1),|b/t|,0.04033876954344126,0.020581004869102686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.96,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Legal status: 1(building permit),other,other,binary,binary,-0.013,,-0.22,,,,,high,"Legal status dummy (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.4637679888011124,5.8327698887562445,100*(exp(b - se^2/2) - 1),|b/t|,-0.0045314629197395215,0.020597558726088736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Legal status: 2(red book),other,other,binary,binary,0.064,,1.28,,,,,high,"Land-use-rights certificate (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,6.476061580081294,5.330461993807527,100*(exp(b - se^2/2) - 1),|b/t|,0.02635598715614808,0.020590614965740685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.28,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,17,Legal status: 3 (pink book),other,other,binary,binary,0.155,,5.36,,***,,,high,"Land-use rights + house ownership certificate (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,16.716983877593393,3.3766228352853425,100*(exp(b - se^2/2) - 1),|b/t|,0.10973727718560869,0.02047337260925535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.36,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.123,,2.34,,**,,,high,"Orientation the house faces (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,12.932319133211667,5.944392469082956,100*(exp(b - se^2/2) - 1),|b/t|,0.04814289379973707,0.02057388623920388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.34,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.089,,1.38,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,9.080978492975845,7.049578145872709,100*(exp(b - se^2/2) - 1),|b/t|,0.028413446507244575,0.02058945399075694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.38,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 3 (east),sunlight,E_environment,binary,binary,-0.133,,-2.28,,**,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-12.60231480748929,5.106879703967832,100*(exp(b - se^2/2) - 1),|b/t|,-0.046911212859032304,0.020575093359224698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.28,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.052,,1.13,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.226100345212892,4.847392797406634,100*(exp(b - se^2/2) - 1),|b/t|,0.023269178171121428,0.020592193071788875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.13,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.084,,1.95,,*,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.662024812539748,4.6851706194841745,100*(exp(b - se^2/2) - 1),|b/t|,0.04013329183771814,0.02058117530139392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.95,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.23,,2.69,,***,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,25.40078674521553,10.76126402541933,100*(exp(b - se^2/2) - 1),|b/t|,0.05532314461545527,0.020566224764109766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.69,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.193,,2.8,,***,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,21.000492036823193,8.36022782675679,100*(exp(b - se^2/2) - 1),|b/t|,0.05757807618639667,0.020563598637998812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c5,6,5,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.039,,0.56,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,other,2367.0,0.656,t,"SDM model, Eq. (6); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.7252028856397335,7.241258725420741,100*(exp(b - se^2/2) - 1),|b/t|,0.011533984039828969,0.020596400071123157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,other,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,D_TR,flood_risk,E_environment,binary,binary,0.029,,0.66,,,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,2.843133420101429,4.523229278754362,100*(exp(b - se^2/2) - 1),|b/t|,0.013593272299561506,0.020595867120547735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.66,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.079,,-1.84,,*,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",other,-0.079,0.042934782608695654,not harmonised,|b/t|,-0.03787270672006652,0.020582992782644846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.84,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,CBD (km),cbd_distance,D_location,level,km,-0.138,,-4.75,,***,,,high,"Geodesic distance to District 1 (city centre); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-13.8,2.905263157894737,100*b/1 (X per km->km),|b/t|,-0.09737445759535235,0.020499885809547865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.75,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Traffic_congestion (km),other,other,level,km,0.052,,1.44,,,,,high,"Distance to nearest traffic congestion point; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,5.2,3.6111111111111107,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.029647750475658454,0.02058871560809615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.44,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Nearest_river (km),water_proximity,E_environment,level,km,-0.031,,-0.41,,,,,high,"Distance to nearest river; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-3.1,7.560975609756097,100*b/1 (X per km->km),|b/t|,-0.008444784651066562,0.02059703573430869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.41,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Large_parks (km),green_space,E_environment,level,km,-0.322,,-7.48,,***,,,high,"Distance to large parks; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-32.2,4.304812834224599,100*b/1 (X per km->km),|b/t|,-0.1522745765545354,0.020357563710499387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.48,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Hospital (1 km),hospital_distance,D_location,binary,binary,-0.045,,-0.73,,,,,high,"1 if a hospital within 1 km; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-4.5817170993797784,5.893135161984863,100*(exp(b - se^2/2) - 1),|b/t|,-0.015034672733201368,0.02059544210027585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.73,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,School (1 km),school_distance,D_location,binary,binary,0.372,,9.62,,***,,,high,"1 if a school within 1 km; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,44.95488024091308,5.609516310198078,100*(exp(b - se^2/2) - 1),|b/t|,0.19437142298768795,0.02020492962449979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.62,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Area (m2),living_area,A_structural,level,m2,0.004,,28.22,,***,,,high,"Floor area in square metres; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.4,0.01417434443656981,100*b/1 (X per m2->m2),|b/t|,0.502538864130175,0.017807897382359144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.22,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.057,,4.27,,***,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,5.7,1.3348946135831383,100*b/1 (per unit count),|b/t|,0.08761425550809765,0.02051856100892217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.27,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.007,,0.56,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.7000000000000001,1.25,100*b/1 (per unit count),|b/t|,0.011533984039828969,0.020596400071123157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.56,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Floor_level,floor_level,A_structural,level,count,-0.005,,-3.83,,***,,,high,"Floor level of the unit (0-38); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-0.5,0.13054830287206268,100*b/1 (per unit count),|b/t|,-0.0786451136985841,0.02053397224506112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.83,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,FRW (m),other,other,level,m,0.001,,1.38,,,,,high,"Front road width in metres; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.1,0.07246376811594205,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.028413446507244575,0.02058945399075694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.38,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Secured_area,other,other,binary,binary,0.104,,3.98,,***,,,high,"1 if inside a secured (gated/guarded) area; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,10.922169585410657,2.8994584751562957,100*(exp(b - se^2/2) - 1),|b/t|,0.08170503268252559,0.020528902684051654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.98,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,-0.004,,-0.12,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-0.4545194751389592,3.3200266311466384,100*(exp(b - se^2/2) - 1),|b/t|,-0.0024717248742887133,0.02059770728573928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.12,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Fiber_Internet,other,other,binary,binary,-0.099,,-3.5,,***,,,high,"1 if fibre internet installed; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-9.461955356420926,2.5619579455523227,100*(exp(b - se^2/2) - 1),|b/t|,-0.07190558099215934,0.02054445171204552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Fully_furnished,other,other,binary,binary,-0.011,,-0.35,,,,,high,"1 if fully furnished; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.142807528944323,3.108475161865445,100*(exp(b - se^2/2) - 1),|b/t|,-0.007209032237601268,0.020597234964575054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Garden,garden,C_exterior,binary,binary,0.032,,0.98,,,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.196720535362796,3.3714857316085496,100*(exp(b - se^2/2) - 1),|b/t|,0.020181703530428006,0.020593575031048982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.98,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Swimming_pool,pool,C_exterior,binary,binary,-0.135,,-3.98,,***,,,high,"Private or shared (apartment) pool; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-12.678656593955463,2.9636092481880567,100*(exp(b - se^2/2) - 1),|b/t|,-0.08170503268252559,0.020528902684051654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.98,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Washing_machine,appliances,B_interior,binary,binary,0.074,,2.48,,**,,,high,"; spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,7.632754546796328,3.2130525647871075,100*(exp(b - se^2/2) - 1),|b/t|,0.05101595248436874,0.02057094858240675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.48,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Legal status: 1(building permit),other,other,binary,binary,-0.056,,-1.2,,,,,high,"Legal status dummy (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-5.548989081913458,4.412515967488516,100*(exp(b - se^2/2) - 1),|b/t|,-0.02470977722860051,0.02059148102383376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Legal status: 2(red book),other,other,binary,binary,-0.018,,-0.45,,,,,high,"Land-use-rights certificate (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-1.8624382259849859,3.9286441294332026,100*(exp(b - se^2/2) - 1),|b/t|,-0.009268598448727924,0.020596885441617606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,17,Legal status: 3 (pink book),other,other,binary,binary,0.051,,2.17,,**,,,high,"Land-use rights + house ownership certificate (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.20323037701147,2.473201269928267,100*(exp(b - se^2/2) - 1),|b/t|,0.04465257933256283,0.02057722549887688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.17,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.014,,0.34,,,,,high,"Orientation the house faces (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,1.3239120396641546,4.175699536805556,100*(exp(b - se^2/2) - 1),|b/t|,0.0070030701379996535,0.020597265111763685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.053,,1.03,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.303463468073333,5.425705940905424,100*(exp(b - se^2/2) - 1),|b/t|,0.021210930265106457,0.020593136179715005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.03,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 3 (east),sunlight,E_environment,binary,binary,-0.069,,-1.46,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-6.771504633213943,4.410927460643558,100*(exp(b - se^2/2) - 1),|b/t|,-0.03005915527406505,0.02058846251648291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.46,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.055,,1.47,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.580135855441659,3.9530431161328017,100*(exp(b - se^2/2) - 1),|b/t|,0.03026485195774136,0.020588334665130176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.47,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.003,,0.1,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,0.2553254015325246,3.0090135135101312,100*(exp(b - se^2/2) - 1),|b/t|,0.0020597726511340734,0.02059772651134073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.116,,1.7,,*,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,12.038454745399285,7.66279536279162,100*(exp(b - se^2/2) - 1),|b/t|,0.03499476177307857,0.020585153984163867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.108,,1.96,,**,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,11.235777317560313,6.138630433762168,100*(exp(b - se^2/2) - 1),|b/t|,0.04033876954344126,0.020581004869102686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.96,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c6,6,6,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.061,,1.07,,,,,high,"Orientation (base: unknown); spatial Durbin/SLX type model, coefficient of X (not W*X); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,spatial_error,2367.0,0.571,t,"SDEM model, Eq. (7); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,6.1173071292907055,6.0595171743382155,100*(exp(b - se^2/2) - 1),|b/t|,0.022034263258132666,0.020592769400123986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.07,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,D_TR,flood_risk,E_environment,binary,binary,0.184,,4.52,,***,,,high,"Treatment group: house within radius of roads inundated by the 30 Sep 2017 pluvial flood (flood exposure indicator); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,20.102028231234435,4.893161757737157,100*(exp(b - se^2/2) - 1),|b/t|,0.09270102345841659,0.02050907598637535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.52,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,D_POST X D_TR,flood_risk,E_environment,other,binary,-0.091,,-2.08,,**,,,high,"Difference-in-differences interaction: post-flood x flood-affected treatment group; authors' headline flood discount (~9%); interaction term (x_transform other); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",other,-0.091,0.04375,not harmonised,|b/t|,-0.04280409543216274,0.020578892034693624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,CBD (km),cbd_distance,D_location,level,km,-0.191,,-6.54,,***,,,high,"Geodesic distance to District 1 (city centre); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-19.1,2.9204892966360854,100*b/1 (X per km->km),|b/t|,-0.13350354367644793,0.02041338588324892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.54,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Traffic_congestion (km),other,other,level,km,0.163,,4.42,,***,,,high,"Distance to nearest traffic congestion point; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,16.3,3.6877828054298645,100*b (unit as reported: km; no harmonised unit defined for this characteristic),|b/t|,0.09066716453270038,0.020512933152194657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.42,2357.0,km,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Nearest_river (km),water_proximity,E_environment,level,km,0.067,,0.91,,,,,high,"Distance to nearest river; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,6.7,7.362637362637363,100*b/1 (X per km->km),|b/t|,0.018740679034882125,0.020594152785584754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.91,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Large_parks (km),green_space,E_environment,level,km,-0.483,,-11.72,,***,,,high,"Distance to large parks; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-48.3,4.121160409556314,100*b/1 (X per km->km),|b/t|,-0.234664926675515,0.02002260466514633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.72,2357.0,km,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Hospital (1 km),hospital_distance,D_location,binary,binary,0.117,,2.7,,***,,,high,"1 if a hospital within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,12.306450175576877,4.87118419532566,100*(exp(b - se^2/2) - 1),|b/t|,0.05552817389934227,0.020565990333089725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,School (1 km),school_distance,D_location,binary,binary,0.355,,11.03,,***,,,high,"1 if a school within 1 km; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,42.54421759389213,4.5901553242966955,100*(exp(b - se^2/2) - 1),|b/t|,0.22154755139343865,0.020085906744645395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.03,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Area (m2),living_area,A_structural,level,m2,0.004,,28.67,,***,,,high,"Floor area in square metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.4,0.013951866062085802,100*b/1 (X per m2->m2),|b/t|,0.5084925019342756,0.0177360482014048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.67,2357.0,m2,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Number_of_bedrooms,bedrooms,A_structural,level,count,0.057,,4.23,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,5.7,1.3475177304964538,100*b/1 (per unit count),|b/t|,0.08679972571386285,0.020520029719589324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.23,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,16,Number_of_bathrooms,bathrooms,A_structural,level,count,0.014,,1.06,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,1.4000000000000001,1.320754716981132,100*b/1 (per unit count),|b/t|,0.021828434147279824,0.020592862403094176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.06,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Floor_level,floor_level,A_structural,level,count,-0.005,,-3.97,,***,,,high,"Floor level of the unit (0-38); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,-0.5,0.12594458438287154,100*b/1 (per unit count),|b/t|,-0.08150110897979958,0.02052924659440795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.97,2357.0,count,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,FRW (m),other,other,level,m,0.001,,1.7,,*,,,high,"Front road width in metres; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",semi_elasticity,0.1,0.05882352941176471,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/t|,0.03499476177307857,0.020585153984163867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,2357.0,m,unit_not_harmonisable,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Secured_area,other,other,binary,binary,0.116,,4.37,,***,,,high,"1 if inside a secured (gated/guarded) area; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,12.260030083907903,2.9809501411317516,100*(exp(b - se^2/2) - 1),|b/t|,0.08964980786222577,0.020514830174422374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.37,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Balcony_or_Terrace,terrace_balcony,C_exterior,binary,binary,0.007,,0.21,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,0.6465254555727551,3.3567485242228288,100*(exp(b - se^2/2) - 1),|b/t|,0.004325491278010889,0.020597577514337568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Fiber_Internet,other,other,binary,binary,-0.109,,-3.8,,***,,,high,"1 if fibre internet installed; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-10.363841431944643,2.5722004080870438,100*(exp(b - se^2/2) - 1),|b/t|,-0.078032860266136,0.02053496322793053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Fully_furnished,other,other,binary,binary,0.006,,0.19,,,,,high,"1 if fully furnished; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,0.5516545448567722,3.176899061223363,100*(exp(b - se^2/2) - 1),|b/t|,0.003913546369177626,0.020597612469355927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Garden,garden,C_exterior,binary,binary,0.033,,0.98,,,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,3.296473252778087,3.4803232443839884,100*(exp(b - se^2/2) - 1),|b/t|,0.020181703530428006,0.020593575031048982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.98,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Swimming_pool,pool,C_exterior,binary,binary,-0.185,,-5.49,,***,,,high,"Private or shared (apartment) pool; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-16.936745430952037,2.8006246359315714,100*(exp(b - se^2/2) - 1),|b/t|,-0.11236560392612475,0.020467323119512708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.49,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Washing_machine,appliances,B_interior,binary,binary,0.08,,2.62,,***,,,high,"; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,8.278218585820984,3.307746771526591,100*(exp(b - se^2/2) - 1),|b/t|,0.053887745104861835,0.020567841643077036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.62,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Legal status: 1(building permit),other,other,binary,binary,-0.042,,-0.9,,,,,high,"Legal status dummy (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-4.217375382785726,4.4747256426715944,100*(exp(b - se^2/2) - 1),|b/t|,-0.018534808648913253,0.020594231832125835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Legal status: 2(red book),other,other,binary,binary,0.042,,1.04,,,,,high,"Land-use-rights certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,4.204438815409728,4.211689241108852,100*(exp(b - se^2/2) - 1),|b/t|,0.02141676762464164,0.020593045792924654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.04,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,17,Legal status: 3 (pink book),other,other,binary,binary,0.075,,3.16,,***,,,high,"Land-use rights + house ownership certificate (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,7.758060154477553,2.5582693454540304,100*(exp(b - se^2/2) - 1),|b/t|,0.06495151338781506,0.020554276388549068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.16,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,False,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 1 (north),sunlight,E_environment,binary,binary,0.028,,0.64,,,,,high,"Orientation the house faces (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,2.7411948422199517,4.499231119343734,100*(exp(b - se^2/2) - 1),|b/t|,0.013181427645894146,0.020595980696709603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.64,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 2 (northeast),sunlight,E_environment,binary,binary,0.053,,1.01,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.29788781340903,5.533145662507511,100*(exp(b - se^2/2) - 1),|b/t|,0.02079924748024231,0.020593314336873576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.01,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 3 (east),sunlight,E_environment,binary,binary,-0.049,,-1.04,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,-4.8875138149258035,4.486238014925648,100*(exp(b - se^2/2) - 1),|b/t|,-0.02141676762464164,0.020593045792924654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.04,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 4 (southeast),sunlight,E_environment,binary,binary,0.056,,1.47,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,5.6830544277114425,4.028943557091853,100*(exp(b - se^2/2) - 1),|b/t|,0.03026485195774136,0.020588334665130176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.47,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 5 (south),sunlight,E_environment,binary,binary,0.016,,0.46,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,1.5514199864070433,3.534360644890765,100*(exp(b - se^2/2) - 1),|b/t|,0.009474549014873825,0.020596845684508314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 6 (southwest),sunlight,E_environment,binary,binary,0.126,,1.8,,*,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,13.150657845664181,7.939975177981175,100*(exp(b - se^2/2) - 1),|b/t|,0.03705052975432005,0.020583627641288915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 7 (west),sunlight,E_environment,binary,binary,0.13,,2.32,,**,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,13.704190331828192,6.38136594104314,100*(exp(b - se^2/2) - 1),|b/t|,0.047732357608403116,0.02057429207258755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.32,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T6-c7,6,7,18,Direction: 8 (northwest),sunlight,E_environment,binary,binary,0.069,,1.2,,,,,high,"Orientation (base: unknown); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,0.599,t,"SAC model, Eq. (8); listings July 2017-January 2018 (Oct. missing), District 7 HCMC, houses on arterial roads; direct (own-coefficient) estimates; pseudo R2",pct_premium,6.966645438105812,6.160758202602992,100*(exp(b - se^2/2) - 1),|b/t|,0.02470977722860051,0.02059148102383376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T7-total,7,Total effect,22,D_TR,flood_risk,E_environment,binary,binary,0.191,,4.52,,***,,,high,"Total effect of treatment-group indicator (direct 0.184 + spillover 0.007); stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,,t,SAC decomposition: total effect (direct + spillover) of flood DiD variables; same sample as Table 6,pct_premium,20.937922202209847,5.114994587334695,100*(exp(b - se^2/2) - 1),|b/t|,0.09270102345841659,0.02050907598637535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.52,2357.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T7-total,7,Total effect,22,D_POST × D_TR,flood_risk,E_environment,other,binary,-0.094,,-2.08,,**,,,high,"Total effect of DiD interaction (direct -0.091 + spillover -0.003); interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,2367.0,,t,SAC decomposition: total effect (direct + spillover) of flood DiD variables; same sample as Table 6,other,-0.094,0.04519230769230769,not harmonised,|b/t|,-0.04280409543216274,0.020578892034693624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08,2357.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c5m,8,5 months,24,D_TR = 1,flood_risk,E_environment,binary,binary,0.324,,12.9,,***,,,high,"Flood-affected treatment group indicator; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,3780.0,0.747,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 5 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",pct_premium,38.221126993484745,3.47269556206753,100*(exp(b - se^2/2) - 1),|b/t|,0.20560778150316553,0.015938587713423682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.9,3770.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,True
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c5m,8,5 months,24,D_TR = 1 X D_POST_5m = 1,flood_risk,E_environment,other,binary,0.03,,1.28,,,,,high,"DiD interaction (post window x treatment), interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,3780.0,0.747,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 5 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",other,0.03,0.0234375,not harmonised,|b/t|,0.020842266527030178,0.016283020724242328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.28,3770.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c6m,8,6 months,24,D_TR = 1,flood_risk,E_environment,binary,binary,0.306,,12.54,,***,,,high,"Flood-affected treatment group indicator; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,4389.0,0.751,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 6 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",pct_premium,35.757805913717775,3.3137367288967714,100*(exp(b - se^2/2) - 1),|b/t|,0.1861868349566933,0.014847435004520999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.54,4379.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c6m,8,6 months,24,D_TR = 1 X D_POST_6m = 1,flood_risk,E_environment,other,binary,0.036,,1.59,,,,,high,"DiD interaction (post window x treatment), interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,4389.0,0.751,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 6 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",other,0.036,0.022641509433962263,not harmonised,|b/t|,0.02402062612934297,0.015107312031033316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,4379.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c7m,8,7 months,24,D_TR = 1,flood_risk,E_environment,binary,binary,0.3,,12.18,,***,,,high,"Flood-affected treatment group indicator; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,4850.0,0.752,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 7 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",pct_premium,34.94494145730407,3.3247753881182343,100*(exp(b - se^2/2) - 1),|b/t|,0.17245219443148344,0.014158636652831154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.18,4840.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c7m,8,7 months,24,D_TR = 1 X D_POST_7m = 1,flood_risk,E_environment,other,binary,0.045,,1.96,,**,,,high,"DiD interaction (post window x treatment), interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,4850.0,0.752,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 7 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",other,0.045,0.02295918367346939,not harmonised,|b/t|,0.028161845075185328,0.014368288303665985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.96,4840.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c8m,8,8 months,24,D_TR = 1,flood_risk,E_environment,binary,binary,0.323,,13.87,,***,,,high,"Flood-affected treatment group indicator; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,5644.0,0.764,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 8 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",pct_premium,38.08908607853976,3.2166453377254403,100*(exp(b - se^2/2) - 1),|b/t|,0.1817093076055691,0.013100887354402966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.87,5634.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c8m,8,8 months,24,D_TR = 1 X D_POST_8m = 1,flood_risk,E_environment,other,binary,0.051,,2.25,,**,,,high,"DiD interaction (post window x treatment), interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,5644.0,0.764,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 8 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",other,0.051,0.022666666666666665,not harmonised,|b/t|,0.02996257016633534,0.013316697851704596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,5634.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c9m,8,9 months,24,D_TR = 1,flood_risk,E_environment,binary,binary,0.32,,13.96,,***,,,high,"Flood-affected treatment group indicator; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,6117.0,0.768,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 9 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",pct_premium,37.67660078635,3.1567398609420216,100*(exp(b - se^2/2) - 1),|b/t|,0.17585320932154466,0.012596934765153629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.96,6107.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c9m,8,9 months,24,D_TR = 1 X D_POST_9m = 1,flood_risk,E_environment,other,binary,0.05,,2.25,,**,,,high,"DiD interaction (post window x treatment), interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,6117.0,0.768,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 9 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",other,0.05,0.022222222222222223,not harmonised,|b/t|,0.02877985654499521,0.012791047353331204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,6107.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c10m,8,10 months,24,D_TR = 1,flood_risk,E_environment,binary,binary,0.319,,13.98,,***,,,high,"Flood-affected treatment group indicator; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,6183.0,0.77,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 10 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",pct_premium,37.539321161728246,3.1392322792920355,100*(exp(b - se^2/2) - 1),|b/t|,0.1751824625822494,0.012530934376412688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.98,6173.0,binary,,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,True,True,False
819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d,819ec939f0241cca8513d3a72e45a56254da737d:T8-c10m,8,10 months,24,D_TR = 1 X D_POST_10m = 1,flood_risk,E_environment,other,binary,0.05,,2.23,,**,,,high,"DiD interaction (post window x treatment), interaction term; stars: * p<0.10, ** p<0.05, *** p<0.01",Ln_Price: natural log of listing price (VND),log,SAC,6183.0,0.77,t,"Price-recovery SAC model, Panel A direct effects; post period extended to 10 months after the 30 Sep 2017 flood; other controls (location, structure, neighbourhood, facilities, direction, legal status) included but not reported; pseudo R2",other,0.05,0.022421524663677132,not harmonised,|b/t|,0.028371471651500398,0.012722633027578652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.23,6173.0,binary,nonstandard_x_transform,2022.0,Vietnam,Asia,Lower middle income,2017.0,2018.0,mixed,listing,journal,False,False,unknown,,2017.5,spatial,journal,False,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,Ln_PARCEL_AREA,lot_size,A_structural,log,m2,0.761,,9.7,,***,,,high,Log parcel area (sq m),Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,elasticity,0.761,0.07845360824742269,b (log-log),|b/t|,0.7997977173045793,0.08245337291799784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.7,53.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,Ln_PARCEL_AREA,lot_size,A_structural,log,m2,0.913,,8.74,,***,,,high,"Log parcel area (sq m); model includes Ln_PARCEL_AREA x DESIGNATED interaction, main effect refers to non-designated buildings",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,elasticity,0.913,0.10446224256292906,b (log-log),|b/t|,0.7683606300861865,0.08791311557050191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.74,53.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,True
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M3,3,Model 3,8,Ln_PARCEL_AREA,lot_size,A_structural,log,m2,1.014,,11.49,,***,,,high,"Log parcel area (sq m); model includes Ln_PARCEL_AREA x DESIGNATED interaction, main effect refers to non-designated buildings",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.851,t,"All buildings, stepwise; includes interactions with DESIGNATED; adj. R2",elasticity,1.014,0.08825065274151436,b (log-log),|b/t|,0.8447157134217734,0.0735174685310508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.49,53.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,True
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,Ln_PARCEL_AREA,lot_size,A_structural,log,m2,0.881,,8.22,,***,,,high,Log parcel area (sq m),Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,elasticity,0.881,0.10717761557177614,b (log-log),|b/t|,0.888618011652937,0.10810438097967599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.22,18.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M5,3,Model 5,8,Ln_PARCEL_AREA,lot_size,A_structural,log,m2,0.901,,9.13,,***,,,high,Log parcel area (sq m),Ln nominal selling price of the building (NIS),log,OLS,28.0,0.872,t,"Non-designated buildings only, stepwise; adj. R2",elasticity,0.901,0.09868565169769988,b (log-log),|b/t|,0.9068680843847836,0.0993283772601077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.13,18.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,Ln_BUILD_%_USED,other,other,log,share,0.572,,5.82,,***,,,high,Log development rights used (gross built-up area / plot area x 100; 0 if best use is demolish-and-rebuild),Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,elasticity,0.572,0.09828178694158074,b (log-log),|b/t|,0.6244275768568365,0.10728996165925025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.82,53.0,share,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,Ln_BUILD_%_USED,other,other,log,share,0.351,,2.13,,**,,,high,Log development rights used (gross built-up area / plot area x 100; 0 if best use is demolish-and-rebuild); model includes interaction with DESIGNATED,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,elasticity,0.351,0.1647887323943662,b (log-log),|b/t|,0.2808059940202674,0.13183380000951525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.13,53.0,share,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,Ln_BUILD_%_USED,other,other,log,share,0.273,,1.28,,,,,high,Log development rights used (gross built-up area / plot area x 100; 0 if best use is demolish-and-rebuild),Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,elasticity,0.273,0.21328125,b (log-log),|b/t|,0.28883972234527805,0.22565603308224846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.28,18.0,share,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,UNEMPLOYMENT,unemployment,F_neighbourhood,level,share,-0.203,,-8.8,,***,,,high,Unemployment rate in Tel Aviv in the transaction year (percent),Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,semi_elasticity,-20.3,2.3068181818181817,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,-0.7705082214680729,0.0875577524395537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.8,53.0,share,unit_not_harmonisable,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,UNEMPLOYMENT,unemployment,F_neighbourhood,level,share,-0.211,,-9.88,,***,,,high,Unemployment rate in Tel Aviv in the transaction year (percent),Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,semi_elasticity,-21.099999999999998,2.1356275303643724,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,-0.8050515612258508,0.081482951541078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.88,53.0,share,unit_not_harmonisable,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M3,3,Model 3,8,UNEMPLOYMENT,unemployment,F_neighbourhood,level,share,-0.196,,-9.06,,***,,,high,Unemployment rate in Tel Aviv in the transaction year (percent),Ln nominal selling price of the building (NIS),log,OLS,63.0,0.851,t,"All buildings, stepwise; includes interactions with DESIGNATED; adj. R2",semi_elasticity,-19.6,2.163355408388521,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,-0.7795193221123231,0.08603966027729834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.06,53.0,share,unit_not_harmonisable,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,UNEMPLOYMENT,unemployment,F_neighbourhood,level,share,-0.176,,-5.32,,***,,,high,Unemployment rate in Tel Aviv in the transaction year (percent),Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,semi_elasticity,-17.599999999999998,3.308270676691729,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,-0.7818255896520991,0.1469596973030262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.32,18.0,share,unit_not_harmonisable,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M5,3,Model 5,8,UNEMPLOYMENT,unemployment,F_neighbourhood,level,share,-0.152,,-5.25,,***,,,high,Unemployment rate in Tel Aviv in the transaction year (percent),Ln nominal selling price of the building (NIS),log,OLS,28.0,0.872,t,"Non-designated buildings only, stepwise; adj. R2",semi_elasticity,-15.2,2.895238095238095,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/t|,-0.7777777777777778,0.14814814814814814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.25,18.0,share,unit_not_harmonisable,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.35,,4.59,,***,,,high,"Building exposed to positive externalities, e.g. located on a pedestrian street",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,41.494797140454985,10.820776514327667,100*(exp(b - se^2/2) - 1),|b/t|,0.5333313137916339,0.11619418601124924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.59,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.367,,5.11,,***,,,high,"Building exposed to positive externalities, e.g. located on a pedestrian street",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,pct_premium,43.968011508843794,10.36647820577828,100*(exp(b - se^2/2) - 1),|b/t|,0.574512448923743,0.11242905067000838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.11,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M3,3,Model 3,8,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.383,,5.15,,***,,,high,"Building exposed to positive externalities, e.g. located on a pedestrian street",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.851,t,"All buildings, stepwise; includes interactions with DESIGNATED; adj. R2",pct_premium,46.26277269450423,10.907527874100905,100*(exp(b - se^2/2) - 1),|b/t|,0.5775136008735541,0.1121385632764183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.15,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.417,,2.46,,**,,,high,"Building exposed to positive externalities, e.g. located on a pedestrian street",Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,pct_premium,49.57575603839051,25.721823085119116,100*(exp(b - se^2/2) - 1),|b/t|,0.5016064598495418,0.20390506497948854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.46,18.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M5,3,Model 5,8,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.359,,2.25,,**,,,high,"Building exposed to positive externalities, e.g. located on a pedestrian street",Ln nominal selling price of the building (NIS),log,OLS,28.0,0.872,t,"Non-designated buildings only, stepwise; adj. R2",pct_premium,41.378571728624756,22.84670896729891,100*(exp(b - se^2/2) - 1),|b/t|,0.4685212856658182,0.20823168251814142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,18.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.111,,-1.55,,,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,-10.735311688827842,6.4089162020079,100*(exp(b - se^2/2) - 1),|b/t|,-0.20824137224001543,0.1343492724129132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.55,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.12,,-1.71,,*,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,pct_premium,-11.526073755682742,6.224003064681807,100*(exp(b - se^2/2) - 1),|b/t|,-0.22866337464643094,0.13372127172305903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.71,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.079,,-0.87,,,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,pct_premium,-7.976180743006145,8.390707359903661,100*(exp(b - se^2/2) - 1),|b/t|,-0.20088093498583928,0.23089762642050493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.87,18.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,FAÇADE,other,other,binary,binary,0.147,,2.4,,**,,,high,Wide facade or corner building dummy,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,15.618318097777761,7.094918023557865,100*(exp(b - se^2/2) - 1),|b/t|,0.3130908301713296,0.13045451257138735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,FAÇADE,other,other,binary,binary,0.164,,2.77,,***,,,high,Wide facade or corner building dummy,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,pct_premium,17.615111225142055,6.975709302354024,100*(exp(b - se^2/2) - 1),|b/t|,0.35561690041605126,0.1283815524967694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.77,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M3,3,Model 3,8,FAÇADE,other,other,binary,binary,0.195,,3.24,,***,,,high,Wide facade or corner building dummy,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.851,t,"All buildings, stepwise; includes interactions with DESIGNATED; adj. R2",pct_premium,21.31118944399737,7.314371677947453,100*(exp(b - se^2/2) - 1),|b/t|,0.4065990455636216,0.12549353258136467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.24,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,FAÇADE,other,other,binary,binary,0.252,,2.79,,**,,,high,Wide facade or corner building dummy,Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,pct_premium,28.135859285593455,11.620867433540495,100*(exp(b - se^2/2) - 1),|b/t|,0.5494500469684042,0.19693550070552124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.79,18.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M5,3,Model 5,8,FAÇADE,other,other,binary,binary,0.283,,3.39,,***,,,high,Wide facade or corner building dummy,Ln nominal selling price of the building (NIS),log,OLS,28.0,0.872,t,"Non-designated buildings only, stepwise; adj. R2",pct_premium,32.24888792742702,11.078783504255323,100*(exp(b - se^2/2) - 1),|b/t|,0.6242331802964942,0.1841395812084054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.39,18.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,NOT_FOR_DEMOLITION,other,other,binary,binary,-3.0,,-5.79,,***,,,high,Dummy = 1 for designated buildings and for non-designated buildings whose highest and best use is not demolition,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,-95.64668164493537,2.579640848075852,100*(exp(b - se^2/2) - 1),|b/t|,-0.622457950489226,0.1075056909307817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.79,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M2,3,Model 2,8,NOT_FOR_DEMOLITION,other,other,binary,binary,-1.904,,-2.28,,**,,,high,Dummy = 1 for designated buildings and for non-designated buildings whose highest and best use is not demolition,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.869,t,All buildings; includes interactions Ln_PARCEL_AREA x DESIGNATED and Ln_BUILD_%_USED x DESIGNATED; adj. R2,pct_premium,-89.48834715533324,12.440430332916748,100*(exp(b - se^2/2) - 1),|b/t|,-0.2988679360816127,0.1310824281059705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.28,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M4,3,Model 4,8,NOT_FOR_DEMOLITION,other,other,binary,binary,-1.489,,-1.35,,,,,high,Dummy = 1 for designated buildings and for non-designated buildings whose highest and best use is not demolition,Ln nominal selling price of the building (NIS),log,OLS,28.0,0.876,t,Non-designated buildings only; adj. R2,pct_premium,-87.72080290848767,24.88263939468211,100*(exp(b - se^2/2) - 1),|b/t|,-0.3032177042381441,0.22460570684306969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.35,18.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,DESIGNATED,other,other,binary,binary,0.074,,0.88,,,,,high,Heritage preservation designation dummy (all types),Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,7.300632627167691,9.054966318945485,100*(exp(b - se^2/2) - 1),|b/t|,0.12000376877257947,0.1363679190597494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.88,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,DESIGNATED_LOCAL_REGULAR,other,other,binary,binary,-0.093,,-1.05,,,,,high,Designated for regular (grade 2) preservation,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,-9.23736092432097,8.070571002622959,100*(exp(b - se^2/2) - 1),|b/t|,-0.1427514841598556,0.13595379443795771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.05,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T3-M1,3,Model 1,8,BUFFER_DESIGNATED,other,other,binary,binary,0.029,,0.39,,,,,high,More than 25 designated buildings within 100 m,Ln nominal selling price of the building (NIS),log,OLS,63.0,0.861,t,All buildings (designated + non-designated); adj. R2; year dummy 2009,pct_premium,2.6582548278979434,7.654695702507382,100*(exp(b - se^2/2) - 1),|b/t|,0.05349391620870837,0.13716388771463683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39,53.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,Ln_TOTAL_APT_AREA,living_area,A_structural,log,m2,0.702,,22.84,,***,,,high,Log total apartment area (sq m),Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,elasticity,0.702,0.03073555166374781,b (log-log),|b/t|,0.8347417977944027,0.0365473641766376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.84,227.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,Ln_TOTAL_APT_AREA,living_area,A_structural,log,m2,0.7,,22.79,,***,,,high,Log total apartment area (sq m),Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",elasticity,0.7,0.030715225976305396,b (log-log),|b/t|,0.8341864584989062,0.036603179398811164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.79,227.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,Ln_TOTAL_APT_AREA,living_area,A_structural,log,m2,0.708,,16.37,,***,,,high,Log total apartment area (sq m),Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,elasticity,0.708,0.0432498472816127,b (log-log),|b/t|,0.8144612252404024,0.0497532819328285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.37,136.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,Ln_TOTAL_APT_AREA,living_area,A_structural,log,m2,0.721,,17.07,,***,,,high,Log total apartment area (sq m),Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",elasticity,0.721,0.042237844171060336,b (log-log),|b/t|,0.8257031361677261,0.04837159555757034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.07,136.0,m2,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,PARKING,parking,A_structural,binary,binary,0.116,,4.43,,***,,,high,Parking spot within the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,12.261094170060293,2.9405760985882066,100*(exp(b - se^2/2) - 1),|b/t|,0.28208842443236587,0.06367684524432637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.43,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,PARKING,parking,A_structural,binary,binary,0.126,,5.01,,***,,,high,Parking spot within the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",pct_premium,13.392350404509612,2.8526856926878477,100*(exp(b - se^2/2) - 1),|b/t|,0.31553767189726445,0.06298157123697894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.01,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,PARKING,parking,A_structural,binary,binary,0.123,,4.43,,***,,,high,Parking spot within the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,pct_premium,13.044860080493436,3.139927399018988,100*(exp(b - se^2/2) - 1),|b/t|,0.35511104760481876,0.08016050736000424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.43,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,PARKING,parking,A_structural,binary,binary,0.128,,4.67,,***,,,high,Parking spot within the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",pct_premium,13.612616348209695,3.115177394972671,100*(exp(b - se^2/2) - 1),|b/t|,0.37175013444658306,0.07960388317913984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.67,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,ELEVATOR1,accessibility,B_interior,binary,binary,0.07,,2.93,,***,,,high,Elevator in building when apartment is on 2nd floor or higher,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,7.220214738838737,2.562306235078333,100*(exp(b - se^2/2) - 1),|b/t|,0.19089471066534605,0.06515177838407715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.93,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,ELEVATOR1,accessibility,B_interior,binary,binary,0.085,,3.98,,***,,,high,Elevator in building when apartment is on 2nd floor or higher,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",pct_premium,8.846880642932131,2.3251495143056258,100*(exp(b - se^2/2) - 1),|b/t|,0.25540100524668813,0.06417110684590153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.98,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,ELEVATOR1,accessibility,B_interior,binary,binary,0.044,,1.48,,,,,high,Elevator in building when apartment is on 2nd floor or higher,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,pct_premium,4.452064952062296,3.1067042983169952,100*(exp(b - se^2/2) - 1),|b/t|,0.12589914432601668,0.08506698940947073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,ELEVATOR1,accessibility,B_interior,binary,binary,0.056,,2.01,,**,,,high,Elevator in building when apartment is on 2nd floor or higher,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",pct_premium,5.718729998214389,2.9465408104104593,100*(exp(b - se^2/2) - 1),|b/t|,0.16985168175475968,0.08450332425609935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,FLOOR,floor_level,A_structural,level,count,0.023,,2.93,,***,,,high,Floor on which the apartment is located,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,semi_elasticity,2.3,0.7849829351535836,100*b/1 (per unit count),|b/t|,0.19089471066534605,0.06515177838407715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.93,227.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,FLOOR,floor_level,A_structural,level,count,0.023,,2.92,,***,,,high,Floor on which the apartment is located,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",semi_elasticity,2.3,0.7876712328767124,100*b/1 (per unit count),|b/t|,0.19026681769766302,0.06515986907454213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.92,227.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,FLOOR,floor_level,A_structural,level,count,0.021,,2.26,,**,,,high,Floor on which the apartment is located,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,semi_elasticity,2.1,0.929203539823009,100*b/1 (per unit count),|b/t|,0.19025375472848016,0.08418307731348679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.26,136.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,FLOOR,floor_level,A_structural,level,count,0.019,,2.04,,**,,,high,Floor on which the apartment is located,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",semi_elasticity,1.9,0.9313725490196079,100*b/1 (per unit count),|b/t|,0.17231204797842015,0.08446669018550007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.04,136.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,Ln_UNITS_PER_DUNAM,density,F_neighbourhood,log,count,-0.112,,-3.16,,***,,,high,Log number of apartments per dunam (1 dunam = 1000 sq m) of the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,elasticity,-0.112,0.035443037974683546,b (log-log),|b/t|,-0.20527029373215963,0.06495895371270874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.16,227.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,Ln_UNITS_PER_DUNAM,density,F_neighbourhood,log,count,-0.109,,-3.08,,***,,,high,Log number of apartments per dunam (1 dunam = 1000 sq m) of the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",elasticity,-0.109,0.03538961038961039,b (log-log),|b/t|,-0.20028463413412165,0.06502747861497456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.08,227.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,Ln_UNITS_PER_DUNAM,density,F_neighbourhood,log,count,-0.154,,-3.46,,***,,,high,Log number of apartments per dunam (1 dunam = 1000 sq m) of the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,elasticity,-0.154,0.04450867052023121,b (log-log),|b/t|,-0.28443753997644194,0.08220738149608148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.46,136.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,True
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,Ln_UNITS_PER_DUNAM,density,F_neighbourhood,log,count,-0.14,,-3.16,,***,,,high,Log number of apartments per dunam (1 dunam = 1000 sq m) of the building's plot,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",elasticity,-0.14,0.04430379746835443,b (log-log),|b/t|,-0.2615363587998367,0.08276467050627744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.16,136.0,count,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,DESIGNATED,other,other,binary,binary,-0.025,,-1.15,,,,,high,Heritage preservation designation dummy (all types),Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,-2.4920521499721904,2.1202389391920278,100*(exp(b - se^2/2) - 1),|b/t|,-0.0761068040157567,0.06617982957891887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.15,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,BUILD_PRESERVED,renovation,A_structural,binary,binary,0.129,,3.14,,***,,,high,Designated building preserved (restored) before the transaction,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,13.673043436421084,4.673949873164948,100*(exp(b - se^2/2) - 1),|b/t|,0.2040253597538615,0.064976229220975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.14,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,BUILD_PRESERVED,renovation,A_structural,binary,binary,0.105,,2.8,,***,,,high,Designated building preserved (restored) before the transaction,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",pct_premium,10.992991645266281,4.165164788833894,100*(exp(b - se^2/2) - 1),|b/t|,0.18271407158090092,0.06525502556460747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,BUILD_RENOVATED,renovation,A_structural,binary,binary,0.022,,0.8,,,,,high,Non-designated building renovated before the transaction,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,2.1857321609674685,2.8111704072937047,100*(exp(b - se^2/2) - 1),|b/t|,0.053023171217831094,0.06627896402228886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,BUILD_RENOVATED,renovation,A_structural,binary,binary,0.033,,1.15,,,,,high,Non-designated building renovated before the transaction,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,pct_premium,3.312509316722223,2.965840677822877,100*(exp(b - se^2/2) - 1),|b/t|,0.09813569210756053,0.08533538444135698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.15,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.08,,3.65,,***,,,high,"Positive location externality, e.g. pedestrian street",Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,8.302689861289082,2.374327819561553,100*(exp(b - se^2/2) - 1),|b/t|,0.23544834814227883,0.06450639675130927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.65,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.075,,3.46,,***,,,high,"Positive location externality, e.g. pedestrian street",Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",pct_premium,7.763095222281491,2.336454084287496,100*(exp(b - se^2/2) - 1),|b/t|,0.22382209327568586,0.0646884662646491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.46,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.074,,2.53,,**,,,high,"Positive location externality, e.g. pedestrian street",Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,pct_premium,7.6346297356292725,3.149553502241908,100*(exp(b - se^2/2) - 1),|b/t|,0.21201381171104144,0.08379992557748674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.53,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,LOCATION_PEDESTRIAN_STREET,other,other,binary,binary,0.066,,2.27,,**,,,high,"Positive location externality, e.g. pedestrian street",Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",pct_premium,6.777530026243439,3.105857415548703,100*(exp(b - se^2/2) - 1),|b/t|,0.19106491902336806,0.08416956785170399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.27,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.182,,-4.01,,***,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,-16.725680088388295,3.78342753006643,100*(exp(b - se^2/2) - 1),|b/t|,-0.25719923318763943,0.06413945964779037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.01,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.19,,-4.23,,***,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",pct_premium,-17.387466527669325,3.714473651282242,100*(exp(b - se^2/2) - 1),|b/t|,-0.27030387415698603,0.06390162509621419,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.23,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.213,,-2.25,,**,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,pct_premium,-19.545701951680094,7.650544765649424,100*(exp(b - se^2/2) - 1),|b/t|,-0.18944220058835148,0.08419653359482288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,True
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M9,4,Model 9,9,LOCATION_MAIN_ROAD,highway_distance,D_location,binary,binary,-0.212,,-2.24,,**,,,high,"Dummy: located on a main transportation artery (negative externality), not a distance",Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.894,t,"Apartments in non-designated buildings, stepwise; adj. R2; transaction-year dummies",pct_premium,-19.465025912827315,7.656273030540086,100*(exp(b - se^2/2) - 1),|b/t|,-0.1886302580176659,0.0842099366150294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.24,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,True,True,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M6,4,Model 6,9,BUFFER_DESIGNATED,other,other,binary,binary,0.061,,2.27,,**,,,high,More than 25 designated buildings within 100 m,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,All apartments; adj. R2; transaction-year dummies,pct_premium,6.251521443139407,2.8562481834986304,100*(exp(b - se^2/2) - 1),|b/t|,0.14898371539474026,0.0656315926849076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.27,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M7,4,Model 7,9,BUFFER_DESIGNATED,other,other,binary,binary,0.057,,2.12,,**,,,high,More than 25 designated buildings within 100 m,Ln nominal selling price of the apartment (NIS),log,OLS,237.0,0.891,t,"All apartments, stepwise; adj. R2; transaction-year dummies",pct_premium,5.827322860633832,2.846385905308656,100*(exp(b - se^2/2) - 1),|b/t|,0.13933673063142732,0.0657248729393525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.12,227.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2,828367334a6cb48f8254ab88d9172664e3afd7a2:T4-M8,4,Model 8,9,BUFFER_DESIGNATED,other,other,binary,binary,0.073,,1.62,,,,,high,More than 25 designated buildings within 100 m,Ln nominal selling price of the apartment (NIS),log,OLS,146.0,0.895,t,Apartments in non-designated buildings; adj. R2; transaction-year dummies,pct_premium,7.463892379629722,4.847427728072429,100*(exp(b - se^2/2) - 1),|b/t|,0.13759263089288984,0.08493372277338879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.62,136.0,binary,,2022.0,Israel,Asia,High income,1998.0,2009.0,mixed,transaction,journal,True,False,unknown,,2003.5,ols,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Detach,dwelling_type,A_structural,binary,binary,434.886,,,,***,,,medium,"1=detached house, 0=townhouse; chi2(1) statistic in parentheses = 26.884; no SE printed","house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,434.886,,not harmonised,,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Age,age,A_structural,level,years,-0.398,,,,,,,medium,house age in years; chi2(1) statistic in parentheses = 0.033; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,-0.398,,not harmonised,,,,,,182.0,years,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Bed,bedrooms,A_structural,level,count,156.273,,,,***,,,medium,number of bedrooms; chi2(1) statistic in parentheses = 6.852; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,156.273,,not harmonised,,,,,,182.0,count,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Bath,bathrooms,A_structural,level,count,262.351,,,,***,,,medium,number of bathrooms; chi2(1) statistic in parentheses = 17.368; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,262.351,,not harmonised,,,,,,182.0,count,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Area,living_area,A_structural,level,m2,-0.408,,,,*,,,medium,utilization (floor) area in m2; chi2(1) statistic in parentheses = 3.087; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,-0.408,,not harmonised,,,,,,182.0,m2,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Lot,lot_size,A_structural,level,m2,0.44,,,,***,,,medium,land size in m2; chi2(1) statistic in parentheses = 7.316; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,0.44,,not harmonised,,,,,,182.0,m2,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Discbd,cbd_distance,D_location,level,m,-0.029,,,,,,,medium,distance to district center in metres; chi2(1) statistic in parentheses = 1.765; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,-0.029,,not harmonised,,,,,,182.0,m,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,Main,other,other,binary,binary,-73.568,,,,,,,medium,main road location (1=main road); chi2(1) statistic in parentheses = 1.094; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,-73.568,,not harmonised,,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lhs,5,lhsonly Model,9,AQI,air_pollution,E_environment,level,index,9.955,,,,,,,medium,daily average Air Quality Index (two station values); chi2(1) statistic in parentheses = 0.59; no SE printed,"house price (THB), Box-Cox theta=0.476",boxcox,other,192.0,,none,"Box-Cox ML, dependent variable transformed only; chi2(1) in parentheses; AIC 2180.956",other,9.955,,not harmonised,,,,,,182.0,index,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Detach,dwelling_type,A_structural,binary,binary,391533.5,,,,**,,,medium,"1=detached house, 0=townhouse; chi2(1) statistic in parentheses = 5.373; no SE printed","house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",monetary,391533.5,,"b (currency per unit, as reported)",,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Age,age,A_structural,other,years,-45908.14,,,,,,,medium,house age in years; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.3; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,-45908.14,,not harmonised,,,,,,182.0,years,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Bed,bedrooms,A_structural,other,count,286382.1,,,,,,,medium,number of bedrooms; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.976; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,286382.1,,not harmonised,,,,,,182.0,count,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Bath,bathrooms,A_structural,other,count,569925.7,,,,***,,,medium,number of bathrooms; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 7.381; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,569925.7,,not harmonised,,,,,,182.0,count,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Area,living_area,A_structural,other,m2,-166003.9,,,,,,,medium,utilization (floor) area in m2; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.427; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,-166003.9,,not harmonised,,,,,,182.0,m2,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Lot,lot_size,A_structural,other,m2,1561252.0,,,,***,,,medium,land size in m2; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 9.93; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,1561252.0,,not harmonised,,,,,,182.0,m2,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Discbd,cbd_distance,D_location,other,m,-80836.09,,,,,,,medium,distance to district center in metres; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.106; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,-80836.09,,not harmonised,,,,,,182.0,m,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,Main,other,other,binary,binary,-184539.0,,,,*,,,medium,main road location (1=main road); chi2(1) statistic in parentheses = 3.26; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",monetary,-184539.0,,"b (currency per unit, as reported)",,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-rhs,5,rhsonly Model,9,AQI,air_pollution,E_environment,other,index,-1144197.0,,,,,,,medium,daily average Air Quality Index (two station values); Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 1.101; no SE printed,"house price (THB), level",level,other,192.0,,none,"Box-Cox ML, regressors transformed (lambda=-0.178); chi2(1) in parentheses; AIC 2187.270",other,-1144197.0,,not harmonised,,,,,,182.0,index,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Detach,dwelling_type,A_structural,binary,binary,3.048,,,,**,,,medium,"1=detached house, 0=townhouse; chi2(1) statistic in parentheses = 5.31; no SE printed","house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,3.048,,not harmonised,,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Age,age,A_structural,other,years,-0.247,,,,,,,medium,house age in years; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.751; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,-0.247,,not harmonised,,,,,,182.0,years,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Bed,bedrooms,A_structural,other,count,3.289,,,,**,,,medium,number of bedrooms; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 4.344; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,3.289,,not harmonised,,,,,,182.0,count,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Bath,bathrooms,A_structural,other,count,4.093,,,,***,,,medium,number of bathrooms; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 10.071; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,4.093,,not harmonised,,,,,,182.0,count,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Area,living_area,A_structural,other,m2,-0.178,,,,,,,medium,utilization (floor) area in m2; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.302; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,-0.178,,not harmonised,,,,,,182.0,m2,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Lot,lot_size,A_structural,other,m2,1.48,,,,***,,,medium,land size in m2; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 14.003; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,1.48,,not harmonised,,,,,,182.0,m2,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Discbd,cbd_distance,D_location,other,m,-0.035,,,,,,,medium,distance to district center in metres; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.027; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,-0.035,,not harmonised,,,,,,182.0,m,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,Main,other,other,binary,binary,-1.488,,,,*,,,medium,main road location (1=main road); chi2(1) statistic in parentheses = 2.703; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,-1.488,,not harmonised,,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-lambda,5,Lambda Model,9,AQI,air_pollution,E_environment,other,index,-2.144,,,,,,,medium,daily average Air Quality Index (two station values); Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.511; no SE printed,"house price (THB), Box-Cox lambda=0.175",boxcox,other,192.0,,none,"Box-Cox ML, common lambda=0.175 both sides; chi2(1) in parentheses; AIC 2175.152",other,-2.144,,not harmonised,,,,,,182.0,index,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Detach,dwelling_type,A_structural,binary,binary,10.738,,,,*,,,medium,"1=detached house, 0=townhouse; chi2(1) statistic in parentheses = 3.356; no SE printed","house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,10.738,,not harmonised,,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Age,age,A_structural,other,years,-2.656,,,,,,,medium,house age in years; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.265; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,-2.656,,not harmonised,,,,,,182.0,years,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Bed,bedrooms,A_structural,other,count,17.64,,,,,,,medium,number of bedrooms; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 2.115; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,17.64,,not harmonised,,,,,,182.0,count,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Bath,bathrooms,A_structural,other,count,22.805,,,,***,,,medium,number of bathrooms; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 7.779; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,22.805,,not harmonised,,,,,,182.0,count,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Area,living_area,A_structural,other,m2,0.131,,,,,,,medium,utilization (floor) area in m2; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.0; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,0.131,,not harmonised,,,,,,182.0,m2,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Lot,lot_size,A_structural,other,m2,241.498,,,,***,,,medium,land size in m2; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 19.346; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,241.498,,not harmonised,,,,,,182.0,m2,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Discbd,cbd_distance,D_location,other,m,-13.424,,,,,,,medium,distance to district center in metres; Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 0.08; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,-13.424,,not harmonised,,,,,,182.0,m,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,Main,other,other,binary,binary,-6.97,,,,,,,medium,main road location (1=main road); chi2(1) statistic in parentheses = 2.265; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,-6.97,,not harmonised,,,,,,182.0,binary,,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f,84a6e8ef31bf481e52cb738679f636c9a2fcc69f:T5-theta,5,Theta Model,9,AQI,air_pollution,E_environment,other,index,-144.509,,,,*,,,medium,daily average Air Quality Index (two station values); Box-Cox transformed regressor (see model lambda); chi2(1) statistic in parentheses = 3.582; no SE printed,"house price (THB), Box-Cox theta=0.288",boxcox,other,192.0,,none,"Box-Cox ML, theta=0.288 (lhs), lambda=-0.461 (rhs); preferred model; chi2(1) in parentheses; AIC 2170.988",other,-144.509,,not harmonised,,,,,,182.0,index,nonstandard_x_transform,2016.0,Thailand,Asia,Upper middle income,2016.0,2016.0,mixed,appraisal,journal,False,False,unknown,,2016.0,other,journal,False,False,False
8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258:T1-c1,1,1,4,Housing area (X1) (per square meter),living_area,A_structural,level,m2,450.0,35.0,12.86,0.001,,,,low,"Housing area in square metres; p printed as '<0.001' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis",house price (yuan; total vs per-m2 not stated),level,OLS,,0.85,se,"All Tianjin transactions 2018-2022 (n not reported); R2 0.85, adjusted R2 0.83, F 75.00",monetary,450.0,35.0,"b (currency per unit, as reported)",reported,,,,12.86,,m2,,2024.0,China,Asia,Upper middle income,2018.0,2022.0,unknown,transaction,conference,False,False,OLS,,2020.0,ols,other,False,False,False
8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258:T1-c1,1,1,4,Room age (X2) (per year),age,A_structural,level,years,-200.0,60.0,-3.33,0.01,,,,low,"Age of the house in years; p printed as '<0.01' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis",house price (yuan; total vs per-m2 not stated),level,OLS,,0.85,se,"All Tianjin transactions 2018-2022 (n not reported); R2 0.85, adjusted R2 0.83, F 75.00",monetary,-200.0,60.0,"b (currency per unit, as reported)",reported,,,,-3.33,,years,,2024.0,China,Asia,Upper middle income,2018.0,2022.0,unknown,transaction,conference,False,False,OLS,,2020.0,ols,other,False,False,False
8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258:T1-c1,1,1,4,Geographical location (X3) (per kilometer),cbd_distance,D_location,level,km,-800.0,120.0,-6.67,0.001,,,,low,"Distance from the city centre in km; p printed as '<0.001' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis",house price (yuan; total vs per-m2 not stated),level,OLS,,0.85,se,"All Tianjin transactions 2018-2022 (n not reported); R2 0.85, adjusted R2 0.83, F 75.00",monetary,-800.0,120.0,"b (currency per unit, as reported)",reported,,,,-6.67,,km,,2024.0,China,Asia,Upper middle income,2018.0,2022.0,unknown,transaction,conference,False,False,OLS,,2020.0,ols,other,False,False,False
8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258,8506f16b0710a57ed835f9583338b207fd204258:T1-c1,1,1,4,Supporting facilities (X4) (each score),neighbourhood_amenities,F_neighbourhood,level,score,1200.0,150.0,8.0,0.001,,,,low,"Composite score of surrounding facilities (schools, hospitals, shopping centres); p printed as '<0.001' (upper bound recorded); values appear illustrative (round numbers, no sample size) -- do not use in primary synthesis",house price (yuan; total vs per-m2 not stated),level,OLS,,0.85,se,"All Tianjin transactions 2018-2022 (n not reported); R2 0.85, adjusted R2 0.83, F 75.00",monetary,1200.0,150.0,"b (currency per unit, as reported)",reported,,,,8.0,,score,,2024.0,China,Asia,Upper middle income,2018.0,2022.0,unknown,transaction,conference,False,False,OLS,,2020.0,ols,other,False,False,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,FRE,flood_risk,E_environment,level,count,-0.185,0.07,,,***,,,high,Flood frequency: number of flood events in the 5-year period 2008-2013,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,-18.5,7.000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.10157439950896371,0.038433556570959246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.642857142857143,670.0,count,unit_not_harmonisable,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,False,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,FRE,flood_risk,E_environment,level,count,-0.136,0.081,,,*,,,high,Flood frequency: number of flood events in the 5-year period 2008-2013,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,-13.600000000000001,8.1,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.06472987066868327,0.03855234944237754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6790123456790125,670.0,count,unit_not_harmonisable,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,False,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,BUS,transit_distance,D_location,level,km,-0.016,0.011,,,,,,high,Distance to nearest bus station,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,-1.6,1.0999999999999999,100*b/1 (X per km->km),reported,-0.05610547926785547,0.03857251699665063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4545454545454546,670.0,km,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,BUS,transit_distance,D_location,level,km,0.003,0.006,,,,,,high,Distance to nearest bus station,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,0.3,0.6,100*b/1 (X per km->km),reported,0.019313082381200473,0.03862616476240095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,670.0,km,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,BUS2,transit_distance,D_location,squared,km,0.0001,0.0001,,,,,,high,Square of distance to nearest bus station,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),other,0.0001,0.0001,not harmonised,reported,0.038604571824109146,0.038604571824109146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,670.0,km,nonstandard_x_transform,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,False,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,BUS2,transit_distance,D_location,squared,km,-0.0001,0.0003,,,,,,high,Square of distance to nearest bus station,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),other,-0.0001,0.0003,not harmonised,reported,-0.012876722477435865,0.03863016743230759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33333333333333337,670.0,km,nonstandard_x_transform,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,False,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,CITY,cbd_distance,D_location,level,km,0.041,0.012,,,***,,,high,Distance to nearest city centre,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,4.1000000000000005,1.2,100*b/1 (X per km->km),reported,0.13086224595033083,0.03830114515619439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4166666666666665,670.0,km,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,CITY,cbd_distance,D_location,level,km,-0.01,0.008,,,,,,high,Distance to nearest city centre,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,-1.0,0.8,100*b/1 (X per km->km),reported,-0.04823550108010261,0.03858840086408209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,670.0,km,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,CITY2,cbd_distance,D_location,squared,km,-0.001,0.0003,,,***,,,high,Square of distance to city centre,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),other,-0.001,0.0003,not harmonised,reported,-0.12772319171982632,0.038316957515947894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,670.0,km,nonstandard_x_transform,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,False,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,CITY2,cbd_distance,D_location,squared,km,0.0003,0.0003,,,,,,high,Square of distance to city centre,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),other,0.0003,0.0003,not harmonised,reported,0.038604571824109146,0.038604571824109146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,670.0,km,nonstandard_x_transform,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,False,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,LOT,lot_size,A_structural,level,sqft,0.0001,5e-05,,,***,,,high,Lot size in square feet,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,0.10763910416709722,0.05381955208354861,100*b/0.092903 (X per sqft->m2),reported,0.07703712157713453,0.03851856078856727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,670.0,sqft,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,LOT,lot_size,A_structural,level,sqft,0.0001,4e-05,,,***,,,high,Lot size in square feet,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,0.10763910416709722,0.04305564166683889,100*b/0.092903 (X per sqft->m2),reported,0.09613607115671603,0.03845442846268641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,670.0,sqft,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,AGE,age,A_structural,level,years,-0.012,0.003,,,***,,,high,Age of house in years,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,-1.2,0.3,100*b/1 (per year),reported,-0.1527207096642425,0.038180177416060626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,670.0,years,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,AGE,age,A_structural,level,years,-0.01,0.002,,,***,,,high,Age of house in years,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,-1.0,0.2,100*b/1 (per year),reported,-0.18966081045272037,0.03793216209054408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,670.0,years,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,BED,bedrooms,A_structural,level,count,0.077,0.034,,,***,,,high,printed *** although beta/se = 2.3 (inconsistent stars in source),ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,7.7,3.4000000000000004,100*b/1 (per unit count),reported,0.08716024873081361,0.038486343595424195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.264705882352941,670.0,count,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,BED,bedrooms,A_structural,level,count,0.059,0.045,,,**,,,high,printed ** although beta/se = 1.3 (inconsistent stars in source),ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,5.8999999999999995,4.5,100*b/1 (per unit count),reported,0.05058778655728977,0.038583905001322714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3111111111111111,670.0,count,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,BATH,bathrooms,A_structural,level,count,0.233,0.035,,,***,,,high,,ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),semi_elasticity,23.3,3.5000000000000004,100*b/1 (per unit count),reported,0.24908192655558284,0.037415740040538194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.657142857142857,670.0,count,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,BATH,bathrooms,A_structural,level,count,0.18,0.042,,,***,,,high,,ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),semi_elasticity,18.0,4.2,100*b/1 (per unit count),reported,0.16334772268318037,0.038114468626075426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.285714285714286,670.0,count,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,DAIRPORT,airport_distance,D_location,binary,binary,0.059,0.074,,,,,,high,"Dummy = 1 if within 10 km of airport (proximity dummy, not distance)",ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),pct_premium,5.787481063178834,7.849736781477176,100*(exp(b - se^2/2) - 1),reported,0.030787679943138198,0.03861505619986825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7972972972972973,670.0,binary,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,DAIRPORT,airport_distance,D_location,binary,binary,-0.112,0.09,,,,,,high,"Dummy = 1 if within 10 km of airport (proximity dummy, not distance)",ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),pct_premium,-10.956929935061677,8.046398317503215,100*(exp(b - se^2/2) - 1),reported,-0.04802161655277529,0.038588799015623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2444444444444445,670.0,binary,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-urban,7,Urban area,13,DSCHOOL,school_distance,D_location,binary,binary,-0.139,0.094,,,,,,high,"Dummy = 1 if within 2 km of a school (proximity dummy, not distance)",ln residential property value (RM),log,OLS,680.0,0.539,se,Urban-area properties (R2 printed as 53.90 %),pct_premium,-13.360815859448982,8.180143467507564,100*(exp(b - se^2/2) - 1),reported,-0.0570350746046699,0.038570482106755184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4787234042553192,670.0,binary,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42,85ea3296a5d9175ea12142cf209523d5db6d0b42:T7-rural,7,Rural area,13,DSCHOOL,school_distance,D_location,binary,binary,0.256,0.137,,,*,,,high,"Dummy = 1 if within 2 km of a school (proximity dummy, not distance)",ln residential property value (RM),log,OLS,680.0,0.47,se,Rural-area properties (R2 printed as 47.00 %),pct_premium,27.96869785282794,17.697012372773948,100*(exp(b - se^2/2) - 1),reported,0.07200344394239547,0.03853309304729758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8686131386861313,670.0,binary,,2019.0,Malaysia,Asia,Upper middle income,2013.0,2014.0,single_family,appraisal,journal,False,False,unknown,,2013.5,ols,journal,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,BATHRMS,bathrooms,A_structural,level,count,0.0439,,1.705,,,,,high,number of bathrooms,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,4.390000000000001,2.5747800586510263,100*b/1 (per unit count),|b/t|,0.045088987535967955,0.026445153980039854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.705,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,FIREPLC,fireplace,B_interior,level,count,0.0775,,5.38,,,,,high,number of fireplaces,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,7.75,1.4405204460966543,100*b/1 (per unit count),|b/t|,0.14099700267789986,0.026207621315594776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.38,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,GARAGE,garage,A_structural,level,count (cars),0.0379,,2.638,,,,,high,number of cars that fit in garage,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,3.7900000000000005,1.43669446550417,100*b/1 (per unit count),|b/t|,0.0696636808131014,0.02640776376539098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.638,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,SQFTBLD,living_area,A_structural,level,sqft,0.0006,,8.467,,,,,high,building area; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,0.6458346250025833,0.0762766770996319,100*b/0.092903 (X per sqft->m2),|b/t|,0.2187125187850698,0.025831170282871126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.467,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,SQFTBD2,living_area,A_structural,squared,sqft squared,-1e-07,,-4.341,,,,,high,square of building area,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,other,-1e-07,2.3036166781847497e-08,not harmonised,|b/t|,-0.11416396009816863,0.026299000252975956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.341,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,SQFTLOT,lot_size,A_structural,level,sqft,1e-05,,1.23,,,,,high,lot size; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,0.010763910416709722,0.008751146680251808,100*b/0.092903 (X per sqft->m2),|b/t|,0.03254340778463125,0.026458055109456303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.23,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,SQFTLOT2,lot_size,A_structural,squared,sqft squared,-1e-11,,-0.325,,,,,high,square of lot size,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,other,-1e-11,3.0769230769230764e-11,not harmonised,|b/t|,-0.008603106556224954,0.026471097096076777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.325,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,STORIES,storeys,A_structural,level,"count (1, 1.5, 2)",0.0512,,1.497,,,,,high,number of stories,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,5.12,3.420173680694723,100*b/1 (per unit count),|b/t|,0.03959761838715894,0.02645131488788172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.497,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,VIEW,view,E_environment,level,index 0-6,0.0556,,4.39,,,,,high,"view index, 0 = no view, max 6",ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,5.56,1.2665148063781322,100*b (unit as reported: index 0-6; no harmonised unit defined for this characteristic),|b/t|,0.11543553393889247,0.026295110236649766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.39,1427.0,index 0-6,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,STRUCAGE,age,A_structural,level,years,-0.0037,,-2.56,,,,,high,age of structure; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,-0.37,0.14453125,100*b/1 (per year),|b/t|,-0.06761343457776167,0.026411497881938154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.56,1427.0,years,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,STRUCAG2,age,A_structural,squared,years squared,2e-05,,1.57,,,,,high,square of structure age,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,other,2e-05,1.2738853503184714e-05,not harmonised,|b/t|,0.0415253120255234,0.026449243328358848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.57,1427.0,years sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,NEGHAGE,other,other,level,years,-0.0041,,-2.931,,,,,high,age of the neighborhood in years,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,-0.41000000000000003,0.13988399863527806,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07735715535164699,0.02639275174058239,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.931,1427.0,years,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,VANDAL,crime,F_neighbourhood,level,vandalism incidents per resident per year (neighborhood),0.0042,,2.49,,,,,high,neighborhood vandalism rate,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,0.42,0.1686746987951807,100*b (unit as reported: vandalism incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,0.0657727396763612,0.026414754890104898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.49,1427.0,vandalism incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,BURGLAR,crime,F_neighbourhood,level,burglary incidents per resident per year (neighborhood),-0.0304,,-4.117,,,,,high,neighborhood burglary rate,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,-3.04,0.7384017488462472,100*b (unit as reported: burglary incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,-0.10834399270848241,0.02631624792530542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.117,1427.0,burglary incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,MDFAMINC,income,F_neighbourhood,level,USD (median family income of neighborhood),-9e-07,,-0.3963,,,,,medium,neighborhood median family income; coefficient printed with 6-7 decimals in scan,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,-8.999999999999999e-05,0.00022710068130204388,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.010490306760974847,0.026470620138720282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3963,1427.0,usd,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,NONWHTE,demographics,F_neighbourhood,level,percent,-0.0027,,-2.766,,,,,high,percent of neighborhood population non-white,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,-0.27,0.09761388286334056,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.07302626352374735,0.02640139679094264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.766,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,PCTBADEG,education_level,F_neighbourhood,level,percent,-0.0161,,7.064,,,,,medium,percent of neighborhood population with a B.A. degree; printed negative coefficient with positive t-score (likely sign typo; Table 6 gives +0.0163),ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,semi_elasticity,-1.6099999999999999,0.22791619479048697,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.18381253781996673,0.026021027437707636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.064,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,UNKNDUM,other,other,dummy,binary,-0.0107,,-0.477,,,,,high,nearest arterial in medium traffic group,ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,pct_premium,-1.089184391297704,2.219312440811628,100*(exp(b - se^2/2) - 1),|b/t|,-0.01262617405962998,0.026469966582033506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.477,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m1,5,1,46,NOTRADUM,other,other,dummy,binary,0.0272,,,,,,,medium,nearest arterial in low traffic group; t-score illegible in scan (printed '1.1.6'),ln(1994 sale price),log,OLS,1437.0,0.595,none,Test model 1: structural + neighbourhood + traffic-group dummies; adj. R2 reported; t-scores in column,pct_premium,2.7573296872767994,,100*(exp(b) - 1),,,,,,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,BATHRMS,bathrooms,A_structural,level,count,0.0438,,1.702,,,,,high,number of bathrooms,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,4.38,2.573443008225617,100*b/1 (per unit count),|b/t|,0.045009812939773686,0.026445248495754223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.702,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,FIREPLC,fireplace,B_interior,level,count,0.0781,,5.495,,,,,high,number of fireplaces,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,7.8100000000000005,1.4212920837124658,100*b/1 (per unit count),|b/t|,0.1439490678196906,0.026196372669643427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.495,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,GARAGE,garage,A_structural,level,count (cars),0.0387,,2.72,,,,,high,number of cars that fit in garage,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,3.8699999999999997,1.4227941176470587,100*b/1 (per unit count),|b/t|,0.07181811597578265,0.026403719108743624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.72,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,SQFTBLD,living_area,A_structural,level,sqft,0.0006,,8.541,,,,,high,building area; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.6458346250025833,0.0756158090390567,100*b/0.092903 (X per sqft->m2),|b/t|,0.22053144424971338,0.02582033066967725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.541,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,SQFTBD2,living_area,A_structural,squared,sqft squared,-1e-07,,-4.398,,,,,high,square of building area,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,other,-1e-07,2.273760800363802e-08,not harmonised,|b/t|,-0.11564308412126877,0.026294471150811453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.398,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,SQFTLOT,lot_size,A_structural,level,sqft,1e-06,,1.047,,,,,high,lot size; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.0010763910416709721,0.001028071673038178,100*b/0.092903 (X per sqft->m2),|b/t|,0.027705624799442996,0.026461914803670484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.047,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,SQFTLOT2,lot_size,A_structural,squared,sqft squared,-4e-11,,-0.144,,,,,high,square of lot size,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,other,-4e-11,2.7777777777777777e-10,not harmonised,|b/t|,-0.00381195135730823,0.026471884425751598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.144,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,STORIES,storeys,A_structural,level,"count (1, 1.5, 2)",0.0515,,1.506,,,,,high,number of stories,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,5.1499999999999995,3.4196547144754312,100*b/1 (per unit count),|b/t|,0.03983530357940319,0.02645106479376042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.506,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,VIEW,view,E_environment,level,index 0-6,0.057,,4.537,,,,,high,"view index, 0 = no view, max 6",ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,5.7,1.2563367864227464,100*b (unit as reported: index 0-6; no harmonised unit defined for this characteristic),|b/t|,0.11924682835184093,0.026283188968887133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.537,1427.0,index 0-6,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,STRUCAGE,age,A_structural,level,years,-0.0036,,-2.512,,,,,high,age of structure; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.36,0.14331210191082802,100*b/1 (per year),|b/t|,-0.06635131703873316,0.026413740859368297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.512,1427.0,years,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,STRUCAG2,age,A_structural,squared,years squared,2e-05,,1.547,,,,,high,square of structure age,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,other,2e-05,1.2928248222365872e-05,not harmonised,|b/t|,0.04091800550855998,0.02644990659893987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.547,1427.0,years sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,NEGHAGE,other,other,level,years,-0.004,,-2.831,,,,,high,age of the neighborhood in years,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.4,0.1412928293889085,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07473287937093993,0.026398049936750243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.831,1427.0,years,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,VANDAL,crime,F_neighbourhood,level,vandalism incidents per resident per year (neighborhood),0.0041,,2.459,,,,,high,neighborhood vandalism rate,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.41000000000000003,0.16673444489629932,100*b (unit as reported: vandalism incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,0.06495735909555082,0.02641616880664938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.459,1427.0,vandalism incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,BURGLAR,crime,F_neighbourhood,level,burglary incidents per resident per year (neighborhood),-0.0297,,-4.117,,,,,high,neighborhood burglary rate,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,-2.97,0.7213990769978139,100*b (unit as reported: burglary incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,-0.10834399270848241,0.02631624792530542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.117,1427.0,burglary incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,MDFAMINC,income,F_neighbourhood,level,USD (median family income of neighborhood),-1e-06,,-0.504,,,,,medium,neighborhood median family income; coefficient printed with 6-7 decimals in scan,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,-9.999999999999999e-05,0.0001984126984126984,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.013340739366807984,0.026469720965888854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.504,1427.0,usd,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,NONWHTE,demographics,F_neighbourhood,level,percent,-0.0029,,-3.007,,,,,high,percent of neighborhood population non-white,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.29,0.09644163618224143,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.07935053326629908,0.026388604345294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.007,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,PCTBADEG,education_level,F_neighbourhood,level,percent,0.0164,,7.186,,,,,high,percent of neighborhood population with a B.A. degree,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,1.6400000000000001,0.22822154188700253,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.18687714638422182,0.02600572590929889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.186,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m2,5,2,46,LINKDIST,highway_distance,D_location,level,feet,8e-05,,3.083,,,,,high,distance to nearest arterial link,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 2: structural + neighbourhood + continuous distance to arterial; adj. R2 reported; t-scores in column,semi_elasticity,26.246719160104988,8.513369821636388,100*b/0.0003048 (X per ft->km),|b/t|,0.08134295973133068,0.026384352815871127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.083,1427.0,ft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,True
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,BATHRMS,bathrooms,A_structural,level,count,0.0422,,1.635,,,,,high,number of bathrooms,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,4.22,2.581039755351682,100*b/1 (per unit count),|b/t|,0.043241362037477346,0.026447316230872993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.635,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,FIREPLC,fireplace,B_interior,level,count,0.0784,,5.494,,,,,high,number of fireplaces,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,7.84,1.4270112850382235,100*b/1 (per unit count),|b/t|,0.14392341412571272,0.026196471446252773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.494,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,GARAGE,garage,A_structural,level,count (cars),0.0392,,2.758,,,,,high,number of cars that fit in garage,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,3.92,1.4213197969543148,100*b/1 (per unit count),|b/t|,0.07281617384784791,0.02640180342561563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.758,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,SQFTBLD,living_area,A_structural,level,sqft,0.00061,,8.512,,,,,high,building area; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.656598535419293,0.07713798583403347,100*b/0.092903 (X per sqft->m2),|b/t|,0.2198188950797638,0.0258245882377542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.512,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,SQFTBD2,living_area,A_structural,squared,sqft squared,-1e-07,,-4.386,,,,,high,square of building area,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,other,-1e-07,2.2799817601459187e-08,not harmonised,|b/t|,-0.1153317531759286,0.026295429360676834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.386,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,SQFTLOT,lot_size,A_structural,level,sqft,7e-06,,0.947,,,,,high,lot size; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.007534737291696805,0.007956427974336648,100*b/0.092903 (X per sqft->m2),|b/t|,0.025061182982124782,0.02646376238872733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.947,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,SQFTLOT2,lot_size,A_structural,squared,sqft squared,-1e-11,,-0.034,,,,,high,square of lot size,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,other,-1e-11,2.941176470588235e-10,not harmonised,|b/t|,-0.00090005024524366,0.026472066036578232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.034,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,STORIES,storeys,A_structural,level,"count (1, 1.5, 2)",0.0528,,1.538,,,,,high,number of stories,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,5.28,3.4330299089726917,100*b/1 (per unit count),|b/t|,0.04068035144529692,0.02645016348848955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.538,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,VIEW,view,E_environment,level,index 0-6,0.0573,,4.654,,,,,high,"view index, 0 = no view, max 6",ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,5.7299999999999995,1.231198968629136,100*b (unit as reported: index 0-6; no harmonised unit defined for this characteristic),|b/t|,0.12227655294589301,0.02627343208979222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.654,1427.0,index 0-6,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,STRUCAGE,age,A_structural,level,years,-0.0036,,-2.485,,,,,high,age of structure; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.36,0.1448692152917505,100*b/1 (per year),|b/t|,-0.06564123554770517,0.026414984123825015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.485,1427.0,years,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,STRUCAG2,age,A_structural,squared,years squared,2e-05,,1.522,,,,,high,square of structure age,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,other,2e-05,1.314060446780552e-05,not harmonised,|b/t|,0.04025783831048492,0.026450616498347516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.522,1427.0,years sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,NEGHAGE,other,other,level,years,-0.004,,-2.819,,,,,high,age of the neighborhood in years,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.4,0.14189428875487764,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07441786079935146,0.02639867357195866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.819,1427.0,years,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,VANDAL,crime,F_neighbourhood,level,vandalism incidents per resident per year (neighborhood),0.0042,,2.436,,,,,high,neighborhood vandalism rate,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.42,0.1724137931034483,100*b (unit as reported: vandalism incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,0.06435231513054608,0.026417206539633042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.436,1427.0,vandalism incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,BURGLAR,crime,F_neighbourhood,level,burglary incidents per resident per year (neighborhood),-0.0295,,-4.112,,,,,high,neighborhood burglary rate,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-2.9499999999999997,0.7174124513618677,100*b (unit as reported: burglary incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,-0.10821395324515828,0.02631662287090425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.112,1427.0,burglary incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,MDFAMINC,income,F_neighbourhood,level,USD (median family income of neighborhood),-1e-06,,-0.497,,,,,medium,neighborhood median family income; coefficient printed with 6-7 decimals in scan,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-9.999999999999999e-05,0.00020120724346076458,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.013155483613032377,0.026469785941715042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.497,1427.0,usd,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,NONWHTE,demographics,F_neighbourhood,level,percent,-0.003,,-3.04,,,,,high,percent of neighborhood population non-white,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.3,0.09868421052631579,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.08021578404981013,0.026386771069016487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.04,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,PCTBADEG,education_level,F_neighbourhood,level,percent,0.0165,,7.214,,,,,high,percent of neighborhood population with a B.A. degree,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,1.6500000000000001,0.22872192958136955,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.1875797335258225,0.02600218097114257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.214,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,WIN85,highway_distance,D_location,dummy,binary (within 85 feet of any arterial),-0.0883,,-2.36,,,,,high,= 1 if house within 85 ft of an arterial (any traffic level),ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-8.51543812548824,3.425314853705816,100*(exp(b - se^2/2) - 1),|b/t|,-0.06235253825753938,0.026420567058279398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.36,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,WIN350,highway_distance,D_location,dummy,binary (85-350 feet of any arterial),-0.0603,,-3.265,,,,,high,= 1 if house between 85 and 350 ft of an arterial,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-5.867850449397249,1.738786138129868,100*(exp(b - se^2/2) - 1),|b/t|,-0.08611029099525008,0.02637374915627874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.265,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m3,5,3,46,WIN550,highway_distance,D_location,dummy,binary (350-550 feet of any arterial),-0.0153,,-0.8,,,,,high,= 1 if house between 350 and 550 ft of an arterial,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 3: structural + neighbourhood + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-1.5363639187307099,1.8834614612882288,100*(exp(b - se^2/2) - 1),|b/t|,-0.021172913983928718,0.026466142479910897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,BATHRMS,bathrooms,A_structural,level,count,0.0422,,1.637,,,,,high,number of bathrooms,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,4.22,2.5778863775198535,100*b/1 (per unit count),|b/t|,0.043294157585533614,0.026447255702830554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.637,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,FIREPLC,fireplace,B_interior,level,count,0.0786,,5.47,,,,,high,number of fireplaces,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,7.86,1.436928702010969,100*b/1 (per unit count),|b/t|,0.1433076385291062,0.026198837025430754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.47,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,GARAGE,garage,A_structural,level,count (cars),0.0393,,2.76,,,,,high,number of cars that fit in garage,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,3.93,1.423913043478261,100*b/1 (per unit count),|b/t|,0.07286869717667181,0.026401701875605733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.76,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,SQFTBLD,living_area,A_structural,level,sqft,0.0006,,8.491,,,,,high,building area; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.6458346250025833,0.07606107937846936,100*b/0.092903 (X per sqft->m2),|b/t|,0.21930269140898595,0.025827663574253437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.491,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,SQFTBD2,living_area,A_structural,squared,sqft squared,-1e-07,,-4.78,,,,,high,square of building area,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,other,-1e-07,2.0920502092050206e-08,not harmonised,|b/t|,-0.12553551246256756,0.026262659510997398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.78,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,SQFTLOT,lot_size,A_structural,level,sqft,7e-06,,0.948,,,,,high,lot size; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.007534737291696805,0.007948035117823635,100*b/0.092903 (X per sqft->m2),|b/t|,0.025087630097286987,0.0264637448283618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.948,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,SQFTLOT2,lot_size,A_structural,squared,sqft squared,-1e-11,,-0.034,,,,,high,square of lot size,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,other,-1e-11,2.941176470588235e-10,not harmonised,|b/t|,-0.00090005024524366,0.026472066036578232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.034,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,STORIES,storeys,A_structural,level,"count (1, 1.5, 2)",0.0533,,1.55,,,,,high,number of stories,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,5.33,3.4387096774193546,100*b/1 (per unit count),|b/t|,0.04099722198840291,0.026449820637679294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.55,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,VIEW,view,E_environment,level,index 0-6,0.0575,,4.715,,,,,high,"view index, 0 = no view, max 6",ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,5.75,1.2195121951219512,100*b (unit as reported: index 0-6; no harmonised unit defined for this characteristic),|b/t|,0.1238548038076983,0.026268251072682568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.715,1427.0,index 0-6,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,STRUCAGE,age,A_structural,level,years,-0.0036,,-2.47,,,,,high,age of structure; squared term also included,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.36,0.145748987854251,100*b/1 (per year),|b/t|,-0.06524670266691247,0.026415669095915976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.47,1427.0,years,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,STRUCAG2,age,A_structural,squared,years squared,2e-05,,1.52,,,,,high,square of structure age,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,other,2e-05,1.3157894736842106e-05,not harmonised,|b/t|,0.0402050226455275,0.026450672793110194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,1427.0,years sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,NEGHAGE,other,other,level,years,-0.004,,-2.83,,,,,high,age of the neighborhood in years,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.4,0.1413427561837456,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07470662867652608,0.026398102005839606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.83,1427.0,years,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,VANDAL,crime,F_neighbourhood,level,vandalism incidents per resident per year (neighborhood),0.0042,,2.465,,,,,high,neighborhood vandalism rate,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,0.42,0.17038539553752535,100*b (unit as reported: vandalism incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,0.06511518489748983,0.026415896510137864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.465,1427.0,vandalism incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,BURGLAR,crime,F_neighbourhood,level,burglary incidents per resident per year (neighborhood),-0.03,,-4.036,,,,,high,neighborhood burglary rate,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-3.0,0.7433102081268583,100*b (unit as reported: burglary incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,-0.10623667317210918,0.0263222678820885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.036,1427.0,burglary incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,MDFAMINC,income,F_neighbourhood,level,USD (median family income of neighborhood),-1e-06,,-0.495,,,,,medium,neighborhood median family income; coefficient printed with 6-7 decimals in scan,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-9.999999999999999e-05,0.00020202020202020202,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.013102553148007925,0.02646980433940995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.495,1427.0,usd,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,NONWHTE,demographics,F_neighbourhood,level,percent,-0.003,,-3.007,,,,,high,percent of neighborhood population non-white,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-0.3,0.09976720984369804,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.07935053326629908,0.026388604345294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.007,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,PCTBADEG,education_level,F_neighbourhood,level,percent,-0.0165,,7.229,,,,,medium,percent of neighborhood population with a B.A. degree; printed negative coefficient with positive t-score (likely sign typo; Table 6 gives +0.0163),ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,semi_elasticity,-1.6500000000000001,0.22824733711440032,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.1879560011672962,0.026000276824913016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.229,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,UNKNDUM,other,other,dummy,binary,0.00093,,-0.042,,,,,medium,nearest arterial in medium traffic group; sign of coefficient vs t-score inconsistent as printed,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,0.06850814999834753,2.216345957864772,100*(exp(b - se^2/2) - 1),|b/t|,-0.0011118265366792729,0.026472060397125543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.042,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,NOTRADUM,other,other,dummy,binary,-0.0082,,-0.38,,,,,high,nearest arterial in low traffic group,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-0.8397368948197648,2.1402723505278827,100*(exp(b - se^2/2) - 1),|b/t|,-0.01005888024564557,0.026470737488540976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.38,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,WIN85,highway_distance,D_location,dummy,binary (within 85 feet of any arterial),-0.094,,-2.39,,,,,high,= 1 if house within 85 ft of an arterial (any traffic level),ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-9.042101997387576,3.5801916171812596,100*(exp(b - se^2/2) - 1),|b/t|,-0.06314201492853798,0.026419253108174882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.39,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,WIN350,highway_distance,D_location,dummy,binary (85-350 feet of any arterial),-0.066,,-2.944,,,,,high,= 1 if house between 85 and 350 ft of an arterial,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-6.4104350300953294,2.0986629430450696,100*(exp(b - se^2/2) - 1),|b/t|,-0.07769819433726627,0.026392049706951855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.944,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T5-m4,5,4,46,WIN550,highway_distance,D_location,dummy,binary (350-550 feet of any arterial),-0.021,,-0.91,,,,,high,= 1 if house between 350 and 550 ft of an arterial,ln(1994 sale price),log,OLS,1437.0,0.597,none,Test model 4: structural + neighbourhood + traffic-group dummies + distance bands to any arterial; adj. R2 reported; t-scores in column,pct_premium,-2.1041739497426826,2.2597360720833684,100*(exp(b - se^2/2) - 1),|b/t|,-0.024082603196385933,0.02646439911690762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.91,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,BATHRMS,bathrooms,A_structural,level,count,0.0407,,1.557,0.12,,,,high,number of bathrooms; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,4.07,2.6140012845215157,100*b/1 (per unit count),|b/t|,0.041182057432323206,0.026449619417034817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.557,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,FIREPLC,fireplace,B_interior,level,count,0.076,,5.283,0.0,,,,high,number of fireplaces; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,7.6,1.4385765663448795,100*b/1 (per unit count),|b/t|,0.13850406918141944,0.026216935298394743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.283,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,GARAGE,garage,A_structural,level,count (cars),0.039,,2.733,0.006,,,,high,number of cars that fit in garage; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,3.9,1.4270032930845225,100*b/1 (per unit count),|b/t|,0.07215958125703849,0.02640306668753695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.733,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,SQFTBLD,living_area,A_structural,level,sqft,0.0006,,8.346,0.0,,,,high,building area; squared term also included; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,0.6458346250025833,0.07738253354931503,100*b/0.092903 (X per sqft->m2),|b/t|,0.21573341885144742,0.025848720207458358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.346,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,SQFTBD2,living_area,A_structural,squared,sqft squared,-1e-07,,-4.214,0.0,,,,high,square of building area; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",other,-1e-07,2.3730422401518744e-08,not harmonised,|b/t|,-0.11086565009462805,0.026308887065645006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.214,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,SQFTLOT,lot_size,A_structural,level,sqft,8e-06,,1.08,0.28,,,,high,lot size; squared term also included; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,0.008611128333367777,0.007973266975340534,100*b/0.092903 (X per sqft->m2),|b/t|,0.02857816568745809,0.02646126452542416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.08,1427.0,sqft,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,SQFTLOT2,lot_size,A_structural,squared,sqft squared,-6e-11,,-0.232,0.817,,,,high,square of lot size; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",other,-6e-11,2.586206896551724e-10,not harmonised,|b/t|,-0.006141405987506458,0.02647157753235542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.232,1427.0,sqft sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,STORIES,storeys,A_structural,level,"count (1, 1.5, 2)",0.053,,1.547,0.122,,,,high,number of stories; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,5.3,3.4259857789269557,100*b/1 (per unit count),|b/t|,0.04091800550855998,0.02644990659893987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.547,1427.0,count,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,VIEW,view,E_environment,level,index 0-6,0.058,,4.781,0.0,,,,high,"view index, 0 = no view, max 6; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005",ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,5.800000000000001,1.2131353273373773,100*b (unit as reported: index 0-6; no harmonised unit defined for this characteristic),|b/t|,0.12556136111665755,0.02626257291710051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.781,1427.0,index 0-6,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,STRUCAGE,age,A_structural,level,years,-0.0036,,-2.466,0.014,,,,high,age of structure; squared term also included; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,-0.36,0.145985401459854,100*b/1 (per year),|b/t|,-0.06514148872305812,0.02641585106368942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.466,1427.0,years,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,STRUCAG2,age,A_structural,squared,years squared,2e-05,,1.48,0.139,,,,high,square of structure age; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",other,2e-05,1.3513513513513515e-05,not harmonised,|b/t|,0.0391486391610485,0.026451783216924662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,1427.0,years sqd,nonstandard_x_transform,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,NEGHAGE,other,other,level,years,-0.0042,,-3.001,0.003,,,,high,age of the neighborhood in years; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,-0.42,0.13995334888370542,100*b (unit as reported: years; no harmonised unit defined for this characteristic),|b/t|,-0.07919319561329129,0.026388935559244018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.001,1427.0,years,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,False,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,VANDAL,crime,F_neighbourhood,level,vandalism incidents per resident per year (neighborhood),0.0044,,2.58,0.01,,,,high,neighborhood vandalism rate; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,0.44,0.17054263565891475,100*b (unit as reported: vandalism incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,0.06813922145047052,0.026410550949794772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.58,1427.0,vandalism incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,BURGLAR,crime,F_neighbourhood,level,burglary incidents per resident per year (neighborhood),-0.0326,,-4.56,0.0,,,,high,neighborhood burglary rate; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,-3.26,0.7149122807017544,100*b (unit as reported: burglary incidents per resident per year; no harmonised unit defined for this characteristic),|b/t|,-0.11984268109825093,0.026281289714528717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.56,1427.0,burglary incidents per resident per year,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,MDFAMINC,income,F_neighbourhood,level,USD (median family income of neighborhood),-1e-06,,-0.5,0.618,,,,high,neighborhood median family income; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,-9.999999999999999e-05,0.00019999999999999998,100*b (unit as reported: usd; no harmonised unit defined for this characteristic),|b/t|,-0.013234879103074857,0.026469758206149715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,1427.0,usd,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,NONWHTE,demographics,F_neighbourhood,level,percent,-0.00282,,-2.868,0.004,,,,high,percent of neighborhood population non-white; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,-0.28200000000000003,0.09832635983263599,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.07570404539498199,0.026396110667706408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.868,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,PCTBADEG,education_level,F_neighbourhood,level,percent,0.0163,,7.151,0.0,,,,high,percent of neighborhood population with a B.A. degree; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",semi_elasticity,1.63,0.2279401482310166,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.18599850914703844,0.0260101397213031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.151,1427.0,percent,unit_not_harmonisable,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,False,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,HIGH85,highway_distance,D_location,dummy,binary (within 85 feet of high-traffic arterial),-0.1022,,-2.331,0.02,,,,high,= 1 if house within 85 ft of a high-traffic arterial; reference: not near high-traffic arterial; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",pct_premium,-9.80183847392302,3.9584368984209553,100*(exp(b - se^2/2) - 1),|b/t|,-0.06158926618772857,0.026421821616357175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.331,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,HIGH350,highway_distance,D_location,dummy,binary (85-350 feet of high-traffic arterial),-0.0463,,-2.237,0.025,,,,high,= 1 if house between 85 and 350 ft of a high-traffic arterial; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",pct_premium,-4.544898499453376,1.9760920566028706,100*(exp(b - se^2/2) - 1),|b/t|,-0.05911447581545815,0.02642578266225219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.237,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09,85ec88163a881d2f9a23ced6110d5e4e05b48d09:T6-c1,6,1,47,HIGH550,highway_distance,D_location,dummy,binary (350-550 feet of high-traffic arterial),0.00558,,0.26,0.794,,,,high,= 1 if house between 350 and 550 ft of a high-traffic arterial; coefficients printed in scientific notation (e.g. 4.07E-02); p printed as 0 when <0.0005,ln(1994 sale price),log,OLS,1437.0,0.6,none,"Final model, White heteroskedasticity-consistent covariance; t-score and p-value in columns; R2 .60, adj. R2 .5963 (Table 7, p.48)",pct_premium,0.5364036386425131,2.1581628586003414,100*(exp(b - se^2/2) - 1),|b/t|,0.006882576938168896,0.02647144976218806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26,1427.0,binary,,1997.0,United States,North America,High income,1994.0,1994.0,single_family,transaction,thesis,False,False,robust,,1994.0,ols,other,True,True,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H1_FLARA,living_area,A_structural,other,m2 (total floor area; standardized beta),-0.007,,,,,,,medium,Total floor area m2 (EPC). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.007,,not harmonised,,,,,,49593.0,m2,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H1_FLARA,living_area,A_structural,other,m2 (total floor area; standardized beta),-0.009,,,,**,,,medium,Total floor area m2 (EPC). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.009,,not harmonised,,,,,,49593.0,m2,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H1_FLARA,living_area,A_structural,other,m2 (total floor area; standardized beta),-0.012,,,,**,,,medium,Total floor area m2 (EPC). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.012,,not harmonised,,,,,,49593.0,m2,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H1_FLARA,living_area,A_structural,other,m2 (total floor area; standardized beta),-0.014,,,,**,,,medium,Total floor area m2 (EPC). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.014,,not harmonised,,,,,,49593.0,m2,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H1_FLARA,living_area,A_structural,other,m2 (total floor area; standardized beta),-0.016,,,,***,,,medium,Total floor area m2 (EPC). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.016,,not harmonised,,,,,,49593.0,m2,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H2_INSUP,insulation,B_interior,other,ordinal 1-5 (EPC insulation performance; standardized beta),-0.009,,,,**,,,medium,House insulation performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.009,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H2_INSUP,insulation,B_interior,other,ordinal 1-5 (EPC insulation performance; standardized beta),-0.006,,,,,,,medium,House insulation performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.006,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H2_INSUP,insulation,B_interior,other,ordinal 1-5 (EPC insulation performance; standardized beta),-0.001,,,,,,,medium,House insulation performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.001,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H2_INSUP,insulation,B_interior,other,ordinal 1-5 (EPC insulation performance; standardized beta),-0.001,,,,,,,medium,House insulation performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.001,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H2_INSUP,insulation,B_interior,other,ordinal 1-5 (EPC insulation performance; standardized beta),0.004,,,,,,,medium,House insulation performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.004,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H3_LIGTP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC lighting performance; standardized beta),-0.039,,,,***,,,medium,House lighting performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.039,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H3_LIGTP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC lighting performance; standardized beta),-0.037,,,,***,,,medium,House lighting performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.037,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H3_LIGTP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC lighting performance; standardized beta),-0.036,,,,***,,,medium,House lighting performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.036,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H3_LIGTP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC lighting performance; standardized beta),-0.036,,,,***,,,medium,House lighting performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.036,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H3_LIGTP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC lighting performance; standardized beta),-0.035,,,,***,,,medium,House lighting performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.035,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H4_HOTWP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC hot water performance; standardized beta),0.002,,,,,,,medium,House hot water performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.002,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H4_HOTWP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC hot water performance; standardized beta),0.002,,,,,,,medium,House hot water performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.002,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H4_HOTWP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC hot water performance; standardized beta),0.001,,,,,,,medium,House hot water performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.001,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H4_HOTWP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC hot water performance; standardized beta),0.002,,,,,,,medium,House hot water performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.002,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H4_HOTWP,energy_efficiency,A_structural,other,ordinal 1-5 (EPC hot water performance; standardized beta),0.003,,,,,,,medium,House hot water performance rating. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.003,,not harmonised,,,,,,49593.0,ordinal 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H5_CO2EM,energy_efficiency,A_structural,other,tonnes CO2/yr (EPC CO2 emission; standardized beta),0.054,,,,***,,,medium,"House CO2 emission (EPC), mean 2.00. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.054,,not harmonised,,,,,,49593.0,tonnes co2/yr,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H5_CO2EM,energy_efficiency,A_structural,other,tonnes CO2/yr (EPC CO2 emission; standardized beta),0.059,,,,***,,,medium,"House CO2 emission (EPC), mean 2.00. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.059,,not harmonised,,,,,,49593.0,tonnes co2/yr,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H5_CO2EM,energy_efficiency,A_structural,other,tonnes CO2/yr (EPC CO2 emission; standardized beta),0.059,,,,***,,,medium,"House CO2 emission (EPC), mean 2.00. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.059,,not harmonised,,,,,,49593.0,tonnes co2/yr,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H5_CO2EM,energy_efficiency,A_structural,other,tonnes CO2/yr (EPC CO2 emission; standardized beta),0.06,,,,***,,,medium,"House CO2 emission (EPC), mean 2.00. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.06,,not harmonised,,,,,,49593.0,tonnes co2/yr,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H5_CO2EM,energy_efficiency,A_structural,other,tonnes CO2/yr (EPC CO2 emission; standardized beta),0.059,,,,***,,,medium,"House CO2 emission (EPC), mean 2.00. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.059,,not harmonised,,,,,,49593.0,tonnes co2/yr,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H6_FLLEV,floor_level,A_structural,other,"ordinal 1-3 (1 low, 2 mid, 3 high; standardized beta)",0.068,,,,***,,,medium,Floor level category. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.068,,not harmonised,,,,,,49593.0,ordinal 1-3,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H6_FLLEV,floor_level,A_structural,other,"ordinal 1-3 (1 low, 2 mid, 3 high; standardized beta)",0.062,,,,***,,,medium,Floor level category. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.062,,not harmonised,,,,,,49593.0,ordinal 1-3,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H6_FLLEV,floor_level,A_structural,other,"ordinal 1-3 (1 low, 2 mid, 3 high; standardized beta)",0.063,,,,***,,,medium,Floor level category. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.063,,not harmonised,,,,,,49593.0,ordinal 1-3,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H6_FLLEV,floor_level,A_structural,other,"ordinal 1-3 (1 low, 2 mid, 3 high; standardized beta)",0.062,,,,***,,,medium,Floor level category. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.062,,not harmonised,,,,,,49593.0,ordinal 1-3,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H6_FLLEV,floor_level,A_structural,other,"ordinal 1-3 (1 low, 2 mid, 3 high; standardized beta)",0.066,,,,***,,,medium,Floor level category. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.066,,not harmonised,,,,,,49593.0,ordinal 1-3,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H7_PROTY,dwelling_type,A_structural,other,"nominal code 1-4 (1 detached, 2 flat, 3 semi, 4 terrace; standardized beta)",-0.026,,,,***,,,medium,"Property type entered as a single numeric code, not dummies. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.026,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H7_PROTY,dwelling_type,A_structural,other,"nominal code 1-4 (1 detached, 2 flat, 3 semi, 4 terrace; standardized beta)",-0.02,,,,***,,,medium,"Property type entered as a single numeric code, not dummies. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.02,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H7_PROTY,dwelling_type,A_structural,other,"nominal code 1-4 (1 detached, 2 flat, 3 semi, 4 terrace; standardized beta)",-0.021,,,,***,,,medium,"Property type entered as a single numeric code, not dummies. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.021,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H7_PROTY,dwelling_type,A_structural,other,"nominal code 1-4 (1 detached, 2 flat, 3 semi, 4 terrace; standardized beta)",-0.021,,,,***,,,medium,"Property type entered as a single numeric code, not dummies. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.021,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H7_PROTY,dwelling_type,A_structural,other,"nominal code 1-4 (1 detached, 2 flat, 3 semi, 4 terrace; standardized beta)",-0.02,,,,***,,,medium,"Property type entered as a single numeric code, not dummies. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.02,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,H8_MENER,heating,A_structural,other,"nominal code 1-4 (1 electricity, 2 gas/LPG, 3 oil/coal, 4 others; standardized beta)",0.122,,,,***,,,medium,Main energy source entered as a single numeric code. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.122,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,H8_MENER,heating,A_structural,other,"nominal code 1-4 (1 electricity, 2 gas/LPG, 3 oil/coal, 4 others; standardized beta)",0.135,,,,***,,,medium,Main energy source entered as a single numeric code. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.135,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,H8_MENER,heating,A_structural,other,"nominal code 1-4 (1 electricity, 2 gas/LPG, 3 oil/coal, 4 others; standardized beta)",0.136,,,,***,,,medium,Main energy source entered as a single numeric code. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.136,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,H8_MENER,heating,A_structural,other,"nominal code 1-4 (1 electricity, 2 gas/LPG, 3 oil/coal, 4 others; standardized beta)",0.134,,,,***,,,medium,Main energy source entered as a single numeric code. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.134,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,H8_MENER,heating,A_structural,other,"nominal code 1-4 (1 electricity, 2 gas/LPG, 3 oil/coal, 4 others; standardized beta)",0.137,,,,***,,,medium,Main energy source entered as a single numeric code. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.137,,not harmonised,,,,,,49593.0,nominal code 1-4,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S1_IMBLY,visual_quality,H_ai_derived,other,"score 1-5 (ML-predicted perceived imageability of street view images, mean within 1 km; standardized beta)",0.024,,,,***,,,medium,AI-derived: BP neural network prediction of subjective perception from SVI segmentation. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.024,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S1_IMBLY,visual_quality,H_ai_derived,other,"score 1-5 (ML-predicted perceived imageability of street view images, mean within 1 km; standardized beta)",0.032,,,,***,,,medium,AI-derived: BP neural network prediction of subjective perception from SVI segmentation. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.032,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S1_IMBLY,visual_quality,H_ai_derived,other,"score 1-5 (ML-predicted perceived imageability of street view images, mean within 1 km; standardized beta)",0.031,,,,***,,,medium,AI-derived: BP neural network prediction of subjective perception from SVI segmentation. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.031,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S1_IMBLY,visual_quality,H_ai_derived,other,"score 1-5 (ML-predicted perceived imageability of street view images, mean within 1 km; standardized beta)",0.035,,,,***,,,medium,AI-derived: BP neural network prediction of subjective perception from SVI segmentation. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.035,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S1_IMBLY,visual_quality,H_ai_derived,other,"score 1-5 (ML-predicted perceived imageability of street view images, mean within 1 km; standardized beta)",0.029,,,,***,,,medium,AI-derived: BP neural network prediction of subjective perception from SVI segmentation. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.029,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S2_ENCLS,visual_quality,H_ai_derived,other,score 1-5 (perceived enclosure; standardized beta),0.135,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.135,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S2_ENCLS,visual_quality,H_ai_derived,other,score 1-5 (perceived enclosure; standardized beta),0.157,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.157,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S2_ENCLS,visual_quality,H_ai_derived,other,score 1-5 (perceived enclosure; standardized beta),0.16,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.16,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S2_ENCLS,visual_quality,H_ai_derived,other,score 1-5 (perceived enclosure; standardized beta),0.164,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.164,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S2_ENCLS,visual_quality,H_ai_derived,other,score 1-5 (perceived enclosure; standardized beta),0.125,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.125,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S3_HMSCL,visual_quality,H_ai_derived,other,score 1-5 (perceived human scale; standardized beta),0.043,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.043,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S3_HMSCL,visual_quality,H_ai_derived,other,score 1-5 (perceived human scale; standardized beta),0.037,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.037,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S3_HMSCL,visual_quality,H_ai_derived,other,score 1-5 (perceived human scale; standardized beta),0.035,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.035,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S3_HMSCL,visual_quality,H_ai_derived,other,score 1-5 (perceived human scale; standardized beta),0.033,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.033,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S3_HMSCL,visual_quality,H_ai_derived,other,score 1-5 (perceived human scale; standardized beta),0.027,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.027,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S4_TRANS,visual_quality,H_ai_derived,other,score 1-5 (perceived transparency; standardized beta),-0.022,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.022,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S4_TRANS,visual_quality,H_ai_derived,other,score 1-5 (perceived transparency; standardized beta),-0.009,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.009,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S4_TRANS,visual_quality,H_ai_derived,other,score 1-5 (perceived transparency; standardized beta),-0.009,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.009,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S4_TRANS,visual_quality,H_ai_derived,other,score 1-5 (perceived transparency; standardized beta),-0.01,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.01,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S4_TRANS,visual_quality,H_ai_derived,other,score 1-5 (perceived transparency; standardized beta),-0.017,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.017,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S5_CMPLY,visual_quality,H_ai_derived,other,score 1-5 (perceived complexity; standardized beta),0.028,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.028,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S5_CMPLY,visual_quality,H_ai_derived,other,score 1-5 (perceived complexity; standardized beta),0.034,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.034,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S5_CMPLY,visual_quality,H_ai_derived,other,score 1-5 (perceived complexity; standardized beta),0.028,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.028,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S5_CMPLY,visual_quality,H_ai_derived,other,score 1-5 (perceived complexity; standardized beta),0.028,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.028,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S5_CMPLY,visual_quality,H_ai_derived,other,score 1-5 (perceived complexity; standardized beta),0.026,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.026,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S6_SAFTY,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of safety; standardized beta),-0.039,,,,***,,,medium,"AI-derived subjective perception score (street view images); perceived safety, not crime. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.039,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S6_SAFTY,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of safety; standardized beta),-0.051,,,,***,,,medium,"AI-derived subjective perception score (street view images); perceived safety, not crime. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.051,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S6_SAFTY,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of safety; standardized beta),-0.051,,,,***,,,medium,"AI-derived subjective perception score (street view images); perceived safety, not crime. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.051,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S6_SAFTY,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of safety; standardized beta),-0.052,,,,***,,,medium,"AI-derived subjective perception score (street view images); perceived safety, not crime. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.052,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S6_SAFTY,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of safety; standardized beta),-0.048,,,,***,,,medium,"AI-derived subjective perception score (street view images); perceived safety, not crime. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.048,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S7_COFRT,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of comfort; standardized beta),0.112,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.112,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S7_COFRT,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of comfort; standardized beta),0.124,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.124,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S7_COFRT,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of comfort; standardized beta),0.126,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.126,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S7_COFRT,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of comfort; standardized beta),0.123,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.123,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S7_COFRT,visual_quality,H_ai_derived,other,score 1-5 (perceived sense of comfort; standardized beta),0.112,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.112,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,S8_INTST,visual_quality,H_ai_derived,other,score 1-5 (perceived level of interest; standardized beta),-0.043,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.043,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,S8_INTST,visual_quality,H_ai_derived,other,score 1-5 (perceived level of interest; standardized beta),-0.044,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.044,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,S8_INTST,visual_quality,H_ai_derived,other,score 1-5 (perceived level of interest; standardized beta),-0.044,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.044,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,S8_INTST,visual_quality,H_ai_derived,other,score 1-5 (perceived level of interest; standardized beta),-0.041,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.041,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,S8_INTST,visual_quality,H_ai_derived,other,score 1-5 (perceived level of interest; standardized beta),-0.037,,,,***,,,medium,AI-derived subjective perception score (street view images). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.037,,not harmonised,,,,,,49593.0,score 1-5,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,15,L1_D2CBD,cbd_distance,D_location,other,"cost network distance index (0.02-0.81, unit not stated; standardized beta)",-0.098,,,,***,,,medium,Cost network distance to CBD (Ordnance Survey network). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.098,,not harmonised,,,,,,49593.0,cost network distance index,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,M1_INT400,other,other,other,space-syntax integration [HH] R400 (index; standardized beta),0.115,,,,***,,,medium,Street-network closeness centrality (space syntax integration) radius 400 m; taxonomy other. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.115,,not harmonised,,,,,,49593.0,space-syntax integration [hh] r400,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,15,M2_CH400,other,other,other,space-syntax choice R400 (index; standardized beta),-0.052,,,,***,,,medium,Street-network betweenness centrality (space syntax choice) radius 400 m; taxonomy other. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.052,,not harmonised,,,,,,49593.0,space-syntax choice r400,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,M3_INT800,other,other,other,space-syntax integration R800 (index; standardized beta),0.217,,,,***,,,medium,Space syntax integration radius 800 m. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.217,,not harmonised,,,,,,49593.0,space-syntax integration r800,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,15,M4_CH800,other,other,other,space-syntax choice R800 (index; standardized beta),-0.14,,,,***,,,medium,Space syntax choice radius 800 m. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.14,,not harmonised,,,,,,49593.0,space-syntax choice r800,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,M5_INT2000,other,other,other,space-syntax integration R2000 (index; standardized beta),0.286,,,,***,,,medium,Space syntax integration radius 2000 m. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.286,,not harmonised,,,,,,49593.0,space-syntax integration r2000,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,15,M6_CH2000,other,other,other,space-syntax choice R2000 (index; standardized beta),-0.185,,,,***,,,medium,Space syntax choice radius 2000 m. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.185,,not harmonised,,,,,,49593.0,space-syntax choice r2000,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,M7_INT6000,other,other,other,space-syntax integration R6000 (index; standardized beta),0.324,,,,***,,,medium,Space syntax integration radius 6000 m. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.324,,not harmonised,,,,,,49593.0,space-syntax integration r6000,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,15,M8_CH6000,other,other,other,space-syntax choice R6000 (index; standardized beta),-0.087,,,,***,,,medium,Space syntax choice radius 6000 m. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.087,,not harmonised,,,,,,49593.0,space-syntax choice r6000,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F1_DENLS,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of urban services POI; standardized beta),0.135,,,,***,,,medium,Density of urban services within 1 km (OS POI). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.135,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F1_DENLS,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of urban services POI; standardized beta),0.111,,,,***,,,medium,Density of urban services within 1 km (OS POI). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.111,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F1_DENLS,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of urban services POI; standardized beta),0.088,,,,***,,,medium,Density of urban services within 1 km (OS POI). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.088,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F1_DENLS,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of urban services POI; standardized beta),0.087,,,,***,,,medium,Density of urban services within 1 km (OS POI). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.087,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F1_DENLS,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of urban services POI; standardized beta),0.102,,,,***,,,medium,Density of urban services within 1 km (OS POI). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.102,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F2_DENWK,employment_access,D_location,other,count within 1 km (density of workplaces; standardized beta),-0.137,,,,***,,,medium,Density of workplaces within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.137,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F2_DENWK,employment_access,D_location,other,count within 1 km (density of workplaces; standardized beta),-0.177,,,,***,,,medium,Density of workplaces within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.177,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F2_DENWK,employment_access,D_location,other,count within 1 km (density of workplaces; standardized beta),-0.165,,,,***,,,medium,Density of workplaces within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.165,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F2_DENWK,employment_access,D_location,other,count within 1 km (density of workplaces; standardized beta),-0.164,,,,***,,,medium,Density of workplaces within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.164,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F2_DENWK,employment_access,D_location,other,count within 1 km (density of workplaces; standardized beta),-0.181,,,,***,,,medium,Density of workplaces within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.181,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F3_DENAT,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of attractions; standardized beta),0.419,,,,***,,,medium,Density of attractions within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.419,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F3_DENAT,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of attractions; standardized beta),0.486,,,,***,,,medium,Density of attractions within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.486,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F3_DENAT,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of attractions; standardized beta),0.479,,,,***,,,medium,Density of attractions within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.479,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F3_DENAT,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of attractions; standardized beta),0.456,,,,***,,,medium,Density of attractions within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.456,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F3_DENAT,neighbourhood_amenities,F_neighbourhood,other,count within 1 km (density of attractions; standardized beta),0.437,,,,***,,,medium,Density of attractions within 1 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.437,,not harmonised,,,,,,49593.0,count within 1 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F4_D2UDG,transit_distance,D_location,other,km (network distance to nearest TfL station; standardized beta),-0.028,,,,***,,,medium,"Distance to TfL (underground/rail) station, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.028,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F4_D2UDG,transit_distance,D_location,other,km (network distance to nearest TfL station; standardized beta),-0.019,,,,***,,,medium,"Distance to TfL (underground/rail) station, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.019,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F4_D2UDG,transit_distance,D_location,other,km (network distance to nearest TfL station; standardized beta),-0.018,,,,***,,,medium,"Distance to TfL (underground/rail) station, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.018,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F4_D2UDG,transit_distance,D_location,other,km (network distance to nearest TfL station; standardized beta),-0.021,,,,***,,,medium,"Distance to TfL (underground/rail) station, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.021,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F4_D2UDG,transit_distance,D_location,other,km (network distance to nearest TfL station; standardized beta),-0.02,,,,***,,,medium,"Distance to TfL (underground/rail) station, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.02,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F5_A2UDG,transit_distance,D_location,other,count within 3 km (number of TfL stations; standardized beta),0.048,,,,***,,,medium,Accessibility to TfL stations: count within 3 km (sign positive = more stations). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.048,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F5_A2UDG,transit_distance,D_location,other,count within 3 km (number of TfL stations; standardized beta),0.044,,,,***,,,medium,Accessibility to TfL stations: count within 3 km (sign positive = more stations). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.044,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F5_A2UDG,transit_distance,D_location,other,count within 3 km (number of TfL stations; standardized beta),0.046,,,,***,,,medium,Accessibility to TfL stations: count within 3 km (sign positive = more stations). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.046,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F5_A2UDG,transit_distance,D_location,other,count within 3 km (number of TfL stations; standardized beta),0.041,,,,***,,,medium,Accessibility to TfL stations: count within 3 km (sign positive = more stations). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.041,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F5_A2UDG,transit_distance,D_location,other,count within 3 km (number of TfL stations; standardized beta),0.009,,,,***,,,medium,Accessibility to TfL stations: count within 3 km (sign positive = more stations). Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.009,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F6_D2SCH,school_distance,D_location,other,km (network distance to nearest Ofsted good+ school; standardized beta),0.041,,,,***,,,medium,"Distance to quality school, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,0.041,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F6_D2SCH,school_distance,D_location,other,km (network distance to nearest Ofsted good+ school; standardized beta),0.031,,,,***,,,medium,"Distance to quality school, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,0.031,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F6_D2SCH,school_distance,D_location,other,km (network distance to nearest Ofsted good+ school; standardized beta),0.032,,,,***,,,medium,"Distance to quality school, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,0.032,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F6_D2SCH,school_distance,D_location,other,km (network distance to nearest Ofsted good+ school; standardized beta),0.03,,,,***,,,medium,"Distance to quality school, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,0.03,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F6_D2SCH,school_distance,D_location,other,km (network distance to nearest Ofsted good+ school; standardized beta),0.034,,,,***,,,medium,"Distance to quality school, km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.","House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,0.034,,not harmonised,,,,,,49593.0,km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m0,5,Model 0,16,F7_A2SCH,school_quality,F_neighbourhood,other,count within 3 km (number of Ofsted good+ schools; standardized beta),-0.071,,,,***,,,medium,Accessibility to quality schools: count within 3 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.496,none,Baseline location attributes: cost network distance to CBD + postcode district code; Greater London transactions since 2017; R2 is adjusted,other,-0.071,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m1,5,Model 1,16,F7_A2SCH,school_quality,F_neighbourhood,other,count within 3 km (number of Ofsted good+ schools; standardized beta),-0.039,,,,***,,,medium,Accessibility to quality schools: count within 3 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.482,none,Space-syntax integration/choice at R400 replace location attributes; Greater London transactions since 2017; R2 is adjusted,other,-0.039,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m2,5,Model 2,16,F7_A2SCH,school_quality,F_neighbourhood,other,count within 3 km (number of Ofsted good+ schools; standardized beta),-0.045,,,,***,,,medium,Accessibility to quality schools: count within 3 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.484,none,Space-syntax integration/choice at R800; Greater London transactions since 2017; R2 is adjusted,other,-0.045,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m3,5,Model 3,16,F7_A2SCH,school_quality,F_neighbourhood,other,count within 3 km (number of Ofsted good+ schools; standardized beta),-0.067,,,,***,,,medium,Accessibility to quality schools: count within 3 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.486,none,Space-syntax integration/choice at R2000; Greater London transactions since 2017; R2 is adjusted,other,-0.067,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635,8674ce1937729ab9e1f1084511ae7a29ecdbc635:T5-m4,5,Model 4,16,F7_A2SCH,school_quality,F_neighbourhood,other,count within 3 km (number of Ofsted good+ schools; standardized beta),-0.126,,,,***,,,medium,Accessibility to quality schools: count within 3 km. Standardized (beta) regression coefficient on price per m2 in levels (eq. 3; Figure 8 and text describe Table 5 values as standardized coefficients); not a raw marginal price. No SE.,"House transaction price per m2 (GBP/m2), Land Registry PPD + EPC",per_m2,OLS,49603.0,0.494,none,Space-syntax integration/choice at R6000; Greater London transactions since 2017; R2 is adjusted,other,-0.126,,not harmonised,,,,,,49593.0,count within 3 km,nonstandard_x_transform,2023.0,United Kingdom,Europe,High income,2017.0,2021.0,mixed,transaction,journal,False,False,unknown,backpropagation neural network (SVI semantic segmentation view indices -> perceived streetscape scores);street view image semantic segmentation,2019.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Ln (Floor),living_area,A_structural,log,m2,0.5825,0.0086,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",elasticity,0.5825,0.0086,b (log-log),reported,0.516340359331432,0.00762322247253273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),67.73255813953489,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Ln (Age),age,A_structural,log,years,-0.0679,0.0023,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",elasticity,-0.0679,0.0023,b (log-log),reported,-0.2541623907415493,0.008609329877843347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.521739130434785,12620.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Ln (Lot),lot_size,A_structural,log,m2,0.0097,0.0013,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",elasticity,0.0097,0.0013,b (log-log),reported,0.06627394786719135,0.00888207548735554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.461538461538462,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Co-op,other,other,dummy,binary,-0.0382,0.0107,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",pct_premium,-3.753468034825158,1.0298968467849163,100*(exp(b - se^2/2) - 1),reported,-0.031763672576526104,0.008897154360440558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5700934579439254,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Twin-house,dwelling_type,A_structural,dummy,binary,-0.074,0.0069,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",pct_premium,-7.13504129252579,0.6407834687504882,100*(exp(b - se^2/2) - 1),reported,-0.09503484044742373,0.008861356744421943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.72463768115942,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Row-house,dwelling_type,A_structural,dummy,binary,-0.1168,0.0074,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",pct_premium,-11.026123338600158,0.6584247147162512,100*(exp(b - se^2/2) - 1),reported,-0.13913505551717684,0.008815063448862232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.783783783783782,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-A,5,Regression A,16,Apartment,dwelling_type,A_structural,dummy,binary,0.0017,0.0086,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.71,se,"Treatment + control regions; years 2003-2006 and 2010-2013 (2007-2009 excluded); zip-code, year, month dummies",pct_premium,0.16644035846302696,0.8614632434044961,100*(exp(b - se^2/2) - 1),reported,0.00175962498667754,0.008901632285545203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19767441860465115,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Ln (Floor),living_area,A_structural,log,m2,0.5828,0.0086,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,elasticity,0.5828,0.0086,b (log-log),reported,0.5165353476024656,0.007622175685280035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),67.76744186046511,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Ln (Age),age,A_structural,log,years,-0.0676,0.0023,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,elasticity,-0.0676,0.0023,b (log-log),reported,-0.2531115267173383,0.008611782713755593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.391304347826086,12620.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Ln (Lot),lot_size,A_structural,log,m2,0.0098,0.0013,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,elasticity,0.0098,0.0013,b (log-log),reported,0.06695413714907955,0.008881671254469736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.538461538461538,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Co-op,other,other,dummy,binary,-0.0389,0.0107,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,pct_premium,-3.820817032301582,1.0291761712580287,100*(exp(b - se^2/2) - 1),reported,-0.03234512592519113,0.008896988365026866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.635514018691589,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0737,0.0069,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,pct_premium,-7.107177625572481,0.6409757326292531,100*(exp(b - se^2/2) - 1),reported,-0.09465302279545323,0.00886168056022561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.681159420289855,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Row-house,dwelling_type,A_structural,dummy,binary,-0.116,0.0074,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,pct_premium,-10.954915758036554,0.6589516652401298,100*(exp(b - se^2/2) - 1),reported,-0.138200338520747,0.008816228491840758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.675675675675675,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-B,5,Regression B,16,Apartment,dwelling_type,A_structural,dummy,binary,0.0029,0.0086,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7117,se,As Regression A with town-specific treatment dummies,pct_premium,0.2867122355868279,0.8624976197982924,100*(exp(b - se^2/2) - 1),reported,0.0030017043365377916,0.008901605963525865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3372093023255814,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Ln (Floor),living_area,A_structural,log,m2,0.6338,0.0055,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,elasticity,0.6338,0.0055,b (log-log),reported,0.642312635100817,0.00557387108402413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),115.23636363636365,18908.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Ln (Age),age,A_structural,log,years,-0.0728,0.0018,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,elasticity,-0.0728,0.0018,b (log-log),reported,-0.28217529053218315,0.006976861579092441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.44444444444445,18908.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Ln (Lot),lot_size,A_structural,log,m2,0.0076,0.0013,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,elasticity,0.0076,0.0013,b (log-log),reported,0.04247714205263422,0.007265826930055854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.846153846153847,18908.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Co-op,other,other,dummy,binary,-0.0549,0.004,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,pct_premium,-5.342777140725641,0.37863192048034505,100*(exp(b - se^2/2) - 1),reported,-0.09932003840211086,0.007236432670463451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.725,18908.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0866,0.0053,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,pct_premium,-8.296903997492954,0.48603323510213664,100*(exp(b - se^2/2) - 1),reported,-0.1179979671966721,0.00722158459748686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.339622641509433,18908.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Row-house,dwelling_type,A_structural,dummy,binary,-0.1104,0.0053,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,pct_premium,-10.453670357073342,0.4746022128487805,100*(exp(b - se^2/2) - 1),reported,-0.14977649850018548,0.007190357264954557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.830188679245282,18908.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-C,5,Regression C,16,Apartment,dwelling_type,A_structural,dummy,binary,-0.0486,0.0062,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,18918.0,0.8146,se,Core town (Kristiansand) + control region; 2007-2009 excluded,pct_premium,-4.745622973217223,0.59058848856772,100*(exp(b - se^2/2) - 1),reported,-0.056913758039093466,0.0072606028774152166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.838709677419355,18908.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Ln (Floor),living_area,A_structural,log,m2,0.585,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",elasticity,0.585,0.0072,b (log-log),reported,0.5269139585733711,0.006485094874749183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.25,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Ln (Age),age,A_structural,log,years,-0.0628,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",elasticity,-0.0628,0.002,b (log-log),reported,-0.23299590632085806,0.007420251793657902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.399999999999995,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Co-op,other,other,dummy,binary,-0.0421,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",pct_premium,-4.126756289211642,0.8916597255014131,100*(exp(b - se^2/2) - 1),reported,-0.034520669346012986,0.0076257060550575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.526881720430108,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0694,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",pct_premium,-6.706226808227078,0.5411129859561633,100*(exp(b - se^2/2) - 1),reported,-0.09092177261175431,0.007598649584267651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.965517241379311,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Row-house,dwelling_type,A_structural,dummy,binary,-0.1127,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",pct_premium,-10.65996515166483,0.5717879331263037,100*(exp(b - se^2/2) - 1),reported,-0.1331672975132452,0.00756229551095625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.609375,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T5-D,5,Regression D,16,Apartment,dwelling_type,A_structural,dummy,binary,0.003,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6911,se,"Treatment + control regions, all years 2003-2013, four treatment-period dummies",pct_premium,0.29777798043959525,0.7321932882874652,100*(exp(b - se^2/2) - 1),reported,0.003135705326685974,0.007630216294935872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.410958904109589,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Ln (Floor),living_area,A_structural,log,m2,0.5845,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",elasticity,0.5845,0.0072,b (log-log),reported,0.5265885244291887,0.006486633662771872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.18055555555556,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Ln (Age),age,A_structural,log,years,-0.0629,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",elasticity,-0.0629,0.002,b (log-log),reported,-0.23334673220192167,0.007419609926929148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.45,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Co-op,other,other,dummy,binary,-0.0423,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",pct_premium,-4.145929020616745,0.8914814113883186,100*(exp(b - se^2/2) - 1),reported,-0.034684466204060634,0.007625662782453046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.548387096774194,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0695,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",pct_premium,-6.715555719092947,0.5410588773630425,100*(exp(b - se^2/2) - 1),reported,-0.0910516984496127,0.007598559007305806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.982758620689657,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Row-house,dwelling_type,A_structural,dummy,binary,-0.1131,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",pct_premium,-10.695694019354251,0.5715592636899893,100*(exp(b - se^2/2) - 1),reported,-0.13363151546728688,0.007561818735549391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.671875,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2005,6,2005,18,Apartment,dwelling_type,A_structural,dummy,binary,0.0031,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2005",pct_premium,0.30780825974323633,0.7322665112773824,100*(exp(b - se^2/2) - 1),reported,0.0032402277578750553,0.007630213752415452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4246575342465753,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Ln (Floor),living_area,A_structural,log,m2,0.5848,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",elasticity,0.5848,0.0072,b (log-log),reported,0.5267838127110238,0.006485710416414794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.22222222222223,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Ln (Age),age,A_structural,log,years,-0.0629,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",elasticity,-0.0629,0.002,b (log-log),reported,-0.23334673220192167,0.007419609926929148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.45,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Co-op,other,other,dummy,binary,-0.0422,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",pct_premium,-4.136343134232479,0.8915705639870132,100*(exp(b - se^2/2) - 1),reported,-0.03460256812359159,0.007625684444298619,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.537634408602151,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0693,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",pct_premium,-6.696896964423493,0.5411670999604141,100*(exp(b - se^2/2) - 1),reported,-0.09079184213087274,0.007598740034041297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.948275862068966,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Row-house,dwelling_type,A_structural,dummy,binary,-0.1131,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",pct_premium,-10.695694019354251,0.5715592636899893,100*(exp(b - se^2/2) - 1),reported,-0.13363151546728688,0.007561818735549391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.671875,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2006,6,2006,18,Apartment,dwelling_type,A_structural,dummy,binary,0.0032,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2006",pct_premium,0.3178395421249869,0.7323397415899648,100*(exp(b - se^2/2) - 1),reported,0.003344750082865261,0.0076302111265363765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4383561643835617,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Ln (Floor),living_area,A_structural,log,m2,0.585,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",elasticity,0.585,0.0072,b (log-log),reported,0.5269139585733711,0.006485094874749183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.25,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Ln (Age),age,A_structural,log,years,-0.0629,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",elasticity,-0.0629,0.002,b (log-log),reported,-0.23334673220192167,0.007419609926929148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.45,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Co-op,other,other,dummy,binary,-0.0419,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",pct_premium,-4.107579722876775,0.8918380752808969,100*(exp(b - se^2/2) - 1),reported,-0.03435686970609543,0.007625749123309965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.505376344086022,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0692,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",pct_premium,-6.6875661875888675,0.5412212193763357,100*(exp(b - se^2/2) - 1),reported,-0.09066190701338228,0.007598830356612965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.931034482758621,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Row-house,dwelling_type,A_structural,dummy,binary,-0.1129,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",pct_premium,-10.677831371952918,0.5716735869746747,100*(exp(b - se^2/2) - 1),reported,-0.13339941746583642,0.007562057323129787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.640625,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2007,6,2007,18,Apartment,dwelling_type,A_structural,dummy,binary,0.0032,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6902,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2007",pct_premium,0.3178395421249869,0.7323397415899648,100*(exp(b - se^2/2) - 1),reported,0.003344750082865261,0.0076302111265363765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4383561643835617,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Ln (Floor),living_area,A_structural,log,m2,0.5853,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",elasticity,0.5853,0.0072,b (log-log),reported,0.5271091078908645,0.006484171496350973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.29166666666667,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Ln (Age),age,A_structural,log,years,-0.0628,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",elasticity,-0.0628,0.002,b (log-log),reported,-0.23299590632085806,0.007420251793657902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.399999999999995,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Co-op,other,other,dummy,binary,-0.0417,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",pct_premium,-4.088399320845082,0.8920164607339037,100*(exp(b - se^2/2) - 1),reported,-0.034193067297445326,0.007625791987200036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.483870967741936,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Twin-house,dwelling_type,A_structural,dummy,binary,-0.069,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",pct_premium,-6.668901834453289,0.5413294744453572,100*(exp(b - se^2/2) - 1),reported,-0.09040202289423764,0.007599010620095337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.896551724137932,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Row-house,dwelling_type,A_structural,dummy,binary,-0.1128,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",pct_premium,-10.668898708464381,0.5717307571918354,100*(exp(b - se^2/2) - 1),reported,-0.13328336023121914,0.007562176467019526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.625,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2008,6,2008,18,Apartment,dwelling_type,A_structural,dummy,binary,0.0032,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6905,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2008",pct_premium,0.3178395421249869,0.7323397415899648,100*(exp(b - se^2/2) - 1),reported,0.003344750082865261,0.0076302111265363765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4383561643835617,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Ln (Floor),living_area,A_structural,log,m2,0.5853,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",elasticity,0.5853,0.0072,b (log-log),reported,0.5271091078908645,0.006484171496350973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.29166666666667,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Ln (Age),age,A_structural,log,years,-0.0628,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",elasticity,-0.0628,0.002,b (log-log),reported,-0.23299590632085806,0.007420251793657902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.399999999999995,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Co-op,other,other,dummy,binary,-0.0419,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",pct_premium,-4.107579722876775,0.8918380752808969,100*(exp(b - se^2/2) - 1),reported,-0.03435686970609543,0.007625749123309965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.505376344086022,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0693,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",pct_premium,-6.696896964423493,0.5411670999604141,100*(exp(b - se^2/2) - 1),reported,-0.09079184213087274,0.007598740034041297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.948275862068966,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Row-house,dwelling_type,A_structural,dummy,binary,-0.1126,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",pct_premium,-10.651030701464936,0.5718451147786513,100*(exp(b - se^2/2) - 1),reported,-0.13305122931634453,0.007562414454925444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.59375,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2009,6,2009,18,Apartment,dwelling_type,A_structural,dummy,binary,0.0029,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2009",pct_premium,0.28774870411372167,0.7321200726194809,100*(exp(b - se^2/2) - 1),reported,0.003031182792723618,0.007630218754097384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3972602739726027,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Ln (Floor),living_area,A_structural,log,m2,0.5852,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",elasticity,0.5852,0.0072,b (log-log),reported,0.5270440673810172,0.00648447929792092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.27777777777779,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Ln (Age),age,A_structural,log,years,-0.0627,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",elasticity,-0.0627,0.002,b (log-log),reported,-0.2326449894471912,0.007420892805333051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.35,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Co-op,other,other,dummy,binary,-0.0418,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",pct_premium,-4.097990001370977,0.891927263547764,100*(exp(b - se^2/2) - 1),reported,-0.03427496884704082,0.007625770580800948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.494623655913979,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0693,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",pct_premium,-6.696896964423493,0.5411670999604141,100*(exp(b - se^2/2) - 1),reported,-0.09079184213087274,0.007598740034041297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.948275862068966,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Row-house,dwelling_type,A_structural,dummy,binary,-0.1128,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",pct_premium,-10.668898708464381,0.5717307571918354,100*(exp(b - se^2/2) - 1),reported,-0.13328336023121914,0.007562176467019526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.625,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T6-c2010,6,2010,18,Apartment,dwelling_type,A_structural,dummy,binary,0.0034,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6909,se,"Treatment + control regions, all years 2003-2013; treatment assumed to start in 2010",pct_premium,0.33790511652396216,0.732486224186054,100*(exp(b - se^2/2) - 1),reported,0.003553794400546739,0.007630205624703293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4657534246575342,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Ln (Floor),living_area,A_structural,log,m2,0.582,0.0086,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,elasticity,0.582,0.0086,b (log-log),reported,0.5160152003458163,0.007624966877962235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),67.67441860465115,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Ln (Age),age,A_structural,log,years,-0.069,0.0023,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,elasticity,-0.069,0.0023,b (log-log),reported,-0.25800784355766804,0.008600261451922268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.000000000000004,12620.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Ln (Lot),lot_size,A_structural,log,m2,0.0098,0.0013,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,elasticity,0.0098,0.0013,b (log-log),reported,0.06695413714907955,0.008881671254469736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.538461538461538,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Co-op,other,other,dummy,binary,0.0032,0.0107,,,,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,pct_premium,0.31476986320131406,1.0734294842483045,100*(exp(b - se^2/2) - 1),reported,0.0026621650909800613,0.00890161452296458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2990654205607477,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0752,0.0069,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,pct_premium,-7.246412406941571,0.6400149897675949,100*(exp(b - se^2/2) - 1),reported,-0.09656169005196033,0.008860048688278275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.898550724637682,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Row-house,dwelling_type,A_structural,dummy,binary,-0.1168,0.0074,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,pct_premium,-11.026123338600158,0.6584247147162512,100*(exp(b - se^2/2) - 1),reported,-0.13913505551717684,0.008815063448862232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.783783783783782,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob1,7,Rob-1,19,Apartment,dwelling_type,A_structural,dummy,binary,-0.0006,0.0086,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7095,se,Regression A with mutual-debt parameter zeta = 1.0,pct_premium,-0.06367771713080561,0.8594841547690446,100*(exp(b - se^2/2) - 1),reported,-0.0006210449546429375,0.008901644349882103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06976744186046512,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Ln (Floor),living_area,A_structural,log,m2,0.5832,0.0086,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,elasticity,0.5832,0.0086,b (log-log),reported,0.5167952070187263,0.007620779801716472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),67.81395348837209,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Ln (Age),age,A_structural,log,years,-0.0664,0.0023,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,elasticity,-0.0664,0.0023,b (log-log),reported,-0.24889913073219927,0.00862150603439847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.869565217391305,12620.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Ln (Lot),lot_size,A_structural,log,m2,0.0097,0.0013,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,elasticity,0.0097,0.0013,b (log-log),reported,0.06627394786719135,0.00888207548735554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.461538461538462,12620.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Co-op,other,other,dummy,binary,-0.0896,0.0117,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,pct_premium,-8.576574603544518,1.06972729211741,100*(exp(b - se^2/2) - 1),reported,-0.06801202350274647,0.008881034318997028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.658119658119658,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0725,0.0069,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,pct_premium,-6.995639329129899,0.6417453651955922,100*(exp(b - se^2/2) - 1),reported,-0.0931253360558319,0.00886296301772745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.507246376811594,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Row-house,dwelling_type,A_structural,dummy,binary,-0.1167,0.0075,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,pct_premium,-11.017291798192119,0.6673890815675231,100*(exp(b - se^2/2) - 1),reported,-0.13719978226421534,0.008817466726491988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.56,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob2,7,Rob-2,19,Apartment,dwelling_type,A_structural,dummy,binary,0.0048,0.0086,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,12630.0,0.7101,se,Regression A with zeta = 0.7,pct_premium,0.47743812104892314,0.86413792307056,100*(exp(b - se^2/2) - 1),reported,0.0049682992754049365,0.008901536201767177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5581395348837209,12620.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Ln (Floor),living_area,A_structural,log,m2,0.5844,0.0072,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,elasticity,0.5844,0.0072,b (log-log),reported,0.526523409802573,0.006486941393871536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.16666666666667,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Ln (Age),age,A_structural,log,years,-0.063,0.002,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,elasticity,-0.063,0.002,b (log-log),reported,-0.23369746698490032,0.007418967205869853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.5,17176.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Ln (Lot),lot_size,A_structural,log,m2,0.0094,0.0011,,,***,,,high,"Lot = lot size exceeding 1000 m2 (0 for lots below 1000 m2, co-ops and apartments)","Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,elasticity,0.0094,0.0011,b (log-log),reported,0.0650658181749187,0.007614085105575593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.545454545454545,17176.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Co-op,other,other,dummy,binary,-0.0423,0.0093,,,***,,,high,Co-operative ownership dummy,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,pct_premium,-4.145929020616745,0.8914814113883186,100*(exp(b - se^2/2) - 1),reported,-0.034684466204060634,0.007625662782453046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.548387096774194,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Twin-house,dwelling_type,A_structural,dummy,binary,-0.0695,0.0058,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,pct_premium,-6.715555719092947,0.5410588773630425,100*(exp(b - se^2/2) - 1),reported,-0.0910516984496127,0.007598559007305806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.982758620689657,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Row-house,dwelling_type,A_structural,dummy,binary,-0.1131,0.0064,,,***,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,pct_premium,-10.695694019354251,0.5715592636899893,100*(exp(b - se^2/2) - 1),reported,-0.13363151546728688,0.007561818735549391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.671875,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob3,7,Rob-3,19,Apartment,dwelling_type,A_structural,dummy,binary,0.0031,0.0073,,,,,,high,Reference category: detached house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,17186.0,0.6901,se,Placebo: treatment assumed to start in 2004; all years,pct_premium,0.30780825974323633,0.7322665112773824,100*(exp(b - se^2/2) - 1),reported,0.0032402277578750553,0.007630213752415452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4246575342465753,17176.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob4,7,Rob-4,19,Ln(Floor),living_area,A_structural,log,m2,0.5716,0.0119,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,7053.0,0.6632,se,Detached houses only,elasticity,0.5716,0.0119,b (log-log),reported,0.4967457472817007,0.01034162769883177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.033613445378144,7043.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob4,7,Rob-4,19,Ln(Age),age,A_structural,log,years,-0.0625,0.0041,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,7053.0,0.6632,se,Detached houses only,elasticity,-0.0625,0.0041,b (log-log),reported,-0.17871805210969513,0.011723904218396003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.243902439024389,7043.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob4,7,Rob-4,19,Ln(Lot),lot_size,A_structural,log,m2,0.0103,0.0014,,,***,,,high,Lot = lot size exceeding 1000 m2,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,7053.0,0.6632,se,Detached houses only,elasticity,0.0103,0.0014,b (log-log),reported,0.08733088952397261,0.011870217993549674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.357142857142858,7043.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob5,7,Rob-5,19,Ln(Floor),living_area,A_structural,log,m2,0.4651,0.0167,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,2122.0,0.7848,se,Small houses only (row-houses and twin-houses),elasticity,0.4651,0.0167,b (log-log),reported,0.5182727867985625,0.018609235733253047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.850299401197606,2112.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob5,7,Rob-5,19,Ln(Age),age,A_structural,log,years,-0.0661,0.0045,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,2122.0,0.7848,se,Small houses only (row-houses and twin-houses),elasticity,-0.0661,0.0045,b (log-log),reported,-0.30445248466266134,0.020726719833312795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.688888888888892,2112.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob5,7,Rob-5,19,Ln(Lot),lot_size,A_structural,log,m2,0.0016,0.0023,,,***,,,high,Lot = lot size exceeding 1000 m2; stars as printed despite SE > coefficient,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,2122.0,0.7848,se,Small houses only (row-houses and twin-houses),elasticity,0.0016,0.0023,b (log-log),reported,0.015135453546857072,0.021757214473607036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6956521739130436,2112.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob5,7,Rob-5,19,Twin-house,dwelling_type,A_structural,dummy,binary,0.0499,0.0082,,,***,,,high,Small-house subsample: reference category row-house,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,2122.0,0.7848,se,Small houses only (row-houses and twin-houses),pct_premium,5.113063491656544,0.8619560991085049,100*(exp(b - se^2/2) - 1),reported,0.13126993936399756,0.021571412881458515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.085365853658536,2112.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob6,7,Rob-6,19,Ln(Floor),living_area,A_structural,log,m2,0.6615,0.0162,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,3455.0,0.7386,se,Apartments only,elasticity,0.6615,0.0162,b (log-log),reported,0.5710893650190773,0.0139858620004672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.833333333333336,3445.0,m2,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob6,7,Rob-6,19,Ln(Age),age,A_structural,log,years,-0.0786,0.0035,,,***,,,high,,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,3455.0,0.7386,se,Apartments only,elasticity,-0.0786,0.0035,b (log-log),reported,-0.3573494844396483,0.015912508849093752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.45714285714286,3445.0,years,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,True,True,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob5,7,Rob-5,19,Co-op,other,other,dummy,binary,0.0369,0.0552,,,***,,,medium,Co-operative ownership dummy; Table 7 prints six Co-op values for seven columns; since detached houses are never co-ops (footnote 5) the values are assigned to Rob-5 and Rob-6; stars as printed despite SE > coefficient,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,2122.0,0.7848,se,Small houses only (row-houses and twin-houses),pct_premium,3.600967230971719,5.727492697118497,100*(exp(b - se^2/2) - 1),reported,0.014544352501583474,0.02175740536822243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6684782608695653,2112.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c,877cbdabc2aba0920a744f15d91bf843fa770f5c:T7-Rob6,7,Rob-6,19,Co-op,other,other,dummy,binary,-0.0497,0.014,,,***,,,medium,Co-operative ownership dummy; Table 7 prints six Co-op values for seven columns; since detached houses are never co-ops (footnote 5) the values are assigned to Rob-5 and Rob-6,"Ln(Price + 0.86*mutual debt), NOK",log,OLS,3455.0,0.7386,se,Apartments only,pct_premium,-4.857840774724731,1.332120770592737,100*(exp(b - se^2/2) - 1),reported,-0.060372730914563674,0.01700640307452498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.55,3445.0,binary,,2020.0,Norway,Europe,High income,2003.0,2013.0,mixed,transaction,journal,True,True,robust,,2008.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Bedrooms,bedrooms,A_structural,level,count,0.085,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,8.5,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Bedrooms,bedrooms,A_structural,level,count,0.106,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,10.6,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Bedrooms,bedrooms,A_structural,level,count,0.113,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,11.3,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Bedrooms,bedrooms,A_structural,level,count,0.112,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,11.200000000000001,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Baths,bathrooms,A_structural,level,count,0.112,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,11.200000000000001,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Baths,bathrooms,A_structural,level,count,0.096,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,9.6,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Baths,bathrooms,A_structural,level,count,0.079,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,7.9,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Baths,bathrooms,A_structural,level,count,0.08,,,,,,,medium,No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,8.0,,100*b/1 (per unit count),,,,,,36254.0,count,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Parking,parking,A_structural,binary,binary,0.036,,,,,,,medium,Flag for parking (per section 3.2); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,pct_premium,3.6655846490923683,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Parking,parking,A_structural,binary,binary,0.046,,,,,,,medium,Flag for parking (per section 3.2); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,pct_premium,4.707441095693721,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Parking,parking,A_structural,binary,binary,0.04,,,,,,,medium,Flag for parking (per section 3.2); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,pct_premium,4.081077419238821,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Parking,parking,A_structural,binary,binary,0.04,,,,,,,medium,Flag for parking (per section 3.2); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,pct_premium,4.081077419238821,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Dist. to City Centre,cbd_distance,D_location,level,unknown,-0.0,,,,,,,medium,"Euclidean distance to city centre; unit not stated (coefficient printed as −0.000, consistent with metres); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Dist. to City Centre,cbd_distance,D_location,level,unknown,-0.0,,,,,,,medium,"Euclidean distance to city centre; unit not stated (coefficient printed as −0.000, consistent with metres); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Dist. to City Centre,cbd_distance,D_location,level,unknown,-0.0,,,,,,,medium,"Euclidean distance to city centre; unit not stated (coefficient printed as −0.000, consistent with metres); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Dist. to City Centre,cbd_distance,D_location,level,unknown,-0.0,,,,,,,medium,"Euclidean distance to city centre; unit not stated (coefficient printed as −0.000, consistent with metres); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Dist. to Coast,water_proximity,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to coastline; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Dist. to Coast,water_proximity,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to coastline; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Dist. to Coast,water_proximity,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to coastline; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Dist. to Coast,water_proximity,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to coastline; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Dist. to Commercial Zones,commercial_distance,D_location,level,unknown,-0.0,,,,,,,medium,Euclidean distance to commercial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Dist. to Commercial Zones,commercial_distance,D_location,level,unknown,-0.0,,,,,,,medium,Euclidean distance to commercial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Dist. to Commercial Zones,commercial_distance,D_location,level,unknown,-0.0,,,,,,,medium,Euclidean distance to commercial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Dist. to Commercial Zones,commercial_distance,D_location,level,unknown,-0.0,,,,,,,medium,Euclidean distance to commercial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Dist. to Swim Place,other,other,level,unknown,-0.0,,,,,,,medium,Euclidean distance to nearest swimming place (beach/pool); unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,100*b (unit as reported: unknown; no harmonised unit defined for this characteristic),,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Dist. to Swim Place,other,other,level,unknown,-0.0,,,,,,,medium,Euclidean distance to nearest swimming place (beach/pool); unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,100*b (unit as reported: unknown; no harmonised unit defined for this characteristic),,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Dist. to Swim Place,other,other,level,unknown,-0.0,,,,,,,medium,Euclidean distance to nearest swimming place (beach/pool); unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,-0.0,,100*b (unit as reported: unknown; no harmonised unit defined for this characteristic),,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Dist. to Swim Place,other,other,level,unknown,-0.0,,,,,,,medium,Euclidean distance to nearest swimming place (beach/pool); unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,-0.0,,100*b (unit as reported: unknown; no harmonised unit defined for this characteristic),,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Dist. to Industrial Zones,hazard_site,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to industrial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Dist. to Industrial Zones,hazard_site,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to industrial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Dist. to Industrial Zones,hazard_site,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to industrial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Dist. to Industrial Zones,hazard_site,E_environment,level,unknown,-0.0,,,,,,,medium,Euclidean distance to industrial zones; unit not stated (coefficient printed as −0.000); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,-0.0,,"100*b (unit as reported: unknown; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,36254.0,unknown,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Within 400 m of Railway Station,transit_distance,D_location,dummy,binary,0.144,,,,,,,medium,Dummy: within 400 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,pct_premium,15.488410852491352,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Within 400 m of Railway Station,transit_distance,D_location,dummy,binary,0.082,,,,,,,medium,Dummy: within 400 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,pct_premium,8.545580982954903,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Within 400 m of Railway Station,transit_distance,D_location,dummy,binary,0.03,,,,,,,medium,Dummy: within 400 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Within 400 m of Railway Station,transit_distance,D_location,dummy,binary,0.03,,,,,,,medium,Dummy: within 400 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Within 800 m of Railway Station,transit_distance,D_location,dummy,binary,0.126,,,,,,,medium,"Dummy: within 800 m of a railway station (column 3 value 0.241 as printed, out of line with other columns); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,pct_premium,13.428216828302508,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Within 800 m of Railway Station,transit_distance,D_location,dummy,binary,0.077,,,,,,,medium,"Dummy: within 800 m of a railway station (column 3 value 0.241 as printed, out of line with other columns); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,pct_premium,8.004207639260041,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Within 800 m of Railway Station,transit_distance,D_location,dummy,binary,0.241,,,,,,,medium,"Dummy: within 800 m of a railway station (column 3 value 0.241 as printed, out of line with other columns); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,pct_premium,27.2521035308229,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Within 800 m of Railway Station,transit_distance,D_location,dummy,binary,0.025,,,,,,,medium,"Dummy: within 800 m of a railway station (column 3 value 0.241 as printed, out of line with other columns); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,pct_premium,2.531512052442886,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Within 1600 m of Railway Station,transit_distance,D_location,dummy,binary,0.073,,,,,,,medium,Dummy: within 1600 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,pct_premium,7.573053691470344,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Within 1600 m of Railway Station,transit_distance,D_location,dummy,binary,0.044,,,,,,,medium,Dummy: within 1600 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,pct_premium,4.498235488844382,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Within 1600 m of Railway Station,transit_distance,D_location,dummy,binary,0.004,,,,,,,medium,Dummy: within 1600 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,pct_premium,0.4008010677341911,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Within 1600 m of Railway Station,transit_distance,D_location,dummy,binary,0.037,,,,,,,medium,Dummy: within 1600 m of a railway station; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,pct_premium,3.7693020838157176,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Crime Rate,crime,F_neighbourhood,level,rate,-0.227,,,,,,,medium,Local crime rate; exact definition/unit not stated in extracted pages; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-22.7,,100*b (unit as reported: rate; no harmonised unit defined for this characteristic),,,,,,36254.0,rate,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Crime Rate,crime,F_neighbourhood,level,rate,-0.2,,,,,,,medium,Local crime rate; exact definition/unit not stated in extracted pages; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,-20.0,,100*b (unit as reported: rate; no harmonised unit defined for this characteristic),,,,,,36254.0,rate,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Crime Rate,crime,F_neighbourhood,level,rate,-0.015,,,,,,,medium,Local crime rate; exact definition/unit not stated in extracted pages; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,-1.5,,100*b (unit as reported: rate; no harmonised unit defined for this characteristic),,,,,,36254.0,rate,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Crime Rate,crime,F_neighbourhood,level,rate,-0.01,,,,,,,medium,Local crime rate; exact definition/unit not stated in extracted pages; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,-1.0,,100*b (unit as reported: rate; no harmonised unit defined for this characteristic),,,,,,36254.0,rate,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Median Family Income,income,F_neighbourhood,level,currency,0.0,,,,,,,medium,"Median family income (AUD, neighbourhood); coefficient printed as 0.000; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,0.0,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,36254.0,currency,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Median Family Income,income,F_neighbourhood,level,currency,0.0,,,,,,,medium,"Median family income (AUD, neighbourhood); coefficient printed as 0.000; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,semi_elasticity,0.0,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,36254.0,currency,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Median Family Income,income,F_neighbourhood,level,currency,0.0,,,,,,,medium,"Median family income (AUD, neighbourhood); coefficient printed as 0.000; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,semi_elasticity,0.0,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,36254.0,currency,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Median Family Income,income,F_neighbourhood,level,currency,0.0,,,,,,,medium,"Median family income (AUD, neighbourhood); coefficient printed as 0.000; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,semi_elasticity,0.0,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,36254.0,currency,unit_not_harmonisable,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Within 100 m of Main Road,highway_distance,D_location,dummy,binary,0.006,,,,,,,medium,Dummy: within 100 m of a main road; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,pct_premium,0.6018036054064924,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Within 100 m of Main Road,highway_distance,D_location,dummy,binary,-0.016,,,,,,,medium,Dummy: within 100 m of a main road; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,pct_premium,-1.5872679944714863,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Within 100 m of Main Road,highway_distance,D_location,dummy,binary,-0.023,,,,,,,medium,Dummy: within 100 m of a main road; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,pct_premium,-2.273751622672293,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Within 100 m of Main Road,highway_distance,D_location,dummy,binary,-0.022,,,,,,,medium,Dummy: within 100 m of a main road; No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE),"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,pct_premium,-2.175976494878995,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c1,2,1 Baseline,10,Within 100 m of Railway Line,other,other,dummy,binary,-0.065,,,,,,,medium,"Dummy: within 100 m of a railway line (track nuisance, not a station); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.746,none,Baseline hedonic without Airbnb density; no month or postcode controls; adjusted R2,pct_premium,-6.293253662259657,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Within 100 m of Railway Line,other,other,dummy,binary,-0.047,,,,,,,medium,"Dummy: within 100 m of a railway line (track nuisance, not a station); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,pct_premium,-4.591260240962891,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Within 100 m of Railway Line,other,other,dummy,binary,-0.061,,,,,,,medium,"Dummy: within 100 m of a railway line (track nuisance, not a station); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,pct_premium,-5.917676022399032,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Within 100 m of Railway Line,other,other,dummy,binary,-0.064,,,,,,,medium,"Dummy: within 100 m of a railway line (track nuisance, not a station); No standard error or significance printed for control variables (only the Airbnb density coefficient has a SE)","log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,pct_premium,-6.199500046927053,,100*(exp(b) - 1),,,,,,36254.0,binary,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c2,2,2 Baseline OLS,10,Airbnb Density,other,other,log,"listings per km2 (postcode, monthly)",0.0844,0.002,,,***,,,high,log of active Airbnb listings per km2 of the property's postcode in the sale month (variable of interest); authors interpret as elasticity (x100); *** p<0.01; robust SE,"log(sale price), AUD",log,OLS,36264.0,0.796,se,Baseline OLS with log Airbnb density; no month or postcode controls; adjusted R2,elasticity,0.0844,0.002,b (log-log),reported,0.2163822608321203,0.0051275417258796275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.2,36254.0,listings per km2,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c3,2,3 Control OLS,10,Airbnb Density,other,other,log,"listings per km2 (postcode, monthly)",0.0301,0.0019,,,***,,,high,log of active Airbnb listings per km2 of the property's postcode in the sale month (variable of interest); authors interpret as elasticity (x100); *** p<0.01; robust SE,"log(sale price), AUD",log,OLS,36264.0,0.868,se,OLS with month fixed effects and postcode-level controls; adjusted R2,elasticity,0.0301,0.0019,b (log-log),reported,0.08291572128611385,0.005233882738990576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.842105263157894,36254.0,listings per km2,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,ols,journal,False,False,False
87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f,87f031f0ad9dc17cd7151e97fc7ad56a0e7b728f:T2-c4,2,4 IV,10,Airbnb Density,other,other,log,"listings per km2 (postcode, monthly)",0.0201,0.0024,,,***,,,high,log of active Airbnb listings per km2 of the property's postcode in the sale month (variable of interest); authors interpret as elasticity (x100); *** p<0.01; robust SE,"log(sale price), AUD",log,IV,36264.0,0.871,se,Two-stage least squares; Airbnb density instrumented by shift-share (Dec-2018 density x Google Trends); month FE and postcode controls; adjusted R2,elasticity,0.0201,0.0024,b (log-log),reported,0.043942740826809254,0.005246894427081703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.375,36254.0,listings per km2,,2022.0,Australia,Oceania,High income,2019.0,2019.0,mixed,transaction,journal,True,True,robust,,2019.0,iv,journal,False,False,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,STRQUAL,condition,A_structural,binary,binary,0.191025,,1.733654,,,,,high,"Structural quality of property: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,20.316367633931055,13.337943880757662,100*(exp(b - se^2/2) - 1),|b/t|,0.16999422017023924,0.09805544830181757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.733654,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,REPAIR,condition,A_structural,binary,binary,0.081544,,1.380998,,,,,high,"State of repair: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,8.307120834647508,6.406385534193269,100*(exp(b - se^2/2) - 1),|b/t|,0.13613514640616256,0.09857736680731077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.380998,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,LIVIN,other,other,level,count,0.084432,,3.994811,,,,,high,Number of living rooms (1-2); printed name LIVIN,natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",semi_elasticity,8.4432,2.113541792089788,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.36938590058914644,0.09246642721999775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.994811,101.0,count,unit_not_harmonisable,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,False,False,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,BED,bedrooms,A_structural,level,count,0.072237,,3.410468,,,,,high,Number of bedrooms (3-5),natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",semi_elasticity,7.223699999999999,2.118096402018726,100*b/1 (per unit count),|b/t|,0.3213545445467387,0.0942259374803513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.410468,101.0,count,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,BATH,bathrooms,A_structural,level,count,0.061126,,4.736524,,,,,high,Number of bathrooms (2-5),natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",semi_elasticity,6.1126,1.29052444366375,100*b/1 (per unit count),|b/t|,0.42632537266292564,0.09000806765951691,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.736524,101.0,count,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,STORE,storage,B_interior,binary,binary,0.090283,,3.028461,,,,,high,Store room available=1,natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,9.399773858460158,3.2628221747702977,100*(exp(b - se^2/2) - 1),|b/t|,0.2885274943188608,0.09527198610741919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.028461,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,True
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,BCNY,terrace_balcony,C_exterior,binary,binary,0.152038,,3.842652,,,,,high,Balcony available=1,natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,16.329357369397645,4.606280246689829,100*(exp(b - se^2/2) - 1),|b/t|,0.3571417231550555,0.09294146936934582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.842652,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,True
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,PLOT,lot_size,A_structural,level,m2,0.008826,,3.947261,,,,,high,Plot size in square metres (range 593-1407 m2),natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",semi_elasticity,0.8826,0.2235980848492157,100*b/1 (X per m2->m2),|b/t|,0.36557980663970313,0.09261607140741471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.947261,101.0,m2,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,NTLIGHT,sunlight,E_environment,binary,binary,0.009435,,6.884632,,,,,high,"Natural lighting: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,0.947870196668732,0.13834349456953088,100*(exp(b - se^2/2) - 1),|b/t|,0.5651535713057985,0.08208914743820708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.884632,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,DISCBD,cbd_distance,D_location,dummy,binary,0.280001,,1.920375,,,,,high,"Distance to closest CBD: 1 if within 15 minutes walking distance, 0 otherwise (positive = proximity premium)",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,30.914126070699275,19.29196334372977,100*(exp(b - se^2/2) - 1),|b/t|,0.1876886124548838,0.09773539670891561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.920375,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,SECRTY,crime,F_neighbourhood,binary,binary,0.000983,,1.677463,,,,,high,"Neighbourhood security: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,0.09833114336652571,0.058658030852942855,100*(exp(b - se^2/2) - 1),|b/t|,0.16463616219591817,0.09814592762756508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.677463,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,ROAD,public_services,F_neighbourhood,binary,binary,0.120567,,1.814028,,,,,high,"Condition of road surface: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,12.564734591706216,7.4980106218619795,100*(exp(b - se^2/2) - 1),|b/t|,0.1776319976991941,0.09792130975883179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.814028,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,WATER,public_services,F_neighbourhood,binary,binary,0.097635,,2.376251,,,,,high,"Government water supply: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,10.162999326494004,4.530181056628356,100*(exp(b - se^2/2) - 1),|b/t|,0.23010119228593862,0.0968337066605921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.376251,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,WASTE,public_services,F_neighbourhood,binary,binary,-0.039012,,-3.429712,,,,,high,"Government waste disposal services: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,-3.832304668976283,1.093950991514608,100*(exp(b - se^2/2) - 1),|b/t|,-0.32297915039804626,0.09417092467182268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.429712,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,SRTLIGHT,public_services,F_neighbourhood,binary,binary,0.050754,,1.092836,,,,,high,"Street lighting: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,5.093006534903011,4.8860450223989575,100*(exp(b - se^2/2) - 1),|b/t|,0.1081039782921921,0.09892058670485976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.092836,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,DRAIN,public_services,F_neighbourhood,binary,binary,0.009127,,2.348283,,,,,high,"Nature of drainage: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,0.9161155746629346,0.3922305547265911,100*(exp(b - se^2/2) - 1),|b/t|,0.227533948861164,0.09689375124768353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.348283,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,GREEN,green_space,E_environment,binary,binary,0.170362,,2.736555,,,,,high,"Green environment in neighbourhood (trees, flowers): good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,18.343852857220135,7.381690384472183,100*(exp(b - se^2/2) - 1),|b/t|,0.2627313285765856,0.09600805705589166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.736555,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,WINDQUA,other,other,binary,binary,0.011275,,1.983463,,,,,high,"Wind quality in building (ventilation): good=1, poor=0; printed name WINDQUA",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,1.132246252781699,0.574895775560612,100*(exp(b - se^2/2) - 1),|b/t|,0.19362691665268317,0.09762063454306087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.983463,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,False,False,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,LSCAPE,landscaping,C_exterior,binary,binary,0.093552,,5.012254,,,,,high,"Landscape of neighbourhood: good=1, poor=0",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,9.787645143644541,2.0495056635046964,100*(exp(b - se^2/2) - 1),|b/t|,0.4463098361859125,0.08904373884202844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.012254,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111,8885ddb1a206684210159fea8394d2ea9c611111:T3-c1,3,1,13,VIEWWAT,view,E_environment,binary,binary,0.0349,,4.773652,,,,,high,"View of navigable water body: good=1, poor=0 (mean 0.0078)",natural log of annual net rental value (Naira),log,OLS,111.0,0.790123,t,"Log-linear model, detached houses, Yaba LCDA; t-statistics printed in a separate column",pct_premium,3.548847810324407,0.757062175982513,100*(exp(b - se^2/2) - 1),|b/t|,0.42905396041648713,0.08987960589009988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.773652,101.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2016.0,2016.0,single_family,rent,journal,False,False,OLS,,2016.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,logSQFT,living_area,A_structural,log,sqft,0.6321,0.0274,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,elasticity,0.6321,0.0274,b (log-log),reported,0.04541005198496497,0.001968415479177409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.06934306569343,257555.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,logSQFT,living_area,A_structural,log,sqft,0.6321,0.0275,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,elasticity,0.6321,0.0275,b (log-log),reported,0.04524526317680663,0.0019684302125647563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.985454545454544,257555.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,logSQFT,living_area,A_structural,log,sqft,0.6309,0.0277,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,elasticity,0.6309,0.0277,b (log-log),reported,0.044834139489778956,0.0019684667361973005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.776173285198556,257555.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,bedrooms,bedrooms,A_structural,level,count,-0.0188,0.0039,,,***,,,high,,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-1.8800000000000001,0.38999999999999996,100*b/1 (per unit count),reported,-0.009498142014342476,0.0019703592476561517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.820512820512821,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,bedrooms,bedrooms,A_structural,level,count,-0.0188,0.0039,,,***,,,high,,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.8800000000000001,0.38999999999999996,100*b/1 (per unit count),reported,-0.009498142014342476,0.0019703592476561517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.820512820512821,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,bedrooms,bedrooms,A_structural,level,count,-0.0187,0.004,,,***,,,high,,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.87,0.4,100*b/1 (per unit count),reported,-0.009211454189182913,0.001970364532445543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.675,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,fullBaths,bathrooms,A_structural,level,count,0.0848,0.0057,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,8.48,0.5700000000000001,100*b/1 (per unit count),reported,0.029302149358981965,0.0019696020205919483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.87719298245614,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,fullBaths,bathrooms,A_structural,level,count,0.085,0.0057,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,8.5,0.5700000000000001,100*b/1 (per unit count),reported,0.02937119865394858,0.001969598027382434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.912280701754387,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,fullBaths,bathrooms,A_structural,level,count,0.0845,0.0058,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,8.450000000000001,0.58,100*b/1 (per unit count),reported,0.02869556910006875,0.00196963669562602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.56896551724138,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,halfBaths,bathrooms,A_structural,level,count,0.058,0.0066,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,5.800000000000001,0.66,100*b/1 (per unit count),reported,0.017313463845037645,0.0019701527823663524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.787878787878789,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,halfBaths,bathrooms,A_structural,level,count,0.0584,0.0065,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,5.84,0.65,100*b/1 (per unit count),reported,0.017700944883192113,0.001970139413368985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.984615384615385,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,halfBaths,bathrooms,A_structural,level,count,0.0589,0.0066,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,5.89,0.66,100*b/1 (per unit count),reported,0.017582038627459304,0.001970143547389328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.924242424242424,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,distanceCBD,cbd_distance,D_location,level,km,-0.0307,0.013,,,*,,,high,distance to CBD in km,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-3.0700000000000003,1.3,100*b/1 (X per km->km),reported,-0.004653238670417924,0.0019704267985483064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3615384615384616,257555.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,distanceCBD,cbd_distance,D_location,level,km,-0.0282,0.013,,,*,,,high,distance to CBD in km,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-2.82,1.3,100*b/1 (X per km->km),reported,-0.004274317669729429,0.001970430131435552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.169230769230769,257555.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,distanceCBD,cbd_distance,D_location,level,km,-0.0362,0.0137,,,**,,,high,distance to CBD in km,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-3.62,1.37,100*b/1 (X per km->km),reported,-0.005206515003349711,0.001970421423919642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6423357664233578,257555.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,house,dwelling_type,A_structural,binary,binary,0.008,0.0448,,,,,,high,single-family house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,0.7021012546645666,4.515983743059145,100*(exp(b - se^2/2) - 1),reported,0.000351865715959997,0.001970448009375983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17857142857142858,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,house,dwelling_type,A_structural,binary,binary,-0.027,0.0461,,,,,,high,single-family house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-2.767250766691376,4.487195323427193,100*(exp(b - se^2/2) - 1),reported,-0.0011540577899785318,0.0019704468191855677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5856832971800433,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,house,dwelling_type,A_structural,binary,binary,-0.0804,0.0571,,,,,,high,single-family house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-7.875586790291045,5.268886361755872,100*(exp(b - se^2/2) - 1),reported,-0.0027744907180684013,0.001970440547284897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4080560420315236,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,rowhouse,dwelling_type,A_structural,binary,binary,0.1527,0.03,,,***,,,high,row house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,16.44513128742595,3.494926301650306,100*(exp(b - se^2/2) - 1),reported,0.010029076576370376,0.001970349032685732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.09,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,rowhouse,dwelling_type,A_structural,binary,binary,0.1516,0.03,,,***,,,high,row house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,16.317112066489912,3.4910839963738254,100*(exp(b - se^2/2) - 1),reported,0.009956837633535632,0.0019703504551851513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.053333333333334,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,rowhouse,dwelling_type,A_structural,binary,binary,0.1522,0.0318,,,***,,,high,row house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,16.38045001434851,3.7027700318100156,100*(exp(b - se^2/2) - 1),reported,0.009430467597063745,0.0019703605097675897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.786163522012578,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0001,0.0232,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-0.016910570001893444,2.3202320116003867,100*(exp(b - se^2/2) - 1),reported,8.493310910711198e-06,0.0019704481312849977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.004310344827586208,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0005,0.0273,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.08722643560823418,2.728635341193132,100*(exp(b - se^2/2) - 1),reported,-3.6088793591078964e-05,0.0019704481300729113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.018315018315018316,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0551,0.0237,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-5.387524755091944,2.242945494885167,100*(exp(b - se^2/2) - 1),reported,-0.0045810359829076995,0.0019704274554430577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3248945147679327,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0266,0.015,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-2.6358876887674465,1.460625995850344,100*(exp(b - se^2/2) - 1),reported,-0.0034942400209082808,0.0019704361020159477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7733333333333332,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0,0.0143,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.010223977315815258,1.43,100*(exp(b - se^2/2) - 1),reported,0.0,0.0019704481313560685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.001,0.0309,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-0.14763141744961183,3.0869115444851287,100*(exp(b - se^2/2) - 1),reported,-6.376854781067073e-05,0.001970448127349726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.032362459546925564,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0044,0.0157,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-0.4513030533982554,1.563107175334684,100*(exp(b - se^2/2) - 1),reported,-0.0005522274175790099,0.0019704478309069216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.28025477707006374,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0011,0.0165,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.12353613121958151,1.6481859978840756,100*(exp(b - se^2/2) - 1),reported,-0.00013136320762365024,0.0019704481143547536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06666666666666667,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0218,0.0144,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-2.166553631137147,1.4089476998366344,100*(exp(b - se^2/2) - 1),reported,-0.002983026259954699,0.001970439364373746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5138888888888888,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.002,0.0217,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-0.22329482536189715,2.165664337108113,100*(exp(b - se^2/2) - 1),reported,-0.00018160811971077343,0.0019704480988618914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09216589861751152,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0015,0.0199,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.16965642051176566,1.9870172376310447,100*(exp(b - se^2/2) - 1),reported,-0.00014852623941874235,0.001970448109621982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07537688442211055,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0004,0.0161,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,0.027043156002304336,1.6106441288171751,100*(exp(b - se^2/2) - 1),reported,4.895523301850598e-05,0.0019704481289948656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.02484472049689441,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0145,0.0312,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,1.411192625544988,3.1655695810483633,100*(exp(b - se^2/2) - 1),reported,0.000915752753993174,0.001970447305143933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4647435897435898,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0127,0.0166,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,1.2641456131941675,1.6812164392232107,100*(exp(b - se^2/2) - 1),reported,0.0015075098092064208,0.0019704458923485504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7650602409638554,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0032,0.0189,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-0.3372903936481486,1.8839616664863321,100*(exp(b - se^2/2) - 1),reported,-0.00033362082907052894,0.001970448021697811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16931216931216933,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.061,0.0327,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-5.9679632224041494,3.0764919940675517,100*(exp(b - se^2/2) - 1),reported,-0.003675734679368364,0.0019704348199236967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8654434250764527,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0593,0.0255,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-5.78823562542733,2.4031811988369842,100*(exp(b - se^2/2) - 1),reported,-0.004582209705075012,0.0019704274448467587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3254901960784315,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0033,0.0236,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,0.30260893925664334,2.367800864346889,100*(exp(b - se^2/2) - 1),reported,0.0002755287536717637,0.001970448056561704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1398305084745763,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0217,0.0325,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,2.139758926248647,3.3212957613272702,100*(exp(b - se^2/2) - 1),reported,0.0013156519213543964,0.0019704464259916077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6676923076923077,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0486,0.0296,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,4.934055079134647,3.107409025954231,100*(exp(b - se^2/2) - 1),reported,0.0032352458787726474,0.0019704378191701725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6418918918918917,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.017,0.0248,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,1.6832577885768973,2.5225203993665968,100*(exp(b - se^2/2) - 1),reported,0.0013507091804941846,0.0019704463338973987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.685483870967742,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0296,0.0503,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,pct_premium,2.8740208394693045,5.181113445863497,100*(exp(b - se^2/2) - 1),reported,0.0011595472261931579,0.001970446806672832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5884691848906561,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0443,0.0323,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,4.475076544066425,3.376305746094936,100*(exp(b - se^2/2) - 1),reported,0.0027024932958337757,0.001970440935788509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.371517027863777,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0111,0.0296,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,1.0718960810317313,2.993039027374411,100*(exp(b - se^2/2) - 1),reported,0.0007389178475340238,0.0019704475934240635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.375,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1025,0.0341,,,**,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.1025,0.0341,not harmonised,reported,0.005922797387399737,0.001970413569856888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.005865102639296,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1077,0.0413,,,**,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.1077,0.0413,not harmonised,reported,0.005138364701318162,0.001970422118518478,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6077481840193704,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1284,0.052,,,*,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.1284,0.052,not harmonised,reported,0.0048654335657439315,0.0019704248085567326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.469230769230769,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.104,0.0277,,,***,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.104,0.0277,not harmonised,reported,0.00739786996113677,0.0019703942108027744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7545126353790614,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0986,0.0311,,,**,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0986,0.0311,not harmonised,reported,0.006247022336898288,0.0019704096823279595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.170418006430868,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0264,0.0312,,,,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0264,0.0312,not harmonised,reported,0.0016672999475342766,0.0019704453925405087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8461538461538461,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0948,0.0193,,,***,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.0948,0.0193,not harmonised,reported,0.009678224566044736,0.0019703558451968713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9119170984455955,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0849,0.0217,,,***,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0849,0.0217,not harmonised,reported,0.007709035727607488,0.0019703895793767075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9124423963133643,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0643,0.0241,,,**,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0643,0.0241,not harmonised,reported,0.005257181077690494,0.001970420901591616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.66804979253112,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0335,0.019,,,+,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.0335,0.019,not harmonised,reported,0.003474190212046295,0.001970436239668048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7631578947368423,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0114,0.0201,,,,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0114,0.0201,not harmonised,reported,0.001117566898992047,0.0019704469008543987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5671641791044776,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0672,0.0288,,,*,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0672,0.0288,not harmonised,reported,0.004597663711843207,0.0019704273050756603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.333333333333333,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0478,0.0159,,,**,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.0478,0.0159,not harmonised,reported,0.00592363321841807,0.0019704135601014082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0062893081761004,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0403,0.0176,,,*,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0403,0.0176,not harmonised,reported,0.004511832468126398,0.00197042807541004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.289772727272727,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.047,0.0254,,,+,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.047,0.0254,not harmonised,reported,0.0036460805744261806,0.0019704350338388293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8503937007874016,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0345,0.0162,,,*,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.0345,0.0162,not harmonised,reported,0.0041962877778348884,0.001970430782635513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1296296296296298,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0325,0.0172,,,+,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0325,0.0172,not harmonised,reported,0.0037232046744890388,0.00197043447388343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8895348837209303,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0587,0.0183,,,**,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0587,0.0183,not harmonised,reported,0.006320382242248848,0.0019704087739889934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.207650273224044,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0174,0.0146,,,,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.0174,0.0146,not harmonised,reported,0.002348335818345567,0.0019704426981520277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.191780821917808,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0145,0.0147,,,,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0145,0.0147,not harmonised,reported,0.0019436356419768373,0.00197044440945238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9863945578231293,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0956,0.0214,,,***,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0956,0.0214,not harmonised,reported,0.008802221666313422,0.0019703717955973556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.4672897196261685,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c1,2,All craft breweries,11,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.05,0.0117,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: All craft breweries,other,0.05,0.0117,not harmonised,reported,0.008420419975684856,0.0019703782743102564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.273504273504273,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c2,2,Brewpubs/taprooms only,11,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0492,0.0115,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0492,0.0115,not harmonised,reported,0.008429791603585047,0.0019703781187241473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.278260869565218,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T2-c3,2,Regional/microbreweries only,11,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0816,0.0216,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.741,se,all home transactions; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0816,0.0216,not harmonised,reported,0.007443708930830803,0.0019703935405140363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7777777777777777,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,logSQFT,living_area,A_structural,log,sqft,0.5024,0.0224,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,elasticity,0.5024,0.0224,b (log-log),reported,0.05266315391185602,0.00234803870944581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.428571428571427,180877.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,logSQFT,living_area,A_structural,log,sqft,0.5031,0.0225,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,elasticity,0.5031,0.0225,b (log-log),reported,0.05250259004496712,0.0023480585887731276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.36,180877.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,logSQFT,living_area,A_structural,log,sqft,0.5014,0.0225,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,elasticity,0.5014,0.0225,b (log-log),reported,0.052325667736150834,0.0023480804229425484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.284444444444443,180877.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,bedrooms,bedrooms,A_structural,level,count,-0.0195,0.0032,,,***,,,high,,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-1.95,0.32,100*b/1 (per unit count),reported,-0.014326773154812456,0.002351060210020505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.09375,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,bedrooms,bedrooms,A_structural,level,count,-0.0198,0.0033,,,***,,,high,,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.9800000000000002,0.33,100*b/1 (per unit count),reported,-0.014106405462621058,0.0023510675771035096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.000000000000001,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,bedrooms,bedrooms,A_structural,level,count,-0.0195,0.0032,,,***,,,high,,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.95,0.32,100*b/1 (per unit count),reported,-0.014326773154812456,0.002351060210020505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.09375,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,fullBaths,bathrooms,A_structural,level,count,0.0763,0.0051,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,7.630000000000001,0.51,100*b/1 (per unit count),reported,0.03515557029001013,0.002349848079673023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.96078431372549,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,fullBaths,bathrooms,A_structural,level,count,0.0767,0.0051,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,7.670000000000001,0.51,100*b/1 (per unit count),reported,0.03533964252687534,0.002349832814694449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.03921568627451,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,fullBaths,bathrooms,A_structural,level,count,0.0759,0.0051,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,7.59,0.51,100*b/1 (per unit count),reported,0.03497149447516826,0.002349863265129883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.88235294117647,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,halfBaths,bathrooms,A_structural,level,count,0.0621,0.0073,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,6.21,0.73,100*b/1 (per unit count),reported,0.0199981677288279,0.0023508313111182556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.506849315068493,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,halfBaths,bathrooms,A_structural,level,count,0.0622,0.0073,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,6.22,0.73,100*b/1 (per unit count),reported,0.020030357987453295,0.00235082979595513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.520547945205479,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,halfBaths,bathrooms,A_structural,level,count,0.0625,0.0072,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,6.25,0.72,100*b/1 (per unit count),reported,0.02040635345210413,0.002350811917682396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.680555555555555,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,distanceCBD,cbd_distance,D_location,level,km,-0.0235,0.0129,,,+,,,high,distance to CBD in km,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-2.35,1.29,100*b/1 (X per km->km),reported,-0.0042833394666106145,0.0023512799625224224,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.821705426356589,180877.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,distanceCBD,cbd_distance,D_location,level,km,-0.0212,0.0131,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-2.12,1.31,100*b/1 (X per km->km),reported,-0.0038051321840201656,0.0023512845099369893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.618320610687023,180877.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,distanceCBD,cbd_distance,D_location,level,km,-0.0177,0.0115,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.77,1.15,100*b/1 (X per km->km),reported,-0.0036189360513975456,0.002351286135088801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5391304347826087,180877.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0334,0.0144,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-3.29486475241223,1.392698335043465,100*(exp(b - se^2/2) - 1),reported,-0.005453632173209724,0.0023512665656952106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3194444444444446,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.035,0.0163,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.4522851073930827,1.5739368284998332,100*(exp(b - se^2/2) - 1),reported,-0.0050487426242032925,0.002351271564986104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1472392638036815,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0259,0.0154,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-2.5683013369038443,1.500626093103904,100*(exp(b - se^2/2) - 1),reported,-0.003954430748816904,0.0023512831479451865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6818181818181817,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0264,0.0146,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-2.6158364063511352,1.4219603329236088,100*(exp(b - se^2/2) - 1),reported,-0.004251630096611272,0.0023512802807016888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8082191780821917,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0117,0.0162,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.1761506320135284,1.6011564497264097,100*(exp(b - se^2/2) - 1),reported,-0.0016981597692229992,0.0023512981420010753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7222222222222223,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0476,0.0165,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-4.661467155815102,1.5732999428077907,100*(exp(b - se^2/2) - 1),reported,-0.006782992618126354,0.0023512474411572447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8848484848484848,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0178,0.0168,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-1.778113629757594,1.6503605734716282,100*(exp(b - se^2/2) - 1),reported,-0.002491252226010809,0.0023512942357854826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0595238095238095,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0086,0.0179,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.8721946203986253,1.7746720048505693,100*(exp(b - se^2/2) - 1),reported,-0.0011296748756889343,0.002351300031957201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48044692737430167,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0376,0.0135,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-3.698965522123432,1.3001824391780628,100*(exp(b - se^2/2) - 1),reported,-0.0065486697690000885,0.0023512511138697122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.785185185185185,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0031,0.0208,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-0.33108270845949583,2.0735619840804524,100*(exp(b - se^2/2) - 1),reported,-0.00035043434146698804,0.0023513013879075326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14903846153846154,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0032,0.0188,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.3371025292634777,1.8739936153409018,100*(exp(b - se^2/2) - 1),reported,-0.00040022150535657926,0.002351301343969903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1702127659574468,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0058,0.0167,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,0.5676606213421032,1.6797141437850087,100*(exp(b - se^2/2) - 1),reported,0.0008166194215580671,0.002351300748279262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34730538922155685,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0149,0.0274,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-1.5159303173733152,2.6994766486803266,100*(exp(b - se^2/2) - 1),reported,-0.0012786264303790182,0.002351299610227188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5437956204379562,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0071,0.0175,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.7226884568903413,1.7376190045443225,100*(exp(b - se^2/2) - 1),reported,-0.0009539561876030485,0.0023513004624018804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4057142857142857,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0129,0.0192,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-1.299909240505981,1.8953910688691993,100*(exp(b - se^2/2) - 1),reported,-0.001579778745670955,0.0023512985982079328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6718750000000001,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0426,0.0274,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-4.206502586422722,2.6257272896693538,100*(exp(b - se^2/2) - 1),reported,-0.0036556487583619237,0.002351285821106026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5547445255474452,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0357,0.0225,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.531448794331793,2.171091890214725,100*(exp(b - se^2/2) - 1),reported,-0.003730705801992846,0.0023512851693232215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.586666666666667,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0165,0.0277,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,1.6246923937974111,2.816084148697374,100*(exp(b - se^2/2) - 1),reported,0.0014005934017999287,0.0023512992260520016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5956678700361011,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.037,0.0267,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-3.6667300979293826,2.5730152813819727,100*(exp(b - se^2/2) - 1),reported,-0.003258340631944531,0.002351289050619432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3857677902621721,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0148,0.0252,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.5003823949679918,2.4829786338702298,100*(exp(b - se^2/2) - 1),reported,-0.0013809218054612485,0.0023512992903799634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5873015873015873,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0254,0.0271,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,2.53487462851818,2.77971564052981,100*(exp(b - se^2/2) - 1),reported,0.0022037975605387426,0.002351295822464564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9372693726937269,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0542,0.0229,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-5.30057001689771,2.169185643373838,100*(exp(b - se^2/2) - 1),reported,-0.005565003042132975,0.002351265122967622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3668122270742358,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0271,0.0097,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-2.678187585681757,0.9440659929588235,100*(exp(b - se^2/2) - 1),reported,-0.006568958423346533,0.0023512508009764345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7938144329896906,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.032,0.0323,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,3.1979038167618823,3.3350315421355323,100*(exp(b - se^2/2) - 1),reported,0.0023294564980909653,0.0023512951527605683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9907120743034056,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1592,0.0341,,,***,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.1592,0.0341,not harmonised,reported,0.010976676023494957,0.002351159876891822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.668621700879766,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1741,0.0306,,,***,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.1741,0.0306,not harmonised,reported,0.013376633029875932,0.0023510911586111633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.689542483660131,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1507,0.0429,,,***,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.1507,0.0429,not harmonised,reported,0.008259418519530704,0.0023512213303773536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5128205128205128,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1035,0.0195,,,***,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.1035,0.0195,not harmonised,reported,0.012479013290468752,0.0023511184460303443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3076923076923075,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1037,0.0202,,,***,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.1037,0.0202,not harmonised,reported,0.01206991125443748,0.002351130253998429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.133663366336634,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1075,0.031,,,***,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.1075,0.031,not harmonised,reported,0.0081534358987867,0.0023512233754640712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.467741935483871,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0846,0.0175,,,***,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.0846,0.0175,not harmonised,reported,0.011366129147532275,0.0023511496463571495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.834285714285714,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0709,0.0203,,,***,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0709,0.0203,not harmonised,reported,0.008211904313317465,0.002351222250498512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.492610837438424,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0819,0.0242,,,***,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0819,0.0242,not harmonised,reported,0.007957252015983202,0.0023512270914138405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.384297520661157,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0502,0.0147,,,***,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.0502,0.0147,not harmonised,reported,0.008029355915464317,0.0023512257362017022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.414965986394558,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0383,0.0158,,,*,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0383,0.0158,not harmonised,reported,0.005699581389138653,0.002351263340689053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.424050632911392,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0921,0.0216,,,***,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0921,0.0216,not harmonised,reported,0.010025184652728758,0.0023511833713240086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.263888888888888,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0391,0.0132,,,**,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.0391,0.0132,not harmonised,reported,0.006964671222303365,0.0023512445047162256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9621212121212124,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0366,0.0149,,,*,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0366,0.0149,not harmonised,reported,0.005775583942373694,0.0023512623153379244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4563758389261747,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0712,0.0176,,,***,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0712,0.0176,not harmonised,reported,0.009511653175324266,0.0023511951669340884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.045454545454545,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0381,0.0131,,,**,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.0381,0.0131,not harmonised,reported,0.006838358299767782,0.002351246554513332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9083969465648853,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0371,0.0148,,,*,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0371,0.0148,not harmonised,reported,0.005894038622012894,0.0023512606901830413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5067567567567566,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0561,0.019,,,**,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0561,0.019,not harmonised,reported,0.006942359851558396,0.002351244869511756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9526315789473685,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0286,0.0135,,,*,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.0286,0.0135,not harmonised,reported,0.004981214039441222,0.0023512723612747026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1185185185185187,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0266,0.0148,,,+,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0266,0.0148,not harmonised,reported,0.00422595015370447,0.0023512805366476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7972972972972971,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0895,0.0179,,,***,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0895,0.0179,not harmonised,reported,0.011755695279993842,0.0023511390559987685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c1,3,All craft breweries,12,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0379,0.0111,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; Euclidean distance bins; brewery type: All craft breweries,other,0.0379,0.0111,not harmonised,reported,0.008028059128818927,0.0023512257606831156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4144144144144146,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c2,3,Brewpubs/taprooms only,12,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0324,0.0116,,,**,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.757,se,single-family homes; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0324,0.0116,not harmonised,reported,0.00656728679207716,0.0023512508267930572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.793103448275862,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T3-c3,3,Regional/microbreweries only,12,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0901,0.02,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.759,se,single-family homes; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0901,0.02,not harmonised,reported,0.010592019189006222,0.0023511696312999383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.505,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,logSQFT,living_area,A_structural,log,sqft,0.7124,0.0549,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,elasticity,0.7124,0.0549,b (log-log),reported,0.08449661077172943,0.006511600128253713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.97632058287796,23416.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,logSQFT,living_area,A_structural,log,sqft,0.7148,0.0539,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,elasticity,0.7148,0.0539,b (log-log),reported,0.086340507329021,0.006510567074754102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.261595547309833,23416.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,logSQFT,living_area,A_structural,log,sqft,0.7157,0.0545,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,elasticity,0.7157,0.0545,b (log-log),reported,0.08550367699088629,0.006511038697783015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.132110091743119,23416.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,bedrooms,bedrooms,A_structural,level,count,-0.0403,0.0101,,,***,,,high,,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-4.03,1.01,100*b/1 (per unit count),reported,-0.02606632021761351,0.006532750228235643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9900990099009905,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,bedrooms,bedrooms,A_structural,level,count,-0.0397,0.0102,,,***,,,high,,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-3.9699999999999998,1.02,100*b/1 (per unit count),reported,-0.025426907503547515,0.00653285784725906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8921568627450975,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,bedrooms,bedrooms,A_structural,level,count,-0.0405,0.0103,,,***,,,high,,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-4.05,1.03,100*b/1 (per unit count),reported,-0.02568727971401918,0.0065328143470221615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9320388349514563,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,fullBaths,bathrooms,A_structural,level,count,0.1023,0.0096,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,10.23,0.96,100*b/1 (per unit count),reported,0.06947003820102224,0.00651918247047716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.656250000000002,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,fullBaths,bathrooms,A_structural,level,count,0.1021,0.0097,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,10.209999999999999,0.97,100*b/1 (per unit count),reported,0.06862346645372842,0.006519565373174982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.525773195876289,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,fullBaths,bathrooms,A_structural,level,count,0.1009,0.0102,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,10.09,1.02,100*b/1 (per unit count),reported,0.06451030255381003,0.006521358632793482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.892156862745098,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,halfBaths,bathrooms,A_structural,level,count,0.065,0.0167,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,6.5,1.67,100*b/1 (per unit count),reported,0.025427290774304754,0.006532857783552144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8922155688622757,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,halfBaths,bathrooms,A_structural,level,count,0.0655,0.0165,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,6.550000000000001,1.6500000000000001,100*b/1 (per unit count),reported,0.02593312865038028,0.0065327728661263304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9696969696969697,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,halfBaths,bathrooms,A_structural,level,count,0.0661,0.0162,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,6.61,1.6199999999999999,100*b/1 (per unit count),reported,0.026654820163767988,0.006532648814720747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.080246913580248,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,distanceCBD,cbd_distance,D_location,level,km,-0.0102,0.0151,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-1.02,1.51,100*b/1 (X per km->km),reported,-0.004414308065038278,0.006534907037458628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6754966887417219,23416.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,distanceCBD,cbd_distance,D_location,level,km,-0.0176,0.0157,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.76,1.5699999999999998,100*b/1 (X per km->km),reported,-0.007325630463745907,0.006534795356864245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.121019108280255,23416.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,distanceCBD,cbd_distance,D_location,level,km,-0.0206,0.0134,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-2.06,1.34,100*b/1 (X per km->km),reported,-0.010045791314592772,0.006534640952210831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.537313432835821,23416.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0139,0.0188,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-1.3978106238743626,1.8540487788221278,100*(exp(b - se^2/2) - 1),reported,-0.0048316506682290995,0.0065348944289717326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7393617021276595,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0094,0.0197,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.9548168979691907,1.9515687625312483,100*(exp(b - se^2/2) - 1),reported,-0.003118194214112814,0.006534938938087492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4771573604060914,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0623,0.0301,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-6.082458332438312,2.8281989022888125,100*(exp(b - se^2/2) - 1),reported,-0.013524632502267203,0.006534373006713368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0697674418604652,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0346,0.0166,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-3.4141349490090622,1.603546281233454,100*(exp(b - se^2/2) - 1),reported,-0.013619820113982955,0.006534364563355984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08433734939759,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0478,0.0213,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-4.689179945557253,2.0305810420745867,100*(exp(b - se^2/2) - 1),reported,-0.014663756529799465,0.006534268077086373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.244131455399061,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0124,0.0175,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-1.2474663183191015,1.728433985620973,100*(exp(b - se^2/2) - 1),reported,-0.0046304438890031,0.0065349006498027625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7085714285714285,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0366,0.0189,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-3.611048795356553,1.822076580695296,100*(exp(b - se^2/2) - 1),reported,-0.012654009414903024,0.006534447484745004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9365079365079365,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0355,0.0191,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.505329292773185,1.843384422379301,100*(exp(b - se^2/2) - 1),reported,-0.01214525389065746,0.0065344887130016184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8586387434554974,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0444,0.0168,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-4.356372937443565,1.6070397040927615,100*(exp(b - se^2/2) - 1),reported,-0.017268418732565665,0.006533996277187008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6428571428571432,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0044,0.0317,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-0.48904476300001276,3.1560826406439157,100*(exp(b - se^2/2) - 1),reported,-0.000907061807193258,0.006534968020005971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13880126182965302,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0021,0.0328,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.2634443746401538,3.273119227339977,100*(exp(b - se^2/2) - 1),reported,-0.0004183974782433311,0.00653497013637203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06402439024390243,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0073,0.0131,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-0.7358596967865116,1.3004718201694028,100*(exp(b - se^2/2) - 1),reported,-0.0036416007522152034,0.0065349273772629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5572519083969465,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0205,0.0366,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,2.002814494619254,3.7358043397700884,100*(exp(b - se^2/2) - 1),reported,0.0036602732814482665,0.006534926931756417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5601092896174864,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0095,0.0218,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.9690362893340798,2.159388061725523,100*(exp(b - se^2/2) - 1),reported,-0.0028477967884046273,0.006534944209181145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43577981651376146,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0346,0.0241,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,3.490495853653819,2.4948453604486693,100*(exp(b - se^2/2) - 1),reported,0.009381744212244688,0.006534683107372746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4356846473029046,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0652,0.0557,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,6.57180048927386,5.945264699862558,100*(exp(b - se^2/2) - 1),reported,0.007649328986610108,0.006534779517702195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1705565529622979,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0088,0.0453,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,0.7804250642941435,4.57003991724528,100*(exp(b - se^2/2) - 1),reported,0.0012694855605750923,0.006534965442505872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19426048565121415,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0209,0.0256,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,2.078538877312708,2.6140670324180606,100*(exp(b - se^2/2) - 1),reported,0.0053351150003613295,0.006534877703791869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8164062499999999,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0952,0.0416,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,9.89275173821258,4.575495844989769,100*(exp(b - se^2/2) - 1),reported,0.014953357033753072,0.0065342400483626864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2884615384615388,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1438,0.0434,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,15.356623745007148,5.011194691829184,100*(exp(b - se^2/2) - 1),reported,0.02164766298598375,0.006533439315658517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.313364055299539,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0388,0.0334,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,3.898286483164104,3.472138918160487,100*(exp(b - se^2/2) - 1),reported,0.007591304113916343,0.006534782407340356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1616766467065869,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1198,0.0385,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-11.355936827521496,3.415326678394755,100*(exp(b - se^2/2) - 1),reported,-0.02033058901734227,0.0065336200097468895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1116883116883116,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0952,0.0419,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-9.160666422438835,3.809510617103177,100*(exp(b - se^2/2) - 1),reported,-0.01484631610843371,0.006534250472094248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.272076372315036,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.035,0.0561,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,3.399133399314591,5.809826566365887,100*(exp(b - se^2/2) - 1),reported,0.004077042314235873,0.006534916395103784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6238859180035652,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0368,0.0339,,,,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0368,0.0339,not harmonised,reported,0.007093831004354203,0.006534806278467594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0855457227138643,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0327,0.0427,,,,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0327,0.0427,not harmonised,reported,0.005004469934413005,0.006534888874600469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.765807962529274,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0505,0.0411,,,,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0505,0.0411,not harmonised,reported,0.008029328041767817,0.0065347600498347974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2287104622871048,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0302,0.0289,,,,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0302,0.0289,not harmonised,reported,0.006828772102808308,0.006534818336793381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0449826989619377,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0201,0.0349,,,,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0201,0.0349,not harmonised,reported,0.003763667074940066,0.006534924423652155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5759312320916905,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0247,0.0311,,,,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0247,0.0311,not harmonised,reported,0.005190083681128448,0.006534882691623268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7942122186495177,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0575,0.0284,,,*,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0575,0.0284,not harmonised,reported,0.013229856681402911,0.0065343987782929165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0246478873239435,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0323,0.0313,,,,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0323,0.0313,not harmonised,reported,0.006743602373179471,0.006534822113947909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0319488817891374,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0737,0.0236,,,**,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0737,0.0236,not harmonised,reported,0.02040368972447462,0.006533610278122129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1228813559322037,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0742,0.0297,,,*,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0742,0.0297,not harmonised,reported,0.016324249649703613,0.0065340999271724706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4983164983164983,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0624,0.0322,,,+,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0624,0.0322,not harmonised,reported,0.012663027220347366,0.006534446738704892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9378881987577639,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0405,0.023,,,+,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0405,0.023,not harmonised,reported,0.01150646923274686,0.006534538082794514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7608695652173914,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0377,0.0212,,,+,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0377,0.0212,not harmonised,reported,0.011620366095441786,0.006534529475420846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7783018867924527,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.04,0.0192,,,*,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.04,0.0192,not harmonised,reported,0.01361326072310193,0.0065343651470889256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0833333333333335,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0203,0.0214,,,,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0203,0.0214,not harmonised,reported,0.0061989418926252675,0.0065348451478906755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9485981308411214,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0514,0.0198,,,*,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0514,0.0198,not harmonised,reported,0.016962079295022497,0.006534030545553414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.595959595959596,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0531,0.0222,,,*,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0531,0.0222,not harmonised,reported,0.015629034274914543,0.006534172521715684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3918918918918917,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0397,0.0224,,,+,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0397,0.0224,not harmonised,reported,0.011581291865349263,0.006534532437879684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7723214285714286,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0193,0.0184,,,,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.0193,0.0184,not harmonised,reported,0.006854454985754549,0.006534817188491381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.048913043478261,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0031,0.019,,,,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0031,0.019,not harmonised,reported,0.0010662314568711216,0.0065349669937262295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1631578947368421,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0905,0.0201,,,***,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0905,0.0201,not harmonised,reported,0.02941089585134041,0.0065321437194689755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.502487562189055,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c1,4,All craft breweries,13,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.058,0.0168,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: All craft breweries,other,0.058,0.0168,not harmonised,reported,0.022555468669829885,0.006533308166433484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4523809523809526,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c2,4,Brewpubs/taprooms only,13,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0411,0.0175,,,*,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0411,0.0175,not harmonised,reported,0.015346038170190382,0.006534201167356003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3485714285714283,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T4-c3,4,Regional/microbreweries only,13,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0907,0.0214,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.783,se,row houses; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0907,0.0214,not harmonised,reported,0.027686664664355067,0.006532465532714425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.238317757009346,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,logSQFT,living_area,A_structural,log,sqft,0.8709,0.0432,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,elasticity,0.8709,0.0432,b (log-log),reported,0.08703743790837049,0.004317392717466536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.15972222222222,53242.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,logSQFT,living_area,A_structural,log,sqft,0.8711,0.0432,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,elasticity,0.8711,0.0432,b (log-log),reported,0.08705727436698012,0.004317385205663576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.16435185185185,53242.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,logSQFT,living_area,A_structural,log,sqft,0.8677,0.0435,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,elasticity,0.8677,0.0435,b (log-log),reported,0.086126422884849,0.004317735848208978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.947126436781613,53242.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,bedrooms,bedrooms,A_structural,level,count,-0.0145,0.0125,,,,,,high,,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,-1.4500000000000002,1.25,100*b/1 (per unit count),reported,-0.005027190246079786,0.004333784694896367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.16,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,bedrooms,bedrooms,A_structural,level,count,-0.0128,0.0125,,,,,,high,,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.28,1.25,100*b/1 (per unit count),reported,-0.004437807905996752,0.004333796783199952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.024,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,bedrooms,bedrooms,A_structural,level,count,-0.0147,0.013,,,,,,high,,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.47,1.3,100*b/1 (per unit count),reported,-0.004900513467308614,0.004333787420068843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1307692307692307,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,fullBaths,bathrooms,A_structural,level,count,0.0959,0.0165,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,9.59,1.6500000000000001,100*b/1 (per unit count),reported,0.025180813209926406,0.004332465255096827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.812121212121212,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,fullBaths,bathrooms,A_structural,level,count,0.0944,0.0166,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,9.44,1.66,100*b/1 (per unit count),reported,0.02463796709818325,0.004332523875316123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.686746987951807,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,fullBaths,bathrooms,A_structural,level,count,0.0929,0.0171,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,9.29,1.71,100*b/1 (per unit count),reported,0.023538136693967444,0.004332638724077968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.432748538011696,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,halfBaths,bathrooms,A_structural,level,count,0.0793,0.0128,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,7.93,1.28,100*b/1 (per unit count),reported,0.026839817170075084,0.004332278181298375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.195312499999999,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,halfBaths,bathrooms,A_structural,level,count,0.0794,0.0124,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,7.9399999999999995,1.24,100*b/1 (per unit count),reported,0.027739873577013,0.004332171692127976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.403225806451613,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,halfBaths,bathrooms,A_structural,level,count,0.083,0.0128,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,8.3,1.28,100*b/1 (per unit count),reported,0.028091150136308612,0.004332129177647593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.484375,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,distanceCBD,cbd_distance,D_location,level,km,0.0064,0.0385,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,semi_elasticity,0.64,3.85,100*b/1 (X per km->km),reported,0.0007204302685679571,0.004333838334354118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16623376623376623,53242.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,distanceCBD,cbd_distance,D_location,level,km,0.0033,0.0391,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,semi_elasticity,0.33,3.91,100*b/1 (X per km->km),reported,0.00036577159227856216,0.0043338391691187216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.08439897698209718,53242.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,distanceCBD,cbd_distance,D_location,level,km,0.0015,0.0385,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,semi_elasticity,0.15,3.85,100*b/1 (X per km->km),reported,0.00016885088560707345,0.004333839397248218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03896103896103896,53242.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0569,0.0922,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,5.406021661446192,9.759830539991734,100*(exp(b - se^2/2) - 1),reported,0.0026745616402813516,0.004333823958417234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6171366594360086,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.078,0.0939,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,7.636690672999813,10.15174176491208,100*(exp(b - se^2/2) - 1),reported,0.0035999711111444257,0.004333811376108482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8306709265175719,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_0to0.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.137,0.042,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-0.5km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-12.879851254374797,3.6622749497548592,100*(exp(b - se^2/2) - 1),reported,-0.014135159235412932,0.004333406480929512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.261904761904762,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0556,0.0531,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,5.568536914082012,5.613597812368749,100*(exp(b - se^2/2) - 1),reported,0.004537834142697907,0.0043337948377204834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.047080979284369,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0527,0.0693,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,5.158521579245856,7.305005610235939,100*(exp(b - se^2/2) - 1),reported,0.003295701286053672,0.0043338159226474285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7604617604617604,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_0.5to1km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1377,0.0981,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0.5-1km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,14.212229620883177,11.25826216286837,100*(exp(b - se^2/2) - 1),reported,0.006083166683991905,0.004333759271602077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.403669724770642,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0904,0.0882,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-8.998083567937588,8.057649349654467,100*(exp(b - se^2/2) - 1),reported,-0.004441895942066979,0.0043337967045388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0249433106575963,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0453,0.0973,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-4.880257907489016,9.299065360128234,100*(exp(b - se^2/2) - 1),reported,-0.002017703266863805,0.004333830637215192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4655704008221994,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_1to1.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0962,0.0709,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1-1.5km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-9.39976734441359,6.439721800763951,100*(exp(b - se^2/2) - 1),reported,-0.005880227757951149,0.004333764532627198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3568406205923835,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0737,0.0501,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-7.221476891413414,4.654041189090663,100*(exp(b - se^2/2) - 1),reported,-0.006375199147115523,0.0043337513876592635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.471057884231537,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0665,0.0597,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,-6.600299276162824,5.585907607662295,100*(exp(b - se^2/2) - 1),reported,-0.0048274198642909,0.004333788960874688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1139028475711892,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_1.5to2km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0532,0.0541,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 1.5-2km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-5.3196216255542055,5.129709822963604,100*(exp(b - se^2/2) - 1),reported,-0.004261703613299918,0.004333800102998602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9833641404805914,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1464,0.1003,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,15.18507020959543,11.611321365913984,100*(exp(b - se^2/2) - 1),reported,0.006325637117163261,0.004333752751717726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4596211365902294,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0514,0.055,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,5.115283503000723,5.79009148650666,100*(exp(b - se^2/2) - 1),reported,0.004050136748328949,0.004333803913581561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9345454545454546,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_2to2.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.2326,0.1111,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 2-2.5km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,25.411280587127116,14.019449333886817,100*(exp(b - se^2/2) - 1),reported,0.009072993396337981,0.004333661076238821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0936093609360937,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,True
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0175,0.0579,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,-1.8993511235685046,5.689556444500774,100*(exp(b - se^2/2) - 1),reported,-0.0013098812688838364,0.00433383574104995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.30224525043177897,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0642,0.0751,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,6.330288720909505,8.007955345619598,100*(exp(b - se^2/2) - 1),reported,0.0037048013823650933,0.004333809716754182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8548601864181091,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_2.5to3km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.09,0.0521,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 2.5-3km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,9.269027217822279,5.700648018104146,100*(exp(b - se^2/2) - 1),reported,0.007486269122054237,0.004333718013989175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.727447216890595,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0582,0.0508,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,5.85601969206746,5.384428956318124,100*(exp(b - se^2/2) - 1),reported,0.004965085580838132,0.004333786039631909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1456692913385829,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0321,0.0439,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,3.1626202946931414,4.533205146138813,100*(exp(b - se^2/2) - 1),reported,0.003168919091731931,0.004333817698661427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7312072892938496,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_3to3.5km,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1687,0.1034,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3-3.5km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,-15.974157025300617,8.73484212061919,100*(exp(b - se^2/2) - 1),reported,-0.007070603877564066,0.004333731125904709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6315280464216633,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0732,0.0523,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,pct_premium,7.447519856932572,5.627196034734427,100*(exp(b - se^2/2) - 1),reported,0.006065606357954182,0.004333759733893494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3996175908221797,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0709,0.0522,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,pct_premium,7.201234667179013,5.6035336176524595,100*(exp(b - se^2/2) - 1),reported,0.005886281502288595,0.004333764378271717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3582375478927202,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_3.5to4km,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1359,0.1111,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 3.5-4km of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,pct_premium,13.851911384303882,12.727253048895639,100*(exp(b - se^2/2) - 1),reported,0.005301174677678395,0.004333778562840837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2232223222322232,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.016,0.0364,,,,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.016,0.0364,not harmonised,reported,0.0019049809210430573,0.004333831595372956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43956043956043955,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0129,0.045,,,,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0129,0.045,not harmonised,reported,0.001242366352806867,0.0043338361144425585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2866666666666667,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_0to0.5km × i.Post_0to0.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0067,0.061,,,,,,high,DID interaction: brewery within 0-0.5km × i.Post_0-0.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0067,0.061,not harmonised,reported,0.0004760118210789277,0.004333838968032028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1098360655737705,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0987,0.0417,,,*,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.0987,0.0417,not harmonised,reported,0.010257253044667567,0.0043336114687197325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.366906474820144,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1162,0.0453,,,*,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.1162,0.0453,not harmonised,reported,0.011116137530054125,0.004333571687706127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.565121412803532,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_0.5to1km × i.Post_0.5to1km,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0828,0.0401,,,*,,,high,DID interaction: brewery within 0.5-1km × i.Post_0.5-1km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,-0.0828,0.0401,not harmonised,reported,-0.008948317712104779,0.004333665945113546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.064837905236908,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0798,0.0398,,,*,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.0798,0.0398,not harmonised,reported,0.008689128968416207,0.0043336758514156025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0050251256281406,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0728,0.0396,,,+,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0728,0.0396,not harmonised,reported,0.007967007561820727,0.004333701915495891,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8383838383838382,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_1to1.5km × i.Post_1to1.5km,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0408,0.0232,,,+,,,high,DID interaction: brewery within 1-1.5km × i.Post_1-1.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,-0.0408,0.0232,not harmonised,reported,-0.007621358384952559,0.004333713591443611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7586206896551726,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0052,0.0191,,,,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.0052,0.0191,not harmonised,reported,0.0011798926439925775,0.004333836442357352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27225130890052357,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,-0.015,0.0249,,,,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,-0.015,0.0249,not harmonised,reported,-0.002610737764688625,0.0043338246893831174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6024096385542169,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_1.5to2km × i.Post_1.5to2km,neighbourhood_amenities,F_neighbourhood,other,binary,0.088,0.0296,,,**,,,high,DID interaction: brewery within 1.5-2km × i.Post_1.5-2km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.088,0.0296,not harmonised,reported,0.012883318261909103,0.00433347977900579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.972972972972973,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0497,0.0322,,,,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.0497,0.0322,not harmonised,reported,0.006689037341550711,0.004333742502976517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5434782608695652,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0503,0.0343,,,,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0503,0.0343,not harmonised,reported,0.006355327182906847,0.0043337519358589436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.466472303206997,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_2to2.5km × i.Post_2to2.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0702,0.0295,,,*,,,high,DID interaction: brewery within 2-2.5km × i.Post_2-2.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0702,0.0295,not harmonised,reported,0.010312520412265741,0.004333609005154406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3796610169491528,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,-0.011,0.0359,,,,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,-0.011,0.0359,not harmonised,reported,-0.0013279162121895296,0.00433383563796401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3064066852367688,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0061,0.0367,,,,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.0061,0.0367,not harmonised,reported,0.0007203382518068806,0.004333838334641397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1662125340599455,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_2.5to3km × i.Post_2.5to3km,neighbourhood_amenities,F_neighbourhood,other,binary,0.045,0.0375,,,,,,high,DID interaction: brewery within 2.5-3km × i.Post_2.5-3km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.045,0.0375,not harmonised,reported,0.005200537023623675,0.004333780853019729,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0073,0.0194,,,,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.0073,0.0194,not harmonised,reported,0.001630772473341503,0.004333833696277419,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37628865979381443,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.027,0.0206,,,,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.027,0.0206,not harmonised,reported,0.005680183382806659,0.004333769543919155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3106796116504853,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_3to3.5km × i.Post_3to3.5km,neighbourhood_amenities,F_neighbourhood,other,binary,0.071,0.029,,,*,,,high,DID interaction: brewery within 3-3.5km × i.Post_3-3.5km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.071,0.029,not harmonised,reported,0.01060983731967886,0.0043335955249392535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4482758620689653,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c1,5,All craft breweries,14,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1108,0.0231,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: All craft breweries,other,0.1108,0.0231,not harmonised,reported,0.020782930593260952,0.004332903399858556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.796536796536796,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c2,5,Brewpubs/taprooms only,14,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.1158,0.0262,,,***,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.784,se,condominiums; Euclidean distance bins; brewery type: Brewpubs/taprooms only,other,0.1158,0.0262,not harmonised,reported,0.01915139565188703,0.004333044612084976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.419847328244274,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T5-c3,5,Regional/microbreweries only,14,i.distBrew_3.5to4km × i.Post_3.5to4km,neighbourhood_amenities,F_neighbourhood,other,binary,0.0089,0.0315,,,,,,high,DID interaction: brewery within 3.5-4km × i.Post_3.5-4km bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; Euclidean distance bins; brewery type: Regional/microbreweries only,other,0.0089,0.0315,not harmonised,reported,0.001224480706966403,0.004333836210049628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.28253968253968254,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,logSQFT,living_area,A_structural,log,sqft,0.6314,0.0278,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,elasticity,0.6314,0.0278,b (log-log),reported,0.04470852163326648,0.0019684778292759077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.712230215827336,257555.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,logSQFT,living_area,A_structural,log,sqft,0.6319,0.028,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,elasticity,0.6319,0.028,b (log-log),reported,0.0444248891097972,0.001968502761630514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.567857142857143,257555.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,logSQFT,living_area,A_structural,log,sqft,0.6296,0.0277,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,elasticity,0.6296,0.0277,b (log-log),reported,0.04474194170084676,0.001968474881056949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.72924187725632,257555.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,bedrooms,bedrooms,A_structural,level,count,-0.019,0.0039,,,***,,,high,,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,semi_elasticity,-1.9,0.38999999999999996,100*b/1 (per unit count),reported,-0.009599176816699669,0.001970357346585721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.871794871794872,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,bedrooms,bedrooms,A_structural,level,count,-0.0186,0.0039,,,***,,,high,,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.8599999999999999,0.38999999999999996,100*b/1 (per unit count),reported,-0.009397106921086211,0.001970361128614851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.769230769230769,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,bedrooms,bedrooms,A_structural,level,count,-0.0185,0.004,,,***,,,high,,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.8499999999999999,0.4,100*b/1 (per unit count),reported,-0.009112944188301553,0.0019703663109841194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.625,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,fullBaths,bathrooms,A_structural,level,count,0.085,0.0058,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,semi_elasticity,8.5,0.58,100*b/1 (per unit count),reported,0.028865224308240765,0.001969627070444664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.655172413793105,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,fullBaths,bathrooms,A_structural,level,count,0.0851,0.0058,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,8.51,0.58,100*b/1 (per unit count),reported,0.028899155051000908,0.001969625138611108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.672413793103448,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,fullBaths,bathrooms,A_structural,level,count,0.0851,0.0058,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,8.51,0.58,100*b/1 (per unit count),reported,0.028899155051000908,0.001969625138611108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.672413793103448,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,halfBaths,bathrooms,A_structural,level,count,0.0584,0.0065,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,semi_elasticity,5.84,0.65,100*b/1 (per unit count),reported,0.017700944883192113,0.001970139413368985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.984615384615385,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,halfBaths,bathrooms,A_structural,level,count,0.0586,0.0065,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,5.86,0.65,100*b/1 (per unit count),reported,0.01776154546623387,0.0019701372957426646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.015384615384615,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,halfBaths,bathrooms,A_structural,level,count,0.0588,0.0066,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,5.88,0.66,100*b/1 (per unit count),reported,0.01755219717184811,0.0019701445805135635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.909090909090908,257555.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,distanceCBD,cbd_distance,D_location,level,km,-0.0335,0.0129,,,*,,,high,distance to CBD in km,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,semi_elasticity,-3.35,1.29,100*b/1 (X per km->km),reported,-0.005116988233017683,0.0019704223345053166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5968992248062017,257555.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,distanceCBD,cbd_distance,D_location,level,km,-0.0345,0.0127,,,**,,,high,distance to CBD in km,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-3.45,1.27,100*b/1 (X per km->km),reported,-0.005352715484376594,0.001970419902944427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7165354330708666,257555.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,distanceCBD,cbd_distance,D_location,level,km,-0.0425,0.0129,,,**,,,high,distance to CBD in km,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-4.25,1.29,100*b/1 (X per km->km),reported,-0.0064916496909717845,0.0019704066120832006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.294573643410853,257555.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,house,dwelling_type,A_structural,binary,binary,-0.023,0.0328,,,,,,high,single-family house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,-2.326306389801991,3.205420946776349,100*(exp(b - se^2/2) - 1),reported,-0.0013817153585312166,0.0019704462504271263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7012195121951219,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,house,dwelling_type,A_structural,binary,binary,-0.0345,0.0362,,,,,,high,single-family house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.4544453383461238,3.4972397896650995,100*(exp(b - se^2/2) - 1),reported,-0.0018779099630821514,0.001970444656915185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9530386740331491,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,house,dwelling_type,A_structural,binary,binary,-0.026,0.0606,,,,,,high,single-family house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-2.7452323487801578,5.904470643029021,100*(exp(b - se^2/2) - 1),reported,-0.0008454064869193596,0.0019704474272043537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.429042904290429,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,rowhouse,dwelling_type,A_structural,binary,binary,0.1565,0.0311,,,***,,,high,row house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,16.88453661525575,3.636867470812224,100*(exp(b - se^2/2) - 1),reported,0.009915111707117018,0.0019703512721491326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.032154340836013,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,rowhouse,dwelling_type,A_structural,binary,binary,0.1556,0.0311,,,***,,,high,row house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,16.779387856341078,3.6335957625780386,100*(exp(b - se^2/2) - 1),reported,0.009858097452711068,0.001970352382900477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.003215434083601,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,rowhouse,dwelling_type,A_structural,binary,binary,0.1514,0.0315,,,***,,,high,row house dummy (reference: condominium),log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,16.288487040094356,3.6649051418611553,100*(exp(b - se^2/2) - 1),reported,0.009470237112769266,0.001970359769169299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.806349206349206,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0053,0.0179,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,-0.5445325175295168,1.7805380961938158,100*(exp(b - se^2/2) - 1),reported,-0.0005834286770269217,0.0019704477959965844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.29608938547486036,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0161,0.0212,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.6192193888991735,2.086141293956755,100*(exp(b - se^2/2) - 1),reported,-0.0014964235563706579,0.0019704459251588782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7594339622641509,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0251,0.0299,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,2.495939277425485,3.0659987949180514,100*(exp(b - se^2/2) - 1),reported,0.0016541197470011564,0.0019704454356707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8394648829431438,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0115,0.0232,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,-1.1700134804111917,2.293472823615158,100*(exp(b - se^2/2) - 1),reported,-0.0009767302888653308,0.0019704471914500587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4956896551724138,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0077,0.0261,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.8008365801038142,2.589980175239847,100*(exp(b - se^2/2) - 1),reported,-0.0005813198485749964,0.001970447798416546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2950191570881226,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0441,0.0243,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-4.342419905671302,2.3251655884744404,100*(exp(b - se^2/2) - 1),reported,-0.003575975596314488,0.0019704355326630855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8148148148148149,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0115,0.0157,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,1.1441716393385093,1.5881592153590696,100*(exp(b - se^2/2) - 1),reported,0.0014433203763098395,0.0019704460789621288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7324840764331211,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0332,0.0165,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,3.361655939049557,1.7056994955355456,100*(exp(b - se^2/2) - 1),reported,0.003964749320701311,0.001970432644324447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.012121212121212,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0233,0.0204,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-2.3233917632986545,1.9930174719611415,100*(exp(b - se^2/2) - 1),reported,-0.0022505551563606774,0.001970443141191323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.142156862745098,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.025,0.0145,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,-2.479261203715366,1.4141993724410824,100*(exp(b - se^2/2) - 1),reported,-0.0033973047589357457,0.001970436760182732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7241379310344829,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0259,0.0165,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-2.5700107610609146,1.6078136711827542,100*(exp(b - se^2/2) - 1),reported,-0.0030929916659254396,0.001970438706091496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5696969696969696,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0059,0.026,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,0.5577496639447954,2.615385342128993,100*(exp(b - se^2/2) - 1),reported,0.0004471401081853226,0.0019704479343759978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.22692307692307692,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0105,0.0099,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,-1.0493559383387474,0.9796593832422203,100*(exp(b - se^2/2) - 1),reported,-0.002089864666433352,0.001970443828351446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0606060606060606,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0075,0.0106,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.752770384070589,1.0520797381082867,100*(exp(b - se^2/2) - 1),reported,-0.0013941836436281595,0.001970446216327799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7075471698113207,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0499,0.0144,,,***,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-4.877407601712724,1.3699073551672365,100*(exp(b - se^2/2) - 1),reported,-0.006827990950831373,0.0019704021982359073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4652777777777777,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0142,0.0197,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,-1.4290945997618043,1.9422236786150515,100*(exp(b - se^2/2) - 1),reported,-0.0014203215859218657,0.001970446143849349,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7208121827411168,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0156,0.0191,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.5658515460792,1.8804352049728232,100*(exp(b - se^2/2) - 1),reported,-0.0016093691644541146,0.0019704455795559995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8167539267015707,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0037,0.0154,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-0.38112984946772066,1.5343125283110803,100*(exp(b - se^2/2) - 1),reported,-0.00047341930318214187,0.0019704479105418877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24025974025974026,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0109,0.0174,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,1.080659394306105,1.7590697412840417,100*(exp(b - se^2/2) - 1),reported,0.0012343602453699332,0.001970446630223563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6264367816091955,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.007,0.0159,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,0.6897272372580199,1.6011690460542893,100*(exp(b - se^2/2) - 1),reported,0.0008674925616053337,0.0019704473899321154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.440251572327044,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0017,0.012,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,0.16293259114303904,1.202041734983018,100*(exp(b - se^2/2) - 1),reported,0.0002791468077328057,0.0019704480545845106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.14166666666666666,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0139,0.0265,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,pct_premium,1.3641076947186193,2.6870921935317424,100*(exp(b - se^2/2) - 1),reported,0.0010335552602550452,0.001970447078903503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5245283018867924,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0073,0.0206,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,0.711299804684562,2.075093022506611,100*(exp(b - se^2/2) - 1),reported,0.0006982654297182583,0.00197044765098577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35436893203883496,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0064,0.0165,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,0.6283534090935294,1.6605938642050913,100*(exp(b - se^2/2) - 1),reported,0.0007642948095382263,0.0019704475558407395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3878787878787879,257555.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.045,0.0258,,,+,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.045,0.0258,not harmonised,reported,0.0034368078387239324,0.001970436494201721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.744186046511628,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.038,0.0275,,,,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.038,0.0275,not harmonised,reported,0.002722790961352593,0.00197044082729464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3818181818181818,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1292,0.0608,,,*,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.1292,0.0608,not harmonised,reported,0.004187165573211226,0.0019704308579817534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.125,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1092,0.0471,,,*,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.1092,0.0471,not harmonised,reported,0.004568379842730913,0.0019704275695295423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3184713375796178,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1036,0.0559,,,+,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.1036,0.0559,not harmonised,reported,0.003651825853775744,0.0019704349925295764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8533094812164579,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1326,0.0503,,,**,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.1326,0.0503,not harmonised,reported,0.005194391596123912,0.001970421548152585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.636182902584493,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1105,0.0215,,,***,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.1105,0.0215,not harmonised,reported,0.010126667625897501,0.0019703470946316405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.1395348837209305,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.109,0.0211,,,***,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.109,0.0211,not harmonised,reported,0.010178564935708238,0.00197034605636187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.165876777251184,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0544,0.0283,,,+,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.0544,0.0283,not harmonised,reported,0.0037876893788132897,0.0019704339966988256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9222614840989398,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.084,0.0221,,,***,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.084,0.0221,not harmonised,reported,0.007489276067334417,0.0019703928700963167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8009049773755654,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0805,0.0233,,,***,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0805,0.0233,not harmonised,reported,0.006807613690791078,0.0019704024719929456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4549356223175964,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0613,0.0303,,,*,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.0613,0.0303,not harmonised,reported,0.003986386491972838,0.001970432474825073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0231023102310233,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0623,0.0226,,,**,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.0623,0.0226,not harmonised,reported,0.005431730426958096,0.001970419063390898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.756637168141593,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0502,0.0216,,,*,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0502,0.0216,not harmonised,reported,0.004579419397947189,0.0019704274700330536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.324074074074074,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0738,0.0491,,,,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.0738,0.0491,not harmonised,reported,0.0029616789066788988,0.001970439489402899,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5030549898167007,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0504,0.0209,,,*,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.0504,0.0209,not harmonised,reported,0.004751649028155404,0.0019704258866755546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.411483253588517,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0283,0.0208,,,,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0283,0.0208,not harmonised,reported,0.0026809366210808687,0.001970441050123041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3605769230769231,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0646,0.0401,,,,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.0646,0.0401,not harmonised,reported,0.003174321894559848,0.0019704382038986047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6109725685785539,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0348,0.0141,,,*,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.0348,0.0141,not harmonised,reported,0.004863176176648399,0.001970424830193748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4680851063829787,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0183,0.015,,,,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0183,0.015,not harmonised,reported,0.0024039397741287397,0.0019704424378104426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.22,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0751,0.0347,,,*,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.0751,0.0347,not harmonised,reported,0.0042645334020891094,0.0019704302137482305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.164265129682997,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c1,6,All craft breweries,16,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0276,0.0155,,,+,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: All craft breweries,other,0.0276,0.0155,not harmonised,reported,0.003508647333685681,0.0019704360026133352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7806451612903225,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c2,6,Brewpubs/taprooms only,16,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0155,0.0168,,,,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.739,se,all home transactions; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0155,0.0168,not harmonised,reported,0.0018179699741181614,0.001970444875173233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9226190476190477,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T6-c3,6,Regional/microbreweries only,16,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.076,0.0258,,,**,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,257565.0,0.74,se,all home transactions; walk-time bins; brewery type: Regional/microbreweries only,other,0.076,0.0258,not harmonised,reported,0.005804323075281028,0.0019704149387138226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.945736434108527,257555.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,logSQFT,living_area,A_structural,log,sqft,0.5034,0.0224,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,elasticity,0.5034,0.0224,b (log-log),reported,0.052767685485024696,0.0023480257347329226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.473214285714285,180877.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,logSQFT,living_area,A_structural,log,sqft,0.5046,0.0226,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,elasticity,0.5046,0.0226,b (log-log),reported,0.05242633262225449,0.0023480680088445328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.327433628318587,180877.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,logSQFT,living_area,A_structural,log,sqft,0.5024,0.0226,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,elasticity,0.5024,0.0226,b (log-log),reported,0.05219838377636025,0.002348096085481174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.23008849557522,180877.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,bedrooms,bedrooms,A_structural,level,count,-0.0195,0.0033,,,***,,,high,,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,semi_elasticity,-1.95,0.33,100*b/1 (per unit count),reported,-0.013892713615933297,0.0023510746119271733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.909090909090909,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,bedrooms,bedrooms,A_structural,level,count,-0.0196,0.0033,,,***,,,high,,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.96,0.33,100*b/1 (per unit count),reported,-0.01396394444444998,0.0023510722789124965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.9393939393939394,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,bedrooms,bedrooms,A_structural,level,count,-0.0194,0.0032,,,***,,,high,,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.94,0.32,100*b/1 (per unit count),reported,-0.01425331748778819,0.0023510626783980523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0625,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,fullBaths,bathrooms,A_structural,level,count,0.0762,0.0051,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,semi_elasticity,7.62,0.51,100*b/1 (per unit count),reported,0.03510955167100679,0.0023498518834925803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.941176470588236,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,fullBaths,bathrooms,A_structural,level,count,0.0764,0.0051,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,7.64,0.51,100*b/1 (per unit count),reported,0.03520158868538992,0.002349844270883359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.980392156862743,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,fullBaths,bathrooms,A_structural,level,count,0.0763,0.0051,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,7.630000000000001,0.51,100*b/1 (per unit count),reported,0.03515557029001013,0.002349848079673023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.96078431372549,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,halfBaths,bathrooms,A_structural,level,count,0.062,0.0072,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,semi_elasticity,6.2,0.72,100*b/1 (per unit count),reported,0.02024316979201654,0.002350819717782566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.61111111111111,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,halfBaths,bathrooms,A_structural,level,count,0.0621,0.0072,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,6.21,0.72,100*b/1 (per unit count),reported,0.020275806653877865,0.002350818162768448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.625,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,halfBaths,bathrooms,A_structural,level,count,0.0621,0.0073,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,6.21,0.73,100*b/1 (per unit count),reported,0.0199981677288279,0.0023508313111182556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.506849315068493,180877.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,distanceCBD,cbd_distance,D_location,level,km,-0.0212,0.0121,,,+,,,high,distance to CBD in km,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,semi_elasticity,-2.12,1.21,100*b/1 (X per km->km),reported,-0.004119600784966067,0.0023512815800985566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7520661157024795,180877.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,distanceCBD,cbd_distance,D_location,level,km,-0.0215,0.012,,,+,,,high,distance to CBD in km,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-2.15,1.2,100*b/1 (X per km->km),reported,-0.004212711196817896,0.002351280667991384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7916666666666665,180877.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,distanceCBD,cbd_distance,D_location,level,km,-0.0292,0.012,,,*,,,high,distance to CBD in km,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-2.92,1.2,100*b/1 (X per km->km),reported,-0.005721406749240604,0.002351263047633125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.433333333333333,180877.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0303,0.0148,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-2.995180194242253,1.4358285764601064,100*(exp(b - se^2/2) - 1),reported,-0.0048137574983803085,0.002351274289637906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0472972972972974,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0327,0.018,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.232791087641873,1.7420919564608965,100*(exp(b - se^2/2) - 1),reported,-0.004271492148381425,0.0023512800816778484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8166666666666669,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0315,0.0131,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-3.109218424828386,1.269378152954399,100*(exp(b - se^2/2) - 1),reported,-0.005653802632037203,0.0023512639517361067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.404580152671756,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0235,0.011,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-2.3285118369041236,1.0744513721357283,100*(exp(b - se^2/2) - 1),reported,-0.005023171717522365,0.002351271867776426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1363636363636367,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0262,0.0122,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-2.5932250743093688,1.188451095333001,100*(exp(b - se^2/2) - 1),reported,-0.005049452031309468,0.002351271556563951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1475409836065573,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0103,0.0136,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-1.0338664765646266,1.34606389415169,100*(exp(b - se^2/2) - 1),reported,-0.001780762307559624,0.0023512978041563964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7573529411764707,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0243,0.0142,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-2.4105526667346,1.3859098725222934,100*(exp(b - se^2/2) - 1),reported,-0.0040236735713415515,0.002351282498479426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7112676056338025,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0122,0.0181,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.2287687187835683,1.78805215408585,100*(exp(b - se^2/2) - 1),reported,-0.0015848531860666857,0.0023512985793284437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6740331491712707,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0506,0.0148,,,***,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-4.944525242386821,1.406975109888945,100*(exp(b - se^2/2) - 1),reported,-0.00803864955190372,0.002351225560635871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.418918918918919,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0182,0.0099,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-1.8083500203382896,0.9721449735958029,100*(exp(b - se^2/2) - 1),reported,-0.004322554353217427,0.0023512795657611276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8383838383838382,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0103,0.0108,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.0304857357007369,1.0689330924145775,100*(exp(b - se^2/2) - 1),reported,-0.002242439341749213,0.002351295620474903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9537037037037037,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0308,0.0111,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-3.0390247067341725,1.0763331312154045,100*(exp(b - se^2/2) - 1),reported,-0.006524193323767897,0.0023512514900592097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.774774774774775,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0095,0.0095,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-0.9499714890941036,0.9410177333207554,100*(exp(b - se^2/2) - 1),reported,-0.0023512950325843313,0.0023512950325843317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0099,0.0109,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.9909974362041551,1.0792622396144234,100*(exp(b - se^2/2) - 1),reported,-0.0021355809254808675,0.002351296170478935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9082568807339451,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0336,0.0137,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-3.3132529360067053,1.3247327484882885,100*(exp(b - se^2/2) - 1),reported,-0.0057665998461030295,0.0023512624372503425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.452554744525547,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0249,0.0138,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-2.468542597914569,1.3460622780968217,100*(exp(b - se^2/2) - 1),reported,-0.004242527627019654,0.002351280371601254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8043478260869565,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0173,0.0164,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.7283378851200681,1.6118720086571605,100*(exp(b - se^2/2) - 1),reported,-0.0024803287429041956,0.0023512942996317234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0548780487804876,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0093,0.0109,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-0.9315742094805568,1.0799099912642542,100*(exp(b - se^2/2) - 1),reported,-0.002006152316192038,0.0023512968006981954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8532110091743118,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0025,0.0166,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,0.23650122398595208,1.66415519182562,100*(exp(b - se^2/2) - 1),reported,0.00035411165434662864,0.0023513013848616136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15060240963855423,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0064,0.015,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-0.6491339632425097,1.4904306545687234,100*(exp(b - se^2/2) - 1),reported,-0.0010032214822589783,0.0023513003490444806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4266666666666667,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0072,0.0131,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-0.7259327864189369,1.3006018738539955,100*(exp(b - se^2/2) - 1),reported,-0.0012923173202817714,0.002351299568846001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5496183206106869,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0148,0.0251,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,pct_premium,-1.5001346681181915,2.4731255440532847,100*(exp(b - se^2/2) - 1),reported,-0.001386423475406911,0.0023512992724806395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5896414342629482,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0165,0.0176,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.6516954259865213,1.7311982677275837,100*(exp(b - se^2/2) - 1),reported,-0.0022043398309258863,0.0023512958196542787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9375,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0043,0.0161,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-0.4419808769586808,1.6030918631385362,100*(exp(b - se^2/2) - 1),reported,-0.0006279872419358049,0.0023513010686433623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2670807453416149,180877.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1607,0.0422,,,***,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.1607,0.0422,not harmonised,reported,0.008953531053538086,0.002351207283505334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.808056872037915,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1747,0.0492,,,***,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.1747,0.0492,not harmonised,reported,0.00834874109185747,0.002351219586258658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5508130081300813,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1857,0.0594,,,**,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.1857,0.0594,not harmonised,reported,0.007350587515101221,0.0023512380096769657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1262626262626263,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1542,0.032,,,***,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.1542,0.032,not harmonised,reported,0.011329607054519068,0.0023511506209118694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.81875,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1678,0.0256,,,***,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.1678,0.0256,not harmonised,reported,0.01541021666753593,0.0023510223283010716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5546875,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.128,0.0621,,,*,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.128,0.0621,not harmonised,reported,0.004846426112445329,0.0023512739186160544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0611916264090175,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1044,0.019,,,***,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.1044,0.019,not harmonised,reported,0.012918705001054291,0.002351105316283827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.494736842105263,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1048,0.0191,,,***,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.1048,0.0191,not harmonised,reported,0.01290030867393925,0.002351105874735111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.486910994764399,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1056,0.0352,,,**,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.1056,0.0352,not harmonised,reported,0.0070537291108199625,0.002351243036939988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0858,0.0186,,,***,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.0858,0.0186,not harmonised,reported,0.010845688483307324,0.002351163237640049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.612903225806452,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0873,0.023,,,***,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0873,0.023,not harmonised,reported,0.008924367363660618,0.0023512078964970703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7956521739130435,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0786,0.031,,,*,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.0786,0.031,not harmonised,reported,0.005961581169421049,0.0023512597487538485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.535483870967742,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0851,0.0164,,,***,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.0851,0.0164,not harmonised,reported,0.012200052962640524,0.0023511265403913587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.189024390243902,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0689,0.0182,,,***,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0689,0.0182,not harmonised,reported,0.008901003176124234,0.0023512083861460245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7857142857142856,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1209,0.034,,,***,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.1209,0.034,not harmonised,reported,0.008360659402105114,0.0023512193521221998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.555882352941176,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.056,0.0155,,,***,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.056,0.0155,not harmonised,reported,0.00849471838377806,0.0023512166955099987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6129032258064515,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.04,0.0173,,,*,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.04,0.0173,not harmonised,reported,0.005436454995645856,0.0023512667856168327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3121387283236996,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1207,0.0274,,,***,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.1207,0.0274,not harmonised,reported,0.010357185132507414,0.002351175415333083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.405109489051095,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0469,0.0131,,,***,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.0469,0.0131,not harmonised,reported,0.008417720215587362,0.002351218226528666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.580152671755725,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0352,0.0154,,,*,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0352,0.0154,not harmonised,reported,0.005374325886333453,0.0023512675752708854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2857142857142856,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1256,0.019,,,***,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.1256,0.019,not harmonised,reported,0.015541463395552768,0.002351017551875021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.610526315789473,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c1,7,All craft breweries,17,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0366,0.0133,,,**,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: All craft breweries,other,0.0366,0.0133,not harmonised,reported,0.006470363506408208,0.002351252312437955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7518796992481205,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c2,7,Brewpubs/taprooms only,17,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0257,0.0138,,,+,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.756,se,single-family homes; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0257,0.0138,not harmonised,reported,0.004378831162639974,0.0023512789900557054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8623188405797102,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T7-c3,7,Regional/microbreweries only,17,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.1041,0.0224,,,***,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,180887.0,0.758,se,single-family homes; walk-time bins; brewery type: Regional/microbreweries only,other,0.1041,0.0224,not harmonised,reported,0.01092660167070099,0.002351161166414046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.647321428571429,180877.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,logSQFT,living_area,A_structural,log,sqft,0.7188,0.0531,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,elasticity,0.7188,0.0531,b (log-log),reported,0.08811797584205765,0.0065095499682989175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.536723163841808,23416.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,logSQFT,living_area,A_structural,log,sqft,0.7192,0.0516,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,elasticity,0.7192,0.0516,b (log-log),reported,0.09070882140469437,0.006508030011793979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.937984496124031,23416.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,logSQFT,living_area,A_structural,log,sqft,0.72,0.0531,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,elasticity,0.72,0.0531,b (log-log),reported,0.08826393921158884,0.006509465516854677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.559322033898304,23416.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,bedrooms,bedrooms,A_structural,level,count,-0.0411,0.0102,,,***,,,high,,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,semi_elasticity,-4.109999999999999,1.02,100*b/1 (per unit count),reported,-0.02632296354257223,0.006532706280638363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.029411764705882,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,bedrooms,bedrooms,A_structural,level,count,-0.0402,0.0099,,,***,,,high,,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-4.02,0.9900000000000001,100*b/1 (per unit count),reported,-0.026526603871512077,0.006532671102685811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0606060606060606,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,bedrooms,bedrooms,A_structural,level,count,-0.0405,0.01,,,***,,,high,,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-4.05,1.0,100*b/1 (per unit count),reported,-0.026457366528934396,0.006532683093564049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.05,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,fullBaths,bathrooms,A_structural,level,count,0.1024,0.0107,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,semi_elasticity,10.24,1.0699999999999998,100*b/1 (per unit count),reported,0.06241833155760033,0.006522228004553939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.570093457943926,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,fullBaths,bathrooms,A_structural,level,count,0.1029,0.0108,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,10.290000000000001,1.08,100*b/1 (per unit count),reported,0.062143407229775106,0.006522340117410798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.527777777777779,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,fullBaths,bathrooms,A_structural,level,count,0.1006,0.0103,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,10.059999999999999,1.03,100*b/1 (per unit count),reported,0.06369737942790371,0.006521699881783381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.766990291262136,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,halfBaths,bathrooms,A_structural,level,count,0.0665,0.0164,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,semi_elasticity,6.65,1.6400000000000001,100*b/1 (per unit count),reported,0.02648921092896801,0.006532677582482336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.054878048780488,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,halfBaths,bathrooms,A_structural,level,count,0.0666,0.0165,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,6.660000000000001,1.6500000000000001,100*b/1 (per unit count),reported,0.026368346529127416,0.006532698464423459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.036363636363636,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,halfBaths,bathrooms,A_structural,level,count,0.0665,0.0164,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,6.65,1.6400000000000001,100*b/1 (per unit count),reported,0.02648921092896801,0.006532677582482336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.054878048780488,23416.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,distanceCBD,cbd_distance,D_location,level,km,-0.0278,0.012,,,*,,,high,distance to CBD in km,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,semi_elasticity,-2.78,1.2,100*b/1 (X per km->km),reported,-0.015137614137416985,0.006534221929820281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3166666666666664,23416.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,distanceCBD,cbd_distance,D_location,level,km,-0.0379,0.0118,,,**,,,high,distance to CBD in km,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-3.7900000000000005,1.18,100*b/1 (X per km->km),reported,-0.02098481782672863,0.00653353167164638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2118644067796613,23416.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,distanceCBD,cbd_distance,D_location,level,km,-0.0346,0.0131,,,**,,,high,distance to CBD in km,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-3.46,1.31,100*b/1 (X per km->km),reported,-0.017257733809459788,0.006533997482772348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6412213740458013,23416.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0135,0.0194,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,1.3400816740257282,1.9663675807134,100*(exp(b - se^2/2) - 1),reported,0.00454748414220535,0.0065349031376876875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6958762886597938,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0032,0.0197,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,0.3010477412985768,1.9763140971674393,100*(exp(b - se^2/2) - 1),reported,0.0010615174865380066,0.006534967026499601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16243654822335027,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0554,0.023,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,5.668378562839571,2.4310156255485347,100*(exp(b - se^2/2) - 1),reported,0.015738805840229617,0.006534161269409408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.408695652173913,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0147,0.0203,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,1.459950167887314,2.0600614100348644,100*(exp(b - se^2/2) - 1),reported,0.004732167182566459,0.006534897537829872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7241379310344828,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0138,0.0232,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,1.3622836640124092,2.3522379301033864,100*(exp(b - se^2/2) - 1),reported,0.003887151484731768,0.006534921336650508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5948275862068966,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0332,0.0227,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-3.290412829039746,2.195873311684372,100*(exp(b - se^2/2) - 1),reported,-0.009557317992462434,0.006534672241834256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.462555066079295,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0388,0.0244,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,-3.834323007483642,2.347141111599762,100*(exp(b - se^2/2) - 1),reported,-0.010391113696024589,0.006534617891314433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5901639344262295,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0287,0.0268,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-2.864096397717286,2.6041772607724742,100*(exp(b - se^2/2) - 1),reported,-0.00699809950388096,0.0065348106865508624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0708955223880596,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0182,0.0184,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-1.8201593108441494,1.8068149004204819,100*(exp(b - se^2/2) - 1),reported,-0.006463803382536623,0.006534834188938124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9891304347826088,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0143,0.0422,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,1.3499891646804318,4.28077953796601,100*(exp(b - se^2/2) - 1),reported,0.0022144514691783044,0.006534954685267444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33886255924170616,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0048,0.047,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,0.3702336779303428,4.722614230734456,100*(exp(b - se^2/2) - 1),reported,0.0006674011151947986,0.00653496925294907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.10212765957446808,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0133,0.016,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-1.3338246593388003,1.5788608867106018,100*(exp(b - se^2/2) - 1),reported,-0.005432114254508091,0.006534874291137553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8312499999999999,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0505,0.0196,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,-4.9428674604512395,1.8634777001977607,100*(exp(b - se^2/2) - 1),reported,-0.016835165837987127,0.006534044562862329,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5765306122448983,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0436,0.0246,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-4.295281153120822,2.3550485664315,100*(exp(b - se^2/2) - 1),reported,-0.011581529004430205,0.0065345324199308046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7723577235772356,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0451,0.0171,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-4.423786055448975,1.6345922265387134,100*(exp(b - se^2/2) - 1),reported,-0.017232948097624625,0.006534000276482951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6374269005847952,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0051,0.024,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,-0.5373510762350819,2.387791159007183,100*(exp(b - se^2/2) - 1),reported,-0.0013886799365385421,0.006534964407240197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21250000000000002,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0303,0.0227,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.0095479690363858,2.202250586867866,100*(exp(b - se^2/2) - 1),reported,-0.008722558578623876,0.006534722103457491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3348017621145374,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0068,0.0158,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,0.6697508668995544,1.5907806125413793,100*(exp(b - se^2/2) - 1),reported,0.002812507915226647,0.006534944861850153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43037974683544294,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0103,0.0426,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,-1.1144811278721023,4.216347197857499,100*(exp(b - se^2/2) - 1),reported,-0.0015800496308510344,0.006534962550898452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24178403755868547,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0084,0.0333,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,0.7876412010633604,3.3580898120426794,100*(exp(b - se^2/2) - 1),reported,0.0016484588398087022,0.006534961829241641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25225225225225223,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0087,0.0193,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,0.8550100083257739,1.9468642531299731,100*(exp(b - se^2/2) - 1),reported,0.0029458030299810245,0.0065349423538659515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4507772020725388,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.092,0.0309,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,pct_premium,9.584153695233866,3.3877673002297244,100*(exp(b - se^2/2) - 1),reported,0.019453188870806153,0.006533734088129459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.977346278317152,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0118,0.0212,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-1.1952711968919227,2.09513101557058,100*(exp(b - se^2/2) - 1),reported,-0.003637365294358121,0.0065349274779993366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5566037735849056,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0158,0.0211,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,1.5699355067962273,2.143602762779391,100*(exp(b - se^2/2) - 1),reported,0.004893426585857141,0.006534892465923143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7488151658767773,23416.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0567,0.0441,,,,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0567,0.0441,not harmonised,reported,0.008401808637302983,0.0065347400512356536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2857142857142858,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0986,0.0494,,,*,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0986,0.0494,not harmonised,reported,0.013042374626884592,0.00653441487391581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9959514170040484,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0367,0.0501,,,,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,-0.0367,0.0501,not harmonised,reported,-0.0047870394610835185,0.006534895831070415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7325349301397206,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0533,0.0289,,,+,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0533,0.0289,not harmonised,reported,0.012051510154716063,0.006534496125164995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8442906574394464,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0476,0.0288,,,,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0476,0.0288,not harmonised,reported,0.010800224414841436,0.006534589561920868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.652777777777778,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0321,0.042,,,,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.0321,0.042,not harmonised,reported,0.004994522459697388,0.00653488919960406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7642857142857141,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0692,0.0244,,,**,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0692,0.0244,not harmonised,reported,0.018530423175337537,0.006533848634078554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8360655737704916,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0628,0.0294,,,*,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0628,0.0294,not harmonised,reported,0.013957693279674456,0.006534334115006833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.136054421768707,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.053,0.0308,,,+,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.053,0.0308,not harmonised,reported,0.011244530859653554,0.006534557556176027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7207792207792207,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0406,0.0283,,,,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0406,0.0283,not harmonised,reported,0.009374846336526872,0.006534683530140652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4346289752650176,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0135,0.0327,,,,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0135,0.0327,not harmonised,reported,0.0026979138681809036,0.006534946925149299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.41284403669724773,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0941,0.0356,,,**,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.0941,0.0356,not harmonised,reported,0.01727103993939032,0.0065339959813208864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6432584269662924,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0463,0.0253,,,+,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0463,0.0253,not harmonised,reported,0.011958399558412032,0.006534503430406574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8300395256916997,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.03,0.0306,,,,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.03,0.0306,not harmonised,reported,0.006406702539526987,0.006534836590317527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9803921568627451,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0731,0.0314,,,*,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.0731,0.0314,not harmonised,reported,0.015211817996809296,0.006534214570448862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3280254777070066,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0225,0.021,,,,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0225,0.021,not harmonised,reported,0.007001582707727282,0.00653481052721213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0714285714285714,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0134,0.0199,,,,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0134,0.0199,not harmonised,reported,0.004400389933348587,0.006534907438331111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6733668341708543,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0649,0.0341,,,+,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.0649,0.0341,not harmonised,reported,0.012436563015203926,0.0065344653130732485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.903225806451613,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0521,0.0295,,,+,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,0.0521,0.0295,not harmonised,reported,0.0115406542366624,0.006534535508282933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7661016949152544,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0368,0.0335,,,,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0368,0.0335,not harmonised,reported,0.007178529125031357,0.006534802328493219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0985074626865672,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0783,0.0282,,,**,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.0783,0.0282,not harmonised,reported,0.018141985572545823,0.006533895187047154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.776595744680851,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c1,8,All craft breweries,18,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0026,0.0119,,,,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: All craft breweries,other,-0.0026,0.0119,not harmonised,reported,-0.0014278072708075443,0.006534964047157607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21848739495798317,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c2,8,Brewpubs/taprooms only,18,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0109,0.0108,,,,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.78,se,row houses; walk-time bins; brewery type: Brewpubs/taprooms only,other,-0.0109,0.0108,not harmonised,reported,-0.006595336248241283,0.006534828576239069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0092592592592593,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T8-c3,8,Regional/microbreweries only,18,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0611,0.0294,,,*,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,23426.0,0.781,se,row houses; walk-time bins; brewery type: Regional/microbreweries only,other,0.0611,0.0294,not harmonised,reported,0.013579928281077321,0.006534368109061756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.078231292517007,23416.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,logSQFT,living_area,A_structural,log,sqft,0.8589,0.0456,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,elasticity,0.8589,0.0456,b (log-log),reported,0.08135952841320968,0.004319471993995065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.835526315789473,53242.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,logSQFT,living_area,A_structural,log,sqft,0.8614,0.0456,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,elasticity,0.8614,0.0456,b (log-log),reported,0.08159476721109075,0.004319388651991803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.890350877192983,53242.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,logSQFT,living_area,A_structural,log,sqft,0.8463,0.0487,,,***,,,high,logged above-ground living area in square feet,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,elasticity,0.8463,0.0487,b (log-log),reported,0.07510001423119163,0.004321600724399187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.37782340862423,53242.0,sqft,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,bedrooms,bedrooms,A_structural,level,count,-0.0112,0.0125,,,,,,high,,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,semi_elasticity,-1.1199999999999999,1.25,100*b/1 (per unit count),reported,-0.003883090879569792,0.004333806785234143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8959999999999999,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,bedrooms,bedrooms,A_structural,level,count,-0.0098,0.0128,,,,,,high,,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-0.98,1.28,100*b/1 (per unit count),reported,-0.0033180775702501145,0.0043338156019593335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7656249999999999,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,bedrooms,bedrooms,A_structural,level,count,-0.0102,0.0133,,,,,,high,,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.02,1.3299999999999998,100*b/1 (per unit count),reported,-0.0033236780690096067,0.004333815521355664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7669172932330828,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,fullBaths,bathrooms,A_structural,level,count,0.0951,0.0166,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,semi_elasticity,9.51,1.66,100*b/1 (per unit count),reported,0.02482055174993679,0.004332504301250796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.728915662650603,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,fullBaths,bathrooms,A_structural,level,count,0.0943,0.0169,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,9.43,1.69,100*b/1 (per unit count),reported,0.024175243684050697,0.0043325728341511854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.579881656804734,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,fullBaths,bathrooms,A_structural,level,count,0.0988,0.0173,,,***,,,high,number of full bathrooms,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,9.879999999999999,1.73,100*b/1 (per unit count),reported,0.024742904574591426,0.004332512643121777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.710982658959538,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,halfBaths,bathrooms,A_structural,level,count,0.0839,0.0123,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,semi_elasticity,8.39,1.23,100*b/1 (per unit count),reported,0.02954880947461911,0.004331947038591359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.821138211382114,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,halfBaths,bathrooms,A_structural,level,count,0.0837,0.0123,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,8.37,1.23,100*b/1 (per unit count),reported,0.029478432593668943,0.00433195604423092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.804878048780488,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,halfBaths,bathrooms,A_structural,level,count,0.0868,0.013,,,***,,,high,number of half bathrooms,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,8.68,1.3,100*b/1 (per unit count),reported,0.028924605461311145,0.004332026163560425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.676923076923077,53242.0,count,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,distanceCBD,cbd_distance,D_location,level,km,-0.0156,0.0341,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,semi_elasticity,-1.5599999999999998,3.4099999999999997,100*b/1 (X per km->km),reported,-0.001982632336748033,0.004333830941224867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4574780058651026,53242.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,distanceCBD,cbd_distance,D_location,level,km,-0.0107,0.0332,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,semi_elasticity,-1.0699999999999998,3.32,100*b/1 (X per km->km),reported,-0.0013967481017413484,0.004333835231571288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.322289156626506,53242.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,distanceCBD,cbd_distance,D_location,level,km,-0.0142,0.0394,,,,,,high,distance to CBD in km,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,semi_elasticity,-1.4200000000000002,3.94,100*b/1 (X per km->km),reported,-0.001561940234753006,0.004333834172483693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3604060913705584,53242.0,km,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0012,0.0414,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,-0.20548658664650832,4.1350349796080375,100*(exp(b - se^2/2) - 1),reported,-0.00012561853405317153,0.004333839424834418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02898550724637681,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0027,0.0483,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,0.1534731496799724,4.843058621205517,100*(exp(b - se^2/2) - 1),reported,0.00024226431047595285,0.0043338393318476005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.055900621118012424,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,i.walkBrew_0to5min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0093,0.1116,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 0-5min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,0.30774456430522346,11.264272113788943,100*(exp(b - se^2/2) - 1),reported,0.0003611532646994546,0.004333839176393456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.08333333333333333,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0026,0.0936,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,-0.6956173040451374,9.335695609399252,100*(exp(b - se^2/2) - 1),reported,-0.00012038442854512655,0.004333839427624556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.027777777777777776,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0151,0.0853,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,1.1527894898774038,8.659780375912216,100*(exp(b - se^2/2) - 1),reported,0.0007671858918275728,0.004333838183635229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17702227432590856,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,i.walkBrew_5to10min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0398,0.0495,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 5-10min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-4.019498545613997,4.7568590004732565,100*(exp(b - se^2/2) - 1),reported,-0.003484560874435173,0.0043338131478527905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.804040404040404,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,18,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1655,0.1387,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,16.868730219376495,16.366363692918906,100*(exp(b - se^2/2) - 1),reported,0.005171166837925318,0.004333781513113241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1932227829848596,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,18,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1963,0.1965,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,19.362378215908336,23.911926191747757,100*(exp(b - se^2/2) - 1),reported,0.004329387851774511,0.004333798842963277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9989821882951654,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,True
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,18,i.walkBrew_10to15min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.032,0.049,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 10-15min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-3.2655412445819265,4.745682252188069,100*(exp(b - se^2/2) - 1),reported,-0.002830251168176071,0.004333822101269609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6530612244897959,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1027,0.0787,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,-10.03926107479327,7.101869522604144,100*(exp(b - se^2/2) - 1),reported,-0.005655377315608935,0.004333770153246575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3049555273189326,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0614,0.1,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-6.42435131736131,9.404469857359242,100*(exp(b - se^2/2) - 1),reported,-0.0026609680069678517,0.004333824115582821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.614,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_15to20min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.164,0.1509,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 15-20min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,16.48759434050573,17.77925401474918,100*(exp(b - se^2/2) - 1),reported,0.004710018472526245,0.004333791387220795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0868124585818424,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1252,0.0665,,,+,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,13.087183992828844,7.536944451055766,100*(exp(b - se^2/2) - 1),reported,0.008159077285586827,0.004333695203606423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8827067669172932,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.1842,0.0703,,,*,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,19.928908851177063,8.451861439628965,100*(exp(b - se^2/2) - 1),reported,0.01135479038714611,0.004333560066321235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.620199146514936,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,True
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_20to25min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1525,0.095,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 20-25min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-14.530662491627666,8.156309492585748,100*(exp(b - se^2/2) - 1),reported,-0.0069567844664705685,0.004333734585670191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6052631578947367,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1383,0.0504,,,**,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,-13.026792975485101,4.389020502722333,100*(exp(b - se^2/2) - 1),reported,-0.011891421001199567,0.004333533032975114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.744047619047619,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.1636,0.0466,,,***,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-15.183982888994429,3.956720193543767,100*(exp(b - se^2/2) - 1),reported,-0.0152131777494952,0.004333337916421005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5107296137339055,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_25to30min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0648,0.0491,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 25-30min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,-6.387419548202855,4.601921537458248,100*(exp(b - se^2/2) - 1),reported,-0.00571951534594873,0.00433376857231609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.319755600814664,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0122,0.0383,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,1.153254793779257,3.8770121912602558,100*(exp(b - se^2/2) - 1),reported,0.0013804906271226826,0.004333835329409732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31853785900783294,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0269,0.0402,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,2.643535565560251,4.1296055859019205,100*(exp(b - se^2/2) - 1),reported,0.0028999948066792745,0.004333821235260477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6691542288557214,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_30to35min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0569,0.0499,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 30-35min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,5.723287018260237,5.282164251036741,100*(exp(b - se^2/2) - 1),reported,0.004941732548714609,0.00433378654096413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1402805611222444,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0225,0.0717,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,pct_premium,-2.475879125849023,7.010476370676222,100*(exp(b - se^2/2) - 1),reported,-0.0013599902043356477,0.004333835451149598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3138075313807531,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,-0.0321,0.0658,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,pct_premium,-3.3684428938690947,6.372136064612915,100*(exp(b - se^2/2) - 1),reported,-0.002114223947015598,0.004333829773010167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48784194528875374,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_35to40min,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.0566,0.0881,,,,,,high,time-invariant dummy: at least one craft brewery (ever) within 35-40min of the home,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,pct_premium,5.413359757061942,9.32302773254966,100*(exp(b - se^2/2) - 1),reported,0.002784271993117111,0.0043338226606646196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6424517593643587,53242.0,binary,,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,True,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0182,0.0541,,,,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,-0.0182,0.0541,not harmonised,reported,-0.0014579629264840203,0.004333834852900302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3364140480591497,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.027,0.062,,,,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,-0.027,0.062,not harmonised,reported,-0.0018873138224994688,0.0043338317405543355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43548387096774194,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_0to5min × i.Post_0to5min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0591,0.0897,,,,,,high,DID interaction: brewery within 0-5min × i.Post_0-5min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,0.0591,0.0897,not harmonised,reported,0.0028553942907185,0.004333821791496607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6588628762541806,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0394,0.0389,,,,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,0.0394,0.0389,not harmonised,reported,0.0043895020479164885,0.004333797707206889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.012853470437018,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0361,0.0421,,,,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0361,0.0421,not harmonised,reported,0.003716164469795327,0.004333809534027237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8574821852731592,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_5to10min × i.Post_5to10min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0369,0.0519,,,,,,high,DID interaction: brewery within 5-10min × i.Post_5-10min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,0.0369,0.0519,not harmonised,reported,0.003081270074843543,0.004333818885755553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7109826589595376,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0712,0.0328,,,*,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,0.0712,0.0328,not harmonised,reported,0.009407186455284954,0.0043336476928840795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.170731707317073,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0844,0.0366,,,*,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0844,0.0366,not harmonised,reported,0.009993382111532549,0.004333623048366011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3060109289617485,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_10to15min × i.Post_10to15min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0427,0.0294,,,,,,high,DID interaction: brewery within 10-15min × i.Post_10-15min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,-0.0427,0.0294,not harmonised,reported,-0.006294261195113666,0.004333753609750393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4523809523809526,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0642,0.0271,,,*,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,0.0642,0.0271,not harmonised,reported,0.010266340603098115,0.004333611064547647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3690036900369003,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0672,0.0328,,,*,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0672,0.0328,not harmonised,reported,0.008878735736225564,0.004333668633157716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0487804878048776,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_15to20min × i.Post_15to20min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.056,0.0594,,,,,,high,DID interaction: brewery within 15-20min × i.Post_15-20min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,-0.056,0.0594,not harmonised,reported,-0.004085740471546224,0.004333803285890101,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9427609427609428,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0022,0.0274,,,,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,-0.0022,0.0274,not harmonised,reported,-0.0003479724902417524,0.00433383919664728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08029197080291971,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0233,0.029,,,,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,-0.0233,0.029,not harmonised,reported,-0.0034819947326751727,0.0043338131865914165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.803448275862069,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_20to25min × i.Post_20to25min,neighbourhood_amenities,F_neighbourhood,other,binary,-0.0007,0.0445,,,,,,high,DID interaction: brewery within 20-25min × i.Post_20-25min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,-0.0007,0.0445,not harmonised,reported,-6.817275537686167e-05,0.004333839448957634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.015730337078651686,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0556,0.0228,,,*,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,0.0556,0.0228,not harmonised,reported,0.010567895535414765,0.0043335974497744015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4385964912280698,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0349,0.0283,,,,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0349,0.0283,not harmonised,reported,0.0053444818719703795,0.004333777563804061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2332155477031803,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_25to30min × i.Post_25to30min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0696,0.0291,,,*,,,high,DID interaction: brewery within 25-30min × i.Post_25-30min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,0.0696,0.0291,not harmonised,reported,0.010364914891982994,0.004333606657423925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3917525773195876,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0191,0.0163,,,,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,0.0191,0.0163,not harmonised,reported,0.00507823719767545,0.004333783577073813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1717791411042946,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0093,0.0171,,,,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0093,0.0171,not harmonised,reported,0.002356993860469023,0.004333827420862397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5438596491228069,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_30to35min × i.Post_30to35min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0633,0.0369,,,+,,,high,DID interaction: brewery within 30-35min × i.Post_30-35min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,0.0633,0.0369,not harmonised,reported,0.00743426711965538,0.004333719695344132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7154471544715444,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c1,9,All craft breweries,19,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0613,0.036,,,+,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: All craft breweries,other,0.0613,0.036,not harmonised,reported,0.00737936459335393,0.004333721457760871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.702777777777778,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c2,9,Brewpubs/taprooms only,19,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.0558,0.0366,,,,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.783,se,condominiums; walk-time bins; brewery type: Brewpubs/taprooms only,other,0.0558,0.0366,not harmonised,reported,0.0066071847886183055,0.004333744861351792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5245901639344264,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c,8965e19cc07a82548ccce5e2fba1b9f697ca577c:T9-c3,9,Regional/microbreweries only,19,i.walkBrew_35to40min × i.Post_35to40min,neighbourhood_amenities,F_neighbourhood,other,binary,0.071,0.0308,,,*,,,high,DID interaction: brewery within 35-40min × i.Post_35-40min bin opened >= 1 year before sale (price premium of craft brewery proximity); interaction term,log sale price (USD),log,OLS,53252.0,0.782,se,condominiums; walk-time bins; brewery type: Regional/microbreweries only,other,0.071,0.0308,not harmonised,reported,0.009989845691787649,0.004333623201507882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.305194805194805,53242.0,binary,nonstandard_x_transform,2024.0,United States,North America,High income,1990.0,2016.0,mixed,transaction,journal,True,True,clustered,,2003.0,ols,journal,False,True,False
89e86c34d31d60b83fe1893b6d6fc9985407503c,89e86c34d31d60b83fe1893b6d6fc9985407503c,89e86c34d31d60b83fe1893b6d6fc9985407503c:T1-c1,1,1,6,Freight station neighborhood x 2011-2018,neighbourhood_amenities,F_neighbourhood,other,binary,0.03,0.013,,,***,,,medium,DID interaction: apartment in neighbourhood of converted freight station x sold after opening (2011-2018); captures access to new arts and culture centre (bundled with brownfield removal). Printed *** inconsistent with beta/SE = 2.3.,log apartment sale price,log,OLS,9742.0,0.652,se,Outer ring model (control area = within outer ring road); postal-code FE; opening dummy interacted with other covariates,other,0.03,0.013,not harmonised,reported,0.023386113856123755,0.01013398267098696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.307692307692308,9732.0,binary,nonstandard_x_transform,2025.0,Denmark,Europe,High income,2006.0,2018.0,condo_apartment,transaction,journal,True,True,robust,,2012.0,ols,journal,False,True,False
89e86c34d31d60b83fe1893b6d6fc9985407503c,89e86c34d31d60b83fe1893b6d6fc9985407503c,89e86c34d31d60b83fe1893b6d6fc9985407503c:T1-c2,1,2,6,Freight station neighborhood x 2011-2018,neighbourhood_amenities,F_neighbourhood,other,binary,0.023,0.011,,,**,,,medium,DID interaction: apartment in neighbourhood of converted freight station x sold after opening (2011-2018).,log apartment sale price,log,OLS,7112.0,0.596,se,Inner ring model (control area = within inner ring road); postal-code FE; opening dummy interacted with other covariates,other,0.023,0.011,not harmonised,reported,0.02480339816141773,0.011862494772851959,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.090909090909091,7102.0,binary,nonstandard_x_transform,2025.0,Denmark,Europe,High income,2006.0,2018.0,condo_apartment,transaction,journal,True,True,robust,,2012.0,ols,journal,False,True,False
8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906:T2-c1,2,1,7,Number of bedrooms (NBR),bedrooms,A_structural,level,count,0.09,,2.72,,***,,,medium,count of bedrooms,Residential property monthly rentals (RPMR); specification says LN(MRP),log,OLS,90.0,0.96,t,Full sample; N not printed in table (90 per data section); t-statistics in separate column with stars; adj R2 0.92,semi_elasticity,9.0,3.308823529411764,100*b/1 (per unit count),|b/t|,0.2909491970543131,0.1069666165640857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.72,80.0,count,,2025.0,Somalia,,,,,rental,rent,journal,False,False,unknown,ANN,2022.0,ols,journal,True,True,False
8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906:T2-c1,2,1,7,Dedicated parking space (DPS),parking,A_structural,binary,binary,-0.01,,-0.36,,,,,medium,presumed dummy for dedicated parking space (scale not described),Residential property monthly rentals (RPMR); specification says LN(MRP),log,OLS,90.0,0.96,t,Full sample; N not printed in table (90 per data section); t-statistics in separate column with stars; adj R2 0.92,pct_premium,-1.0332056249470867,2.7501384270810227,100*(exp(b - se^2/2) - 1),|b/t|,-0.04021666128175814,0.11171294800488372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36,80.0,binary,,2025.0,Somalia,,,,,rental,rent,journal,False,False,unknown,ANN,2022.0,ols,journal,True,True,False
8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906:T2-c1,2,1,7,Residential property size (RPS),living_area,A_structural,log,unknown,0.01,,0.26,,,,,medium,"specification enters LN(RPS); area unit not stated; text quotes b=0.07, t=2.26 instead (inconsistent)",Residential property monthly rentals (RPMR); specification says LN(MRP),log,OLS,90.0,0.96,t,Full sample; N not printed in table (90 per data section); t-statistics in separate column with stars; adj R2 0.92,elasticity,0.01,0.038461538461538464,b (log-log),|b/t|,0.029056609882120176,0.11175619185430835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26,80.0,unknown,,2025.0,Somalia,,,,,rental,rent,journal,False,False,unknown,ANN,2022.0,ols,journal,True,True,False
8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906:T2-c1,2,1,7,Access to electricity (AEL),public_services,F_neighbourhood,other,index,-0.18,,-5.96,,***,,,medium,access to electricity (essential services); scale not described; sign negative,Residential property monthly rentals (RPMR); specification says LN(MRP),log,OLS,90.0,0.96,t,Full sample; N not printed in table (90 per data section); t-statistics in separate column with stars; adj R2 0.92,other,-0.18,0.030201342281879193,not harmonised,|b/t|,-0.5545167409054279,0.09303972162842751,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.96,80.0,index,nonstandard_x_transform,2025.0,Somalia,,,,,rental,rent,journal,False,False,unknown,ANN,2022.0,ols,journal,False,True,False
8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906,8a99113b012c7dc5288863734eba9456a85fd906:T2-c1,2,1,7,Environmental factors,other,other,other,index,-0.12,,-3.71,,***,,,medium,"composite environmental factors (climate change exposure, sustainable practices); survey index",Residential property monthly rentals (RPMR); specification says LN(MRP),log,OLS,90.0,0.96,t,Full sample; N not printed in table (90 per data section); t-statistics in separate column with stars; adj R2 0.92,other,-0.12,0.03234501347708895,not harmonised,|b/t|,-0.3831383414647588,0.10327179015222611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.71,80.0,index,nonstandard_x_transform,2025.0,Somalia,,,,,rental,rent,journal,False,False,unknown,ANN,2022.0,ols,journal,False,False,False
8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4:T4-rural,4,Rural region,8,Drinking-Water Characteristics (piped water connection),public_services,F_neighbourhood,binary,binary,0.0238,,,,,,,medium,"Dummy = 1 if household has a piped water connection inside the house; coefficient reproduced in the WTP table (Table 4), no SE/t printed; text claims significance at 5%",log of monthly rental value of the house (PKR),log,OLS,10735.0,,none,"Rural region subsample; semi-log hedonic rent model with house, neighbourhood, sanitation, province and region controls; only the piped-water coefficient is printed (Table 4)",pct_premium,2.4085480311484053,,100*(exp(b) - 1),,,,,,10725.0,binary,,2022.0,Pakistan,Asia,Lower middle income,2013.0,2014.0,mixed,rent,journal,False,True,unknown,,2013.5,ols,journal,False,False,False
8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4:T4-urban,4,Urban region,8,Drinking-Water Characteristics (piped water connection),public_services,F_neighbourhood,binary,binary,0.07087,,,,,,,medium,"Dummy = 1 if household has a piped water connection inside the house; coefficient reproduced in the WTP table (Table 4), no SE/t printed; text claims significance at 5%",log of monthly rental value of the house (PKR),log,OLS,5149.0,,none,"Urban region subsample; semi-log hedonic rent model with house, neighbourhood, sanitation, province and region controls; only the piped-water coefficient is printed (Table 4)",pct_premium,7.344166938036278,,100*(exp(b) - 1),,,,,,5139.0,binary,,2022.0,Pakistan,Asia,Lower middle income,2013.0,2014.0,mixed,rent,journal,False,True,unknown,,2013.5,ols,journal,False,False,False
8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4:T4-rented,4,Rented houses,8,Drinking-Water Characteristics (piped water connection),public_services,F_neighbourhood,binary,binary,0.03192,,,,,,,medium,"Dummy = 1 if household has a piped water connection inside the house; coefficient reproduced in the WTP table (Table 4), no SE/t printed; text claims significance at 5%",log of monthly rental value of the house (PKR),log,OLS,15884.0,,none,"Rented houses subsample; semi-log hedonic rent model with house, neighbourhood, sanitation, province and region controls; only the piped-water coefficient is printed (Table 4)",pct_premium,3.2434907208661823,,100*(exp(b) - 1),,,,,,15874.0,binary,,2022.0,Pakistan,Asia,Lower middle income,2013.0,2014.0,mixed,rent,journal,False,True,unknown,,2013.5,ols,journal,False,False,False
8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4:T4-owned,4,Owner-occupied houses,8,Drinking-Water Characteristics (piped water connection),public_services,F_neighbourhood,binary,binary,0.05122,,,,,,,medium,"Dummy = 1 if household has a piped water connection inside the house; coefficient reproduced in the WTP table (Table 4), no SE/t printed; text claims significance at 5%",log of monthly rental value of the house (PKR),log,OLS,15884.0,,none,"Owner-occupied houses subsample; semi-log hedonic rent model with house, neighbourhood, sanitation, province and region controls; only the piped-water coefficient is printed (Table 4)",pct_premium,5.255442978780778,,100*(exp(b) - 1),,,,,,15874.0,binary,,2022.0,Pakistan,Asia,Lower middle income,2013.0,2014.0,mixed,rent,journal,False,True,unknown,,2013.5,ols,journal,False,False,False
8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4:T4-urban,4,Urban region,8,No of rooms,bedrooms,A_structural,level,count,0.20409,,,,,,,medium,Number of rooms (not bedrooms); coefficient quoted in the text for the urban model (exp(0.20409)=1.26327); no SE/t,log of monthly rental value of the house (PKR),log,OLS,5149.0,,none,"Urban region subsample; semi-log hedonic rent model with house, neighbourhood, sanitation, province and region controls; only the piped-water coefficient is printed (Table 4)",semi_elasticity,20.409,,100*b/1 (per unit count),,,,,,5139.0,count,,2022.0,Pakistan,Asia,Lower middle income,2013.0,2014.0,mixed,rent,journal,False,True,unknown,,2013.5,ols,journal,False,False,False
8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4,8acb5243fed06bb1f23f1332908a7f46169463e4:T4-urban,4,Urban region,8,Toilet facility inside house,other,other,binary,binary,0.06167,,,,,,,medium,Dummy for toilet inside the house (sanitation); coefficient quoted in the text for the urban model (exp(0.06167)=1.07315); no SE/t,log of monthly rental value of the house (PKR),log,OLS,5149.0,,none,"Urban region subsample; semi-log hedonic rent model with house, neighbourhood, sanitation, province and region controls; only the piped-water coefficient is printed (Table 4)",pct_premium,6.361129508065111,,100*(exp(b) - 1),,,,,,5139.0,binary,,2022.0,Pakistan,Asia,Lower middle income,2013.0,2014.0,mixed,rent,journal,False,True,unknown,,2013.5,ols,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,macro,other,other,log,index,0.948,0.008,,,***,,,high,"Estimated 2004 macro-location price level of the village (log price level index from GAM); no taxonomy leaf. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",elasticity,0.948,0.008,b (log-log),reported,,,,118.49999999999999,,index,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,DNWF50,living_area,A_structural,dummy,m2,-1.272,0.152,,,***,,,high,"Segment dummy: dwelling area up to 50 m2 (piecewise log-linear spline in dwelling area). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,-72.29485361556978,4.2601119013219355,100*(exp(b - se^2/2) - 1),reported,,,,-8.368421052631579,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,LNWF50,living_area,A_structural,log,m2,1.114,0.035,,,***,,,high,"Slope of ln dwelling area (m2, SIA 416) for segment up to 50 m2 (piecewise spline). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",elasticity,1.114,0.035,b (log-log),reported,,,,31.82857142857143,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,DNWF5075,living_area,A_structural,dummy,m2,-0.352,0.164,,,**,,,high,"Segment dummy: dwelling area 50-75 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,-30.61142795679942,11.533794000411993,100*(exp(b - se^2/2) - 1),reported,,,,-2.146341463414634,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,LNWF5075,living_area,A_structural,log,m2,0.881,0.034,,,***,,,high,"Slope of ln dwelling area for segment 50-75 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",elasticity,0.881,0.034,b (log-log),reported,,,,25.91176470588235,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,DNWF7500,living_area,A_structural,dummy,m2,-0.056,0.179,,,,,,high,"Segment dummy: dwelling area 75-100 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,-6.948817987663192,16.925150532438092,100*(exp(b - se^2/2) - 1),reported,,,,-0.3128491620111732,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,LNWF7500,living_area,A_structural,log,m2,0.82,0.036,,,***,,,high,"Slope of ln dwelling area for segment 75-100 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",elasticity,0.82,0.036,b (log-log),reported,,,,22.77777777777778,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,LNWF0150,living_area,A_structural,log,m2,0.81,0.017,,,***,,,high,"Slope of ln dwelling area for segment 100-150 m2 (reference segment). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",elasticity,0.81,0.017,b (log-log),reported,,,,47.64705882352941,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,DNWF150P,living_area,A_structural,dummy,m2,-0.442,0.146,,,***,,,high,"Segment dummy: dwelling area above 150 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,-36.4064413589579,9.384144677650754,100*(exp(b - se^2/2) - 1),reported,,,,-3.0273972602739727,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,LNWF150P,living_area,A_structural,log,m2,0.899,0.024,,,***,,,high,"Slope of ln dwelling area for segment above 150 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",elasticity,0.899,0.024,b (log-log),reported,,,,37.458333333333336,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,bauj,age,A_structural,other,years,-0.652,0.073,,,***,,,high,"Construction year (year built), NOT age; enters with quadratic term bauj2; properties <=30 years old. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",other,-0.652,0.073,not harmonised,reported,,,,-8.931506849315069,,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,bauj2,age,A_structural,other,years,0.0,0.0,,,***,,,medium,"Construction year squared; coefficient printed as 0.000 (rounded) with SE 0.000. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",other,0.0,,not harmonised,,,,,,,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,stand: 4,condition,A_structural,dummy,ordinal,0.132,0.003,,,***,,,high,"Standard (quality) factor level 4 vs reference (5-level factor). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,14.110318429135216,0.3423324957801705,100*(exp(b - se^2/2) - 1),reported,,,,44.0,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,stand: 5,condition,A_structural,dummy,ordinal,0.276,0.006,,,***,,,high,"Standard (quality) factor level 5 vs reference. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,31.7824142979241,0.790708718416382,100*(exp(b - se^2/2) - 1),reported,,,,46.0,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,micro: 4,other,other,dummy,ordinal,0.119,0.003,,,***,,,high,"Micro-location factor level 4 vs reference (5-level composite of exposition, view, neighbourhood quality, proximity to services/transport, noise); no single taxonomy leaf. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,12.636484963512506,0.3379109754865059,100*(exp(b - se^2/2) - 1),reported,,,,39.666666666666664,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TA-c1,A,1,20,micro: 5,other,other,dummy,ordinal,0.283,0.005,,,***,,,high,"Micro-location factor level 5 vs reference. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.965,se,"Condominiums, year 2004 cross-section (centred moving sample over six quarters); robust estimation; D.F. residuals 10,345; regional quarterly coefficients not shown",pct_premium,32.7088573106314,0.6635525809085786,100*(exp(b - se^2/2) - 1),reported,,,,56.599999999999994,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,macro,other,other,log,index,0.892,0.01,,,***,,,high,"Estimated 2004 macro-location price level of the village; no taxonomy leaf. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.892,0.01,b (log-log),reported,,,,89.2,,index,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,DVOL600,living_area,A_structural,dummy,m3,-1.566,0.144,,,***,,,high,"Segment dummy: cubic content up to 600 m3 (piecewise spline in ln building volume, SIA 416). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,-79.32754400397609,3.007858027237588,100*(exp(b - se^2/2) - 1),reported,,,,-10.875000000000002,,m3,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,True
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LVOL600,living_area,A_structural,log,m3,0.747,0.023,,,***,,,high,"Slope of ln cubic content (m3) for segment up to 600 m3; building volume used instead of floor area. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.747,0.023,b (log-log),reported,,,,32.47826086956522,,m3,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LVOL1300,living_area,A_structural,log,m3,0.507,0.004,,,***,,,high,"Slope of ln cubic content for segment 600-1300 m3 (reference segment). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.507,0.004,b (log-log),reported,,,,126.75,,m3,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,DVOL2300,living_area,A_structural,dummy,m3,-0.76,0.373,,,*,,,high,"Segment dummy: cubic content above 2300 m3. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,-56.376077285765966,17.443957727469613,100*(exp(b - se^2/2) - 1),reported,,,,-2.037533512064343,,m3,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LVOL2300,living_area,A_structural,log,m3,0.627,0.051,,,***,,,high,"Slope of ln cubic content for segment above 2300 m3. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.627,0.051,b (log-log),reported,,,,12.294117647058824,,m3,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LLA2000,lot_size,A_structural,log,m2,0.002,0.002,,,,,,high,"Slope of ln site area (m2) up to 2000 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.002,0.002,b (log-log),reported,,,,1.0,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LLAE250,lot_size,A_structural,log,m2,0.021,0.001,,,***,,,high,"Slope of ln additional site area (landexc, m2) up to 250 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.021,0.001,b (log-log),reported,,,,21.0,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,DLAE600,lot_size,A_structural,dummy,m2,-0.301,0.085,,,***,,,high,"Segment dummy: additional site area 250-600 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,-26.259093495180565,6.290661068347015,100*(exp(b - se^2/2) - 1),reported,,,,-3.5411764705882347,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,True
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LLAE600,lot_size,A_structural,log,m2,0.077,0.014,,,***,,,high,"Slope of ln additional site area for segment 250-600 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.077,0.014,b (log-log),reported,,,,5.5,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,DLAE1800,lot_size,A_structural,dummy,m2,0.055,0.126,,,,,,high,"Segment dummy: additional site area above 600/1800 m2. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,4.818699465875076,13.312411744911227,100*(exp(b - se^2/2) - 1),reported,,,,0.4365079365079365,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,LLAE1800,lot_size,A_structural,log,m2,0.023,0.019,,,,,,high,"Slope of ln additional site area for upper segment. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",elasticity,0.023,0.019,b (log-log),reported,,,,1.2105263157894737,,m2,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,bauj,age,A_structural,other,years,-0.146,0.087,,,.,,,high,"Construction year (year built), NOT age; quadratic with bauj2; properties <=30 years old. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",other,-0.146,0.087,not harmonised,reported,,,,-1.6781609195402298,,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,bauj2,age,A_structural,other,years,0.0,0.0,,,.,,,medium,"Construction year squared; printed as 0.000 (rounded). Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",other,0.0,,not harmonised,,,,,,,years,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,stand: 4,condition,A_structural,dummy,ordinal,0.123,0.004,,,***,,,high,"Standard (quality) factor level 4 vs reference. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,13.087537390830995,0.4523537683789957,100*(exp(b - se^2/2) - 1),reported,,,,30.75,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,stand: 5,condition,A_structural,dummy,ordinal,0.215,0.007,,,***,,,high,"Standard (quality) factor level 5 vs reference. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,23.983152072169677,0.8679033278762432,100*(exp(b - se^2/2) - 1),reported,,,,30.71428571428571,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,True,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,micro: 4,other,other,dummy,ordinal,0.109,0.004,,,***,,,high,"Micro-location factor level 4 vs reference; no single taxonomy leaf. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,11.515342907833027,0.44606494013657916,100*(exp(b - se^2/2) - 1),reported,,,,27.25,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7,8b40d59c2cf1fb45e2898b8a9d2bc70967606aa7:TB-c1,B,1,21,micro: 5,other,other,dummy,ordinal,0.289,0.006,,,***,,,high,"Micro-location factor level 5 vs reference. Standard error printed in a separate column; significance symbols: '.' 10%, * 5%, ** 1%, *** 0.1% (1 per mille).",ln transaction price (CHF),log,other,,0.937,se,"Single family houses, year 2004 cross-section; robust estimation; D.F. residuals 7,163; regional quarterly coefficients not shown",pct_premium,33.50676970936783,0.801055037117105,100*(exp(b - se^2/2) - 1),reported,,,,48.166666666666664,,ordinal,,2008.0,Switzerland,Europe,High income,1985.0,2004.0,mixed,transaction,journal,True,True,unknown,,1994.5,other,journal,False,False,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),0.0384,0.0136,,,**,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,3.9050712627921147,1.413239659534471,100*(exp(b - se^2/2) - 1),reported,0.06566396622666694,0.023255988038611213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8235294117647056,1841.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.1298,0.0148,,,***,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,-12.182511541041919,1.299841180003341,100*(exp(b - se^2/2) - 1),reported,-0.16180545856400075,0.0184493126868044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.77027027027027,2861.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.1048,0.0096,,,***,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,-9.95368887977116,0.8644844212785346,100*(exp(b - se^2/2) - 1),reported,-0.13949746088542364,0.012778393363550258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.916666666666668,6005.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0828,0.0095,,,***,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,-7.950630235748612,0.8745084740649279,100*(exp(b - se^2/2) - 1),reported,-0.12890070231875414,0.014789331787779761,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.715789473684211,4496.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0031,0.0085,,,,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,-0.3131212496742086,0.8473690800328773,100*(exp(b - se^2/2) - 1),reported,-0.004743592811047946,0.013006625449647598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36470588235294116,5911.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0106,0.0096,,,,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,-1.0589611058926751,0.9498777427413619,100*(exp(b - se^2/2) - 1),reported,-0.016167585144659317,0.014642341263087682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1041666666666667,4663.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Close (within 1/2 mile of station),transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0222,0.0154,,,,,,high,Pre-opening proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,-2.2071363085769957,1.5061886941142584,100*(exp(b - se^2/2) - 1),reported,-0.023357282608144198,0.01620279964709102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4415584415584415,3807.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0256,0.0285,,,,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.0256,0.0285,not harmonised,reported,-0.020930184850573148,0.023301182353177134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8982456140350877,1841.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0069,0.0284,,,,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.0069,0.0284,not harmonised,reported,-0.004542211386243266,0.01869547874917518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24295774647887322,2861.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),0.1019,0.0203,,,***,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,0.1019,0.0203,not harmonised,reported,0.06464164210279305,0.012877579339418048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.019704433497537,6005.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),0.0932,0.0196,,,***,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,0.0932,0.0196,not harmonised,reported,0.0707387471214764,0.014876388879623791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.755102040816327,4496.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0116,0.0203,,,,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.0116,0.0203,not harmonised,reported,-0.007432235741547316,0.013006412547707806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5714285714285714,5911.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.006,0.0226,,,,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.006,0.0226,not harmonised,reported,-0.0038878260130817415,0.014644144649274559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26548672566371684,4663.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.1347,0.0367,,,***,,,high,DiD treatment effect of light rail opening on prices near station; interaction term,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.1347,0.0367,not harmonised,reported,-0.059380393612828375,0.0161786224616986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6702997275204354,3807.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Baths,bathrooms,A_structural,level,count,0.0285,0.0107,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,2.85,1.0699999999999998,100*b/1 (per unit count),reported,0.06195822883483158,0.0232615104748315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6635514018691593,1841.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Baths,bathrooms,A_structural,level,count,0.0565,0.0091,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,5.65,0.91,100*b/1 (per unit count),reported,0.11530332273964798,0.01857097764479286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.208791208791209,2861.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Baths,bathrooms,A_structural,level,count,0.0204,0.0064,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,2.04,0.64,100*b/1 (per unit count),reported,0.04109855909484502,0.01289366559838275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1875,6005.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Baths,bathrooms,A_structural,level,count,0.0347,0.0067,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,3.47,0.67,100*b/1 (per unit count),reported,0.07701048706448495,0.014869460038387582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.179104477611941,4496.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Baths,bathrooms,A_structural,level,count,0.0241,0.0055,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,2.41,0.5499999999999999,100*b/1 (per unit count),reported,0.05690097010306409,0.012985698571238692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.381818181818182,5911.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Baths,bathrooms,A_structural,level,count,0.0185,0.0053,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,1.8499999999999999,0.53,100*b/1 (per unit count),reported,0.0510500888677439,0.014625160594542847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.490566037735849,4663.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Baths,bathrooms,A_structural,level,count,0.0192,0.0057,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,1.92,0.5700000000000001,100*b/1 (per unit count),reported,0.05451157352095769,0.016183123389034316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3684210526315783,3807.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Size (ft2),living_area,A_structural,level,sqft,0.00035,4e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.37673686458484024,0.04305564166683889,100*b/0.092903 (X per sqft->m2),reported,0.19981739325112938,0.022836273514414782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.75,1841.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Size (ft2),living_area,A_structural,level,sqft,0.00048,2e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.5166677000020666,0.021527820833419444,100*b/0.092903 (X per sqft->m2),reported,0.40937514922362117,0.01705729788431755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.0,2861.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Size (ft2),living_area,A_structural,level,sqft,0.0003,2e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.32291731250129163,0.021527820833419444,100*b/0.092903 (X per sqft->m2),reported,0.19004096880261104,0.012669397920174071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.999999999999998,6005.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Size (ft2),living_area,A_structural,level,sqft,0.00031,2e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.33368122291800134,0.021527820833419444,100*b/0.092903 (X per sqft->m2),reported,0.2252238556804993,0.014530571334225764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.499999999999998,4496.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Size (ft2),living_area,A_structural,level,sqft,0.000431,2e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.463924538960189,0.021527820833419444,100*b/0.092903 (X per sqft->m2),reported,0.2698941494043267,0.01252409045959753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.549999999999997,5911.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Size (ft2),living_area,A_structural,level,sqft,0.0003,2e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.32291731250129163,0.021527820833419444,100*b/0.092903 (X per sqft->m2),reported,0.21454858788398362,0.014303239192265577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.999999999999998,4663.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Size (ft2),living_area,A_structural,level,sqft,0.00031,2e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.33368122291800134,0.021527820833419444,100*b/0.092903 (X per sqft->m2),reported,0.24364173432784875,0.015718821569538632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.499999999999998,3807.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Size2,living_area,A_structural,squared,sqft squared,-5.23e-08,1.15e-08,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-5.23e-08,1.15e-08,not harmonised,reported,-0.10540252489357896,0.023176463408721953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.547826086956522,1841.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Size2,living_area,A_structural,squared,sqft squared,-3.44e-08,3.17e-09,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-3.44e-08,3.17e-09,not harmonised,reported,-0.19882977110407996,0.01832239460464923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.851735015772869,2861.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Size2,living_area,A_structural,squared,sqft squared,2.03e-08,5.53e-09,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,2.03e-08,5.53e-09,not harmonised,reported,0.04731813964119363,0.012890113902256197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.670886075949367,6005.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Size2,living_area,A_structural,squared,sqft squared,-2.15e-08,2.77e-09,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-2.15e-08,2.77e-09,not harmonised,reported,-0.11498870428713709,0.014814823761645104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.76173285198556,4496.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Size2,living_area,A_structural,squared,sqft squared,-5.1e-08,4.76e-09,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-5.1e-08,4.76e-09,not harmonised,reported,-0.1380244464048085,0.012882281664448793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.714285714285714,5911.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Size2,living_area,A_structural,squared,sqft squared,-2.37e-08,3.13e-09,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-2.37e-08,3.13e-09,not harmonised,reported,-0.11020915420292282,0.01455504863523833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.571884984025559,4663.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Size2,living_area,A_structural,squared,sqft squared,-1.94e-08,3.68e-09,,,***,,,high,"Columbia City value printed positive (2.03e-08), likely a missing minus sign (Table 3 gives -2.60e-08); recorded as printed",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-1.94e-08,3.68e-09,not harmonised,reported,-0.08513008133315503,0.01614838656216549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.271739130434782,3807.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Age,age,A_structural,level,years,-0.00593,0.00089,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.5930000000000001,0.089,100*b/1 (per year),reported,-0.15344882469082996,0.023030262053092526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.662921348314607,1841.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Age,age,A_structural,level,years,-0.00213,0.000821,,,**,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.213,0.0821,100*b/1 (per year),reported,-0.048447040092301746,0.018673718270319124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5943970767356883,2861.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Age,age,A_structural,level,years,-0.00509,0.000474,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.509,0.0474,100*b/1 (per year),reported,-0.1372627256148782,0.012782422778281387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.738396624472575,6005.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Age,age,A_structural,level,years,-0.00484,0.000497,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.484,0.04970000000000001,100*b/1 (per year),reported,-0.1437285469049373,0.014758902440445011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.738430583501005,4496.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Age,age,A_structural,level,years,-0.004017,0.00039,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.40169999999999995,0.039,100*b/1 (per year),reported,-0.13278345691419913,0.01289159775866011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.299999999999999,5911.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Age,age,A_structural,level,years,-0.006067,0.000376,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.6067,0.037599999999999995,100*b/1 (per year),reported,-0.22996164437386885,0.014251784784007694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.135638297872344,4663.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Age,age,A_structural,level,years,-0.006522,0.000495,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.6522,0.0495,100*b/1 (per year),reported,-0.2088340418918817,0.01584986978480243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.175757575757576,3807.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Age2,age,A_structural,squared,years squared,5e-05,7.49e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,5e-05,7.49e-06,not harmonised,reported,0.15373319028358848,0.023029231904481553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.675567423230975,1841.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Age2,age,A_structural,squared,years squared,3.4e-05,7.43e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,3.4e-05,7.43e-06,not harmonised,reported,0.08524082081246502,0.018627626430488677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.576043068640646,2861.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Age2,age,A_structural,squared,years squared,4.25e-05,4.07e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,4.25e-05,4.07e-06,not harmonised,reported,0.13354583702106732,0.01278897780413515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.442260442260443,6005.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Age2,age,A_structural,squared,years squared,3.81e-05,4.36e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,3.81e-05,4.36e-06,not harmonised,reported,0.12923144062662859,0.014788689793493456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.738532110091743,4496.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Age2,age,A_structural,squared,years squared,3.56e-05,3.48e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,3.56e-05,3.48e-06,not harmonised,reported,0.13189534069971914,0.012893140045927603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.229885057471263,5911.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Age2,age,A_structural,squared,years squared,3.86e-05,3.79e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,3.86e-05,3.79e-06,not harmonised,reported,0.14751558547056937,0.014484043236618081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.184696569920845,4663.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Age2,age,A_structural,squared,years squared,4.83e-05,6.92e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,4.83e-05,6.92e-06,not harmonised,reported,0.11240573026957816,0.016104506282929212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.979768786127168,3807.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Waterfront,waterfront,C_exterior,binary,binary,0.6723,0.0712,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,95.3778675880694,13.946209144501388,100*(exp(b - se^2/2) - 1),reported,0.17384427383745343,0.018410995533581265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.442415730337078,2861.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Waterfront,waterfront,C_exterior,binary,binary,0.2902,0.0253,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,33.62670662167828,3.3818378446411246,100*(exp(b - se^2/2) - 1),reported,0.14642460929480527,0.012765481099788329,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.470355731225297,6005.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Waterfront,waterfront,C_exterior,binary,binary,0.2269,0.0234,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,25.436092527839428,2.9360082754721755,100*(exp(b - se^2/2) - 1),reported,0.1431235748116552,0.014760210007019532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.696581196581196,4496.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Waterfront,waterfront,C_exterior,binary,binary,0.4724,0.0201,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,60.3514840218927,3.223715969815445,100*(exp(b - se^2/2) - 1),reported,0.29233743081934743,0.012438574003956147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.502487562189053,5911.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Waterfront,waterfront,C_exterior,binary,binary,0.0372,0.0498,,,,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,3.661436035409027,5.168744889383892,100*(exp(b - se^2/2) - 1),reported,0.010938427845183125,0.014643379212099992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7469879518072289,4663.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Waterfront,waterfront,C_exterior,binary,binary,0.0584,0.0077,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,6.010753914324862,0.8163070042027487,100*(exp(b - se^2/2) - 1),reported,0.12200402357843669,0.016086146944417165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.584415584415584,3807.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,View,view,E_environment,binary,binary (outstanding view),0.0509,0.0181,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,5.204532185631616,1.90451397592212,100*(exp(b - se^2/2) - 1),reported,0.06540056971235958,0.023256391194375413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8121546961325965,1841.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,View,view,E_environment,binary,binary (outstanding view),0.1599,0.0142,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,17.32752302001814,1.6662188065963393,100*(exp(b - se^2/2) - 1),reported,0.20600811913194086,0.01829465473216736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.260563380281688,2861.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,View,view,E_environment,binary,binary (outstanding view),0.2325,0.0103,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,26.168351858166684,1.299602959749756,100*(exp(b - se^2/2) - 1),reported,0.2796688661174585,0.012389631488214292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.572815533980584,6005.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,View,view,E_environment,binary,binary (outstanding view),0.2869,0.0124,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,33.21885551230355,1.6520408123681216,100*(exp(b - se^2/2) - 1),reported,0.32618776468522465,0.01409804211257158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.137096774193548,4496.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,True
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,View,view,E_environment,binary,binary (outstanding view),0.2198,0.0087,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,24.578039271003238,1.0838699599401886,100*(exp(b - se^2/2) - 1),reported,0.31218453259979345,0.012356712618827129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.264367816091955,5911.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,View,view,E_environment,binary,binary (outstanding view),0.0611,0.0119,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,6.292994597397916,1.2649761991780102,100*(exp(b - se^2/2) - 1),reported,0.07497860231511974,0.014603033838787643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.134453781512605,4663.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,View,view,E_environment,binary,binary (outstanding view),0.1484,0.0112,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,15.990404283189186,1.2991740096103106,100*(exp(b - se^2/2) - 1),reported,0.20995903993277057,0.01584596527794495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.25,3807.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Lot (acres),lot_size,A_structural,level,acres,0.0509,0.0181,,,**,,,low,"Printed value for Beacon Hill (0.0509 (0.0181)) duplicates the View row exactly, apparent typesetting error (Table 3 gives 0.5880 (0.6628)); do not use",log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.0012577663916678715,0.00044726074045556925,100*b/4046.86 (X per acre->m2),reported,0.06540056971235958,0.023256391194375413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8121546961325965,1841.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,False,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Lot (acres),lot_size,A_structural,level,acres,1.3753,0.1827,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.03398440311317925,0.0045146153194051105,100*b/4046.86 (X per acre->m2),reported,0.13936096812286836,0.018513232659091144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.527640941434044,2861.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Lot (acres),lot_size,A_structural,level,acres,0.8103,0.0983,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.020022949060284408,0.0024290458998222353,100*b/4046.86 (X per acre->m2),reported,0.10577730437738944,0.012832172060097968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.243133265513734,6005.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Lot (acres),lot_size,A_structural,level,acres,0.8986,0.0824,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.022204889578639475,0.0020361483432894422,100*b/4046.86 (X per acre->m2),reported,0.16053022383895174,0.014720332121444048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.905339805825243,4496.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Lot (acres),lot_size,A_structural,level,acres,-0.0818,0.0355,,,*,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,-0.002021322020401412,0.0008772241042084369,100*b/4046.86 (X per acre->m2),reported,-0.029957082104010494,0.01300093416494343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.304225352112676,5911.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Lot (acres),lot_size,A_structural,level,acres,0.2,0.0267,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.004942107629343307,0.0006597713685173314,100*b/4046.86 (X per acre->m2),reported,0.1090407175211813,0.014556935789077704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.49063670411985,4663.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Lot (acres),lot_size,A_structural,level,acres,0.5928,0.0494,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.014648407013373561,0.0012207005844477967,100*b/4046.86 (X per acre->m2),reported,0.19090958396133906,0.015909131996778257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,3807.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Lot2,lot_size,A_structural,squared,acres squared,3.095,1.8734,,,,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,3.095,1.8734,not harmonised,reported,0.03847525900724097,0.023289030767097003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6520764385609055,1841.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Lot2,lot_size,A_structural,squared,acres squared,-0.8996,0.1645,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.8996,0.1645,not harmonised,reported,-0.10171066971158937,0.018598716282299303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.468693009118541,2861.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Lot2,lot_size,A_structural,squared,acres squared,-0.2619,0.0904,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.2619,0.0904,not harmonised,reported,-0.03736003395699438,0.012895559639985838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8971238938053103,6005.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Lot2,lot_size,A_structural,squared,acres squared,-0.1283,0.0597,,,*,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.1283,0.0597,not harmonised,reported,-0.032034372612223735,0.01490609543998252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.149078726968174,4496.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Lot2,lot_size,A_structural,squared,acres squared,0.0322,0.0099,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,0.0322,0.0099,not harmonised,reported,0.042267047918991885,0.012995148273230426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.252525252525252,5911.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Lot2,lot_size,A_structural,squared,acres squared,-0.0064,0.0114,,,,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.0064,0.0114,not harmonised,reported,-0.008221058495334008,0.0146437604448137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5614035087719298,4663.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Lot2,lot_size,A_structural,squared,acres squared,-0.2006,0.0319,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,other,-0.2006,0.0319,not harmonised,reported,-0.10139228110899173,0.01612369774365322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.2884012539184955,3807.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Garage (sf),garage,A_structural,level,sqft,0.00033,5e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.033,0.005,100*b (unit as reported: sqft; count expected),reported,0.1520333800870563,0.023035360619250957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6,1841.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Garage (sf),garage,A_structural,level,sqft,0.00022,4.5e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.022000000000000002,0.0045000000000000005,100*b (unit as reported: sqft; count expected),reported,0.09102164755490358,0.018618064272593913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.888888888888888,2861.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Garage (sf),garage,A_structural,level,sqft,0.00021,3e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.021,0.003,100*b (unit as reported: sqft; count expected),reported,0.08996567271059688,0.012852238958656696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,6005.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Garage (sf),garage,A_structural,level,sqft,0.0002,2e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.02,0.002,100*b (unit as reported: sqft; count expected),reported,0.1475061031912819,0.014750610319128193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,4496.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Garage (sf),garage,A_structural,level,sqft,0.000193,1.8e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.0193,0.0018,100*b (unit as reported: sqft; count expected),reported,0.1381247368573065,0.012882099810525995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.722222222222221,5911.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Garage (sf),garage,A_structural,level,sqft,0.000171,1.4e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.0171,0.0014,100*b (unit as reported: sqft; count expected),reported,0.1760746147478935,0.014415465534915257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.214285714285715,4663.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Garage (sf),garage,A_structural,level,sqft,0.000172,1.4e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,semi_elasticity,0.0172,0.0014,100*b (unit as reported: sqft; count expected),reported,0.1952836461743421,0.015895180502562726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.285714285714286,3807.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c1,2,1,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0775,0.014,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1851.0,0.38,se,Beacon Hill station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,8.047634058856534,1.5128151254419837,100*(exp(b - se^2/2) - 1),reported,0.12795640452819382,0.023114705334125338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.535714285714286,1841.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c2,2,2,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.1413,0.0149,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2871.0,0.69,se,Mount Baker station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,15.164228205131746,1.7161374895256616,100*(exp(b - se^2/2) - 1),reported,0.1745727004528992,0.018408586247333318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.483221476510067,2861.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c3,2,3,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0701,0.0087,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,6015.0,0.52,se,Columbia City station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,7.257484507194567,0.9331754305685036,100*(exp(b - se^2/2) - 1),reported,0.10342063024283163,0.0128353706578122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.057471264367816,6005.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c4,2,4,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.042,0.0102,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4506.0,0.66,se,Othello station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,4.284022879103233,1.0637523683257786,100*(exp(b - se^2/2) - 1),reported,0.06129409205737587,0.014885708071077001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.117647058823529,4496.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c5,2,5,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0784,0.0079,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,5921.0,0.57,se,Rainier Beach station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,8.152144483163859,0.8544286034455687,100*(exp(b - se^2/2) - 1),reported,0.12801777635048672,0.012899750423072006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.924050632911392,5911.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c6,2,6,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.048,0.0069,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4673.0,0.63,se,Tukwila/Intl Blvd station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,4.91456801142891,0.7239277521738847,100*(exp(b - se^2/2) - 1),reported,0.10134853410707163,0.014568851777891547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9565217391304355,4663.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T2-c7,2,7,10,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.073,0.0085,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3817.0,0.7,se,SeaTac Airport station neighbourhood (houses within 2 miles of station); sales 1992-2008 (before) vs 2010-2016 (after); DiD,pct_premium,7.569167685097167,0.914370956377498,100*(exp(b - se^2/2) - 1),reported,0.13786234634705907,0.01605246498561647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.588235294117647,3807.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),0.0394,0.0159,,,*,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,4.005499857478778,1.6538964953077941,100*(exp(b - se^2/2) - 1),reported,0.06513940094914977,0.02628722017998684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4779874213836477,1441.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.1449,0.0174,,,***,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,-13.502215059291023,1.5052893114176358,100*(exp(b - se^2/2) - 1),reported,-0.17276745668082177,0.020746402665605925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.327586206896552,2254.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.1234,0.0113,,,***,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,-11.614640360681673,0.9988183314450279,100*(exp(b - se^2/2) - 1),reported,-0.1575191468762377,0.014424362720433436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.920353982300885,4687.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0907,0.0119,,,***,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,-8.677300602408344,1.0868170721899053,100*(exp(b - se^2/2) - 1),reported,-0.13063502848719433,0.017139546185199698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.621848739495798,3346.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0098,0.0104,,,,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,-0.980568763818801,1.029857778058964,100*(exp(b - se^2/2) - 1),reported,-0.01413391020946973,0.014999251650865835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9423076923076923,4444.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.005,0.0119,,,,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,-0.505797017179388,1.184064850239292,100*(exp(b - se^2/2) - 1),reported,-0.007128482720592769,0.01696578887501079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42016806722689076,3474.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Close,transit_distance,D_location,binary,binary (within 800 m of light rail station),-0.0206,0.0182,,,,,,high,Pre-construction proximity differential (DiD main effect),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,-2.0551499191323486,1.782891529515916,100*(exp(b - se^2/2) - 1),reported,-0.02103175312813913,0.018581451792821953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1318681318681318,2895.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0217,0.0297,,,,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.0217,0.0297,not harmonised,reported,-0.019243801319136295,0.026338290284716497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7306397306397306,1441.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),0.0042,0.0297,,,,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,0.0042,0.0297,not harmonised,reported,0.0029786121634608927,0.021063043155902024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1414141414141414,2254.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),0.1183,0.0213,,,***,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,0.1183,0.0213,not harmonised,reported,0.08085990440505397,0.01455888388696238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.553990610328639,4687.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0883,0.0211,,,***,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.0883,0.0211,not harmonised,reported,-0.0721575374665696,0.017242627865737468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.184834123222749,3346.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0079,0.0215,,,,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.0079,0.0215,not harmonised,reported,-0.005511819782158664,0.01500052219195079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36744186046511634,4444.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.0101,0.0243,,,,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.0101,0.0243,not harmonised,reported,-0.007051628025270416,0.016965798120205062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4156378600823045,3474.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Close x After,transit_distance,D_location,other,binary interaction (within 800 m x sale after Dec 2009),-0.133,0.0384,,,***,,,high,DiD treatment effect of light rail opening; interaction term. Othello sign printed negative here (positive in Table 2); text on p.11 says Othello effect was negative,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.133,0.0384,not harmonised,reported,-0.06423891402533523,0.018547175177239642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.463541666666667,2895.0,binary interaction,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Baths,bathrooms,A_structural,level,count,0.0282,0.0121,,,*,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,2.82,1.21,100*b/1 (per unit count),reported,0.061279440015605716,0.026293660432227983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3305785123966944,1441.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Baths,bathrooms,A_structural,level,count,0.0578,0.0101,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,5.779999999999999,1.01,100*b/1 (per unit count),reported,0.11967325652430502,0.020911762818260914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7227722772277225,2254.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Baths,bathrooms,A_structural,level,count,0.0154,0.0072,,,*,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,1.54,0.72,100*b/1 (per unit count),reported,0.031226901967501622,0.014599590530260498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.138888888888889,4687.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Baths,bathrooms,A_structural,level,count,0.0272,0.0078,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,2.7199999999999998,0.7799999999999999,100*b/1 (per unit count),reported,0.0601760368430701,0.01725636350646863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4871794871794872,3346.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Baths,bathrooms,A_structural,level,count,0.0198,0.0065,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,1.9800000000000002,0.65,100*b/1 (per unit count),reported,0.04564696199709757,0.014985113786925967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0461538461538464,4444.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Baths,bathrooms,A_structural,level,count,0.0088,0.0063,,,,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.88,0.63,100*b/1 (per unit count),reported,0.023692194663985897,0.01696145754353536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.396825396825397,3474.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Baths,bathrooms,A_structural,level,count,0.0193,0.0067,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,1.9300000000000002,0.67,100*b/1 (per unit count),reported,0.05346095479835414,0.01855898430823693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8805970149253732,2895.0,count,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Size (ft2),living_area,A_structural,level,sqft,0.000352,5e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.3788896466681822,0.05381955208354861,100*b/0.092903 (X per sqft->m2),reported,0.18234661042923087,0.025901507163243024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.039999999999999,1441.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Size (ft2),living_area,A_structural,level,sqft,0.000483,2.4e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.5198968731270796,0.025833385000103334,100*b/0.092903 (X per sqft->m2),reported,0.39027923438828,0.019392756988237513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.125,2254.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Size (ft2),living_area,A_structural,level,sqft,0.000322,2.6e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.346597915418053,0.027986167083445274,100*b/0.092903 (X per sqft->m2),reported,0.17800936592476865,0.0143734270622484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.384615384615387,4687.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Size (ft2),living_area,A_structural,level,sqft,0.000307,1.7e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.3304520497929884,0.018298647708406526,100*b/0.092903 (X per sqft->m2),reported,0.2980100814415694,0.016502186920217196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.058823529411764,3346.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Size (ft2),living_area,A_structural,level,sqft,0.000447,2.3e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.4811467956269246,0.02475699395843236,100*b/0.092903 (X per sqft->m2),reported,0.2798846682868021,0.014401224542721358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.434782608695652,4444.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Size (ft2),living_area,A_structural,level,sqft,0.000323,1.7e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.347674306459724,0.018298647708406526,100*b/0.092903 (X per sqft->m2),reported,0.30681099337932694,0.016147947019964574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,3474.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Size (ft2),living_area,A_structural,level,sqft,0.000297,2.1e-05,,,***,,,high,Quadratic in size (Size2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.3196881393762787,0.022604211875090412,100*b/0.092903 (X per sqft->m2),reported,0.2542174722241729,0.017974972783527374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.142857142857144,2895.0,sqft,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Size2,living_area,A_structural,squared,sqft squared,-5.52e-08,1.33e-08,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-5.52e-08,1.33e-08,not harmonised,reported,-0.10868636762987906,0.02618711393980781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.150375939849624,1441.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Size2,living_area,A_structural,squared,sqft squared,-3.58e-08,3.73e-09,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-3.58e-08,3.73e-09,not harmonised,reported,-0.19815233424357206,0.020645480634874966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.597855227882038,2254.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Size2,living_area,A_structural,squared,sqft squared,-2.6e-08,6.23e-09,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-2.6e-08,6.23e-09,not harmonised,reported,-0.060846051552417,0.01457965004505992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.173354735152488,4687.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Size2,living_area,A_structural,squared,sqft squared,-2.09e-08,3.03e-09,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-2.09e-08,3.03e-09,not harmonised,reported,-0.1184062785858598,0.017166077708859098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.897689768976897,3346.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Size2,living_area,A_structural,squared,sqft squared,-5.38e-08,5.6e-09,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-5.38e-08,5.6e-09,not harmonised,reported,-0.14264071231127828,0.014847360389278036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.607142857142858,4444.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Size2,living_area,A_structural,squared,sqft squared,-2.92e-08,3.68e-09,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-2.92e-08,3.68e-09,not harmonised,reported,-0.13341968501827847,0.016814535646139203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.934782608695652,3474.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Size2,living_area,A_structural,squared,sqft squared,-1.34e-08,4.59e-09,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-1.34e-08,4.59e-09,not harmonised,reported,-0.054178813471320246,0.01855826521144477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9193899782135073,2895.0,sqft sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Age,age,A_structural,level,years,-0.005313,0.001009,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.5313,0.10089999999999999,100*b/1 (per year),reported,-0.1373972887863963,0.026093330394405017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.265609514370665,1441.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Age,age,A_structural,level,years,-0.001945,0.000932,,,*,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.19449999999999998,0.0932,100*b/1 (per year),reported,-0.04391446225212218,0.021042816873510475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.086909871244635,2254.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Age,age,A_structural,level,years,-0.004767,0.000535,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.4767,0.0535,100*b/1 (per year),reported,-0.12906142070093218,0.01448455214495463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.910280373831776,4687.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Age,age,A_structural,level,years,-0.004454,0.000592,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.44539999999999996,0.059199999999999996,100*b/1 (per year),reported,-0.12898010305657376,0.017143291650087936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.523648648648648,3346.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Age,age,A_structural,level,years,-0.00438,0.000465,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.438,0.0465,100*b/1 (per year),reported,-0.13990766489389198,0.014853210999009077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.419354838709678,4444.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Age,age,A_structural,level,years,-0.006907,0.000462,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.6907,0.0462,100*b/1 (per year),reported,-0.24586282205121046,0.016445435614254992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.95021645021645,3474.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Age,age,A_structural,level,years,-0.006782,0.000571,,,***,,,high,Quadratic in age (Age2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.6782,0.0571,100*b/1 (per year),reported,-0.21555871456094564,0.018148632558876433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.87740805604203,2895.0,years,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Age2,age,A_structural,squared,years squared,4.16e-05,8.55e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,4.16e-05,8.55e-06,not harmonised,reported,0.12713258242684275,0.026129412974747725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.865497076023392,1441.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Age2,age,A_structural,squared,years squared,3.22e-05,8.5e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,3.22e-05,8.5e-06,not harmonised,reported,0.07953931445740706,0.020996402884719254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7882352941176465,2254.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Age2,age,A_structural,squared,years squared,3.55e-05,4.62e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,3.55e-05,4.62e-06,not harmonised,reported,0.11153739988291506,0.01451557147772021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.683982683982685,4687.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Age2,age,A_structural,squared,years squared,3.02e-05,5.18e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,3.02e-05,5.18e-06,not harmonised,reported,0.10028118549561303,0.017200547710836938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.83011583011583,3346.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Age2,age,A_structural,squared,years squared,3.43e-05,4.17e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,3.43e-05,4.17e-06,not harmonised,reported,0.12245880079350309,0.014887848376352998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.225419664268586,4444.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Age2,age,A_structural,squared,years squared,4.13e-05,4.66e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,4.13e-05,4.66e-06,not harmonised,reported,0.14869427014347003,0.01677761014209614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.862660944206008,3474.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Age2,age,A_structural,squared,years squared,4.74e-05,7.97e-06,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,4.74e-05,7.97e-06,not harmonised,reported,0.10986484831682806,0.01847305571909535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.947302383939774,2895.0,years sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Waterfront,waterfront,C_exterior,binary,binary,0.6472,0.0771,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,90.45157846823066,14.727524936057584,100*(exp(b - se^2/2) - 1),reported,0.1741095943390026,0.020741424171101825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.394293125810636,2254.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Waterfront,waterfront,C_exterior,binary,binary,0.2623,0.0283,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,29.93960180411983,3.6787635786209285,100*(exp(b - se^2/2) - 1),reported,0.13415918537468924,0.014474666207028995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.268551236749117,4687.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Waterfront,waterfront,C_exterior,binary,binary,0.204,0.0276,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,22.583116995350583,3.384582903539141,100*(exp(b - se^2/2) - 1),reported,0.12674805579101378,0.017148266371725392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.391304347826087,3346.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Waterfront,waterfront,C_exterior,binary,binary,0.4644,0.0227,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,59.064939121767864,3.6117045358153734,100*(exp(b - se^2/2) - 1),reported,0.2933829991137706,0.014340641860212304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.45814977973568,4444.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Waterfront,waterfront,C_exterior,binary,binary,0.0394,0.0584,,,,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,3.8414177405534433,6.074689014212275,100*(exp(b - se^2/2) - 1),reported,0.011445638340214922,0.016965108605800798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6746575342465753,3474.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Waterfront,waterfront,C_exterior,binary,binary,0.0471,0.0091,,,***,,,high,Not estimable for Beacon Hill (no waterfront lots),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,4.818342558811017,0.953886412134427,100*(exp(b - se^2/2) - 1),reported,0.09575359282742424,0.018500163370054362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.175824175824176,2895.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,View,view,E_environment,binary,binary (outstanding view),0.0611,0.02,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,6.279262961009402,2.1260104187865716,100*(exp(b - se^2/2) - 1),reported,0.08021901895830995,0.0262582713447823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.055,1441.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,View,view,E_environment,binary,binary (outstanding view),0.1537,0.0162,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,16.59879810175471,1.8891484070387803,100*(exp(b - se^2/2) - 1),reported,0.19596504228002876,0.020654740955995222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.487654320987655,2254.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,View,view,E_environment,binary,binary (outstanding view),0.2521,0.0115,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,28.66396214638065,1.4797334088200282,100*(exp(b - se^2/2) - 1),reported,0.30495245233725593,0.013910960737320281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.921739130434784,4687.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,View,view,E_environment,binary,binary (outstanding view),0.2952,0.0145,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,34.32537938446669,1.9479227656923814,100*(exp(b - se^2/2) - 1),reported,0.3319915945870396,0.016307175208374235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.358620689655172,3346.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,True
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,View,view,E_environment,binary,binary (outstanding view),0.2219,0.0102,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,24.838158446998193,1.2734154575085288,100*(exp(b - se^2/2) - 1),reported,0.31023789744752206,0.014260597359011832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.75490196078431,4444.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,View,view,E_environment,binary,binary (outstanding view),0.062,0.0144,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,6.385203883048884,1.5321057764083685,100*(exp(b - se^2/2) - 1),reported,0.07285487866809066,0.016921133110008153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.305555555555555,3474.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,View,view,E_environment,binary,binary (outstanding view),0.1446,0.013,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,15.547960475882515,1.502250420983975,100*(exp(b - se^2/2) - 1),reported,0.20244792056542787,0.01820071208402878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.123076923076924,2895.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Lot (acres),lot_size,A_structural,level,acres,0.588,0.6628,,,,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.014529796430269321,0.01637814468364372,100*b/4046.86 (X per acre->m2),reported,0.0233638424819003,0.02633597754592435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8871454435727218,1441.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Lot (acres),lot_size,A_structural,level,acres,1.4153,0.2061,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.03497282463904791,0.005092841912038277,100*b/4046.86 (X per acre->m2),reported,0.14315200741617176,0.020846201320195715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.867054827753518,2254.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Lot (acres),lot_size,A_structural,level,acres,0.867,0.1091,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.021424036573203235,0.002695919711806774,100*b/4046.86 (X per acre->m2),reported,0.11530299358824118,0.01450929250343381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.9468377635197065,4687.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Lot (acres),lot_size,A_structural,level,acres,0.9429,0.0931,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.023299566418539017,0.0023005511014593093,100*b/4046.86 (X per acre->m2),reported,0.17246312850958617,0.017028653371770574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.12781954887218,3346.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Lot (acres),lot_size,A_structural,level,acres,-0.0913,0.0417,,,*,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,-0.0022560721327952197,0.0010304294407180795,100*b/4046.86 (X per acre->m2),reported,-0.03282566929065133,0.014992666039651265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1894484412470026,4444.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Lot (acres),lot_size,A_structural,level,acres,0.2292,0.0313,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.005663655343227429,0.0007734398439922276,100*b/4046.86 (X per acre->m2),reported,0.12329040329785543,0.01683677846083279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.322683706070287,3474.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Lot (acres),lot_size,A_structural,level,acres,0.578,0.0567,,,***,,,high,Quadratic in lot size (Lot2 also included),log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.014282691048802155,0.0014010875129188275,100*b/4046.86 (X per acre->m2),reported,0.18614977053984294,0.018260712784790824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.194003527336859,2895.0,acre,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Lot2,lot_size,A_structural,squared,acres squared,1.4301,2.4902,,,,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,1.4301,2.4902,not harmonised,reported,0.015126919416043164,0.026340154345731552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5742912215886273,1441.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Lot2,lot_size,A_structural,squared,acres squared,-0.9184,0.1765,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.9184,0.1765,not harmonised,reported,-0.10894751984707635,0.020937758332980155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.203399433427762,2254.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Lot2,lot_size,A_structural,squared,acres squared,-0.2835,0.0937,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.2835,0.0937,not harmonised,reported,-0.04415117760426398,0.014592470340456914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0256136606189963,4687.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Lot2,lot_size,A_structural,squared,acres squared,-0.1501,0.0636,,,*,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.1501,0.0636,not harmonised,reported,-0.04076612540153422,0.017273321622502176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.360062893081761,3346.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Lot2,lot_size,A_structural,squared,acres squared,0.0345,0.0108,,,**,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,0.0345,0.0108,not harmonised,reported,0.0478641404953529,0.014983557024632211,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1944444444444446,4444.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Lot2,lot_size,A_structural,squared,acres squared,-0.0061,0.0123,,,,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.0061,0.0123,not harmonised,reported,-0.008413843766745678,0.01696561939851997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.49593495934959353,3474.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Lot2,lot_size,A_structural,squared,acres squared,-0.2115,0.0349,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,other,-0.2115,0.0349,not harmonised,reported,-0.11192401445350689,0.018468785363722887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.060171919770774,2895.0,acres sqd,nonstandard_x_transform,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Garage (sf),garage,A_structural,level,sqft,0.000354,5.2e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.0354,0.0052,100*b (unit as reported: sqft; count expected),reported,0.17652007650070414,0.0259295027628153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.8076923076923075,1441.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Garage (sf),garage,A_structural,level,sqft,0.000235,5.1e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.0235,0.0051,100*b (unit as reported: sqft; count expected),reported,0.09660171167921197,0.020964626789956645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.607843137254902,2254.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Garage (sf),garage,A_structural,level,sqft,0.000214,2.8e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.0214,0.0028,100*b (unit as reported: sqft; count expected),reported,0.11094780691450319,0.014516535484140601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.642857142857143,4687.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Garage (sf),garage,A_structural,level,sqft,0.000223,2.8e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.0223,0.0028,100*b (unit as reported: sqft; count expected),reported,0.13639735616591062,0.017126125437872185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.964285714285714,3346.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Garage (sf),garage,A_structural,level,sqft,0.0002,2.1e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.02,0.0021,100*b (unit as reported: sqft; count expected),reported,0.1414282863931358,0.014849970071279254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.523809523809526,4444.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Garage (sf),garage,A_structural,level,sqft,0.000165,1.7e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.0165,0.0017,100*b (unit as reported: sqft; count expected),reported,0.1624838405434802,0.016740759328722203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.705882352941176,3474.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Garage (sf),garage,A_structural,level,sqft,0.000191,1.6e-05,,,***,,,high,Garage floor area in square feet,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,semi_elasticity,0.019100000000000002,0.0015999999999999999,100*b (unit as reported: sqft; count expected),reported,0.21659825927813342,0.01814435679816824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.937500000000002,2895.0,sqft,unit_not_harmonisable,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,False,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c1,3,1,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0829,0.0156,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,1451.0,0.42,se,Beacon Hill station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,8.63009706555773,1.6948357292878615,100*(exp(b - se^2/2) - 1),reported,0.1386384220999339,0.026088774243172116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.314102564102565,1441.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c2,3,2,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.1489,0.0168,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2264.0,0.7,se,Mount Baker station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,16.03931614753149,1.9497356385584046,100*(exp(b - se^2/2) - 1),reported,0.18351413462721897,0.020705422845784274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.863095238095239,2254.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c3,3,3,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.066,0.0099,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4697.0,0.55,se,Columbia City station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,6.817436999836546,1.0575444499943334,100*(exp(b - se^2/2) - 1),reported,0.09691965698120014,0.014537948547180022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.666666666666666,4687.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c4,3,4,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0415,0.012,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3356.0,0.67,se,Othello station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,4.229811368558645,1.2508477942217682,100*(exp(b - se^2/2) - 1),reported,0.059680037439536905,0.017256878295769707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4583333333333335,3346.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c5,3,5,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0823,0.0092,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,4454.0,0.57,se,Rainier Beach station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,8.573554612230193,0.9989189757890629,100*(exp(b - se^2/2) - 1),reported,0.1329993518509124,0.014867485261584379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.945652173913043,4444.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c6,3,6,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0452,0.0082,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,3484.0,0.61,se,Tukwila/Intl Blvd station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,4.6201912503082365,0.8579144108501736,100*(exp(b - se^2/2) - 1),reported,0.09311480106198257,0.01689250815726233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.512195121951219,3474.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a,8c46154fbaf164447e17b77d9874009c161a116a:T3-c7,3,7,12,Detached Garage,garage,A_structural,binary,binary (garage is detached),0.0729,0.01,,,***,,,high,,log of real relative sale price (sale price deflated by Seattle metro Case-Shiller index),log,OLS,2905.0,0.7,se,SeaTac Airport station neighbourhood; sales 1992-2003 (before construction) vs 2010-2016 (after opening); DiD,pct_premium,7.5569189435529704,1.0756229692394854,100*(exp(b - se^2/2) - 1),reported,0.13426201930388304,0.018417286598612213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.290000000000001,2895.0,binary,,2018.0,United States,North America,High income,1992.0,2016.0,single_family,transaction,journal,False,False,clustered,,2004.0,ols,journal,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.149,0.034,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,semi_elasticity,14.899999999999999,3.4000000000000004,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.0216798903164514,0.004947089065498978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.38235294117647,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.099,0.02,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",semi_elasticity,9.9,2.0,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.05781076354580079,0.011678942130464805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.95,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Age,age,A_structural,level,years,-0.013,0.001,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",semi_elasticity,-1.3,0.1,100*b/1 (per year),reported,-0.06419459565029369,0.004938045819253361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.0,40841.0,years,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Age,age,A_structural,level,years,-0.012,0.001,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,semi_elasticity,-1.2,0.1,100*b/1 (per year),reported,-0.05927461970912663,0.004939551642427219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,40841.0,years,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Age,age,A_structural,level,years,-0.014,0.002,,,***,,,high,,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",semi_elasticity,-1.4000000000000001,0.2,100*b/1 (per year),reported,-0.08161635209175691,0.011659478870250987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,7307.0,years,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Age,age,A_structural,level,years,-0.014,0.002,,,***,,,high,,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",semi_elasticity,-1.4000000000000001,0.2,100*b/1 (per year),reported,-0.08161635209175691,0.011659478870250987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,7307.0,years,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,AgexAge,age,A_structural,squared,years,0.0001,0.0,,,***,,,high,SE printed as (0.0000) - rounded to zero,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",other,0.0001,,not harmonised,,,,,,40841.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,AgexAge,age,A_structural,squared,years,0.0001,0.0,,,***,,,high,SE printed as (0.0000) - rounded to zero,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,other,0.0001,,not harmonised,,,,,,40841.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,AgexAge,age,A_structural,squared,years,0.0001,0.0,,,***,,,high,SE printed as (0.0000) - rounded to zero,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",other,0.0001,,not harmonised,,,,,,7307.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,AgexAge,age,A_structural,squared,years,0.0001,0.0,,,***,,,high,SE printed as (0.0000) - rounded to zero,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",other,0.0001,,not harmonised,,,,,,7307.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Bed,bedrooms,A_structural,level,count,-0.047,0.021,,,**,,,high,,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",semi_elasticity,-4.7,2.1,100*b/1 (per unit count),reported,-0.011073980348086692,0.004947948666166394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.238095238095238,40841.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,True
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Bed,bedrooms,A_structural,level,count,-0.042,0.02,,,**,,,high,,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,semi_elasticity,-4.2,2.0,100*b/1 (per unit count),reported,-0.010390768397624072,0.004947984951249558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1,40841.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Bed,bedrooms,A_structural,level,count,-0.014,0.021,,,,,,high,,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",semi_elasticity,-1.4000000000000001,2.1,100*b/1 (per unit count),reported,-0.007798767619131272,0.011698151428696908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,7307.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Bed,bedrooms,A_structural,level,count,-0.013,0.021,,,,,,high,,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",semi_elasticity,-1.3,2.1,100*b/1 (per unit count),reported,-0.007241743126266714,0.011698200434738541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.619047619047619,7307.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Bath,bathrooms,A_structural,level,count,0.247,0.018,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",semi_elasticity,24.7,1.7999999999999998,100*b/1 (per unit count),reported,0.0677450234828826,0.004936884302396302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.722222222222223,40841.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Bath,bathrooms,A_structural,level,count,0.245,0.016,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,semi_elasticity,24.5,1.6,100*b/1 (per unit count),reported,0.07555353987036179,0.004934108726227709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.3125,40841.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Bath,bathrooms,A_structural,level,count,0.32,0.035,,,***,,,high,,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",semi_elasticity,32.0,3.5000000000000004,100*b/1 (per unit count),reported,0.10635118295801178,0.011632160636032542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.142857142857142,7307.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Bath,bathrooms,A_structural,level,count,0.315,0.033,,,***,,,high,,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",semi_elasticity,31.5,3.3000000000000003,100*b/1 (per unit count),reported,0.11097778546723713,0.011626244191805796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.545454545454545,7307.0,count,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Ranch,dwelling_type,A_structural,binary,binary,-0.26,0.021,,,***,,,high,ranch-style house dummy,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",pct_premium,-22.91184123281206,1.6192083301874893,100*(exp(b - se^2/2) - 1),reported,-0.06114942524825239,0.004938992039281924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.380952380952381,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Ranch,dwelling_type,A_structural,binary,binary,-0.244,0.02,,,***,,,high,ranch-style house dummy,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,pct_premium,-21.666904759528194,1.5669752685257252,100*(exp(b - se^2/2) - 1),reported,-0.06025897209183984,0.004939260007527856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.2,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Ranch,dwelling_type,A_structural,binary,binary,-0.036,0.042,,,,,,high,ranch-style house dummy,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",pct_premium,-3.6210144157598156,4.051489232629117,100*(exp(b - se^2/2) - 1),reported,-0.010026787810405125,0.011697919112139315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.857142857142857,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Ranch,dwelling_type,A_structural,binary,binary,-0.038,0.041,,,,,,high,ranch-style house dummy,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",pct_premium,-3.809587938128478,3.947123057653918,100*(exp(b - se^2/2) - 1),reported,-0.010841881591019517,0.011697819611363163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9268292682926829,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Renovated,renovation,A_structural,binary,binary,0.253,0.032,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",pct_premium,28.72240493735221,4.121226485870817,100*(exp(b - se^2/2) - 1),reported,0.03909221338527812,0.004944469677189329,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.90625,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Renovated,renovation,A_structural,binary,binary,0.225,0.027,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,pct_premium,25.186632774366014,3.3812713337180336,100*(exp(b - se^2/2) - 1),reported,0.04120042112451108,0.004944050534941329,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.333333333333334,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Renovated,renovation,A_structural,binary,binary,0.359,0.042,,,***,,,high,,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",pct_premium,43.06344253824079,6.013966566600967,100*(exp(b - se^2/2) - 1),reported,0.09949818512010357,0.011640456197895126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.547619047619047,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Renovated,renovation,A_structural,binary,binary,0.345,0.038,,,***,,,high,,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",pct_premium,41.09708308809805,5.365561694737104,100*(exp(b - se^2/2) - 1),reported,0.1056160956007964,0.01163307719660946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.078947368421051,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Sqft,living_area,A_structural,level,100 sqft,0.009,0.001,,,***,,,high,square footage in hundreds (Table 6: Sqft (100)),Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",semi_elasticity,0.09687519375038749,0.010763910416709722,100*b/9.2903 (X per 100sqft->m2),reported,0.04449017191579412,0.004943352435088236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,40841.0,100sqft,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Sqft,living_area,A_structural,level,100 sqft,0.009,0.001,,,***,,,high,square footage in hundreds (Table 6: Sqft (100)),Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,semi_elasticity,0.09687519375038749,0.010763910416709722,100*b/9.2903 (X per 100sqft->m2),reported,0.04449017191579412,0.004943352435088236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,40841.0,100sqft,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Sqft,living_area,A_structural,level,100 sqft,-0.001,0.001,,,,,,high,square footage in hundreds (Table 6: Sqft (100)),Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",semi_elasticity,-0.010763910416709722,0.010763910416709722,100*b/9.2903 (X per 100sqft->m2),reported,-0.011697706772393275,0.011697706772393273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7307.0,100sqft,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,True
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Sqft,living_area,A_structural,level,100 sqft,-0.001,0.001,,,,,,high,square footage in hundreds (Table 6: Sqft (100)),Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",semi_elasticity,-0.010763910416709722,0.010763910416709722,100*b/9.2903 (X per 100sqft->m2),reported,-0.011697706772393275,0.011697706772393273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,7307.0,100sqft,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,True
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Large Lot,lot_size,A_structural,binary,binary,0.012,0.028,,,,,,high,large lot indicator,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",pct_premium,1.1675634278220715,2.8338024088250178,100*(exp(b - se^2/2) - 1),reported,0.0021206746963994072,0.004948240958265284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42857142857142855,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Large Lot,lot_size,A_structural,binary,binary,0.004,0.025,,,,,,high,large lot indicator,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,pct_premium,0.3694307192727564,2.510020026693355,100*(exp(b - se^2/2) - 1),reported,0.0007917200854776199,0.004948250534235124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Large Lot,lot_size,A_structural,binary,binary,0.066,0.07,,,,,,high,large lot indicator,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",pct_premium,6.561276510772851,7.477587020161954,100*(exp(b - se^2/2) - 1),reported,0.011029350162602938,0.011697795627003118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9428571428571428,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Large Lot,lot_size,A_structural,binary,binary,0.077,0.073,,,,,,high,large lot indicator,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",pct_premium,7.716813478906337,7.884307157665983,100*(exp(b - se^2/2) - 1),reported,0.012338581959024365,0.01169761666245167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0547945205479452,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Pool,pool,C_exterior,binary,binary,0.049,0.018,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",pct_premium,5.005022882345567,1.8903966313320504,100*(exp(b - se^2/2) - 1),reported,0.013469019883144832,0.004947803222379734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7222222222222223,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Pool,pool,C_exterior,binary,binary,0.051,0.016,,,***,,,high,,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,pct_premium,5.218820457312501,1.683716629253126,100*(exp(b - se^2/2) - 1),reported,0.015770591990896973,0.004947636703026502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1874999999999996,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Pool,pool,C_exterior,binary,binary,0.026,0.056,,,,,,high,,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",pct_premium,2.4732906900293283,5.747509311451275,100*(exp(b - se^2/2) - 1),reported,0.005431369653034972,0.011698334637306095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46428571428571425,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Pool,pool,C_exterior,binary,binary,0.01,0.057,,,,,,high,,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",pct_premium,0.8410672627664262,5.757285952379757,100*(exp(b - se^2/2) - 1),reported,0.002052365359996223,0.011698482551978471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.17543859649122806,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,True,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c1,3,1,38,Feature,other,other,binary,binary,0.126,0.031,,,***,,,high,special recreational feature such as access to a lake or golf course,Ln(sale price),log,OLS,40851.0,0.76,se,"full sample hedonic, no uniqueness control",pct_premium,13.37372766218703,3.516274721677378,100*(exp(b - se^2/2) - 1),reported,0.020108183914960942,0.004947251598125312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.064516129032258,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c2,3,2,38,Feature,other,other,binary,binary,0.09,0.03,,,***,,,high,special recreational feature such as access to a lake or golf course,Ln(sale price),log,OLS,40851.0,0.769,se,full sample hedonic with uniqueness control,pct_premium,9.36820160460734,3.282522851115631,100*(exp(b - se^2/2) - 1),reported,0.014843120879858802,0.0049477069599529345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,40841.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c3,3,3,38,Feature,other,other,binary,binary,0.023,0.083,,,,,,high,special recreational feature such as access to a lake or golf course,Ln(sale price),log,FE_panel,7317.0,0.956,se,"repeat-sale sample with property FE, no uniqueness control",pct_premium,1.9747961302000405,8.493112278241906,100*(exp(b - se^2/2) - 1),reported,0.003241737970899747,0.011698445721073002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27710843373493976,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T3-c4,3,4,38,Feature,other,other,binary,binary,-0.004,0.078,,,,,,high,special recreational feature such as access to a lake or golf course,Ln(sale price),log,FE_panel,7317.0,0.958,se,"repeat-sale sample with property FE, with uniqueness control",pct_premium,-0.7017263217532821,7.7688623168831334,100*(exp(b - se^2/2) - 1),reported,-0.0005999233376952759,0.01169850508505788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05128205128205128,7307.0,binary,,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,False,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c2,4,2,39,MLS Area Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.125,0.032,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,OLS,40851.0,0.767,se,full sample; competitor-based uniqueness,semi_elasticity,12.5,3.2,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.019325499901119144,0.004947327974686501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.90625,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c4,4,4,39,MLS Area Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.069,0.071,,,,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,OLS,40851.0,0.767,se,full sample; horse race,semi_elasticity,6.9,7.1,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.004808809100424869,0.004948194871451676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9718309859154931,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c6,4,6,39,MLS Area Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.09,0.02,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,FE_panel,7317.0,0.958,se,repeat sale; competitor-based uniqueness,semi_elasticity,9.0,2.0,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.052570488059505045,0.011682330679890008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c8,4,8,39,MLS Area Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.084,0.047,,,*,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,FE_panel,7317.0,0.957,se,repeat sale; horse race,semi_elasticity,8.4,4.7,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.020903401906446493,0.011695951066702204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7872340425531916,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c3,4,3,39,MLS Area Non-Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.112,0.028,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,OLS,40851.0,0.767,se,full sample; non-competitor-based uniqueness,semi_elasticity,11.200000000000001,2.8000000000000003,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.01978913239314169,0.004947283098285423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c4,4,4,39,MLS Area Non-Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.059,0.061,,,,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,OLS,40851.0,0.767,se,full sample; horse race,semi_elasticity,5.8999999999999995,6.1,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.00478595949866792,0.004948195413877003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9672131147540983,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c7,4,7,39,MLS Area Non-Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.068,0.021,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,FE_panel,7317.0,0.957,se,repeat sale; non-competitor-based uniqueness,semi_elasticity,6.800000000000001,2.1,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.03785373084607038,0.011690122761286443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.238095238095238,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T4-c8,4,8,39,MLS Area Non-Competitor Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.006,0.048,,,,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure); uniqueness relative to competing / non-competing units",Ln(sale price),log,FE_panel,7317.0,0.957,se,repeat sale; horse race,semi_elasticity,0.6,4.8,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.0014623118353073315,0.011698494682458654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.125,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T5A-c2,5A,2,40,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.149,0.032,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,OLS,40851.0,0.784,se,full sample; + transaction characteristics,semi_elasticity,14.899999999999999,3.2,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.023034185671737248,0.004946939204668402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.65625,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T5A-c3,5A,3,40,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.136,0.026,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,OLS,40851.0,0.811,se,full sample; + transaction characteristics + keyword dummies,semi_elasticity,13.600000000000001,2.6,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.02587449904591022,0.004946595405835776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.230769230769232,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T5A-c4,5A,4,40,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.122,0.019,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,OLS,40851.0,0.886,se,full sample; + transaction characteristics + keywords + agent FE,semi_elasticity,12.2,1.9,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.03175696142924708,0.00494575628816143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.421052631578948,40841.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,ols,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T5B-c2,5B,2,40,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.098,0.02,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,FE_panel,7317.0,0.961,se,repeat sale sample with property FE; + transaction characteristics,semi_elasticity,9.8,2.0,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.05722873866648471,0.011679334421731571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T5B-c3,5B,3,40,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.08,0.015,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,FE_panel,7317.0,0.968,se,repeat sale sample with property FE; + transaction characteristics + keyword dummies,semi_elasticity,8.0,1.5,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.06227095293468546,0.011675803675253522,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333334,7307.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7,8d5f6b09f9058fc5a131a22d58a856d18ce849c7:T5B-c4,5B,4,40,MLS Area-Year Unique Score (standard),multimodal_embedding,H_ai_derived,level,standard deviation,0.05,0.018,,,***,,,high,"AI-derived: uniqueness of listing text description from machine-learning text analysis, standardised (1 SD); mapped to H_ai_derived (text-only embedding-type measure)",Ln(sale price),log,FE_panel,4117.0,0.989,se,repeat sale sample with property FE; + transaction characteristics + keywords + agent FE,semi_elasticity,5.0,1.7999999999999998,100*b (unit as reported: standard deviation; no harmonised unit defined for this characteristic),reported,0.0433039551176746,0.015589423842362853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.777777777777778,4107.0,standard deviation,unit_not_harmonisable,2021.0,United States,North America,High income,2010.0,2016.0,single_family,transaction,working_paper,True,True,clustered,NLP text analysis of listing descriptions (uniqueness score),2013.0,panel,other,False,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Secchi,water_quality,E_environment,log,m (Secchi depth),0.231,0.034,,,***,,,high,"ln(summer mean Secchi depth, metres); main effect = elasticity for a lake with mean log lake area (interactions demeaned); excluded categories MN and Ecoregion 8.1","ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,0.231,0.034,b (log-log),reported,0.009746783723741185,0.0014345915437541138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.794117647058823,485850.0,m,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,<150 m,water_proximity,E_environment,binary,binary,0.124,0.009,,,***,,,high,Lakefront buffer dummy: property within 150 m of the lake =1,"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",pct_premium,13.197002528478174,1.0188142838992575,100*(exp(b - se^2/2) - 1),reported,0.019762562064385022,0.0014343795046731064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.777777777777779,485850.0,binary,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Lake Distance,water_proximity,E_environment,log,m,-0.095,0.005,,,***,,,high,"ln(distance from property to nearest lake, metres)","ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,-0.095,0.005,b (log-log),reported,-0.027248412856838294,0.0014341269924651734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.0,485850.0,m,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Lake Area,other,other,log,ha,0.013,0.005,,,*,,,high,"ln(lake surface area, hectares), not demeaned as a control","ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,0.013,0.005,b (log-log),reported,0.003730089248362784,0.0014346497109087636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5999999999999996,485850.0,ha,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,False,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Median Income,income,F_neighbourhood,log,"currency (2016 $, block group median)",0.131,0.014,,,***,,,high,ln(block-group median income),"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,0.131,0.014,b (log-log),reported,0.013423106235694388,0.0014345304374024537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.357142857142858,485850.0,currency,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Slope,other,other,log,degrees,0.019,0.002,,,***,,,high,ln(parcel slope in degrees),"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,0.019,0.002,b (log-log),reported,0.013628001380803062,0.0014345264611371645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.5,485850.0,degrees,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,False,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Elevation,other,other,log,m,-0.023,0.023,,,,,,high,"ln(parcel elevation, metres); printed as -0.0230","ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,-0.023,0.023,b (log-log),reported,-0.00143465821511874,0.0014346582151187402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,485850.0,m,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,False,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Lot Size,lot_size,A_structural,log,sqft,0.101,0.004,,,***,,,high,"ln(lot size, sq ft)","ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,0.101,0.004,b (log-log),reported,0.03620141213025096,0.0014337192922871667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.25,485850.0,sqft,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Building Age,age,A_structural,log,years,-0.125,0.003,,,***,,,high,ln(building age in years),"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,-0.125,0.003,b (log-log),reported,-0.059670969650351464,0.0014321032716084353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-41.666666666666664,485850.0,years,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Building Size,living_area,A_structural,log,sqft,0.547,0.01,,,***,,,high,"ln(building size, sq ft)","ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",elasticity,0.547,0.01,b (log-log),reported,0.07823535007665969,0.00143026234143802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.7,485850.0,sqft,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Quarter 2,transaction_timing,G_market,dummy,binary,0.044,0.002,,,***,,,high,Sale quarter 2 dummy (reference quarter 1),"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",pct_premium,4.4980264925823965,0.20899647097768878,100*(exp(b - se^2/2) - 1),reported,0.03154680378516759,0.0014339456265985268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,485850.0,binary,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Quarter 3,transaction_timing,G_market,dummy,binary,0.05,0.002,,,***,,,high,Sale quarter 3 dummy (reference quarter 1),"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",pct_premium,5.126899383593386,0.21025421927520482,100*(exp(b - se^2/2) - 1),reported,0.03584344510029118,0.0014337378040116472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.0,485850.0,binary,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2,8fb41eefcd89b6d6746d9ab5af9f6f61e2da05b2:T3-c1,3,Estimate,34,Quarter 4,transaction_timing,G_market,dummy,binary,0.015,0.002,,,***,,,high,Sale quarter 4 dummy (reference quarter 1),"ln(sale price, 2020 $)",log,OLS,485860.0,,se,"National baseline model: summer-mean Secchi within +/-5 years, lot size/building size/building age controls, 150 m lakefront buffer, properties within 1,000 m of lake, census tract x year FE, quarter FE; SE clustered by tract",pct_premium,1.511103439161987,0.20302261292314377,100*(exp(b - se^2/2) - 1),reported,0.01075932486637363,0.0014345766488498173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,485850.0,binary,,2023.0,United States,North America,High income,,,single_family,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c1,2,1,10,Grocery store distance 0-399.9 meters,commercial_distance,D_location,dummy,binary (within 0-399.9 m of a grocery store site; ref > 800 m),0.076,0.028,,,***,,,high,Distance-band dummy (pre- and post-opening location effect); clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.589,se,"All observations; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.853970371623564,3.021095207640395,100*(exp(b - se^2/2) - 1),reported,0.014753651786630696,0.0054355559213902565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.714285714285714,33839.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c2,2,2,10,Grocery store distance 0-399.9 meters,commercial_distance,D_location,dummy,binary (within 0-399.9 m of a grocery store site; ref > 800 m),0.073,0.027,,,***,,,high,Distance-band dummy (pre- and post-opening location effect); clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.599,se,"Excludes properties with top 1% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.533850458623048,2.9044724496696994,100*(exp(b - se^2/2) - 1),reported,0.014754236660817946,0.005457046436192939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7037037037037037,33573.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c3,2,3,10,Grocery store distance 0-399.9 meters,commercial_distance,D_location,dummy,binary (within 0-399.9 m of a grocery store site; ref > 800 m),0.072,0.027,,,***,,,high,Distance-band dummy (pre- and post-opening location effect); clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.584,se,"Excludes properties with top 5% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.426370357171819,2.9015694289722966,100*(exp(b - se^2/2) - 1),reported,0.014765652497915996,0.005537119686718498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6666666666666665,32609.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c1,2,1,10,Grocery store distance 400-800 meters,commercial_distance,D_location,dummy,binary (400-800 m of a grocery store site; ref > 800 m),0.081,0.018,,,***,,,high,Distance-band dummy; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.589,se,"All observations; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,8.419524270986312,1.9518676138201687,100*(exp(b - se^2/2) - 1),reported,0.02445534797416125,0.005434521772035832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.500000000000001,33839.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c2,2,2,10,Grocery store distance 400-800 meters,commercial_distance,D_location,dummy,binary (400-800 m of a grocery store site; ref > 800 m),0.08,0.018,,,***,,,high,Distance-band dummy; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.599,se,"Excludes properties with top 1% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,8.311158938412056,1.9499167218149254,100*(exp(b - se^2/2) - 1),reported,0.024249047430402546,0.0054560356718405725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.444444444444445,33573.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c3,2,3,10,Grocery store distance 400-800 meters,commercial_distance,D_location,dummy,binary (400-800 m of a grocery store site; ref > 800 m),0.076,0.017,,,***,,,high,Distance-band dummy; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.584,se,"Excludes properties with top 5% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.880667532927976,1.8342363760673828,100*(exp(b - se^2/2) - 1),reported,0.02474929777956894,0.005536027134903579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.470588235294117,32609.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c1,2,1,10,Grocery store distance 0-399.9 m*Post Grocery store,commercial_distance,D_location,other,binary (0-399.9 m band x post-opening indicator),0.073,0.024,,,***,,,high,DiD interaction: effect of the store opening on homes within 399.9 m; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.589,se,"All observations; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.073,0.024,not harmonised,reported,0.016532689040359927,0.005435404616008743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0416666666666665,33839.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c2,2,2,10,Grocery store distance 0-399.9 m*Post Grocery store,commercial_distance,D_location,other,binary (0-399.9 m band x post-opening indicator),0.07,0.024,,,***,,,high,DiD interaction: effect of the store opening on homes within 399.9 m; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.599,se,"Excludes properties with top 1% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.07,0.024,not harmonised,reported,0.01591610178604237,0.005456949183785954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.916666666666667,33573.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c3,2,3,10,Grocery store distance 0-399.9 m*Post Grocery store,commercial_distance,D_location,other,binary (0-399.9 m band x post-opening indicator),0.071,0.024,,,***,,,high,DiD interaction: effect of the store opening on homes within 399.9 m; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.584,se,"Excludes properties with top 5% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.071,0.024,not harmonised,reported,0.016380233773485603,0.005536980430474007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.958333333333333,32609.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c1,2,1,10,Grocery store distance 400-800 m*Post Grocery store,commercial_distance,D_location,other,binary (400-800 m band x post-opening indicator),0.039,0.019,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.589,se,"All observations; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.039,0.019,not harmonised,reported,0.011157713623922733,0.005435809201398255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0526315789473686,33839.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c2,2,2,10,Grocery store distance 400-800 m*Post Grocery store,commercial_distance,D_location,other,binary (400-800 m band x post-opening indicator),0.039,0.019,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.599,se,"Excludes properties with top 1% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.039,0.019,not harmonised,reported,0.011201822360830081,0.005457298073224911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0526315789473686,33573.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T2-c3,2,3,10,Grocery store distance 400-800 m*Post Grocery store,commercial_distance,D_location,other,binary (400-800 m band x post-opening indicator),0.037,0.018,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.584,se,"Excludes properties with top 5% prices; 800 m split into two buffers (0-399.9, 400-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.037,0.018,not harmonised,reported,0.011382360688189586,0.005537364659119257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.055555555555556,32609.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 0-199.9 meters,commercial_distance,D_location,dummy,binary (0-199.9 m; ref > 800 m),0.10366,0.06349,,,,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,10.698987788569191,7.042458445287401,100*(exp(b - se^2/2) - 1),reported,0.00887523807946204,0.0054359334908840925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6326980626870373,33839.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 0-199.9 meters,commercial_distance,D_location,dummy,binary (0-199.9 m; ref > 800 m),0.10062,0.06409,,,,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,10.358749914002829,7.087433260645231,100*(exp(b - se^2/2) - 1),reported,0.00856807036394695,0.005457440167216855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.569979716024341,33573.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 0-199.9 meters,commercial_distance,D_location,dummy,binary (0-199.9 m; ref > 800 m),0.09851,0.06452,,,,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,10.123093370813962,7.119946136257979,100*(exp(b - se^2/2) - 1),reported,0.008454768040060666,0.005537525468934261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5268133911965283,32609.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 200-399.9 meters,commercial_distance,D_location,dummy,binary (200-399.9 m; ref > 800 m),0.07078,0.0284,,,**,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.291229264990173,3.0482999816958927,100*(exp(b - se^2/2) - 1),reported,0.01354701473481514,0.005435648749205285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4922535211267602,33839.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 200-399.9 meters,commercial_distance,D_location,dummy,binary (200-399.9 m; ref > 800 m),0.06747,0.02844,,,**,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,6.936560829345817,3.0425059817478397,100*(exp(b - se^2/2) - 1),reported,0.012946418562846182,0.005457183102524758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3723628691983123,33573.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 200-399.9 meters,commercial_distance,D_location,dummy,binary (200-399.9 m; ref > 800 m),0.06674,0.02763,,,**,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,6.860952234607365,2.9536953455630184,100*(exp(b - se^2/2) - 1),reported,0.013375121234434201,0.005537228044762016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4154904089757507,32609.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 400-599.9 meters,commercial_distance,D_location,dummy,binary (400-599.9 m; ref > 800 m),0.0749,0.02149,,,***,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.752752719480394,2.316141414528527,100*(exp(b - se^2/2) - 1),reported,0.01894343376999218,0.005435172119053832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4853420195439737,33839.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 400-599.9 meters,commercial_distance,D_location,dummy,binary (400-599.9 m; ref > 800 m),0.0705,0.02099,,,***,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.280821442314478,2.2523205512350666,100*(exp(b - se^2/2) - 1),reported,0.018327728803278546,0.005456723795472578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3587422582181987,33573.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 400-599.9 meters,commercial_distance,D_location,dummy,binary (400-599.9 m; ref > 800 m),0.0713,0.02115,,,***,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,7.366318484544676,2.271305582187701,100*(exp(b - se^2/2) - 1),reported,0.01866529043380363,0.005536758663042732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3711583924349884,32609.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 600-800 meters,commercial_distance,D_location,dummy,binary (600-800 m; ref > 800 m),0.0845,0.02107,,,***,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,8.793132664586324,2.2927801827968324,100*(exp(b - se^2/2) - 1),reported,0.021796172070951393,0.0054348561601768736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0104413858566685,33839.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 600-800 meters,commercial_distance,D_location,dummy,binary (600-800 m; ref > 800 m),0.0845,0.02056,,,***,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,8.794287580535176,2.237283367741,100*(exp(b - se^2/2) - 1),reported,0.022424837176424125,0.0054562680751157385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.109922178988327,33573.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 600-800 meters,commercial_distance,D_location,dummy,binary (600-800 m; ref > 800 m),0.0788,0.02026,,,***,,,high,Distance-ring dummy; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",pct_premium,8.176586514528882,2.192107491171151,100*(exp(b - se^2/2) - 1),reported,0.021533633747108843,0.005536439336502858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.889437314906219,32609.0,binary,,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,True,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 0-199.9 m*Post Grocery store,commercial_distance,D_location,other,binary (0-199.9 m ring x post-opening),0.09786,0.0584,,,+,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.09786,0.0584,not harmonised,reported,0.009108892699653677,0.0054359220688716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6756849315068494,33839.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 0-199.9 m*Post Grocery store,commercial_distance,D_location,other,binary (0-199.9 m ring x post-opening),0.09685,0.05965,,,,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.09685,0.05965,not harmonised,reported,0.00886088401300712,0.005457426240329114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6236378876781223,33573.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 0-199.9 m*Post Grocery store,commercial_distance,D_location,other,binary (0-199.9 m ring x post-opening),0.09464,0.05956,,,,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.09464,0.05956,not harmonised,reported,0.008799023738224571,0.005537509022069479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5889858965748824,32609.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 200-399.9 m*Post Grocery store,commercial_distance,D_location,other,binary (200-399.9 m ring x post-opening),0.06805,0.02634,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.06805,0.02634,not harmonised,reported,0.014043028317798746,0.005435611548726215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5835231586940015,33839.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 200-399.9 m*Post Grocery store,commercial_distance,D_location,other,binary (200-399.9 m ring x post-opening),0.06516,0.02617,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.06516,0.02617,not harmonised,reported,0.013587582181067712,0.005457136674010774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4898739014138327,33573.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 200-399.9 m*Post Grocery store,commercial_distance,D_location,other,binary (200-399.9 m ring x post-opening),0.06703,0.02598,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.06703,0.02598,not harmonised,reported,0.014286209314815673,0.0055371582574803985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5800615858352582,32609.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 400-599.9 m*Post Grocery store,commercial_distance,D_location,other,binary (400-599.9 m ring x post-opening),0.01695,0.02448,,,,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.01695,0.02448,not harmonised,reported,0.003763972592226152,0.005436109088949629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6924019607843138,33839.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 400-599.9 m*Post Grocery store,commercial_distance,D_location,other,binary (400-599.9 m ring x post-opening),0.01561,0.02377,,,,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.01561,0.02377,not harmonised,reported,0.003584064829698721,0.005457605445351608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6567101388304586,33573.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 400-599.9 m*Post Grocery store,commercial_distance,D_location,other,binary (400-599.9 m ring x post-opening),0.01632,0.02409,,,,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.01632,0.02409,not harmonised,reported,0.003751557073171676,0.005537684429700102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6774595267745953,32609.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c1,3,1,11,Grocery store distance 600-800 m*Post Grocery store,commercial_distance,D_location,other,binary (600-800 m ring x post-opening),0.05274,0.02271,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33849.0,0.58925,se,"All observations; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.05274,0.02271,not harmonised,reported,0.012623495375677958,0.005435714447888631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.322324966974901,33839.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c2,3,2,11,Grocery store distance 600-800 m*Post Grocery store,commercial_distance,D_location,other,binary (600-800 m ring x post-opening),0.05241,0.02231,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,33583.0,0.59944,se,"Excludes properties with top 1% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.05241,0.02231,not harmonised,reported,0.012819875963932302,0.005457192000674102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3491707754370235,33573.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06,90decb41b454a38dee63486b408022b766c2dc06:T3-c3,3,3,11,Grocery store distance 600-800 m*Post Grocery store,commercial_distance,D_location,other,binary (600-800 m ring x post-opening),0.04979,0.022,,,**,,,high,DiD interaction; clustered (block group) robust SE,log sale price,log,OLS,32619.0,0.58413,se,"Excludes properties with top 5% prices; four 200 m rings (0-199.9, 200-399.9, 400-599.9, 600-800 m); year FE, housing and neighbourhood controls included (not reported); SE clustered by block group",other,0.04979,0.022,not harmonised,reported,0.01253189073950702,0.005537288537239495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.263181818181818,32609.0,binary,nonstandard_x_transform,2017.0,United States,North America,High income,1988.0,2011.0,single_family,transaction,journal,True,False,clustered,,1999.5,ols,journal,False,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1988,4,1988,12,AGE,age,A_structural,level,years,-0.02119,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,260.0,,p,listings in 1988,semi_elasticity,-2.119,,100*b/1 (per year),,,,,,250.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1988,4,1988,12,BATH,bathrooms,A_structural,level,count,0.08929,,,0.02,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,260.0,,p,listings in 1988,semi_elasticity,8.929,3.838204981996276,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.1455640394806681,0.06257191415995104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3263478740408408,250.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1988,4,1988,12,AREA,living_area,A_structural,level,m2,0.007949,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,260.0,,p,listings in 1988,semi_elasticity,0.7948999999999999,,100*b/1 (X per m2->m2),,,,,,250.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1988,4,1988,12,DIST,cbd_distance,D_location,level,m,-0.0003853,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,260.0,,p,listings in 1988,semi_elasticity,-38.53,,100*b/0.001 (X per m->km),,,,,,250.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1991,4,1991,12,AGE,age,A_structural,level,years,-0.007828,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,247.0,,p,listings in 1991,semi_elasticity,-0.7827999999999999,,100*b/1 (per year),,,,,,237.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1991,4,1991,12,BATH,bathrooms,A_structural,level,count,0.1176,,,0.0,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,247.0,,p,listings in 1991,semi_elasticity,11.76,,100*b/1 (per unit count),,,,,,237.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1991,4,1991,12,AREA,living_area,A_structural,level,m2,0.005782,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,247.0,,p,listings in 1991,semi_elasticity,0.5781999999999999,,100*b/1 (X per m2->m2),,,,,,237.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1991,4,1991,12,DIST,cbd_distance,D_location,level,m,-0.0002532,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,247.0,,p,listings in 1991,semi_elasticity,-25.319999999999997,,100*b/0.001 (X per m->km),,,,,,237.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1991,4,1991,12,REHAB,condition,A_structural,binary,binary,-0.1517,,,0.0,,,,high,=1 if apartment must be remodeled; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,247.0,,p,listings in 1991,pct_premium,-14.075398411631578,,100*(exp(b) - 1),,,,,,237.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1995,4,1995,12,AGE,age,A_structural,level,years,-0.008497,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,293.0,,p,listings in 1995,semi_elasticity,-0.8496999999999999,,100*b/1 (per year),,,,,,283.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1995,4,1995,12,BATH,bathrooms,A_structural,level,count,0.06929,,,0.0,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,293.0,,p,listings in 1995,semi_elasticity,6.929,,100*b/1 (per unit count),,,,,,283.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1995,4,1995,12,AREA,living_area,A_structural,level,m2,0.008361,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,293.0,,p,listings in 1995,semi_elasticity,0.8361000000000001,,100*b/1 (X per m2->m2),,,,,,283.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1995,4,1995,12,DIST,cbd_distance,D_location,level,m,-0.0002111,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,293.0,,p,listings in 1995,semi_elasticity,-21.11,,100*b/0.001 (X per m->km),,,,,,283.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1995,4,1995,12,ELEV,accessibility,B_interior,binary,binary,0.1552,,,0.0,,,,high,=1 if building has an elevator; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,293.0,,p,listings in 1995,pct_premium,16.78915160520622,,100*(exp(b) - 1),,,,,,283.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-1995,4,1995,12,HEAT,heating,A_structural,binary,binary,0.07528,,,0.0,,,,high,=1 if central heating; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,293.0,,p,listings in 1995,pct_premium,7.818600070388193,,100*(exp(b) - 1),,,,,,283.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,AGE,age,A_structural,level,years,-0.006176,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,semi_elasticity,-0.6176,,100*b/1 (per year),,,,,,197.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,BATH,bathrooms,A_structural,level,count,0.09256,,,0.0,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,semi_elasticity,9.256,,100*b/1 (per unit count),,,,,,197.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,AREA,living_area,A_structural,level,m2,0.005951,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,semi_elasticity,0.5951,,100*b/1 (X per m2->m2),,,,,,197.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,DIST,cbd_distance,D_location,level,m,-0.0002257,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,semi_elasticity,-22.57,,100*b/0.001 (X per m->km),,,,,,197.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,REHAB,condition,A_structural,binary,binary,-0.06733,,,0.008,,,,high,=1 if apartment must be remodeled; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,pct_premium,-6.541459777068381,2.373463818258092,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.18566679430835734,0.0700082606253947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.652069807902196,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,ELEV,accessibility,B_interior,binary,binary,0.1187,,,0.0,,,,high,=1 if building has an elevator; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,pct_premium,12.60320579944445,,100*(exp(b) - 1),,,,,,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,HEAT,heating,A_structural,binary,binary,0.04679,,,0.06,,,,high,=1 if central heating; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,pct_premium,4.75777010003946,2.606949052335213,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.13281388079565434,0.07061587205531039,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.880793608151251,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,FLOOR,floor_level,A_structural,binary,binary,-0.1056,,,0.059,,,,high,=1 if apartment on a low floor; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,pct_premium,-10.162157016370067,5.032177599188474,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.13332714827111183,0.07061096216119284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8881933369885062,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T4-2005,4,2005,12,SPORT,recreation_private,C_exterior,binary,binary,0.1278,,,0.011,,,,high,=1 if complex has sports facilities; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,other,207.0,,p,listings in 2005,pct_premium,13.489131424805855,5.711349895112377,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.1782582860176834,0.07010593809132837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.542698819399048,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,other,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1988,6,1988,14,AGE,age,A_structural,level,years,-0.02033,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,260.0,0.8176,p,listings in 1988,semi_elasticity,-2.033,,100*b/1 (per year),,,,,,250.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1988,6,1988,14,BATH,bathrooms,A_structural,level,count,0.1269,,,0.004,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,260.0,0.8176,p,listings in 1988,semi_elasticity,12.690000000000001,4.409064239728263,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.17908804655265503,0.06222306554910177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.878161739095483,250.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1988,6,1988,14,AREA,living_area,A_structural,level,m2,0.008506,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,260.0,0.8176,p,listings in 1988,semi_elasticity,0.8506,,100*b/1 (X per m2->m2),,,,,,250.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1988,6,1988,14,DIST,cbd_distance,D_location,level,m,-0.0003521,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,260.0,0.8176,p,listings in 1988,semi_elasticity,-35.21,,100*b/0.001 (X per m->km),,,,,,250.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1991,6,1991,14,AGE,age,A_structural,level,years,-0.006484,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,247.0,0.8278,p,listings in 1991,semi_elasticity,-0.6484,,100*b/1 (per year),,,,,,237.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1991,6,1991,14,BATH,bathrooms,A_structural,level,count,0.1788,,,0.0,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,247.0,0.8278,p,listings in 1991,semi_elasticity,17.88,,100*b/1 (per unit count),,,,,,237.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1991,6,1991,14,AREA,living_area,A_structural,level,m2,0.005766,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,247.0,0.8278,p,listings in 1991,semi_elasticity,0.5766,,100*b/1 (X per m2->m2),,,,,,237.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1991,6,1991,14,DIST,cbd_distance,D_location,level,m,-0.000251,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,247.0,0.8278,p,listings in 1991,semi_elasticity,-25.099999999999998,,100*b/0.001 (X per m->km),,,,,,237.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1991,6,1991,14,REHAB,condition,A_structural,binary,binary,-0.1981,,,0.0,,,,high,=1 if apartment must be remodeled; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,247.0,0.8278,p,listings in 1991,pct_premium,-17.971217974577026,,100*(exp(b) - 1),,,,,,237.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1995,6,1995,14,AGE,age,A_structural,level,years,-0.01035,,,0.0,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,293.0,0.9234,p,listings in 1995,semi_elasticity,-1.035,,100*b/1 (per year),,,,,,283.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1995,6,1995,14,BATH,bathrooms,A_structural,level,count,0.09536,,,0.002,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,293.0,0.9234,p,listings in 1995,semi_elasticity,9.536,3.0858521480624743,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.18067223701239754,0.05846558417365346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,283.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1995,6,1995,14,AREA,living_area,A_structural,level,m2,0.01188,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,293.0,0.9234,p,listings in 1995,semi_elasticity,1.188,,100*b/1 (X per m2->m2),,,,,,283.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1995,6,1995,14,DIST,cbd_distance,D_location,level,m,-0.0002685,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,293.0,0.9234,p,listings in 1995,semi_elasticity,-26.85,,100*b/0.001 (X per m->km),,,,,,283.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1995,6,1995,14,ELEV,accessibility,B_interior,binary,binary,0.254,,,0.0,,,,high,=1 if building has an elevator; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,293.0,0.9234,p,listings in 1995,pct_premium,28.91718042678042,,100*(exp(b) - 1),,,,,,283.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-1995,6,1995,14,HEAT,heating,A_structural,binary,binary,0.155,,,0.0,,,,high,=1 if central heating; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,293.0,0.9234,p,listings in 1995,pct_premium,16.765796110512497,,100*(exp(b) - 1),,,,,,283.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,AGE,age,A_structural,level,years,-0.006583,,,0.001,,,,high,age of apartment in years adjusted for major rehabilitation; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,semi_elasticity,-0.6583,0.20005915578796496,100*b/1 (per year),|b/z(p)| (two-sided normal),-0.22825160886749504,0.06936628311905792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,197.0,years,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,BATH,bathrooms,A_structural,level,count,0.1107,,,0.002,,,,high,; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,semi_elasticity,11.07,3.582254957954236,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.21502007891332287,0.06958055499069214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0902323061678127,197.0,count,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,AREA,living_area,A_structural,level,m2,0.006125,,,0.0,,,,high,floor area in m2; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,semi_elasticity,0.6125,,100*b/1 (X per m2->m2),,,,,,197.0,m2,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,DIST,cbd_distance,D_location,level,m,-0.0002139,,,0.0,,,,high,Euclidean distance in metres from the CBD; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,semi_elasticity,-21.39,,100*b/0.001 (X per m->km),,,,,,197.0,m,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,REHAB,condition,A_structural,binary,binary,-0.06925,,,0.048,,,,high,=1 if apartment must be remodeled; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,pct_premium,-6.747866232221423,3.2678137000642917,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.13950405581764838,0.07055036068615331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9773684281819468,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,ELEV,accessibility,B_interior,binary,binary,0.1875,,,0.0,,,,high,=1 if building has an elevator; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,pct_premium,20.623024942098066,,100*(exp(b) - 1),,,,,,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,False,False,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,HEAT,heating,A_structural,binary,binary,0.07201,,,0.053,,,,high,=1 if central heating; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,pct_premium,7.392212591811886,3.9994763710419585,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.13656582167783368,0.07057953631142908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9349209248873929,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,FLOOR,floor_level,A_structural,binary,binary,-0.1482,,,0.079,,,,high,=1 if apartment on a low floor; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,pct_premium,-14.080491199404154,7.275002649581315,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.12417808381232374,0.0706955940836923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.756518004011915,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,False
91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3,91a343b71c11d4139d2606d6c4d1c5fc64cbc6c3:T6-2005,6,2005,14,SPORT,recreation_private,C_exterior,binary,binary,0.1376,,,0.041,,,,high,=1 if complex has sports facilities; only p-value printed (no SE),log listing price (PRICE in 1000 euros),log,OLS,207.0,0.7745,p,listings in 2005,pct_premium,14.4918018527753,7.726740649944538,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.14407642339463456,0.0705036984385339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0435300074398564,197.0,binary,,2019.0,Spain,Europe,High income,1988.0,2005.0,condo_apartment,listing,journal,False,False,unknown,,1996.5,ols,journal,True,True,True
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",22,Energy,public_services,F_neighbourhood,level,currency (COP per month),3.55e-06,1.28e-06,,,,,,high,"Monthly energy (electricity) subsidy, Colombian pesos; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,0.000355,0.000128,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.03813319484906329,0.013749433635718592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7734375,5282.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",22,Water and sewerage,public_services,F_neighbourhood,level,currency (COP per month),2.41e-06,5.32e-07,,,,,,high,"Monthly piped water and sewerage subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,0.00024099999999999998,5.3200000000000006e-05,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.06221057105183913,0.013732789958331295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.530075187969924,5282.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",22,Piped gas,public_services,F_neighbourhood,level,currency (COP per month),-2.91e-06,4.75e-06,,,,,,high,"Monthly piped gas subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,-0.000291,0.00047500000000000005,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,-0.008429168817445539,0.01375895253706746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6126315789473684,5282.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",22,Energy squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,1.1e-10,3.91e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",other,1.1e-10,3.91e-11,not harmonised,reported,0.038680456879316365,0.013749144218011544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.813299232736573,5282.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",22,Water and sewerage squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,4.58e-13,7.22e-12,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",other,4.58e-13,7.22e-12,not harmonised,reported,0.0008728284959269394,0.013759436114830794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0634349030470914,5282.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",22,Piped gas squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,6.32e-10,7.05e-10,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",other,6.32e-10,7.05e-10,not harmonised,reported,0.01233376665073658,0.013758394760710902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8964539007092198,5282.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cB,6,"B (whole sample, eq. 2)",22,Energy,public_services,F_neighbourhood,level,currency (COP per month),3.51e-06,1.22e-06,,,,,,high,"Monthly energy (electricity) subsidy, Colombian pesos; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8823,se,Whole sample with stratum-boundary dummies (RD design); only subsidy coefficients reported,semi_elasticity,0.00035099999999999997,0.000122,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.03955560784984188,0.013748672813905157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8770491803278686,5282.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cB,6,"B (whole sample, eq. 2)",22,Water and sewerage,public_services,F_neighbourhood,level,currency (COP per month),2.07e-06,5.59e-07,,,,,,high,"Monthly piped water and sewerage subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8823,se,Whole sample with stratum-boundary dummies (RD design); only subsidy coefficients reported,semi_elasticity,0.00020700000000000002,5.59e-05,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.050885768430482856,0.013741615725913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7030411449016105,5282.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cB,6,"B (whole sample, eq. 2)",22,Piped gas,public_services,F_neighbourhood,level,currency (COP per month),-2.17e-07,4.61e-06,,,,,,high,"Monthly piped gas subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8823,se,Whole sample with stratum-boundary dummies (RD design); only subsidy coefficients reported,semi_elasticity,-2.17e-05,0.000461,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,-0.0006476785570500434,0.013759438470049307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.047071583514099784,5282.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cB,6,"B (whole sample, eq. 2)",22,Energy squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,1.02e-10,3.64e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8823,se,Whole sample with stratum-boundary dummies (RD design); only subsidy coefficients reported,other,1.02e-10,3.64e-11,not harmonised,reported,0.03852804873120411,0.013749225233488527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.802197802197802,5282.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cB,6,"B (whole sample, eq. 2)",22,Water and sewerage squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,-3.63e-14,8.48e-12,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8823,se,Whole sample with stratum-boundary dummies (RD design); only subsidy coefficients reported,other,-3.63e-14,8.48e-12,not harmonised,reported,-5.889949532507933e-05,0.013759441332139744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.004280660377358491,5282.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cB,6,"B (whole sample, eq. 2)",22,Piped gas squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,2.19e-10,6.89e-10,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8823,se,Whole sample with stratum-boundary dummies (RD design); only subsidy coefficients reported,other,2.19e-10,6.89e-10,not harmonised,reported,0.00437342356925218,0.01375930976810389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3178519593613933,5282.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cC,6,C (500m),22,Energy,public_services,F_neighbourhood,level,currency (COP per month),5.21e-06,1.83e-06,,,,,,high,"Monthly energy (electricity) subsidy, Colombian pesos; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,2085.0,0.9049,se,Houses within 500 m of each other across a stratum boundary (~250 m from boundary) with boundary dummies; only subsidy coefficients reported,semi_elasticity,0.000521,0.000183,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.062377937875054315,0.02191010101945286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8469945355191255,2075.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cC,6,C (500m),22,Water and sewerage,public_services,F_neighbourhood,level,currency (COP per month),2.13e-06,9.1e-07,,,,,,high,"Monthly piped water and sewerage subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,2085.0,0.9049,se,Houses within 500 m of each other across a stratum boundary (~250 m from boundary) with boundary dummies; only subsidy coefficients reported,semi_elasticity,0.000213,9.1e-05,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.051316446548279465,0.021923927868044278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.340659340659341,2075.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cC,6,C (500m),22,Piped gas,public_services,F_neighbourhood,level,currency (COP per month),-1.34e-06,6.7e-06,,,,,,high,"Monthly piped gas subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,2085.0,0.9049,se,Houses within 500 m of each other across a stratum boundary (~250 m from boundary) with boundary dummies; only subsidy coefficients reported,semi_elasticity,-0.000134,0.00067,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,-0.0043905280814486025,0.02195264040724301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,2075.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cC,6,C (500m),22,Energy squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,1.1e-10,5.05e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,2085.0,0.9049,se,Houses within 500 m of each other across a stratum boundary (~250 m from boundary) with boundary dummies; only subsidy coefficients reported,other,1.1e-10,5.05e-11,not harmonised,reported,0.04776351732668702,0.021927796590888136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.178217821782178,2075.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cC,6,C (500m),22,Water and sewerage squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,-8.76e-12,2.02e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,2085.0,0.9049,se,Houses within 500 m of each other across a stratum boundary (~250 m from boundary) with boundary dummies; only subsidy coefficients reported,other,-8.76e-12,2.02e-11,not harmonised,reported,-0.009519716306656071,0.02195185723680966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4336633663366337,2075.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cC,6,C (500m),22,Piped gas squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,2.49e-09,1.09e-09,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,2085.0,0.9049,se,Houses within 500 m of each other across a stratum boundary (~250 m from boundary) with boundary dummies; only subsidy coefficients reported,other,2.49e-09,1.09e-09,not harmonised,reported,0.05008623328298425,0.021925298907009172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2844036697247705,2075.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cA,7,"A (all sample, eq. 1)",25,Energy,public_services,F_neighbourhood,level,currency (COP per month),1.59e-06,1.23e-06,,,,,,high,"Monthly energy (electricity) subsidy, Colombian pesos; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8741,se,"IV: stratum instrumented to correct measurement error; whole sample, no boundary dummies",semi_elasticity,0.00015900000000000002,0.000123,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.01801892211869527,0.013939166167292563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2926829268292683,5145.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cA,7,"A (all sample, eq. 1)",25,Water and sewerage,public_services,F_neighbourhood,level,currency (COP per month),3.01e-06,6.04e-07,,,,,,high,"Monthly piped water and sewerage subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8741,se,"IV: stratum instrumented to correct measurement error; whole sample, no boundary dummies",semi_elasticity,0.000301,6.04e-05,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.06930925455708567,0.013907903572252404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.983443708609272,5145.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cA,7,"A (all sample, eq. 1)",25,Piped gas,public_services,F_neighbourhood,level,currency (COP per month),3.29e-06,4.68e-06,,,,,,high,"Monthly piped gas subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8741,se,"IV: stratum instrumented to correct measurement error; whole sample, no boundary dummies",semi_elasticity,0.000329,0.000468,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.009800235197020812,0.013940760097889787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7029914529914529,5145.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cA,7,"A (all sample, eq. 1)",25,Energy squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,7.76e-11,3.87e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8741,se,"IV: stratum instrumented to correct measurement error; whole sample, no boundary dummies",other,7.76e-11,3.87e-11,not harmonised,reported,0.027943991299762806,0.013935985351814702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0051679586563305,5145.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cA,7,"A (all sample, eq. 1)",25,Water and sewerage squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,-1.76e-11,1.2e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8741,se,"IV: stratum instrumented to correct measurement error; whole sample, no boundary dummies",other,-1.76e-11,1.2e-11,not harmonised,reported,-0.020443156931120806,0.013938516089400553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4666666666666666,5145.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cA,7,"A (all sample, eq. 1)",25,Piped gas squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,1.8e-09,7.34e-10,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8741,se,"IV: stratum instrumented to correct measurement error; whole sample, no boundary dummies",other,1.8e-09,7.34e-10,not harmonised,reported,0.03416882828100005,0.013933288865696686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4523160762942777,5145.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cB,7,"B (all sample, eq. 2)",25,Energy,public_services,F_neighbourhood,level,currency (COP per month),1.63e-06,1.21e-06,,,,,,high,"Monthly energy (electricity) subsidy, Colombian pesos; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8837,se,"IV with boundary dummies, whole sample",semi_elasticity,0.000163,0.000121,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.018777292341518585,0.013938971615483122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3471074380165289,5145.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cB,7,"B (all sample, eq. 2)",25,Water and sewerage,public_services,F_neighbourhood,level,currency (COP per month),2.67e-06,5.99e-07,,,,,,high,"Monthly piped water and sewerage subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8837,se,"IV with boundary dummies, whole sample",semi_elasticity,0.000267,5.99e-05,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.06202328969435015,0.01391458821232799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.457429048414023,5145.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cB,7,"B (all sample, eq. 2)",25,Piped gas,public_services,F_neighbourhood,level,currency (COP per month),3.7e-06,4.64e-06,,,,,,high,"Monthly piped gas subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8837,se,"IV with boundary dummies, whole sample",semi_elasticity,0.00037,0.00046399999999999995,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.011116401354564088,0.01394056818518307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7974137931034484,5145.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cB,7,"B (all sample, eq. 2)",25,Energy squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,8.79e-11,3.63e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8837,se,"IV with boundary dummies, whole sample",other,8.79e-11,3.63e-11,not harmonised,reported,0.03373977834950549,0.013933492082901582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4214876033057853,5145.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cB,7,"B (all sample, eq. 2)",25,Water and sewerage squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,-1.89e-11,1.18e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8837,se,"IV with boundary dummies, whole sample",other,-1.89e-11,1.18e-11,not harmonised,reported,-0.022324351874935054,0.013937955138848343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6016949152542372,5145.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cB,7,"B (all sample, eq. 2)",25,Piped gas squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,1.51e-09,7.15e-10,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,5155.0,0.8837,se,"IV with boundary dummies, whole sample",other,1.51e-09,7.15e-10,not harmonised,reported,0.029429986168598012,0.013935390801687141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1118881118881117,5145.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cC,7,C (500m),25,Energy,public_services,F_neighbourhood,level,currency (COP per month),4.78e-06,1.97e-06,,,,,,high,"Monthly energy (electricity) subsidy, Colombian pesos; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,2002.0,0.9057,se,"IV with boundary dummies, houses within 500 m across boundary (preferred RD estimate)",semi_elasticity,0.000478,0.00019700000000000002,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.05428454059380722,0.02237249894765695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4263959390862944,1992.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cC,7,C (500m),25,Water and sewerage,public_services,F_neighbourhood,level,currency (COP per month),2.42e-06,9.35e-07,,,,,,high,"Monthly piped water and sewerage subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,2002.0,0.9057,se,"IV with boundary dummies, houses within 500 m across boundary (preferred RD estimate)",semi_elasticity,0.000242,9.350000000000001e-05,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,0.05789353408034545,0.022367956349224376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.588235294117647,1992.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cC,7,C (500m),25,Piped gas,public_services,F_neighbourhood,level,currency (COP per month),-6.44e-06,7.16e-06,,,,,,high,"Monthly piped gas subsidy, COP; utility cross-subsidy received by the household (negative = contribution)","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,2002.0,0.9057,se,"IV with boundary dummies, houses within 500 m across boundary (preferred RD estimate)",semi_elasticity,-0.000644,0.0007160000000000001,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),reported,-0.020148374181405165,0.022400987443922514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8994413407821229,1992.0,currency,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cC,7,C (500m),25,Energy squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,4.42e-11,5.81e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,2002.0,0.9057,se,"IV with boundary dummies, houses within 500 m across boundary (preferred RD estimate)",other,4.42e-11,5.81e-11,not harmonised,reported,0.01704269961964395,0.02240228162672655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7607573149741824,1992.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cC,7,C (500m),25,Water and sewerage squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,-9.47e-12,2.17e-11,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,2002.0,0.9057,se,"IV with boundary dummies, houses within 500 m across boundary (preferred RD estimate)",other,-9.47e-12,2.17e-11,not harmonised,reported,-0.00977743231585876,0.02240446475756442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43640552995391707,1992.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T7-cC,7,C (500m),25,Piped gas squared,public_services,F_neighbourhood,squared,currency (COP per month) squared,2.06e-09,1.06e-09,,,,,,high,Quadratic term of the monthly utility subsidy; utility cross-subsidy received by the household (negative = contribution),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,IV,2002.0,0.9057,se,"IV with boundary dummies, houses within 500 m across boundary (preferred RD estimate)",other,2.06e-09,1.06e-09,not harmonised,reported,0.04350161409144148,0.02238432569753785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9433962264150941,1992.0,currency sqd,nonstandard_x_transform,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,iv,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Number of rooms,bedrooms,A_structural,level,count,0.0182,,4.9,,,,,high,Total number of rooms,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,1.82,0.37142857142857144,100*b/1 (per unit count),|b/t|,0.06726854714404812,0.013728274927356758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9,5282.0,count,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Number of bathrooms,bathrooms,A_structural,level,count,0.1071,,15.9,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,10.71,0.6735849056603773,100*b/1 (per unit count),|b/t|,0.21372029543539323,0.013441528014804605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.9,5282.0,count,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with piped gas service,other,other,binary,binary,0.0187,,1.9,,,,,high,Piped natural gas connection,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,1.8826593430249616,1.002788425139599,100*(exp(b - se^2/2) - 1),|b/t|,0.026134009415147215,0.013754741797445902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with telephone,other,other,binary,binary,-0.0101,,-0.6,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-1.018941313285593,1.6664172367550603,100*(exp(b - se^2/2) - 1),|b/t|,-0.008255383491432412,0.013758972485720687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with garden,garden,C_exterior,binary,binary,0.0193,,2.2,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,1.9448219478356954,0.8943685347867841,100*(exp(b - se^2/2) - 1),|b/t|,0.030256911657151206,0.013753141662341456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with courtyard,terrace_balcony,C_exterior,binary,binary,0.0269,,1.2,,,,,high,Courtyard (patio),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,2.700699962590636,2.302785867760399,100*(exp(b - se^2/2) - 1),|b/t|,0.016509079394883736,0.013757566162403114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with garage,garage,A_structural,binary,binary,0.0631,,6.7,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,6.508611120190966,1.0031330479235536,100*(exp(b - se^2/2) - 1),|b/t|,0.09179899747248786,0.01370134290634147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.7,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with terrace,terrace_balcony,C_exterior,binary,binary,-0.0139,,-1.4,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-1.3852447752106034,0.9791519006697407,100*(exp(b - se^2/2) - 1),|b/t|,-0.019259644876568793,0.013756889197549138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Parks in neighborhood,green_space,E_environment,binary,binary,0.0722,,5.6,,,,,high,Presence of parks in the neighbourhood (ECV2003 household report),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,7.478096477070095,1.3858149181514536,100*(exp(b - se^2/2) - 1),|b/t|,0.07682514807761463,0.013718776442431187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with damage due to a natural disaster,other,other,binary,binary,0.0042,,0.1,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,0.332351065509906,4.2176770959161,100*(exp(b - se^2/2) - 1),|b/t|,0.0013759428331184664,0.013759428331184664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House in area vulnerable to natural disasters,other,other,binary,binary,-0.0415,,-1.7,,,,,high,Natural hazard exposure (not specific to flood),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-4.093647664135213,2.3419410245100645,100*(exp(b - se^2/2) - 1),|b/t|,-0.02338465382585593,0.013755678721091725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Factories in neighborhood,hazard_site,E_environment,binary,binary,-0.0131,,-1.1,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-1.308455629454075,1.1754099230182498,100*(exp(b - se^2/2) - 1),|b/t|,-0.015133652183149209,0.013757865621044734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Garbage collector in neighborhood,hazard_site,E_environment,binary,binary,-0.0728,,-2.6,,,,,high,Garbage dump/collection site in neighbourhood,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-7.057763620026625,2.603402952598151,100*(exp(b - se^2/2) - 1),|b/t|,-0.035751677016432254,0.013750645006320096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Market places in neighborhood,commercial_distance,D_location,binary,binary (presence in neighbourhood),0.0127,,0.7,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,1.2614316051326124,1.8374740772921496,100*(exp(b - se^2/2) - 1),|b/t|,0.009631162228261011,0.013758803183230018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Airport in neighborhood,airport_distance,D_location,binary,binary (presence in neighbourhood),-0.0315,,-1.3,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-3.1293463712438463,2.347939625253292,100*(exp(b - se^2/2) - 1),|b/t|,-0.01788441289187469,0.013757240686057453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Terminals of ground transportation in neighborhood,transit_distance,D_location,binary,binary (presence in neighbourhood),0.047,,2.2,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,4.788285232765377,2.2391697466701737,100*(exp(b - se^2/2) - 1),|b/t|,0.030256911657151206,0.013753141662341456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House close to open sewers,hazard_site,E_environment,binary,binary,-0.0556,,-4.7,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-5.414875815728504,1.119000190472492,100*(exp(b - se^2/2) - 1),|b/t|,-0.06453456925530268,0.013730759416021846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.7,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Plants of residual water treatment in neighborhood,hazard_site,E_environment,binary,binary,0.2513,,6.4,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,28.47049811657292,5.0483646602510905,100*(exp(b - se^2/2) - 1),|b/t|,0.0877209593555123,0.013706399899298794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Lines of hydrocarbon transportation in neighborhood,hazard_site,E_environment,binary,binary,0.1315,,3.8,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,13.985520123760153,3.9468614438287326,100*(exp(b - se^2/2) - 1),|b/t|,0.05221455345476955,0.013740671961781461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House close to high tension lines of electricity transmission,hazard_site,E_environment,binary,binary,-0.0084,,-0.3,,,,,high,Power lines,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-0.8753463388659677,2.7765785079846754,100*(exp(b - se^2/2) - 1),|b/t|,-0.00412779724018239,0.0137593241339413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Feeling of safety in your neighborhood,crime,F_neighbourhood,binary,binary (perceived safety),0.0121,,1.5,,,,,high,Perceived safety (inverse of crime),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,1.2140570064034462,0.8164866242654321,100*(exp(b - se^2/2) - 1),|b/t|,0.020634767554292468,0.013756511702861645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Toilet inside the house,other,other,binary,binary,-0.0453,,-0.9,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-4.549912797402122,4.810410652207479,100*(exp(b - se^2/2) - 1),|b/t|,-0.012382547818736033,0.013758386465262258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Daily supply of water,public_services,F_neighbourhood,binary,binary,0.0063,,0.2,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,0.5820750113728623,3.1699076432316935,100*(exp(b - se^2/2) - 1),|b/t|,0.0027518778513881998,0.013759389256940997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Provision of water is inside the house,other,other,binary,binary,-0.0062,,-0.1,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-0.8089105674148223,6.161678918108982,100*(exp(b - se^2/2) - 1),|b/t|,-0.0013759428331184664,0.013759428331184664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Kitchen is separate room in the house,kitchen_quality,B_interior,binary,binary,0.1054,,2.7,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,11.03086668042408,4.337619824354986,100*(exp(b - se^2/2) - 1),|b/t|,0.03712488137459201,0.013749956064663706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House,dwelling_type,A_structural,binary,binary (house vs apartment etc.),-0.1301,,-13.1,,,,,high,Dummy = 1 if living in a house (as opposed to an apartment),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-12.203567240660163,0.8719755123083267,100*(exp(b - se^2/2) - 1),|b/t|,-0.177390050350272,0.013541225217578015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.1,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,"Wall material: brick, block, stone, or polished wood",roof_materials,A_structural,binary,binary,0.0254,,0.6,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,2.480663339205802,4.342237224443861,100*(exp(b - se^2/2) - 1),|b/t|,0.008255383491432412,0.013758972485720687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,"Floor material: marmol, parquet, or lacquered wood",flooring,B_interior,binary,binary,0.0049,,0.2,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,0.4610470665023181,2.462034460348915,100*(exp(b - se^2/2) - 1),|b/t|,0.0027518778513881998,0.013759389256940997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Floor material is carpet,flooring,B_interior,binary,binary,0.0548,,2.4,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,5.605399905394215,2.411951964873815,100*(exp(b - se^2/2) - 1),|b/t|,0.03300466842795952,0.013751945178316467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,"Floor material: floor tile, vinyl, tablet, or wood",flooring,B_interior,binary,binary,0.0136,,0.7,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,1.3501599705816103,1.969460492734128,100*(exp(b - se^2/2) - 1),|b/t|,0.009631162228261011,0.013758803183230018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,"Floor material: cement, gravilla, earth, or sand",flooring,B_interior,binary,binary,-0.0971,,-4.1,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,-9.278919926878304,2.149143323593292,100*(exp(b - se^2/2) - 1),|b/t|,-0.05632415475734719,0.013737598721304195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.1,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with toilet connected to the public sewerage,public_services,F_neighbourhood,binary,binary,0.2671,,2.8,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,30.024161046562025,12.45993890355409,100*(exp(b - se^2/2) - 1),|b/t|,0.038497875457120696,0.013749241234685964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,True
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,House with potable water service,public_services,F_neighbourhood,binary,binary,0.0894,,1.0,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,8.91568012050914,9.776050705859946,100*(exp(b - se^2/2) - 1),|b/t|,0.013758139056883268,0.013758139056883268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Number of infantile shelters by censal sector,neighbourhood_amenities,F_neighbourhood,level,count,-0.0148,,-1.2,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,-1.48,1.2333333333333334,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,-0.016509079394883736,0.013757566162403114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,5282.0,count,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Number of asylums by censal sector,neighbourhood_amenities,F_neighbourhood,level,count,0.0153,,1.86,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,1.53,0.8225806451612903,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.02558418373989378,0.013754937494566548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.86,5282.0,count,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Number of prisons by censal sector,other,other,level,count,0.0479,,1.7,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,4.79,2.817647058823529,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.02338465382585593,0.013755678721091725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,5282.0,count,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,False,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Number of convents by censal sector,neighbourhood_amenities,F_neighbourhood,level,count,0.0316,,7.0,,,,,high,,"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,3.16,0.45142857142857146,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.09587242195003015,0.013696060278575735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,5282.0,count,unit_not_harmonisable,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Stratum 2,income,F_neighbourhood,dummy,binary,0.2364,,6.0,,,,,medium,"Official socioeconomic stratum of the block (1-6, base stratum 1), proxy for neighbourhood wealth","logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,26.56980902926933,4.990722671921691,100*(exp(b - se^2/2) - 1),|b/t|,0.08227674157279218,0.013712790262132029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Stratum 3,income,F_neighbourhood,dummy,binary,0.6205,,13.6,,,,,medium,Official socioeconomic stratum (base stratum 1),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,85.7922967608615,8.485600930866015,100*(exp(b - se^2/2) - 1),|b/t|,0.18393567067889652,0.01352468166756592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.6,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Stratum 4,income,F_neighbourhood,dummy,binary,0.917,,17.8,,,,,medium,Official socioeconomic stratum (base stratum 1),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,149.8456164397734,12.888351539038421,100*(exp(b - se^2/2) - 1),|b/t|,0.2378871448895374,0.013364446342108843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.8,5282.0,binary,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Stratum 5,income,F_neighbourhood,dummy,binary,1.1879,,19.9,,,,,medium,Official socioeconomic stratum (base stratum 1),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,227.4346623728815,19.58056507654978,100*(exp(b - se^2/2) - 1),|b/t|,0.2640918506077095,0.013270947266719074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.9,5282.0,binary,implausible_magnitude,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Stratum 6,income,F_neighbourhood,dummy,binary,1.4192,,19.8,,,,,medium,Official socioeconomic stratum (base stratum 1),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",pct_premium,312.3206842751884,29.62982871762238,100*(exp(b - se^2/2) - 1),|b/t|,0.2628566651443546,0.013275589148704777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.8,5282.0,binary,implausible_magnitude,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,False,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",45,Area of the land (square meters),lot_size,A_structural,level,m2,0.001,,5.4,,,,,high,Main effect (stratum 1); Land*stratum interactions also included (not extracted),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,0.1,0.018518518518518517,100*b/1 (X per m2->m2),|b/t|,0.07409673427669865,0.013721617458647894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.4,5282.0,m2,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d,91b7dd63d779b9bfc5961da2cf5599ed5160c16d:T6-cA,6,"A (whole sample, eq. 1)",46,Built area (square meters),living_area,A_structural,level,m2,0.0039,,21.2,,,,,high,Main effect (stratum 1); Constructed area*stratum interactions also included (not extracted),"logarithm of house valuation (DACD cadastral appraisal, Colombian pesos)",log,OLS,5292.0,0.8715,se,"Whole sample, no boundary dummies, 19 Bogota neighbourhood FE, disaggregated subsidies; full set of hedonic controls reported in Appendix A-5 (pp.45-46, t-statistics)",semi_elasticity,0.38999999999999996,0.01839622641509434,100*b/1 (X per m2->m2),|b/t|,0.2800296294799057,0.013208944786788003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.2,5282.0,m2,,2006.0,Colombia,South America,Upper middle income,2000.0,2003.0,mixed,appraisal,working_paper,False,True,robust,,2001.5,ols,other,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Distance<200m (dist2),transit_distance,D_location,dummy,m,0.09,0.052,,,*,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,9.269595964589117,5.689706275267094,100*(exp(b - se^2/2) - 1),reported,0.019759911462649046,0.011416837733975007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7307692307692308,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Distance<200m (dist2),transit_distance,D_location,dummy,m,0.09,0.049,,,*,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,9.286151562432622,5.361453990155531,100*(exp(b - se^2/2) - 1),reported,0.020969185345768786,0.011416556466029671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8367346938775508,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Distance<200m (dist2),transit_distance,D_location,dummy,m,0.172,0.051,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,18.613426211647077,6.05715694939092,100*(exp(b - se^2/2) - 1),reported,0.03848283724195168,0.011410608717090324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.372549019607843,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Distance<200m (dist2),transit_distance,D_location,dummy,m,0.173,0.048,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,18.749731990261665,5.706557304403354,100*(exp(b - se^2/2) - 1),reported,0.041121409893882535,0.0114094085254703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6041666666666665,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,201 to 300m (dist3),transit_distance,D_location,dummy,m,0.118,0.024,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,12.492008772491259,2.700585867281622,100*(exp(b - se^2/2) - 1),reported,0.05605547008959727,0.011401112560596057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.916666666666666,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,201 to 300m (dist3),transit_distance,D_location,dummy,m,0.125,0.023,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,13.284877493517012,2.6062414420537006,100*(exp(b - se^2/2) - 1),reported,0.0619409811298128,0.011397140527885553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.434782608695652,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,201 to 300m (dist3),transit_distance,D_location,dummy,m,0.191,0.023,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,21.013932789451406,2.7840567397869886,100*(exp(b - se^2/2) - 1),reported,0.09440439850806982,0.011368068930291129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.304347826086957,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,201 to 300m (dist3),transit_distance,D_location,dummy,m,0.193,0.022,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,21.2589311395857,2.668342145742063,100*(exp(b - se^2/2) - 1),reported,0.09967746451439607,0.011362198027547737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.772727272727273,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,301 to 400m (dist4),transit_distance,D_location,dummy,m,-0.029,0.022,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,-2.8818589875073575,2.1371162218265303,100*(exp(b - se^2/2) - 1),reported,-0.015050701886498385,0.011417773844929808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3181818181818183,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,301 to 400m (dist4),transit_distance,D_location,dummy,m,0.009,0.021,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,0.8818152844867422,2.118985305725123,100*(exp(b - se^2/2) - 1),reported,0.004893827366096075,0.011418930520890843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4285714285714285,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,301 to 400m (dist4),transit_distance,D_location,dummy,m,0.054,0.022,,,**,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,5.522920578556567,2.322066124741176,100*(exp(b - se^2/2) - 1),reported,0.02801761643577474,0.011414584473834153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4545454545454546,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,301 to 400m (dist4),transit_distance,D_location,dummy,m,0.085,0.021,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,8.84770310500944,2.2863058400666376,100*(exp(b - se^2/2) - 1),reported,0.04617074338370794,0.011406889541857257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0476190476190474,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,401 to 500m (dist5),transit_distance,D_location,dummy,m,-0.063,0.025,,,**,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,-6.134990030432064,2.347358684222833,100*(exp(b - se^2/2) - 1),reported,-0.02876414273298955,0.011414342354360932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.52,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,401 to 500m (dist5),transit_distance,D_location,dummy,m,-0.04,0.023,,,*,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,-3.9464656048760816,2.2098157100503433,100*(exp(b - se^2/2) - 1),reported,-0.019855332429547335,0.011416816146989715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7391304347826089,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,401 to 500m (dist5),transit_distance,D_location,dummy,m,0.014,0.024,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,1.3806440634972272,2.4338363014523816,100*(exp(b - se^2/2) - 1),reported,0.006660974795830864,0.011418813935710051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5833333333333334,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,401 to 500m (dist5),transit_distance,D_location,dummy,m,0.034,0.023,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,3.4310995286346735,2.3795445954746715,100*(exp(b - se^2/2) - 1),reported,0.016877955814530304,0.011417440698064616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4782608695652175,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,501 to 600m (dist6),transit_distance,D_location,dummy,m,0.069,0.026,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,7.107412490533838,2.7857341437857004,100*(exp(b - se^2/2) - 1),reported,0.030290542122463075,0.011413827466435359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.653846153846154,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,501 to 600m (dist6),transit_distance,D_location,dummy,m,0.088,0.025,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,9.164692905424253,2.729970305070494,100*(exp(b - se^2/2) - 1),reported,0.04016268548848491,0.011409853831955941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5199999999999996,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,501 to 600m (dist6),transit_distance,D_location,dummy,m,0.14,0.026,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,14.988507201175128,2.990711877028791,100*(exp(b - se^2/2) - 1),reported,0.061371381735220845,0.011397542322255299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.384615384615385,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,501 to 600m (dist6),transit_distance,D_location,dummy,m,0.158,0.025,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,17.08002624517162,2.927915486769174,100*(exp(b - se^2/2) - 1),reported,0.07198129868692903,0.011389445994767253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.319999999999999,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,601 to 700m (dist7),transit_distance,D_location,dummy,m,-0.035,0.019,,,*,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,-3.4568859791181406,1.8346502908893763,100*(exp(b - se^2/2) - 1),reported,-0.021030471674986208,0.011416541766421083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.842105263157895,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,601 to 700m (dist7),transit_distance,D_location,dummy,m,-0.053,0.018,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,-5.177361263575342,1.7070840224681365,100*(exp(b - se^2/2) - 1),reported,-0.033603820080239426,0.01141261814045867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9444444444444446,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,601 to 700m (dist7),transit_distance,D_location,dummy,m,0.03,0.019,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,3.026855369536152,1.9578636145116823,100*(exp(b - se^2/2) - 1),reported,0.0180271762621068,0.011417211632667639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5789473684210527,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,601 to 700m (dist7),transit_distance,D_location,dummy,m,0.008,0.018,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,0.7868797533104432,1.814457753907692,100*(exp(b - se^2/2) - 1),reported,0.005075075646449613,0.011418920204511627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4444444444444445,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,701 to 800m (dist8),transit_distance,D_location,dummy,m,-0.156,0.019,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,-14.459522412651582,1.625562461704935,100*(exp(b - se^2/2) - 1),reported,-0.09334717550431697,0.01136920727296168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.210526315789474,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,701 to 800m (dist8),transit_distance,D_location,dummy,m,-0.161,0.018,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,-14.884582703831683,1.532325727992872,100*(exp(b - se^2/2) - 1),reported,-0.1016085968305756,0.01135996734751777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.944444444444445,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,701 to 800m (dist8),transit_distance,D_location,dummy,m,-0.087,0.019,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,-8.348834965072339,1.7416864817029893,100*(exp(b - se^2/2) - 1),reported,-0.05221597844552184,0.011403489545573737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.578947368421052,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,701 to 800m (dist8),transit_distance,D_location,dummy,m,-0.096,0.018,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,-9.168314318167582,1.635235228923671,100*(exp(b - se^2/2) - 1),reported,-0.06078906260002511,0.011397949237504708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.333333333333334,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,801 to 900m (dist9),transit_distance,D_location,dummy,m,-0.026,0.017,,,,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,-2.58056916400089,1.6563696523348739,100*(exp(b - se^2/2) - 1),reported,-0.01746179302973811,0.011417326211751844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5294117647058822,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,801 to 900m (dist9),transit_distance,D_location,dummy,m,-0.052,0.016,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,-5.079263947549606,1.5189261869486235,100*(exp(b - se^2/2) - 1),reported,-0.03708643784850335,0.01141121164569334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.25,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,801 to 900m (dist9),transit_distance,D_location,dummy,m,0.062,0.017,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,6.380861327658538,1.8087359860376575,100*(exp(b - se^2/2) - 1),reported,0.041609941711816416,0.011409177566143212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6470588235294117,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,801 to 900m (dist9),transit_distance,D_location,dummy,m,0.033,0.016,,,**,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,3.341825323876746,1.653680862786089,100*(exp(b - se^2/2) - 1),reported,0.02354529698114883,0.011415901566617613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0625,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,901 to 1000m (dist10),transit_distance,D_location,dummy,m,0.03,0.017,,,*,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,3.0305644030916623,1.751772707720979,100*(exp(b - se^2/2) - 1),reported,0.020147204949147738,0.01141674947118372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.764705882352941,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,901 to 1000m (dist10),transit_distance,D_location,dummy,m,0.087,0.016,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,9.075705387951816,1.7454346875492446,100*(exp(b - se^2/2) - 1),reported,0.061971832707566045,0.011397118658862723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.437499999999999,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,901 to 1000m (dist10),transit_distance,D_location,dummy,m,0.113,0.017,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,11.947015782280923,1.9033742860125964,100*(exp(b - se^2/2) - 1),reported,0.0756855015784734,0.011386314396761486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.647058823529411,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,901 to 1000m (dist10),transit_distance,D_location,dummy,m,0.162,0.016,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,17.57097408423467,1.8813763861135997,100*(exp(b - se^2/2) - 1),reported,0.11485295399187302,0.011343501628826965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.125,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,1001 to 1100m (dist11),transit_distance,D_location,dummy,m,0.197,0.013,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,21.76411456818448,1.5830672529176808,100*(exp(b - se^2/2) - 1),reported,0.17050877715631488,0.011251848238741589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.153846153846155,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,1001 to 1100m (dist11),transit_distance,D_location,dummy,m,0.166,0.012,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,18.048810351992394,1.4166877220679315,100*(exp(b - se^2/2) - 1),reported,0.15602909619399122,0.011279211773059607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.833333333333334,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,1101 to 1200m (dist12),transit_distance,D_location,dummy,m,0.279,0.018,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,32.15932284236518,2.3792532189651707,100*(exp(b - se^2/2) - 1),reported,0.17428661960580769,0.011244298039084364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.500000000000002,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,True
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,1101 to 1200m (dist12),transit_distance,D_location,dummy,m,0.229,0.018,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,25.713836609747087,2.2632156702177104,100*(exp(b - se^2/2) - 1),reported,0.14376672767880888,0.011300441476936941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.722222222222223,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,1201 to 1300m (dist13),transit_distance,D_location,dummy,m,0.208,0.022,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,23.09152520113129,2.7086689730075086,100*(exp(b - se^2/2) - 1),reported,0.10733834494383435,0.01135309417675171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.454545454545455,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,1201 to 1300m (dist13),transit_distance,D_location,dummy,m,0.236,0.021,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,26.58951495100814,2.658966051350448,100*(exp(b - se^2/2) - 1),reported,0.12728476841789355,0.011326187020236292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.238095238095237,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,1301 to 1400m (dist14),transit_distance,D_location,dummy,m,0.15,0.018,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,16.164604082572676,2.0913016369109094,100*(exp(b - se^2/2) - 1),reported,0.0947309558482455,0.011367714701789457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.333333333333334,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,1301 to 1400m (dist14),transit_distance,D_location,dummy,m,0.181,0.017,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,19.824202081079335,2.0373058047984394,100*(exp(b - se^2/2) - 1),reported,0.12069075358062646,0.011335595640169336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.647058823529411,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,1401 to 1500m (dist15),transit_distance,D_location,dummy,m,0.055,0.023,,,**,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,5.626119663757723,2.4300434137536366,100*(exp(b - se^2/2) - 1),reported,0.027296290444631256,0.011414812367754889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.391304347826087,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,1401 to 1500m (dist15),transit_distance,D_location,dummy,m,0.097,0.022,,,***,,,high,"Distance-band dummy to nearest existing MRT station (100-m bands); reference = sales beyond the last band up to 2,000 m. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,10.159375567266714,2.424092821966223,100*(exp(b - se^2/2) - 1),reported,0.05028401373602301,0.011404621672087693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.409090909090909,7669.0,m,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Floor level (floor),floor_level,A_structural,level,count,0.013,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",semi_elasticity,1.3,0.1,100*b/1 (per unit count),reported,0.14683876485102718,0.011295289603925165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,7669.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Floor level (floor),floor_level,A_structural,level,count,0.013,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",semi_elasticity,1.3,0.1,100*b/1 (per unit count),reported,0.14683876485102718,0.011295289603925165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,7669.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Floor level (floor),floor_level,A_structural,level,count,0.013,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",semi_elasticity,1.3,0.1,100*b/1 (per unit count),reported,0.14683876485102718,0.011295289603925165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,7669.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Floor level (floor),floor_level,A_structural,level,count,0.013,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",semi_elasticity,1.3,0.1,100*b/1 (per unit count),reported,0.14683876485102718,0.011295289603925165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,7669.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Unit size (area),living_area,A_structural,level,m2,0.006,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,7669.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Unit size (area),living_area,A_structural,level,m2,0.006,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,7669.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Unit size (area),living_area,A_structural,level,m2,0.006,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,7669.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Unit size (area),living_area,A_structural,level,m2,0.006,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",semi_elasticity,0.6,,100*b/1 (X per m2->m2),,,,,,7669.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.226,0.009,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,25.3524896491818,1.128218098750368,100*(exp(b - se^2/2) - 1),reported,0.2756374278113392,0.010976711727000234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.111111111111114,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.219,0.008,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,24.479144272402408,0.995865021350025,100*(exp(b - se^2/2) - 1),reported,0.29835930784584275,0.010898970149619826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.375,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.231,0.009,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,25.98082161832618,1.1338733155043081,100*(exp(b - se^2/2) - 1),reported,0.28125801448813725,0.010958104460576774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.66666666666667,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.235,0.008,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,26.48682922999186,1.0119270149863133,100*(exp(b - se^2/2) - 1),reported,0.3180205875426816,0.010826232767410437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.374999999999996,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Freehold dummy (dumfh),other,other,binary,binary,0.251,0.011,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,28.5232325423324,1.4138410927643146,100*(exp(b - se^2/2) - 1),reported,0.2521435426762415,0.011050115416090262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.81818181818182,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Freehold dummy (dumfh),other,other,binary,binary,0.259,0.011,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,29.555542135398017,1.4251971853108534,100*(exp(b - se^2/2) - 1),reported,0.2596460240544852,0.011027437315055354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.545454545454547,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Freehold dummy (dumfh),other,other,binary,binary,0.223,0.011,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,24.97449618785548,1.3748026311095822,100*(exp(b - se^2/2) - 1),reported,0.22553136151268438,0.011124865366096538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.272727272727273,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Freehold dummy (dumfh),other,other,binary,binary,0.235,0.011,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,26.48322440672779,1.3913996456061808,100*(exp(b - se^2/2) - 1),reported,0.23700235950810103,0.011093727466336644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.363636363636363,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Age (age),age,A_structural,level,years,-0.011,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",semi_elasticity,-1.0999999999999999,0.1,100*b/1 (per year),reported,-0.12463039065457177,0.011330035514051979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.0,7669.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Age (age),age,A_structural,level,years,-0.012,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",semi_elasticity,-1.2,0.1,100*b/1 (per year),reported,-0.13576015773227668,0.011313346477689723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,7669.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Age (age),age,A_structural,level,years,-0.01,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",semi_elasticity,-1.0,0.1,100*b/1 (per year),reported,-0.11345338009741439,0.011345338009741437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,7669.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Age (age),age,A_structural,level,years,-0.011,0.001,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",semi_elasticity,-1.0999999999999999,0.1,100*b/1 (per year),reported,-0.12463039065457177,0.011330035514051979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.0,7669.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c1,2,1,12,Resale dummy (dumresale),other,other,binary,binary,-0.069,0.011,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.674,se,"Pre-CL sales within 2,000 m of 6 existing MRT stations; distance dummies up to 1,000 m",pct_premium,-6.67297844778727,1.0266593480860222,100*(exp(b - se^2/2) - 1),reported,-0.07144564711953282,0.011389885772679142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.272727272727273,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c2,2,2,12,Resale dummy (dumresale),other,other,binary,binary,-0.091,0.011,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.706,se,"Same as col 1, with additional fixed effects (text: year-of-sale FE)",pct_premium,-8.703752500134076,1.0043194819891923,100*(exp(b - se^2/2) - 1),reported,-0.09404811938250573,0.0113684539912919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.272727272727273,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c3,2,3,12,Resale dummy (dumresale),other,other,binary,binary,-0.072,0.011,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.692,se,"Pre-CL sales within 2,000 m; distance dummies up to 1,500 m",pct_premium,-6.952539960503734,1.0235839853923263,100*(exp(b - se^2/2) - 1),reported,-0.07453508004953331,0.01138730389645648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.545454545454545,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T2-c4,2,4,12,Resale dummy (dumresale),other,other,binary,binary,-0.093,0.011,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,7679.0,0.722,se,"Same as col 3, with planning sector FE (table note) / year-of-sale FE (text)",pct_premium,-8.886162524306297,1.0023128503257546,100*(exp(b - se^2/2) - 1),reported,-0.09609622680234471,0.01136622037447088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.454545454545455,7669.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Floor level (floor),floor_level,A_structural,level,count,0.015,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",semi_elasticity,1.5,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,57407.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,57407.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.133,0.003,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",pct_premium,14.224485821746713,0.3426749994992683,100*(exp(b - se^2/2) - 1),reported,0.15363223096886325,0.0034653886684705994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.333333333333336,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.165,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",pct_premium,17.939075992751196,0.23587862374227814,100*(exp(b - se^2/2) - 1),reported,0.27793024027107677,0.0033688513972251733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),82.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.154,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",pct_premium,16.648855379899885,0.23329817735568792,100*(exp(b - se^2/2) - 1),reported,0.3059605332754418,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),77.0,57407.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Freehold dummy (dumfh),other,other,binary,binary,0.149,0.003,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",pct_premium,16.06677661923859,0.3482018967627256,100*(exp(b - se^2/2) - 1),reported,0.17159852701217176,0.0034550038995739283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.666666666666664,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Freehold dummy (dumfh),other,other,binary,binary,0.099,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",pct_premium,10.406409142849093,0.2208132599117764,100*(exp(b - se^2/2) - 1),reported,0.1710395663108036,0.003455344773955628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Freehold dummy (dumfh),other,other,binary,binary,0.086,0.003,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",pct_premium,8.980142418768544,0.32694189849153865,100*(exp(b - se^2/2) - 1),reported,0.11879778940380332,0.004144108932690814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.666666666666664,57407.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Age (age),age,A_structural,level,years,-0.011,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",semi_elasticity,-1.0999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Age (age),age,A_structural,level,years,-0.008,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",semi_elasticity,-0.8,,100*b/1 (per year),,,,,,57407.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Resale dummy (dumresale),other,other,binary,binary,0.017,0.003,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",pct_premium,1.7140745181588812,0.3051435966975722,100*(exp(b - se^2/2) - 1),reported,0.019869211162369508,0.0035063313815946184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.666666666666667,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Resale dummy (dumresale),other,other,binary,binary,-0.052,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",pct_premium,-5.067303181294546,0.18986577336857793,100*(exp(b - se^2/2) - 1),reported,-0.09080590549887042,0.0034925348268796314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Resale dummy (dumresale),other,other,binary,binary,-0.051,0.003,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",pct_premium,-4.972560571196894,0.2850836011597281,100*(exp(b - se^2/2) - 1),reported,-0.07077437923794822,0.0041631987787028365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.0,57407.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.051,0.002,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",pct_premium,5.232078863952205,0.21046457865664076,100*(exp(b - se^2/2) - 1),reported,0.08907362790058182,0.003493083447081641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.499999999999996,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.016,0.002,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",pct_premium,1.6126653150756232,0.203225737081219,100*(exp(b - se^2/2) - 1),reported,0.0280451539914883,0.0035056442489360376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.015,0.003,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",pct_premium,1.5108496617206146,0.3045339193847157,100*(exp(b - se^2/2) - 1),reported,0.020863781900230534,0.004172756380046107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,57407.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.126,0.007,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",pct_premium,13.425437871032564,0.7939975177981176,100*(exp(b - se^2/2) - 1),reported,0.06300102377495459,0.003500056876386366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.042,0.005,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",pct_premium,4.288144265125471,0.5214472393753816,100*(exp(b - se^2/2) - 1),reported,0.029446224746603838,0.0035055029460242665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.4,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.049,0.005,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",pct_premium,5.020722306769199,0.525110175370014,100*(exp(b - se^2/2) - 1),reported,0.040867744839514886,0.004170178044848457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.8,57407.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c1,3,1,17,After x Proximity to MRT (aftdumMRT),transit_distance,D_location,other,binary,-0.078,0.009,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.669,se,"Sales within 4,000 m of CL stations; no FE",other,-0.078,0.009,not harmonised,reported,-0.030380176014710922,0.0035054049247743363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.666666666666668,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c2,3,2,17,After x Proximity to MRT (aftdumMRT),transit_distance,D_location,other,binary,0.132,0.006,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE",other,0.132,0.006,not harmonised,reported,0.07692589835599901,0.0034966317434545003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T3-c3,3,3,17,After x Proximity to MRT (aftdumMRT),transit_distance,D_location,other,binary,0.122,0.006,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,57417.0,0.859,se,"Sales within 2,000 m; planning sector FE + year-of-sale FE",other,0.122,0.006,not harmonised,reported,0.08456056332450557,0.004158716229074045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.333333333333332,57407.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.163,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",pct_premium,17.703433561744177,0.23540733793769342,100*(exp(b - se^2/2) - 1),reported,0.2748172614807181,0.0033719909384137188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.163,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",pct_premium,17.703433561744177,0.23540733793769342,100*(exp(b - se^2/2) - 1),reported,0.2748172614807181,0.0033719909384137188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Freehold dummy (dumfh),other,other,binary,binary,0.094,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",pct_premium,9.85575487998791,0.21971194918343476,100*(exp(b - se^2/2) - 1),reported,0.1626355996164049,0.003460331906732019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Freehold dummy (dumfh),other,other,binary,binary,0.094,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",pct_premium,9.85575487998791,0.21971194918343476,100*(exp(b - se^2/2) - 1),reported,0.1626355996164049,0.003460331906732019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Resale dummy (dumresale),other,other,binary,binary,-0.05,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",pct_premium,-4.877247795623251,0.19024588490014283,100*(exp(b - se^2/2) - 1),reported,-0.08734054163995394,0.0034936216655981575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Resale dummy (dumresale),other,other,binary,binary,-0.05,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",pct_premium,-4.877247795623251,0.19024588490014283,100*(exp(b - se^2/2) - 1),reported,-0.08734054163995394,0.0034936216655981575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.026,0.002,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",pct_premium,2.633889579359794,0.2052681896946884,100*(exp(b - se^2/2) - 1),reported,0.045543999678550995,0.0035033845906577684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.026,0.002,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",pct_premium,2.633889579359794,0.2052681896946884,100*(exp(b - se^2/2) - 1),reported,0.045543999678550995,0.0035033845906577684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.129,0.003,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",pct_premium,13.768500457169196,0.3413070372497195,100*(exp(b - se^2/2) - 1),reported,0.14911600903350777,0.0034678141635699484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),43.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.13,0.003,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",pct_premium,13.882325860842748,0.3416485149973865,100*(exp(b - se^2/2) - 1),reported,0.15024595123343726,0.0034672142592331676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),43.333333333333336,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c1,4,1,19,After x Proximity x Existing MRT station (aftdumMRTold),transit_distance,D_location,other,binary,-0.093,0.009,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with existing-station dummy",other,-0.093,0.009,not harmonised,reported,-0.03621547223718104,0.003504723119727197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.333333333333334,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T4-c2,4,2,19,After x Proximity x Interchange (aftdumMRTinterchg),transit_distance,D_location,other,binary,-0.096,0.009,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; triple interaction with interchange dummy",other,-0.096,0.009,not harmonised,reported,-0.03738210622518647,0.003504572458611231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.666666666666668,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.164,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",pct_premium,17.821195866644835,0.23564286301854445,100*(exp(b - se^2/2) - 1),reported,0.2763748382768127,0.003370424857034301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),82.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.163,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",pct_premium,17.703433561744177,0.23540733793769342,100*(exp(b - se^2/2) - 1),reported,0.2748172614807181,0.0033719909384137188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Freehold dummy (dumfh),other,other,binary,binary,0.096,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",pct_premium,10.075686247805237,0.22015181279879575,100*(exp(b - se^2/2) - 1),reported,0.1660015433027719,0.003458365485474414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Freehold dummy (dumfh),other,other,binary,binary,0.095,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",pct_premium,9.965665581059202,0.21993177102522057,100*(exp(b - se^2/2) - 1),reported,0.16431928895749445,0.0034593534517367254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Resale dummy (dumresale),other,other,binary,binary,-0.048,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",pct_premium,-4.686811918816314,0.19062675741550095,100*(exp(b - se^2/2) - 1),reported,-0.08387200414797394,0.0034946668394989144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Resale dummy (dumresale),other,other,binary,binary,-0.052,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",pct_premium,-5.067303181294546,0.18986577336857793,100*(exp(b - se^2/2) - 1),reported,-0.09080590549887042,0.0034925348268796314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.026,0.002,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",pct_premium,2.633889579359794,0.2052681896946884,100*(exp(b - se^2/2) - 1),reported,0.045543999678550995,0.0035033845906577684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.024,0.002,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",pct_premium,2.4288269312034316,0.2048580635781243,100*(exp(b - se^2/2) - 1),reported,0.04204706649821094,0.0035039222081842446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.134,0.003,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",pct_premium,14.338767438853539,0.3430178458933941,100*(exp(b - se^2/2) - 1),reported,0.1547597952627518,0.0034647715357332483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.66666666666667,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.111,0.003,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",pct_premium,11.738987858869109,0.3352184720563375,100*(exp(b - se^2/2) - 1),reported,0.12868105779648148,0.003477866426931932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c1,5,1,21,After x Proximity x Resale (aftdumMRTresale),transit_distance,D_location,other,binary,-0.064,0.006,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by resale",other,-0.064,0.006,not harmonised,reported,-0.037382106225186465,0.003504572458611231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.666666666666666,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T5-c2,5,2,21,After x Proximity x HDB buyer (aftdumMRThdbbuyer),transit_distance,D_location,other,binary,0.043,0.006,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.864,se,"Sales within 4,000 m; planning sector FE + year-of-sale FE; heterogeneity by HDB-upgrader buyer",other,0.043,0.006,not harmonised,reported,0.02512573520168828,0.003505916539770458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Floor level (floor),floor_level,A_structural,level,count,0.007,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",semi_elasticity,0.7000000000000001,,100*b/1 (per unit count),,,,,,81306.0,count,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Unit size (area),living_area,A_structural,level,m2,0.007,0.0,,,***,,,high,"Unit size in sqm (Table 1); SE printed as (0.000) i.e. <0.0005. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",semi_elasticity,0.7000000000000001,,100*b/1 (X per m2->m2),,,,,,81306.0,m2,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.168,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",pct_premium,18.293424477695552,0.23658732212956216,100*(exp(b - se^2/2) - 1),reported,0.2825833162400669,0.0033640870980960343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),84.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.165,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",pct_premium,17.939075992751196,0.23587862374227814,100*(exp(b - se^2/2) - 1),reported,0.27793024027107677,0.0033688513972251733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),82.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Condominium dummy (dumcond),dwelling_type,A_structural,binary,binary,0.168,0.002,,,***,,,high,"Condominium vs apartment (non-landed) dummy. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",pct_premium,18.293424477695552,0.23658732212956216,100*(exp(b - se^2/2) - 1),reported,0.2825833162400669,0.0033640870980960343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),84.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Freehold dummy (dumfh),other,other,binary,binary,0.099,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",pct_premium,10.406409142849093,0.2208132599117764,100*(exp(b - se^2/2) - 1),reported,0.1710395663108036,0.003455344773955628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Freehold dummy (dumfh),other,other,binary,binary,0.098,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",pct_premium,10.296057918514334,0.22059255702170158,100*(exp(b - se^2/2) - 1),reported,0.1693616949237807,0.003456361120893484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Freehold dummy (dumfh),other,other,binary,binary,0.098,0.002,,,***,,,high,"Freehold (vs leasehold) tenure dummy; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",pct_premium,10.296057918514334,0.22059255702170158,100*(exp(b - se^2/2) - 1),reported,0.1693616949237807,0.003456361120893484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Age (age),age,A_structural,level,years,-0.009,0.0,,,***,,,high,"Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",semi_elasticity,-0.8999999999999999,,100*b/1 (per year),,,,,,81306.0,years,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Resale dummy (dumresale),other,other,binary,binary,-0.052,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",pct_premium,-5.067303181294546,0.18986577336857793,100*(exp(b - se^2/2) - 1),reported,-0.09080590549887042,0.0034925348268796314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Resale dummy (dumresale),other,other,binary,binary,-0.053,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",pct_premium,-5.162188427583003,0.18967600249645963,100*(exp(b - se^2/2) - 1),reported,-0.09253735922597903,0.0034919758198482653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Resale dummy (dumresale),other,other,binary,binary,-0.053,0.002,,,***,,,high,"Resale (secondary market) vs developer/sub-sale; no taxonomy leaf. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",pct_premium,-5.162188427583003,0.18967600249645963,100*(exp(b - se^2/2) - 1),reported,-0.09253735922597903,0.0034919758198482653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.5,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,False,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.038,0.005,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",pct_premium,3.8718248819237244,0.5193656164392488,100*(exp(b - se^2/2) - 1),reported,0.02664391792909864,0.0035057786748814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.031,0.005,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",pct_premium,3.147261039830851,0.5157427519432614,100*(exp(b - se^2/2) - 1),reported,0.021738408853487993,0.0035061949763690314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.2,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,Proximity to MRT (<= 400m) (dumMRT),transit_distance,D_location,binary,binary,0.028,0.005,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",pct_premium,2.838282955571292,0.5141978422107125,100*(exp(b - se^2/2) - 1),reported,0.01963554639868067,0.003506347571192977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,6 months before MRT opening (bef6mth),transaction_timing,G_market,binary,binary,-0.007,0.003,,,**,,,high,"Sold within 180 days before opening of nearest CL station (anticipation dummy). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",pct_premium,-0.6980025666703749,0.2979073328799705,100*(exp(b - se^2/2) - 1),reported,-0.008182781361118227,0.0035069062976220977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3333333333333335,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,6 months before MRT opening (bef6mth),transaction_timing,G_market,binary,binary,-0.017,0.003,,,***,,,high,"Sold within 180 days before opening of nearest CL station (anticipation dummy). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",pct_premium,-1.686073950171696,0.2949431053904729,100*(exp(b - se^2/2) - 1),reported,-0.019869211162369508,0.0035063313815946184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.666666666666667,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,True
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,6 months before MRT opening x Proximity to MRT (bef6mthdumMRT),transit_distance,D_location,other,binary,-0.044,0.01,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",other,-0.044,0.01,not harmonised,reported,-0.015429067508781449,0.003506606251995784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.3999999999999995,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,6 months before MRT opening x Proximity to MRT (bef6mthdumMRT),transit_distance,D_location,other,binary,-0.037,0.01,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",other,-0.037,0.01,not harmonised,reported,-0.012974895444796542,0.0035067284985936605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6999999999999997,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,After the opening of MRT line (after),transaction_timing,G_market,binary,binary,0.021,0.003,,,***,,,high,"Post-Circle-Line-opening period dummy (DiD time indicator). Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",pct_premium,2.121745614863624,0.30636661549125854,100*(exp(b - se^2/2) - 1),reported,0.02454177190026069,0.0035059674143229553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,81306.0,binary,,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,True,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c2,6,2,22,After x Proximity to MRT (aftdumMRT),transit_distance,D_location,other,binary,0.143,0.007,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.865,se,"Sales within 4,000 m; FE; 6-month anticipation window + after",other,0.143,0.007,not harmonised,reported,0.07146032373724888,0.0034980578053198756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.428571428571427,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,After x Phase 1 x Proximity to MRT (aftph1dumMRT),transit_distance,D_location,other,binary,0.192,0.008,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",other,0.192,0.008,not harmonised,reported,0.08387200414797394,0.0034946668394989144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.0,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,After x Phase 1 x Proximity to MRT (aftph1dumMRT),transit_distance,D_location,other,binary,0.202,0.008,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",other,0.202,0.008,not harmonised,reported,0.08820718490108159,0.003493353857468578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.25,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,After x Phase 2 x Proximity to MRT (aftph2dumMRT),transit_distance,D_location,other,binary,0.125,0.008,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",other,0.125,0.008,not harmonised,reported,0.05471515941289115,0.0035017702024250337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.625,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,After x Phase 2 x Proximity to MRT (aftph2dumMRT),transit_distance,D_location,other,binary,0.134,0.008,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",other,0.134,0.008,not harmonised,reported,0.058641557123865186,0.0035009884850068767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.75,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,After x Phase 3 x Proximity to MRT (aftph3dumMRT),transit_distance,D_location,other,binary,0.034,0.011,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",other,0.034,0.011,not harmonised,reported,0.010839254665501166,0.0035068176858974354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0909090909090913,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,After x Phase 3 x Proximity to MRT (aftph3dumMRT),transit_distance,D_location,other,binary,0.044,0.011,,,***,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",other,0.044,0.011,not harmonised,reported,0.014026714771523317,0.003506678692880829,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c1,6,1,22,After x Phase 4 x Proximity to MRT (aftph4dumMRT),transit_distance,D_location,other,binary,-0.08,0.054,,,,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; phase-specific opening dummies (aftph1-4 period dummies not extracted)",other,-0.08,0.054,not harmonised,reported,-0.0051955205591884875,0.003506976377452229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4814814814814816,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68,933204aac69df35d44733b61b90a1210852e7b68:T6-c3,6,3,22,After x Phase 4 x Proximity to MRT (aftph4dumMRT),transit_distance,D_location,other,binary,-0.07,0.054,,,,,,high,"Difference-in-differences interaction term (Circle Line opening x within 400 m of CL station); x_transform other. Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1.",ln sale price (lnprice),log,OLS,81316.0,0.866,se,"Sales within 4,000 m; FE; anticipation window + phase-specific dummies",other,-0.07,0.054,not harmonised,reported,-0.004546094869942901,0.003506987471098809,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2962962962962965,81306.0,binary,nonstandard_x_transform,2017.0,Singapore,Asia,High income,2009.0,2013.0,condo_apartment,transaction,journal,True,True,unknown,,2011.0,ols,journal,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Area,living_area,A_structural,level,m2,0.015,,37.546,,,,,high,"Area in m2; SE printed 0.000 (rounded); p printed '-' meaning <0.000; SE printed as 0.000 (rounded), stored as null; use z-value","ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,1.5,0.03995099344803707,100*b/1 (X per m2->m2),|b/t|,0.422647528698053,0.011256792433229988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.546,6482.0,m2,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Area^2,living_area,A_structural,squared,m2,-0.0,,-19.709,,,,,medium,"Quadratic area term printed as -0.000 (rounded); SE 0.000; SE printed as 0.000 (rounded), stored as null; use z-value","ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,-0.0,0.0,not harmonised,|b/t|,-0.23777827163755189,0.012064451349005627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.709,6482.0,m2,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Number of bathrooms,bathrooms,A_structural,level,count,0.098,0.008,11.842,,,,,high,p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,9.8,0.8,100*b/1 (per unit count),reported,0.14552004801922622,0.012288468841346581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.842,6482.0,count,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Air conditioner,air_conditioning,A_structural,dummy,binary,0.074,0.008,8.905,,,,,high,p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,7.677234822976331,0.8614454443969759,100*(exp(b - se^2/2) - 1),reported,0.10993576640999868,0.012345397687815687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.905,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Central heating,heating,A_structural,dummy,binary,0.041,0.009,4.686,,,,,high,p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,4.180991138964241,0.9376668949909315,100*(exp(b - se^2/2) - 1),reported,0.05810498592427124,0.012399698233946061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.686,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Retrofitted apartment,renovation,A_structural,dummy,binary,0.034,0.01,3.437,0.001,,,,high,,"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,3.453287879099065,1.034584606728118,100*(exp(b - se^2/2) - 1),reported,0.042651041827716216,0.01240938080527094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.437,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Swimming pool,pool,C_exterior,dummy,binary,0.186,0.015,11.976,,,,,high,p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,20.428677049478594,1.8066333905664447,100*(exp(b - se^2/2) - 1),reported,0.14713125186349657,0.012285508672636655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.976,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Lift,accessibility,B_interior,dummy,binary,0.089,0.008,10.452,,,,,high,p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,9.304567830497913,0.8744645250610642,100*(exp(b - se^2/2) - 1),reported,0.12874064971146412,0.012317322016022208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.452,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Inverse of building age,age,A_structural,other,years,0.238,0.045,5.34,,,,,high,Regressor is 1/(building age in years); p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,0.238,0.045,not harmonised,reported,0.0661810365730923,0.012393452541777586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.34,6482.0,years,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class A,energy_efficiency,A_structural,dummy,binary,0.086,0.034,2.492,0.013,,,,high,EPC class A vs reference G (2014 baseline; year-2016 interactions separate),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,8.917660225572831,3.705341516237438,100*(exp(b - se^2/2) - 1),reported,0.030937526398574635,0.012414737720134284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.492,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class C,energy_efficiency,A_structural,dummy,binary,-0.011,0.029,-0.375,0.708,,,,high,EPC class C vs G (2014 baseline); class B absent in 2014,"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,-1.1355533641168813,2.8682748084485694,100*(exp(b - se^2/2) - 1),reported,-0.004657705695564641,0.01242054852150571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.375,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class D,energy_efficiency,A_structural,dummy,binary,0.0,0.019,0.02,0.984,,,,high,EPC class D vs G (2014 baseline),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,-0.018048371085510606,1.9,100*(exp(b - se^2/2) - 1),reported,0.0002484136573506475,0.012420682867532373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.02,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class E,energy_efficiency,A_structural,dummy,binary,0.007,0.013,0.572,0.567,,,,high,EPC class E vs G (2014 baseline),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,0.69394672868619,1.309131924446903,100*(exp(b - se^2/2) - 1),reported,0.007104451520339401,0.01242036979080315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.572,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class F,energy_efficiency,A_structural,dummy,binary,0.019,0.016,1.132,0.258,,,,high,EPC class F vs G (2014 baseline),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,1.9051201715164723,1.630690637787853,100*(exp(b - se^2/2) - 1),reported,0.014058823866895335,0.012419455712805066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.132,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class A * Year 2016,energy_efficiency,A_structural,other,binary,0.067,0.044,1.515,0.13,,,,high,Interaction EPC A x 2016 dummy (incremental 2016 premium),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,0.067,0.044,not harmonised,reported,0.018814004474539202,0.012418484801676042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.515,6482.0,binary,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class B * Year 2016,energy_efficiency,A_structural,other,binary,0.107,0.041,2.619,0.009,,,,high,"Interaction EPC B x 2016 dummy (B exists only in 2016, so equals the 2016 B premium)","ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,0.107,0.041,not harmonised,reported,0.0325125718096093,0.012414116765792018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.619,6482.0,binary,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class C * Year 2016,energy_efficiency,A_structural,other,binary,0.106,0.041,2.597,0.009,,,,high,Interaction EPC C x 2016 dummy (incremental),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,0.106,0.041,not harmonised,reported,0.03223974631334809,0.012414226535752058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.597,6482.0,binary,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class D * Year 2016,energy_efficiency,A_structural,other,binary,0.105,0.026,4.071,,,,,high,Interaction EPC D x 2016 dummy (incremental); p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,0.105,0.026,not harmonised,reported,0.05050008395162726,0.012404835163750249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.071,6482.0,binary,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class E * Year 2016,energy_efficiency,A_structural,other,binary,0.008,0.018,0.462,0.644,,,,high,Interaction EPC E x 2016 dummy (incremental),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,0.008,0.018,not harmonised,reported,0.005738261185818485,0.012420478757182867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.462,6482.0,binary,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Energy class F * Year 2016,energy_efficiency,A_structural,other,binary,-0.033,0.024,-1.386,0.166,,,,high,Interaction EPC F x 2016 dummy (incremental),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",other,-0.033,0.024,not harmonised,reported,-0.01721251663643024,0.01241884317202759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.386,6482.0,binary,nonstandard_x_transform,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Commuting time,employment_access,D_location,level,minutes,0.004,0.001,2.597,0.009,,,,high,"Average commuting time of the zone (minutes), from Mobility Survey; positive sign","ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,0.4,0.1,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,0.03223974631334809,0.012414226535752058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.597,6482.0,minutes,unit_not_harmonisable,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,<200m from sea shore,water_proximity,E_environment,dummy,binary,0.131,0.029,4.532,,,,,high,Within 200 m of the sea shore; p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,13.948852563051762,3.305906565804772,100*(exp(b - se^2/2) - 1),reported,0.05620156608561427,0.012401051651724242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.532,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Highway ramp,highway_distance,D_location,dummy,binary,0.081,0.022,3.712,,,,,high,Municipality with highway access dummy; p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,8.410851055977853,2.385615972446873,100*(exp(b - se^2/2) - 1),reported,0.04605665034661409,0.012407502787342156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.712,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,True
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,<800 m from railway station,transit_distance,D_location,dummy,binary,0.033,0.011,3.12,0.002,,,,high,Within 800 m of a railway station,"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",pct_premium,3.3488011325169875,1.1369055931654362,100*(exp(b - se^2/2) - 1),reported,0.038723465999439204,0.012411367307512565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.12,6482.0,binary,,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,True,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Centrality index,cbd_distance,D_location,level,index,0.025,0.003,8.68,,,,,high,"Centrality index (3.5-20.5, higher = more central); p printed '-' (<0.000)","ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,2.5,0.3,"100*b (unit as reported: index; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.10719037602313068,0.012349121661650999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.68,6482.0,index,unit_not_harmonisable,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,Average gross area floor ratio,density,F_neighbourhood,level,index,0.048,0.005,9.029,,,,,high,"Average gross floor area ratio (m2/m2) of the zone, built density; p printed '-' (<0.000)","ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,4.8,0.5,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1114477108801154,0.012343306111431543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.029,6482.0,index,unit_not_harmonisable,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,PC1 High income,income,F_neighbourhood,level,index,0.104,0.006,17.356,,,,,high,Principal component for high-income jobs/education of neighbourhood; p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,10.4,0.6,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.21073241605900883,0.012141761699643283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.356,6482.0,index,unit_not_harmonisable,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937,942f0ff361a49b68c1333c31381f78c02a3d0937:T2-SEM,2,1,7,PC2 Med income,income,F_neighbourhood,level,index,0.072,0.007,10.979,,,,,high,Principal component for medium-income jobs; p printed '-' (<0.000),"ln(asking price, EUR)",log,spatial_error,6492.0,0.764,se,"Pooled 2014 and 2016 listings, spatial error ML; z-values and p-values in separate columns",semi_elasticity,7.199999999999999,0.7000000000000001,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.13511616677614546,0.012306782655628516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.979,6482.0,index,unit_not_harmonisable,2019.0,Spain,Europe,High income,2014.0,2016.0,condo_apartment,listing,conference,True,False,unknown,,2015.0,spatial,other,False,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,Terraced,dwelling_type,A_structural,binary,"binary (terraced house, reference detached)",-0.12,,-4.63,0.0,,,,high,Property type dummy; reference category detached,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",pct_premium,-11.337740274947539,2.2987138748608835,100*(exp(b - se^2/2) - 1),|b/t|,-0.13030952177259159,0.028144605134469022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.63,1241.0,binary,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,True,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,Semi-detached,dwelling_type,A_structural,binary,"binary (semi-detached, reference detached)",-0.08,,-3.48,0.005,,,,high,Property type dummy; reference category detached,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",pct_premium,-7.712754168232494,2.1221065434175537,100*(exp(b - se^2/2) - 1),|b/t|,-0.09830703025828706,0.028249146625944556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.48,1241.0,binary,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,True,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,Leasehold,other,other,binary,binary (leasehold tenure vs freehold),-0.19,,-2.22,0.027,,,,high,Tenure dummy; taxonomy other,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",pct_premium,-17.6064019970538,7.077578173389137,100*(exp(b - se^2/2) - 1),|b/t|,-0.06289359674452638,0.02833044898402089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.22,1241.0,binary,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,False,False,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,Flat,dwelling_type,A_structural,binary,"binary (flat, reference detached)",-0.33,,,0.002,,,,medium,"Property type dummy; t-statistic printed as '-373' (typo, presumably -3.73), not recorded","LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",pct_premium,-28.516379602962203,7.677249103862427,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.08738576223740169,0.02827805600989541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.090232306167813,1241.0,binary,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,True,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDCL,hazard_site,E_environment,log,m (ln distance to centre line of high-voltage overhead transmission line),0.03,,3.63,0.003,,,,high,Elasticity of price w.r.t. distance to HVOTL centre line (positive = closer to line lowers value),"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",elasticity,0.03,0.008264462809917356,b (log-log),|b/t|,0.10250079322682493,0.028237133120337446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.63,1241.0,m,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,True,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDPK,green_space,E_environment,log,m (ln distance to public park),1.08,,5.98,0.0,,,,high,Main effect of ln distance to public park; enters interactions with LnDRA and LnDPY (full elasticity = 1.08 - 0.09 LnDRA - 0.08 LnDPY),"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",elasticity,1.08,0.1806020066889632,b (log-log),|b/t|,0.16735800181379998,0.027986287928729096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.98,1241.0,m,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,True,True,True
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDRA,other,other,log,m (ln distance to railway lines),0.87,,5.53,0.0,,,,high,"Distance to railway lines (track, a disamenity), not to a station; main effect, also in interactions; taxonomy other","LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",elasticity,0.87,0.15732368896925858,b (log-log),|b/t|,0.15507905052174092,0.02804322794244863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.53,1241.0,m,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,False,False,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDWS,transit_distance,D_location,log,m (ln distance to Welling railway station),-0.89,,-7.77,0.0,,,,high,Main effect of ln distance to Welling station; interacts with LnDPY (full elasticity = -0.89 + 0.11 LnDPY),"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",elasticity,-0.89,0.11454311454311455,b (log-log),|b/t|,-0.21538732345438844,0.02772037624895604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.77,1241.0,m,,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,True,True,True
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDRA*LnDPY,hazard_site,E_environment,other,ln m x ln m (distance to railway x distance to nearest pylon),-0.05,,-5.37,0.0,,,,high,Interaction term; LnDPY (distance to pylon) has no separate main effect in Table 1,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",other,-0.05,0.00931098696461825,not harmonised,|b/t|,-0.1506955105430064,0.028062478685848487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.37,1241.0,ln m x ln m,nonstandard_x_transform,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,False,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDRA*LnDPK,green_space,E_environment,other,ln m x ln m (distance to railway x distance to park),-0.09,,-4.99,0.0,,,,high,Interaction term,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",other,-0.09,0.018036072144288574,not harmonised,|b/t|,-0.14024934075975926,0.028106080312577008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.99,1241.0,ln m x ln m,nonstandard_x_transform,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,False,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDWS*LnDPY,transit_distance,D_location,other,ln m x ln m (distance to station x distance to pylon),0.11,,6.19,0.0,,,,high,Interaction term,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",other,0.11,0.01777059773828756,not harmonised,|b/t|,0.17306199917893714,0.027958319738115852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.19,1241.0,ln m x ln m,nonstandard_x_transform,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,False,True,False
96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503,96e0ec640b350c1ec477b4a65c34f99c77d53503:T1-c1,1,1,5,LnDPY*LnDPK,hazard_site,E_environment,other,ln m x ln m (distance to pylon x distance to park),-0.08,,-6.1,0.0,,,,high,Interaction term,"LnP: natural log of sold house price (Land Registry via Nethouseprices), indexed to 2009Q1 with Nationwide Greater London index",log,OLS,1251.0,0.44,t,"Single LSDV regression (eq. 1); t-statistic and p-value in separate columns; R2 is adjusted; S.E. of regression 0.17; N = 1,251 sold houses (Goldfeld-Quandt subsamples of 469 each); plot/house over-sail and plot-tower dummies in eq. 1 not reported in Table 1",other,-0.08,0.013114754098360657,not harmonised,|b/t|,-0.17061952539334724,0.027970413998909382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.1,1241.0,ln m x ln m,nonstandard_x_transform,2011.0,United Kingdom,Europe,High income,2000.0,2009.0,mixed,transaction,journal,False,False,OLS,,2004.5,ols,journal,False,True,False
98438191d74bdd40ba8f1ee8325b8fb8b7420348,98438191d74bdd40ba8f1ee8325b8fb8b7420348,98438191d74bdd40ba8f1ee8325b8fb8b7420348:EQ1-m3,eq. (1) / Table 5 model 3,3,5,Carpet_area (X_C),living_area,A_structural,level,"not stated (carpet area, mean 1,512; likely sqft)",5764.491,,,,,,,medium,"Coefficient read from printed equation (1); no SE/t table printed, text states t-test p = 0.000 for retained variables","Price_house (house price, currency not stated; mean 6,084,695)",level,OLS,898.0,0.889,none,"Stepwise linear regression, final model with 3 retained variables (Carpet_area, Builtup_area, Hospital_dist); N from ANOVA df (total 897 => 898); adjusted R2 0.888",monetary,5764.491,,"b (currency per unit, as reported)",,,,,,888.0,not stated,,2023.0,Unknown,,,,,unknown,listing,conference,False,False,unknown,,2020.0,ols,other,False,False,False
98438191d74bdd40ba8f1ee8325b8fb8b7420348,98438191d74bdd40ba8f1ee8325b8fb8b7420348,98438191d74bdd40ba8f1ee8325b8fb8b7420348:EQ1-m3,eq. (1) / Table 5 model 3,3,5,Builtup_area (X_B),living_area,A_structural,level,"not stated (built-up/construction area, mean 1,795; likely sqft)",-7804.659,,,,,,,medium,"Built-up (construction) area, entered alongside carpet area; negative sign; coefficient from equation (1), no SE","Price_house (house price, currency not stated; mean 6,084,695)",level,OLS,898.0,0.889,none,"Stepwise linear regression, final model with 3 retained variables (Carpet_area, Builtup_area, Hospital_dist); N from ANOVA df (total 897 => 898); adjusted R2 0.888",monetary,-7804.659,,"b (currency per unit, as reported)",,,,,,888.0,not stated,,2023.0,Unknown,,,,,unknown,listing,conference,False,False,unknown,,2020.0,ols,other,False,False,False
98438191d74bdd40ba8f1ee8325b8fb8b7420348,98438191d74bdd40ba8f1ee8325b8fb8b7420348,98438191d74bdd40ba8f1ee8325b8fb8b7420348:EQ1-m3,eq. (1) / Table 5 model 3,3,5,Hospital_dist (X_H),hospital_distance,D_location,level,"not stated (mean 13,072; likely metres)",87.981,,,,,,,medium,"Distance to hospital; positive sign; coefficient from equation (1), no SE","Price_house (house price, currency not stated; mean 6,084,695)",level,OLS,898.0,0.889,none,"Stepwise linear regression, final model with 3 retained variables (Carpet_area, Builtup_area, Hospital_dist); N from ANOVA df (total 897 => 898); adjusted R2 0.888",monetary,87.981,,"b (currency per unit, as reported)",,,,,,888.0,not stated,,2023.0,Unknown,,,,,unknown,listing,conference,False,False,unknown,,2020.0,ols,other,False,False,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Finsqft,living_area,A_structural,level,1000 sqft,0.283044,0.024368,,,,,,medium,Total finished square footage in thousands (Table 1); not stated whether logged. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,0.30466602599871867,0.02622949690343825,100*b/92.903 (X per 1000sqft->m2),reported,0.34134635295842775,0.029387402414080378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.615397242284963,1023.0,1000sqft,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Finsqft,living_area,A_structural,level,1000 sqft,0.280172,0.010226,,,,,,medium,Total finished square footage in thousands (Table 1); not stated whether logged. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,0.3015746309270396,0.011007174792127363,100*b/92.903 (X per 1000sqft->m2),reported,0.4011093927302134,0.014640094834812767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.39800508507725,3915.0,1000sqft,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Finsqft,living_area,A_structural,level,1000 sqft,0.28005,0.010258,,,,,,medium,Total finished square footage in thousands (Table 1); not stated whether logged. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,0.30144331121995577,0.011041619305460833,100*b/92.903 (X per 1000sqft->m2),reported,0.39991231122084636,0.014648457377266352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.30064340027296,3915.0,1000sqft,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Bdrms,bedrooms,A_structural,level,count,-0.039027,0.016597,,,,,,high,Number of bedrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-3.9027,1.6597,100*b/1 (per unit count),reported,-0.07332080722926594,0.03118111660092057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3514490570585043,1023.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Bdrms,bedrooms,A_structural,level,count,-0.037703,0.00705,,,,,,high,Number of bedrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-3.7703,0.705,100*b/1 (per unit count),reported,-0.08516091808391656,0.015924050406906923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.347943262411348,3915.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Bdrms,bedrooms,A_structural,level,count,-0.037248,0.007058,,,,,,high,Number of bedrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-3.7248,0.7058,100*b/1 (per unit count),reported,-0.08404581936256406,0.0159255636023673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.277415698498158,3915.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Fbath,bathrooms,A_structural,level,count,0.175602,0.026654,,,,,,high,Number of full bathrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,17.560200000000002,2.6654,100*b/1 (per unit count),reported,0.20174653889892968,0.030622386122094693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.588204397088617,1023.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Fbath,bathrooms,A_structural,level,count,0.14164,0.011502,,,,,,high,Number of full bathrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,14.163999999999998,1.1502000000000001,100*b/1 (per unit count),reported,0.1931054270743434,0.015681294988767986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.314380107807336,3915.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Fbath,bathrooms,A_structural,level,count,0.140987,0.011494,,,,,,high,Number of full bathrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,14.098700000000001,1.1494,100*b/1 (per unit count),reported,0.19237699146247278,0.015683581747747397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.26613885505481,3915.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Hbath,bathrooms,A_structural,level,count,0.126607,0.026258,,,,,,high,Number of half bathrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,12.6607,2.6258,100*b/1 (per unit count),reported,0.14906602851855724,0.030915950751856346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.821654352959098,1023.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Hbath,bathrooms,A_structural,level,count,0.124649,0.011963,,,,,,high,Number of half bathrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,12.4649,1.1963,100*b/1 (per unit count),reported,0.16426426062803928,0.015765014961156797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.419543592744295,3915.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Hbath,bathrooms,A_structural,level,count,0.125128,0.011927,,,,,,high,Number of half bathrooms. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,12.512799999999999,1.1927,100*b/1 (per unit count),reported,0.1653624397490031,0.015762082178939648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.49115452335038,3915.0,count,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Age,age,A_structural,level,years,-0.012805,0.002421,,,,,,high,"Age of property (2018 - year built), quadratic. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.",ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-1.2805,0.2421,100*b/1 (per year),reported,-0.16315057451065418,0.030846352275696508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.289136720363486,1023.0,years,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Age,age,A_structural,level,years,-0.011156,0.001245,,,,,,high,"Age of property (2018 - year built), quadratic. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.",ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-1.1156,0.1245,100*b/1 (per year),reported,-0.14176363178942164,0.015820699316764966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.960642570281124,3915.0,years,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Age,age,A_structural,level,years,-0.011028,0.001255,,,,,,high,"Age of property (2018 - year built), quadratic. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.",ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-1.1028,0.1255,100*b/1 (per year),reported,-0.13907402634653993,0.01582679570773555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.787250996015935,3915.0,years,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Age2,age,A_structural,squared,years,7.6e-05,1.6e-05,,,,,,high,Age squared. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,7.6e-05,1.6e-05,not harmonised,reported,0.14689892253697767,0.030926088955153187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.750000000000001,1023.0,years,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Age2,age,A_structural,squared,years,6.7e-05,8e-06,,,,,,high,Age squared. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,6.7e-05,8e-06,not harmonised,reported,0.1326670249314942,0.015840838797790354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.375,3915.0,years,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Age2,age,A_structural,squared,years,6.6e-05,8e-06,,,,,,high,Age squared. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,6.6e-05,8e-06,not harmonised,reported,0.1307210079652737,0.01584497066245741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.250000000000002,3915.0,years,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Waterfront,waterfront,C_exterior,binary,binary,4.498826,1.325469,,,,,,medium,Indicator for homes within 150 m of the water; magnitude in column 1 (4.50) is as printed and implausibly large. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,3635.2205711135953,11917.492348747981,100*(exp(b - se^2/2) - 1),reported,0.10552616082053093,0.031090701186626983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3941389802402018,1023.0,binary,implausible_magnitude,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Waterfront,waterfront,C_exterior,binary,binary,0.987184,0.383445,,,,,,medium,Indicator for homes within 150 m of the water; magnitude in column 1 (4.50) is as printed and implausibly large. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,149.34541698499376,102.90385458087046,100*(exp(b - se^2/2) - 1),reported,0.04111135821333558,0.015968598306002186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.574512641969513,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,True
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Waterfront,waterfront,C_exterior,binary,binary,0.98032,0.38374,,,,,,medium,Indicator for homes within 150 m of the water; magnitude in column 1 (4.50) is as printed and implausibly large. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,147.61174923986283,102.27856773337001,100*(exp(b - se^2/2) - 1),reported,0.04079465177402267,0.01596880576930335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.554646375149841,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Acres,lot_size,A_structural,level,acres,-11.70237,3.848793,,,,,,high,Acreage of lot (quadratic). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-0.28917186029199116,0.09510574624531556,100*b/4046.86 (X per acre->m2),reported,-0.09463633541169228,0.031124948645289235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0405298492280566,1023.0,acre,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,True
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Acres,lot_size,A_structural,level,acres,-1.001644,0.705101,,,,,,high,Acreage of lot (quadratic). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-0.024751162271429736,0.017423425157787973,100*b/4046.86 (X per acre->m2),reported,-0.022697826859662852,0.015977992596746086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4205681171917215,3915.0,acre,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Acres,lot_size,A_structural,level,acres,-1.014829,0.698689,,,,,,high,Acreage of lot (quadratic). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,semi_elasticity,-0.025076970716894194,0.017264981187191226,100*b/4046.86 (X per acre->m2),reported,-0.023207379116548934,0.015977805627906235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4524759943265173,3915.0,acre,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,True
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Acres2,lot_size,A_structural,squared,acres,0.159765,0.038555,,,,,,high,Acreage squared. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,0.159765,0.038555,not harmonised,reported,0.1284838417092294,0.031006130986757672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.143820516145766,1023.0,acre,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Acres2,lot_size,A_structural,squared,acres,0.142691,0.024794,,,,,,high,Acreage squared. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,0.142691,0.024794,not harmonised,reported,0.0915914157668873,0.0159149320035896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.755061708477858,3915.0,acre,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Acres2,lot_size,A_structural,squared,acres,0.142736,0.017641,,,,,,high,Acreage squared. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,0.142736,0.017641,not harmonised,reported,0.12824585076343004,0.015850136288796585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.091151295278046,3915.0,acre,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Cape Cod,architectural_style,H_ai_derived,binary,binary,0.153071,0.05142,,,,,,high,Assessor-classified Cape Cod style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,16.38680593376114,5.992526496000969,100*(exp(b - se^2/2) - 1),reported,0.09267232964992729,0.03113072489628513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9768767016725013,1023.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Cape Cod,architectural_style,H_ai_derived,binary,binary,0.186078,0.017676,,,,,,high,Assessor-classified Cape Cod style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,20.432805356516436,2.129102850989313,100*(exp(b - se^2/2) - 1),reported,0.16591429502256705,0.015760600816963292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.527155465037337,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Cape Cod,architectural_style,H_ai_derived,binary,binary,0.188487,0.024864,,,,,,high,Assessor-classified Cape Cod style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,20.704821896751756,3.0021325351612815,100*(exp(b - se^2/2) - 1),reported,0.12027634979710564,0.01586608711134049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5807191119691115,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,True
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Colonial,architectural_style,H_ai_derived,binary,binary,0.145395,0.127,,,,,,high,Assessor-classified colonial style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,14.72072378645477,14.687502960415266,100*(exp(b - se^2/2) - 1),reported,0.03577090300616851,0.03124526071586644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1448425196850394,1023.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Colonial,architectural_style,H_ai_derived,binary,binary,-0.003397,0.052669,,,,,,high,Assessor-classified colonial style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,-0.47725866227662506,5.249038695302065,100*(exp(b - se^2/2) - 1),reported,-0.0010307998821822851,0.01598210155862784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06449714253165999,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Colonial,architectural_style,H_ai_derived,binary,binary,-0.007865,0.052892,,,,,,high,Assessor-classified colonial style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,-0.9221005198128074,5.247763604224658,100*(exp(b - se^2/2) - 1),reported,-0.0023765208457507798,0.015982064917158326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1486992361793844,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Condo,dwelling_type,A_structural,binary,binary,0.120879,0.048037,,,,,,high,Condominium indicator. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,12.718708429307757,5.4209195205915774,100*(exp(b - se^2/2) - 1),reported,0.07843270917637929,0.0311689544975201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5163727959697733,1023.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Condo,dwelling_type,A_structural,binary,binary,0.081467,0.021277,,,,,,high,Condominium indicator. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,8.463187600630317,2.308293678105219,100*(exp(b - se^2/2) - 1),reported,0.06107926832256162,0.015952270147411147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.828876251351224,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Condo,dwelling_type,A_structural,binary,binary,0.08111,0.021237,,,,,,high,Condominium indicator. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,8.424565344797074,2.303131803603789,100*(exp(b - se^2/2) - 1),reported,0.06092671835113499,0.01595241915451922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8192776757545794,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,Ranch,architectural_style,H_ai_derived,binary,binary,-0.070964,0.084647,,,,,,high,Assessor-classified ranch style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,-7.183573977305802,7.884829327038894,100*(exp(b - se^2/2) - 1),reported,-0.026202308980416065,0.031254535373784996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8383522156721444,1023.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,True
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,Ranch,architectural_style,H_ai_derived,binary,binary,0.142348,0.049215,,,,,,high,Assessor-classified ranch style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,15.158233704622571,5.674380316569813,100*(exp(b - se^2/2) - 1),reported,0.046176868622587325,0.015965061604382467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.892370212333638,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,Ranch,architectural_style,H_ai_derived,binary,binary,0.162981,0.049872,,,,,,high,Assessor-classified ranch style house (not AI-derived). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,17.555149133089444,5.870005847643674,100*(exp(b - se^2/2) - 1),reported,0.052158219817880276,0.015960355739364253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2679860442733397,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,DistancetoDowntown,cbd_distance,D_location,log,100 m,-0.256703,0.070046,,,,,,high,ln straight-line distance to downtown (hundreds of metres). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,-0.256703,0.070046,b (log-log),reported,-0.11383544046116843,0.031062033799928335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.664777431973275,1023.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,DistancetoDowntown,cbd_distance,D_location,log,100 m,-0.322434,0.038786,,,,,,high,ln straight-line distance to downtown (hundreds of metres). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,-0.322434,0.038786,b (log-log),reported,-0.13170439190978614,0.015842890466306176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.31315423090806,3915.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,DistancetoDowntown,cbd_distance,D_location,log,100 m,0.241554,0.092125,,,,,,low,ln straight-line distance to downtown (hundreds of metres). Column 3 value is identical to the DistancetoLake row (0.241554 (0.092125)): probable typesetting error. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,0.241554,0.092125,b (log-log),reported,0.04186873680561891,0.015968095656530804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.622024423337856,3915.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,DistancetoLake,water_proximity,E_environment,log,100 m,0.260477,0.130816,,,,,,high,ln straight-line distance to Lake Michigan (hundreds of metres). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,0.260477,0.130816,b (log-log),reported,0.06213420486956493,0.031204859331983265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.991170804794521,1023.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,True
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,DistancetoLake,water_proximity,E_environment,log,100 m,0.211159,0.088278,,,,,,high,ln straight-line distance to Lake Michigan (hundreds of metres). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,0.211159,0.088278,b (log-log),reported,0.0382009453998449,0.015970444347659856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3919776161671087,3915.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,DistancetoLake,water_proximity,E_environment,log,100 m,0.241554,0.092125,,,,,,high,ln straight-line distance to Lake Michigan (hundreds of metres). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,0.241554,0.092125,b (log-log),reported,0.04186873680561891,0.015968095656530804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.622024423337856,3915.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,17,ln distance x Menominee,water_quality,E_environment,other,100 m,-0.027456,0.070704,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.027456,0.070704,not harmonised,reported,-0.012140133400415053,0.031262965907012885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.38832315003394435,1023.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,17,ln distance x Menominee,water_quality,E_environment,other,100 m,-0.070494,0.036149,,,*,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.070494,0.036149,not harmonised,reported,-0.0311515139160593,0.01597435351308803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9500954383247115,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,17,ln distance x Menominee,water_quality,E_environment,other,100 m,-0.078889,0.03662,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.078889,0.03662,not harmonised,reported,-0.034409231582205296,0.01597264587636246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.154259967231021,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,18,ln distance x Kinnickinnic,water_quality,E_environment,other,100 m,-0.055137,0.070136,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.055137,0.070136,not harmonised,reported,-0.024571585301821127,0.03125583014542914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7861440629633853,1023.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,18,ln distance x Kinnickinnic,water_quality,E_environment,other,100 m,-0.092289,0.036414,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.092289,0.036414,not harmonised,reported,-0.040472467764810074,0.015969015171773387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.534437304333498,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,18,ln distance x Kinnickinnic,water_quality,E_environment,other,100 m,-0.100555,0.036838,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.100555,0.036838,not harmonised,reported,-0.043584178306314586,0.01596692318082658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.729654161463706,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c1,A1,1,18,ln distance x Milwaukee,water_quality,E_environment,other,100 m,0.002291,0.065402,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,1033.0,0.8296,se,Pre-project cross-section (Aug 2010-Jul 2011); month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,0.002291,0.065402,not harmonised,reported,0.0010952064241459083,0.03126525122304264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03502950980092352,1023.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,18,ln distance x Milwaukee,water_quality,E_environment,other,100 m,0.006906,0.035543,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,0.006906,0.035543,not harmonised,reported,0.0031053068070708172,0.015982032992139884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19429986213881778,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,18,ln distance x Milwaukee,water_quality,E_environment,other,100 m,0.044785,0.037371,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Stars from Table 2 (p.10). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,0.044785,0.037371,not harmonised,reported,0.019149274947007293,0.01597917950306151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1983891252575525,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,18,Post,transaction_timing,G_market,binary,binary,-0.014117,0.01218,,,,,,high,Post-remediation period indicator (Dec 2015-Nov 2016 sales). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,-1.4090956099514051,1.2009262923159043,100*(exp(b - se^2/2) - 1),reported,-0.01852058695773975,0.015979368785525975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.159031198686371,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,18,Post,transaction_timing,G_market,binary,binary,-0.014066,0.013048,,,,,,high,Post-remediation period indicator. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,pct_premium,-1.405146861832085,1.2865751589914665,100*(exp(b - se^2/2) - 1),reported,-0.017226471642063163,0.015979738517392303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0780196198651133,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,18,ln distance to LP,water_quality,E_environment,log,100 m,0.014304,0.017707,,,,,,high,"ln distance to Lincoln Park remediation site (hundreds of metres), truncated to homes within 5 km. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.",ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,elasticity,0.014304,0.017707,b (log-log),reported,0.012909530236735496,0.015980778236987934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.807816117919467,3915.0,100m,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,True,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c2,A1,2,18,ln distance to LP x Post,water_quality,E_environment,other,100 m,-0.023126,0.012902,,,*,,,high,DiD interaction: change in Lincoln Park proximity gradient after remediation. Stars from Table 2. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7966,se,Pre + post-project sales; Lincoln Park proximity x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.023126,0.012902,not harmonised,reported,-0.028635150689583434,0.01597555626554551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7924352813517284,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,18,ln distance x Treated,water_quality,E_environment,other,100 m,-0.053156,0.029504,,,*,,,high,ln distance to AOC interacted with treated-segment indicator (Lincoln Park to Humboldt Ave bridge). Stars from Table 2. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.053156,0.029504,not harmonised,reported,-0.028782303362675746,0.015975488720226975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8016540130151846,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:TA1-c3,A1,3,18,ln distance x Treated x Post,water_quality,E_environment,other,100 m,-0.023111,0.013905,,,*,,,high,Triple DiD interaction: change in proximity gradient in treated segment after remediation. Stars from Table 2. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7971,se,Pre + post-project sales; Treated-segment x Post design; month + tract FE; same models as Table 2 (p.10) which reports only coefficients of interest with stars,other,-0.023111,0.013905,not harmonised,reported,-0.02655392316610535,0.015976474476426587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.662064005753326,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c1,3,1,11,ln distance x Menominee,water_quality,E_environment,other,100 m,-0.0797,0.0393,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7968,se,Robustness: treated area 5 km downstream; month + tract FE; only coefficients of interest reported,other,-0.0797,0.0393,not harmonised,reported,-0.032394545598203,0.015973721982551797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0279898218829513,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c2,3,2,11,ln distance x Menominee,water_quality,E_environment,other,100 m,-0.0795,0.0379,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.8079,se,Robustness: tract-by-post fixed effects added; only coefficients of interest reported,other,-0.0795,0.0379,not harmonised,reported,-0.033505655803839705,0.015973136540446853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0976253298153034,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c3,3,3,11,ln distance x Menominee,water_quality,E_environment,other,100 m,-0.0257,0.0311,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,4437.0,0.8124,se,Robustness: mature-project post period (Aug 2017-Jul 2018); tract-by-post FE; only coefficients of interest reported,other,-0.0257,0.0311,not harmonised,reported,-0.012418938605592972,0.015028365394316788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8263665594855306,4427.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c1,3,1,11,ln distance x Kinnickinnic,water_quality,E_environment,other,100 m,-0.1008,0.0391,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7968,se,Robustness: treated area 5 km downstream; month + tract FE; only coefficients of interest reported,other,-0.1008,0.0391,not harmonised,reported,-0.04116703366296998,0.015968561668870302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.578005115089514,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c2,3,2,11,ln distance x Kinnickinnic,water_quality,E_environment,other,100 m,-0.0966,0.038,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.8079,se,Robustness: tract-by-post fixed effects added; only coefficients of interest reported,other,-0.0966,0.038,not harmonised,reported,-0.0405947160307127,0.01596893591270272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.542105263157895,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c3,3,3,11,ln distance x Kinnickinnic,water_quality,E_environment,other,100 m,-0.0573,0.0318,,,*,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,4437.0,0.8124,se,Robustness: mature-project post period (Aug 2017-Jul 2018); tract-by-post FE; only coefficients of interest reported,other,-0.0573,0.0318,not harmonised,reported,-0.027071576155117516,0.015024016086086162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.80188679245283,4427.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c1,3,1,11,ln distance x Milwaukee,water_quality,E_environment,other,100 m,0.0282,0.0367,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7968,se,Robustness: treated area 5 km downstream; month + tract FE; only coefficients of interest reported,other,0.0282,0.0367,not harmonised,reported,0.01227960551003009,0.01598090504319519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.768392370572207,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c2,3,2,11,ln distance x Milwaukee,water_quality,E_environment,other,100 m,0.073,0.0382,,,*,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.8079,se,Robustness: tract-by-post fixed effects added; only coefficients of interest reported,other,0.073,0.0382,not harmonised,reported,0.030527493967130528,0.015974661226635427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9109947643979057,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c3,3,3,11,ln distance x Milwaukee,water_quality,E_environment,other,100 m,0.0771,0.0318,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,4437.0,0.8124,se,Robustness: mature-project post period (Aug 2017-Jul 2018); tract-by-post FE; only coefficients of interest reported,other,0.0771,0.0318,not harmonised,reported,0.036415338561990106,0.015019555982766346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4245283018867925,4427.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c1,3,1,11,Post,transaction_timing,G_market,binary,binary,0.0282,0.0367,,,,,,low,Post-period indicator; printed value identical to the Milwaukee row in the same column (0.0282 (0.0367)) - probable typesetting error. Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7968,se,Robustness: treated area 5 km downstream; month + tract FE; only coefficients of interest reported,pct_premium,2.790891086595959,3.7749670797482713,100*(exp(b - se^2/2) - 1),reported,0.01227960551003009,0.01598090504319519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.768392370572207,3915.0,binary,,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c1,3,1,11,ln distance x Treated,water_quality,E_environment,other,100 m,-0.03,0.0529,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7968,se,Robustness: treated area 5 km downstream; month + tract FE; only coefficients of interest reported,other,-0.03,0.0529,not harmonised,reported,-0.009063206221436063,0.015981453637132256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5671077504725898,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c2,3,2,11,ln distance x Treated,water_quality,E_environment,other,100 m,-0.0239,0.0432,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.8079,se,Robustness: tract-by-post fixed effects added; only coefficients of interest reported,other,-0.0239,0.0432,not harmonised,reported,-0.00884160878996282,0.015981485344200578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5532407407407407,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c3,3,3,11,ln distance x Treated,water_quality,E_environment,other,100 m,-0.0048,0.0407,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,4437.0,0.8124,se,Robustness: mature-project post period (Aug 2017-Jul 2018); tract-by-post FE; only coefficients of interest reported,other,-0.0048,0.0407,not harmonised,reported,-0.0017725209825173367,0.015029500830928251,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11793611793611793,4427.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c1,3,1,11,ln distance x Treated x Post,water_quality,E_environment,other,100 m,-0.0303,0.0133,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.7968,se,Robustness: treated area 5 km downstream; month + tract FE; only coefficients of interest reported,other,-0.0303,0.0133,not harmonised,reported,-0.036386260095922884,0.015971526708771434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2781954887218046,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c2,3,2,11,ln distance x Treated x Post,water_quality,E_environment,other,100 m,-0.0718,0.0471,,,,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,3925.0,0.8079,se,Robustness: tract-by-post fixed effects added; only coefficients of interest reported,other,-0.0718,0.0471,not harmonised,reported,-0.024356158918632333,0.01597736887280756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.524416135881104,3915.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b,9848e68efcee710bc05a55f8576e298c00f7e04b:T3-c3,3,3,11,ln distance x Treated x Post,water_quality,E_environment,other,100 m,-0.1078,0.0414,,,**,,,high,ln straight-line distance to nearest point of the Milwaukee Estuary Area of Concern (hundreds of metres) interacted with river-segment indicator; AOC = PCB-contaminated waterway (remediation 2011-2015). Standard errors in parentheses; * and ** indicate significance at 10% and 5% (Table 2/3 legend); Table A1 prints full coefficients without stars.,ln sale price (USD),log,OLS,4437.0,0.8124,se,Robustness: mature-project post period (Aug 2017-Jul 2018); tract-by-post FE; only coefficients of interest reported,other,-0.1078,0.0414,not harmonised,reported,-0.0391049148223235,0.015018028512469321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.603864734299517,4427.0,100m,nonstandard_x_transform,2022.0,United States,North America,High income,2010.0,2016.0,mixed,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,"Cases per 100,000 ('000) [cumulative cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.382,0.172,,,**,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,-0.382,0.172,"b (currency per unit, as reported)",reported,-0.006342281662896035,0.0028556870314610412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.22093023255814,122620.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,"Cases per 100,000 ('000) [cumulative cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.398,0.174,,,**,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,-0.398,0.174,"b (currency per unit, as reported)",reported,-0.006549346945508663,0.002863282332961073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2873563218390807,121970.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c3,3,3,25,"Cases per 100,000 ('000) [cumulative cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.706,0.358,,,*,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; cumulative cases",other,-0.706,0.358,not harmonised,reported,-0.015822735744035813,0.008023426907032326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9720670391061452,15530.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c4,3,4,25,"Cases per 100,000 ('000) [new cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",10.162,12.208,,,,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; new cases",other,10.162,12.208,not harmonised,reported,0.006679427709616031,0.008024252458078382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8324049803407602,15530.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,"Cases per 100,000 ('000) [cumulative cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.611,0.236,,,**,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,-0.611,0.236,"b (currency per unit, as reported)",reported,-0.007097268664444854,0.002741334541422235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5889830508474576,133062.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,"Cases per 100,000 ('000) [cumulative cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.618,0.235,,,***,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,-0.618,0.235,"b (currency per unit, as reported)",reported,-0.0072275842008188715,0.002748353215521739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.629787234042553,132383.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c7,3,7,25,"Cases per 100,000 ('000) [cumulative cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.633,0.241,,,***,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; cumulative cases",other,-0.633,0.241,not harmonised,reported,-0.02094226752840352,0.007973280370213663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6265560165975104,15723.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c8,3,8,25,"Cases per 100,000 ('000) [new cases]",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-30.759,7.905,,,***,,,high,COVID-19 infection rate of the MODZCTA; no taxonomy leaf,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; new cases",other,-30.759,7.905,not harmonised,reported,-0.031016559954110987,0.007971192380677113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8910815939278938,15723.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.064,0.012,,,***,,,medium,"Row has 6 entries for 8 columns; assigned to cols 2,3,4,6,7,8 (cols 1 and 5 omit unemployment, consistent with N and Table 4 baseline)","Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,-0.064,0.012,"b (currency per unit, as reported)",reported,-0.015269386261160839,0.0028630099239676577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.333333333333333,121970.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c3,3,3,25,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.039,0.024,,,,,,high,Cross-checked with Table 4 column (1),"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; cumulative cases",other,-0.039,0.024,not harmonised,reported,-0.013038592675505252,0.008023749338772462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.625,15530.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c4,3,4,25,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.039,0.024,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; new cases",other,-0.039,0.024,not harmonised,reported,-0.013038592675505252,0.008023749338772462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.625,15530.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.072,0.013,,,***,,,medium,"Row has 6 entries for 8 columns; assigned to cols 2,3,4,6,7,8 (cols 1 and 5 omit unemployment, consistent with N and Table 4 baseline)","Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,-0.072,0.013,"b (currency per unit, as reported)",reported,-0.015220282918897836,0.0027481066381343316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.538461538461538,132383.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c7,3,7,25,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.06,0.024,,,**,,,high,Cross-checked with Table 4 column (1),"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; cumulative cases",other,-0.06,0.024,not harmonised,reported,-0.019933612021352548,0.007973444808541019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,15723.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c8,3,8,25,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.063,0.024,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; new cases",other,-0.063,0.024,not harmonised,reported,-0.020929866408228543,0.00797328244122992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.625,15723.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,Investor Owned (%),other,other,level,share,0.004,0.009,,,,,,high,Share of investor-owned properties in the MODZCTA,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,0.004,0.009,"b (currency per unit, as reported)",reported,0.0012692187410463258,0.002855742167354233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4444444444444445,122620.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,Investor Owned (%),other,other,level,share,0.004,0.009,,,,,,high,Share of investor-owned properties in the MODZCTA,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.004,0.009,"b (currency per unit, as reported)",reported,0.001272596188924304,0.002863341425079684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4444444444444445,121970.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c3,3,3,25,Investor Owned (%),other,other,level,share,-0.009,0.013,,,,,,high,Share of investor-owned properties in the MODZCTA,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; cumulative cases",other,-0.009,0.013,not harmonised,reported,-0.005555289905420674,0.008024307641163195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6923076923076923,15530.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c4,3,4,25,Investor Owned (%),other,other,level,share,-0.009,0.013,,,,,,high,Share of investor-owned properties in the MODZCTA,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; new cases",other,-0.009,0.013,not harmonised,reported,-0.005555289905420674,0.008024307641163195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6923076923076923,15530.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,Investor Owned (%),other,other,level,share,0.002,0.01,,,,,,high,Share of investor-owned properties in the MODZCTA,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,0.002,0.01,"b (currency per unit, as reported)",reported,0.0005482806348335487,0.002741403174167744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,133062.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,Investor Owned (%),other,other,level,share,0.002,0.01,,,,,,high,Share of investor-owned properties in the MODZCTA,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.002,0.01,"b (currency per unit, as reported)",reported,0.0005496849174624306,0.002748424587312153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,132383.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c7,3,7,25,Investor Owned (%),other,other,level,share,-0.011,0.014,,,,,,high,Share of investor-owned properties in the MODZCTA,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; cumulative cases",other,-0.011,0.014,not harmonised,reported,-0.006265971516803732,0.007974872839568386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7857142857142857,15723.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c8,3,8,25,Investor Owned (%),other,other,level,share,-0.01,0.014,,,,,,high,Share of investor-owned properties in the MODZCTA,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; new cases",other,-0.01,0.014,not harmonised,reported,-0.005696357150456286,0.007974900010638799,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7142857142857143,15723.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,Altered (yes==1),renovation,A_structural,binary,binary,1.356,0.228,,,***,,,high,Home altered per PLUTO assessment records,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,1.356,0.228,"b (currency per unit, as reported)",reported,0.016981715352985167,0.002855332669970957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.947368421052632,122620.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,Altered (yes==1),renovation,A_structural,binary,binary,1.351,0.228,,,***,,,high,Home altered per PLUTO assessment records,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,1.351,0.228,"b (currency per unit, as reported)",reported,0.01696412602538754,0.0028629317052467503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.925438596491228,121970.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c3,3,3,25,Altered (yes==1),renovation,A_structural,binary,binary,2.432,0.339,,,***,,,high,Home altered per PLUTO assessment records,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; cumulative cases",other,2.432,0.339,not harmonised,reported,0.057472448808709775,0.008011167823253543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.174041297935102,15530.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c4,3,4,25,Altered (yes==1),renovation,A_structural,binary,binary,2.432,0.34,,,***,,,high,Home altered per PLUTO assessment records,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"Repeat sales, first wave; new cases",other,2.432,0.34,not harmonised,reported,0.057303968083657744,0.008011245538011363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.152941176470588,15530.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,Altered (yes==1),renovation,A_structural,binary,binary,1.997,0.315,,,***,,,high,Home altered per PLUTO assessment records,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,1.997,0.315,"b (currency per unit, as reported)",reported,0.017377004272932137,0.0027409896574730214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.33968253968254,133062.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,Altered (yes==1),renovation,A_structural,binary,binary,1.989,0.315,,,***,,,high,Home altered per PLUTO assessment records,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,1.989,0.315,"b (currency per unit, as reported)",reported,0.017351727989780918,0.0027480112201010505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.314285714285715,132383.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c7,3,7,25,Altered (yes==1),renovation,A_structural,binary,binary,3.056,0.591,,,***,,,high,Home altered per PLUTO assessment records,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; cumulative cases",other,3.056,0.591,not harmonised,reported,0.04120303430219225,0.007968256960927886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.170896785109983,15723.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c8,3,8,25,Altered (yes==1),renovation,A_structural,binary,binary,3.058,0.591,,,***,,,high,Home altered per PLUTO assessment records,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Repeat sales, second wave; new cases",other,3.058,0.591,not harmonised,reported,0.04122995381512091,0.007968248104884388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.174280879864636,15723.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,Year Built,age,A_structural,level,years,0.002,0.002,,,,,,high,"Year built (calendar year), not age","Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,0.002,0.002,"b (currency per unit, as reported)",reported,0.002855732822916092,0.002855732822916092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,122620.0,years,year_built_not_age,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,Year Built,age,A_structural,level,years,0.002,0.002,,,,,,high,"Year built (calendar year), not age","Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.002,0.002,"b (currency per unit, as reported)",reported,0.002863332005845201,0.002863332005845201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,121970.0,years,year_built_not_age,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,Year Built,age,A_structural,level,years,0.003,0.002,,,,,,high,"Year built (calendar year), not age","Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,0.003,0.002,"b (currency per unit, as reported)",reported,0.004112070613126721,0.0027413804087511474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,133062.0,years,year_built_not_age,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,Year Built,age,A_structural,level,years,0.003,0.002,,,,,,high,"Year built (calendar year), not age","Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.003,0.002,"b (currency per unit, as reported)",reported,0.0041226024697874845,0.0027484016465249895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,132383.0,years,year_built_not_age,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,Home Sqft ('000),living_area,A_structural,level,sqft,2.174,0.28,,,***,,,high,Thousands of square feet,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,2.174,0.28,"b (currency per unit, as reported)",reported,0.02216736752931635,0.0028550427360665037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.764285714285713,122620.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,Home Sqft ('000),living_area,A_structural,level,sqft,2.163,0.281,,,***,,,high,Thousands of square feet,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,2.163,0.281,"b (currency per unit, as reported)",reported,0.022035262351064025,0.0028626485070036953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.697508896797151,121970.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,Home Sqft ('000),living_area,A_structural,level,sqft,2.174,0.341,,,***,,,high,Thousands of square feet,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,2.174,0.341,"b (currency per unit, as reported)",reported,0.01747478404364194,0.002740984985686247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.375366568914956,133062.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,Home Sqft ('000),living_area,A_structural,level,sqft,2.159,0.341,,,***,,,high,Thousands of square feet,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,2.159,0.341,"b (currency per unit, as reported)",reported,0.01739868440520071,0.002748008977384643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.331378299120233,132383.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,"Lot Sqft ('00,000)",lot_size,A_structural,level,sqft,0.032,0.014,,,**,,,high,Hundred-thousands of square feet,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,0.032,0.014,"b (currency per unit, as reported)",reported,0.0065272768729208825,0.0028556836319028863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2857142857142856,122620.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,"Lot Sqft ('00,000)",lot_size,A_structural,level,sqft,0.032,0.013,,,**,,,high,Hundred-thousands of square feet,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.032,0.013,"b (currency per unit, as reported)",reported,0.007048055690741737,0.0028632726243638306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4615384615384617,121970.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,"Lot Sqft ('00,000)",lot_size,A_structural,level,sqft,0.027,0.015,,,*,,,high,Hundred-thousands of square feet,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,0.027,0.015,"b (currency per unit, as reported)",reported,0.004934466379534564,0.002741370210852535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,133062.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,"Lot Sqft ('00,000)",lot_size,A_structural,level,sqft,0.026,0.015,,,*,,,high,Hundred-thousands of square feet,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.026,0.015,"b (currency per unit, as reported)",reported,0.004763882612989388,0.0027483938151861855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7333333333333334,132383.0,sqft,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,# of Floors,storeys,A_structural,level,count,0.051,0.069,,,,,,high,,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,0.051,0.069,"b (currency per unit, as reported)",reported,0.002110762947841027,0.0028557381059025667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7391304347826085,122620.0,count,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,# of Floors,storeys,A_structural,level,count,0.06,0.071,,,,,,high,,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.06,0.071,"b (currency per unit, as reported)",reported,0.0024197200235148535,0.002863335361159243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8450704225352114,121970.0,count,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,# of Floors,storeys,A_structural,level,count,0.045,0.159,,,,,,high,,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,0.045,0.159,"b (currency per unit, as reported)",reported,0.0007758687059694747,0.0027414027610921437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2830188679245283,133062.0,count,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,# of Floors,storeys,A_structural,level,count,0.067,0.158,,,,,,high,,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.067,0.158,"b (currency per unit, as reported)",reported,0.0011654705702918342,0.0027484231359120865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42405063291139244,132383.0,count,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c1,3,1,25,Basement (yes==1),basement,A_structural,binary,binary,0.519,0.109,,,***,,,high,,"Sale price ($100,000)",level,OLS,122630.0,0.606,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE",monetary,0.519,0.109,"b (currency per unit, as reported)",reported,0.013596278713854713,0.002855480500597618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.761467889908257,122620.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c2,3,2,25,Basement (yes==1),basement,A_structural,binary,binary,0.502,0.112,,,***,,,high,,"Sale price ($100,000)",level,OLS,121980.0,0.608,se,"All sales, first-wave months (Mar-Jul) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.502,0.112,"b (currency per unit, as reported)",reported,0.01283285890557436,0.00286310796299667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.482142857142857,121970.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c5,3,5,25,Basement (yes==1),basement,A_structural,binary,binary,0.53,0.133,,,***,,,high,,"Sale price ($100,000)",level,OLS,133072.0,0.551,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE",monetary,0.53,0.133,"b (currency per unit, as reported)",reported,0.010923738418190842,0.0027412400181497775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9849624060150375,133062.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T3-c6,3,6,25,Basement (yes==1),basement,A_structural,binary,binary,0.506,0.132,,,***,,,high,,"Sale price ($100,000)",level,OLS,132393.0,0.552,se,"All sales, second-wave months (Aug-Dec) 2003-2020; MODZCTA FE; adds unemployment",monetary,0.506,0.132,"b (currency per unit, as reported)",reported,0.010535044500367656,0.00274827247835678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.833333333333333,132383.0,binary,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,ols,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4A-c2,4,2 (Panel A),28,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-6.459,1.364,,,***,,,high,Cumulative COVID-19 case rate,"Change in sale price between repeat sales ($100,000)",other,other,2915.0,0.168,se,"Repeat sales, first wave, homes priced < 25th percentile",other,-6.459,1.364,not harmonised,reported,-0.08752016803816162,0.018482351634007195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.735337243401759,2905.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4A-c2,4,2 (Panel A),28,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.032,0.027,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,2915.0,0.168,se,"Repeat sales, first wave, homes priced < 25th percentile",other,-0.032,0.027,not harmonised,reported,-0.02198407379888989,0.018549062267813346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1851851851851851,2905.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4A-c3,4,3 (Panel A),28,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.854,0.469,,,*,,,high,Cumulative COVID-19 case rate,"Change in sale price between repeat sales ($100,000)",other,other,4360.0,0.093,se,"Repeat sales, first wave, homes priced > 75th percentile",other,-0.854,0.469,not harmonised,reported,-0.027597830844714632,0.01515618579176951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8208955223880599,4350.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4A-c3,4,3 (Panel A),28,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,0.019,0.064,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,4360.0,0.093,se,"Repeat sales, first wave, homes priced > 75th percentile",other,0.019,0.064,not harmonised,reported,0.004501161535266112,0.01516180727668585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.296875,4350.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4B-c2,4,2 (Panel B),28,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",0.2,0.724,,,,,,high,Cumulative COVID-19 case rate,"Change in sale price between repeat sales ($100,000)",other,other,3208.0,0.182,se,"Repeat sales, second wave, homes priced < 25th percentile",other,0.2,0.724,not harmonised,reported,0.004884802601930732,0.01768298541898925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.27624309392265195,3198.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4B-c2,4,2 (Panel B),28,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.005,0.027,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,3208.0,0.182,se,"Repeat sales, second wave, homes priced < 25th percentile",other,-0.005,0.027,not harmonised,reported,-0.0032746484408668996,0.017683101580681256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1851851851851852,3198.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4B-c3,4,3 (Panel B),28,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-1.242,0.353,,,***,,,high,Cumulative COVID-19 case rate,"Change in sale price between repeat sales ($100,000)",other,other,4856.0,0.07,se,"Repeat sales, second wave, homes priced > 75th percentile",other,-1.242,0.353,not harmonised,reported,-0.05047788902709427,0.014346775222676552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.518413597733711,4846.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T4B-c3,4,3 (Panel B),28,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.07,0.053,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,4856.0,0.07,se,"Repeat sales, second wave, homes priced > 75th percentile",other,-0.07,0.053,not harmonised,reported,-0.018969344075380482,0.014362503371359508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3207547169811322,4846.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c1,5,1 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.594,0.211,,,***,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,36051.0,0.145,se,"All repeat sales, all months 2003-2020 (Panel B col 1 is identical)",other,-0.594,0.211,not harmonised,reported,-0.014827155066645935,0.005266885048926418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.815165876777251,36041.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c1,5,1 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.053,0.018,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,36051.0,0.145,se,"All repeat sales, all months 2003-2020 (Panel B col 1 is identical)",other,-0.053,0.018,not harmonised,reported,-0.015507890264149815,0.0052668306557489926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9444444444444446,36041.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c2,5,2 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.669,0.351,,,*,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15353.0,0.228,se,"First wave, outliers (>3 SD) excluded",other,-0.669,0.351,not harmonised,reported,-0.015385529624299454,0.008072228547278187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9059829059829063,15343.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c2,5,2 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.068,0.018,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15353.0,0.228,se,"First wave, outliers (>3 SD) excluded",other,-0.068,0.018,not harmonised,reported,-0.030484520974177184,0.008069432022576314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.777777777777778,15343.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c3,5,3 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.893,1.856,,,,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,6174.0,0.101,se,"First wave, repeat sales > 3 years apart",other,-0.893,1.856,not harmonised,reported,-0.006128215329386012,0.012736805880560401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48114224137931033,6164.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c3,5,3 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.074,0.026,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,6174.0,0.101,se,"First wave, repeat sales > 3 years apart",other,-0.074,0.026,not harmonised,reported,-0.036227792701793986,0.01272868392225194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8461538461538463,6164.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c4,5,4 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.703,0.359,,,*,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,13757.0,0.136,se,"First wave, properties sold < 4 times",other,-0.703,0.359,not harmonised,reported,-0.016699226183742838,0.008527769843476073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.958217270194986,13747.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c4,5,4 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.041,0.026,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,13757.0,0.136,se,"First wave, properties sold < 4 times",other,-0.041,0.026,not harmonised,reported,-0.013448296213067696,0.008528187842433171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.576923076923077,13747.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c5,5,5 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.719,0.358,,,**,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"First wave, controlling for NYC shutdown (shutdown dummy not extracted)",other,-0.719,0.358,not harmonised,reported,-0.016114014265558117,0.00802338957867845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0083798882681565,15530.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c5,5,5 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.039,0.024,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"First wave, controlling for NYC shutdown (shutdown dummy not extracted)",other,-0.039,0.024,not harmonised,reported,-0.013038592675505252,0.008023749338772462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.625,15530.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c6,5,6 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.631,0.344,,,*,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15323.0,0.146,se,"First wave, 30-day lagged case and unemployment rates",other,-0.631,0.344,not harmonised,reported,-0.01482153103571825,0.00808020075481312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8343023255813955,15313.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c6,5,6 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.037,0.024,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15323.0,0.146,se,"First wave, 30-day lagged case and unemployment rates",other,-0.037,0.024,not harmonised,reported,-0.012457377930099045,0.008080461360064246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5416666666666665,15313.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c7,5,7 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.422,0.394,,,,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15094.0,0.146,se,"First wave, 60-day lagged rates",other,-0.422,0.394,not harmonised,reported,-0.008720501341098065,0.008141889877707672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0710659898477157,15084.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c7,5,7 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.043,0.023,,,*,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15094.0,0.146,se,"First wave, 60-day lagged rates",other,-0.043,0.023,not harmonised,reported,-0.015220609576653731,0.00814125628518688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8695652173913042,15084.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c8,5,8 (Panel A),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-1.005,0.58,,,*,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"First wave, borough-level case and unemployment rates",other,-1.005,0.58,not harmonised,reported,-0.013903058904503622,0.00802365588518617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.732758620689655,15530.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5A-c8,5,8 (Panel A),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.232,0.118,,,*,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15540.0,0.146,se,"First wave, borough-level case and unemployment rates",other,-0.232,0.118,not harmonised,reported,-0.01577488516944953,0.00802343297411657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9661016949152545,15530.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c2,5,2 (Panel B),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.481,0.198,,,**,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15575.0,0.229,se,"Second wave, outliers excluded",other,-0.481,0.198,not harmonised,reported,-0.019468074875905135,0.008013885291952634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.429292929292929,15565.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c2,5,2 (Panel B),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.07,0.019,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15575.0,0.229,se,"Second wave, outliers excluded",other,-0.07,0.019,not harmonised,reported,-0.029517569639947002,0.008011911759414186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.68421052631579,15565.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c3,5,3 (Panel B),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-1.06,0.441,,,**,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,7037.0,0.107,se,"Second wave, repeat sales > 3 years apart",other,-1.06,0.441,not harmonised,reported,-0.028661824947274005,0.011924400756365881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4036281179138324,7027.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c3,5,3 (Panel B),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.064,0.024,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,7037.0,0.107,se,"Second wave, repeat sales > 3 years apart",other,-0.064,0.024,not harmonised,reported,-0.031795387350491804,0.011923270256434425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6666666666666665,7027.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c4,5,4 (Panel B),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.633,0.242,,,***,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,14014.0,0.12,se,"Second wave, properties sold < 4 times",other,-0.633,0.242,not harmonised,reported,-0.022098165805002918,0.008448271919132238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.615702479338843,14004.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c4,5,4 (Panel B),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.064,0.024,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,14014.0,0.12,se,"Second wave, properties sold < 4 times",other,-0.064,0.024,not harmonised,reported,-0.022528508681446093,0.008448190755542286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6666666666666665,14004.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c6,5,6 (Panel B),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.622,0.297,,,**,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15570.0,0.134,se,"Second wave, 30-day lag",other,-0.622,0.297,not harmonised,reported,-0.01678680088241783,0.008015562479225235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0942760942760943,15560.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c6,5,6 (Panel B),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.067,0.022,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15570.0,0.134,se,"Second wave, 30-day lag",other,-0.067,0.022,not harmonised,reported,-0.02440719831571508,0.00801430392456316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.045454545454546,15560.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c7,5,7 (Panel B),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-0.5,0.318,,,,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15410.0,0.134,se,"Second wave, 60-day lag",other,-0.5,0.318,not harmonised,reported,-0.012669155520673075,0.008057582911148076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.572327044025157,15400.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c7,5,7 (Panel B),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.061,0.021,,,***,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15410.0,0.134,se,"Second wave, 60-day lag",other,-0.061,0.021,not harmonised,reported,-0.02340082871354786,0.008056022999745985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9047619047619047,15400.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c8,5,8 (Panel B),31,"Cases per 100,000 ('000)",other,other,level,"COVID-19 cases per 100,000 residents (thousands)",-1.119,0.344,,,***,,,high,Cumulative COVID-19 case rate (lagged / borough-level per column),"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Second wave, borough-level rates",other,-1.119,0.344,not harmonised,reported,-0.025933303832069654,0.007972347201279678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2529069767441863,15723.0,covid-19 cases per 100000 residents,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,False,False
98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f,98c105ab42ddbb39da40f571a9e40579ebb0b16f:T5B-c8,5,8 (Panel B),31,Unemployment Rate (%),unemployment,F_neighbourhood,level,share,-0.166,0.105,,,,,,high,,"Change in sale price between repeat sales ($100,000)",other,other,15733.0,0.131,se,"Second wave, borough-level rates",other,-0.166,0.105,not harmonised,reported,-0.012607139710223158,0.007974395599839949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.580952380952381,15723.0,share,,2021.0,United States,North America,High income,2003.0,2020.0,single_family,transaction,journal,True,True,clustered,,2011.5,other,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c1,2,1,10,Ln Rooms,bedrooms,A_structural,log,count (rooms excluding kitchen and bathrooms),0.5748,,21.73,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.7472,t,"Model 1: tourists, no geographical interaction terms (adj. R2)",elasticity,0.5748,0.026451909802116886,b (log-log),|b/t|,0.7396151467095151,0.03403659211732698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.73,391.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c3,2,3,10,Ln Rooms,bedrooms,A_structural,log,count (rooms excluding kitchen and bathrooms),0.519,,14.56,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8517,t,"Model 3: residents, no geographical interaction terms (adj. R2)",elasticity,0.519,0.035645604395604394,b (log-log),|b/t|,0.8309917537479088,0.057073609460708025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.56,95.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,10,Ln Rooms × Verbier,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.9036,,15.17,,**,,,high,Interaction: log rooms x Verbier resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",other,0.9036,0.05956493078444298,not harmonised,|b/t|,0.6086879615101374,0.040124453626245046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.17,391.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,10,Ln Rooms × Verbier,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.61,,7.87,,**,,,high,Interaction: log rooms x Verbier resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",other,0.61,0.07750952986022872,not harmonised,|b/t|,0.628220635101128,0.07982473127079136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.87,95.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,10,Ln Rooms × Anzere,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.6507,,15.17,,**,,,high,Interaction: log rooms x Anzere resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",other,0.6507,0.042893869479235326,not harmonised,|b/t|,0.6086879615101374,0.040124453626245046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.17,391.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,10,Ln Rooms × Anzere,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.4445,,12.62,,**,,,high,Interaction: log rooms x Anzere resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",other,0.4445,0.03522187004754358,not harmonised,|b/t|,0.7914374118638497,0.0627129486421434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.62,95.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,10,Ln Rooms × Ovronnaz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.5234,,9.5,,**,,,high,Interaction: log rooms x Ovronnaz resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",other,0.5234,0.05509473684210526,not harmonised,|b/t|,0.43305019054662425,0.04558423058385518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.5,391.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,10,Ln Rooms × Ovronnaz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.5264,,9.83,,**,,,high,Interaction: log rooms x Ovronnaz resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",other,0.5264,0.05355035605289928,not harmonised,|b/t|,0.7101057257976108,0.0722386292774782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.83,95.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,10,Ln Rooms × Champery,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.5178,,12.88,,**,,,high,Interaction: log rooms x Champery resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",other,0.5178,0.04020186335403727,not harmonised,|b/t|,0.5457948552830022,0.042375376962966005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.88,391.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,10,Ln Rooms × Champery,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.4082,,8.22,,**,,,high,Interaction: log rooms x Champery resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",other,0.4082,0.04965936739659367,not harmonised,|b/t|,0.6446941832797558,0.07842994930410654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.22,95.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,10,Ln Rooms × Grimentz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.4813,,12.54,,**,,,high,Interaction: log rooms x Grimentz resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",other,0.4813,0.03838118022328549,not harmonised,|b/t|,0.5355593214044237,0.042708079856812096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.54,391.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,10,Ln Rooms × Grimentz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.5168,,14.46,,**,,,high,Interaction: log rooms x Grimentz resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",other,0.5168,0.03573997233748271,not harmonised,|b/t|,0.8292129598724959,0.05734529459699141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.46,95.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,11,Ln Rooms × Haute-Nendaz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.4209,,9.91,,**,,,high,Interaction: log rooms x Haute-Nendaz resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",other,0.4209,0.04247225025227043,not harmonised,|b/t|,0.4480503644141186,0.04521194393684345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.91,391.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,11,Ln Rooms × Haute-Nendaz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy)",0.4182,,4.8,,**,,,high,Interaction: log rooms x Haute-Nendaz resort dummy (resort-specific elasticity); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",other,0.4182,0.08712500000000001,not harmonised,|b/t|,0.441800941420516,0.09204186279594083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,95.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c1,2,1,11,Ln Landscape-Quality (visibility-adjusted MACBETH score),view,E_environment,log,"index (visibility-adjusted MACBETH landscape-quality score, 10-110)",0.0449,,6.57,,**,,,high,Natural landscape quality score; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.7472,t,"Model 1: tourists, no geographical interaction terms (adj. R2)",elasticity,0.0449,0.0068340943683409434,b (log-log),|b/t|,0.3153101960212747,0.04799241948573435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.57,391.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,True
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,11,Ln Landscape-Quality (visibility-adjusted MACBETH score),view,E_environment,log,"index (visibility-adjusted MACBETH landscape-quality score, 10-110)",0.0225,,3.84,,**,,,high,Natural landscape quality score; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.0225,0.005859375,b (log-log),|b/t|,0.19063573575140608,0.04964472285192866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.84,391.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c3,2,3,11,Ln Landscape-Quality (visibility-adjusted MACBETH score),view,E_environment,log,"index (visibility-adjusted MACBETH landscape-quality score, 10-110)",0.0351,,4.28,,**,,,high,Natural landscape quality score; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8517,t,"Model 3: residents, no geographical interaction terms (adj. R2)",elasticity,0.0351,0.008200934579439252,b (log-log),|b/t|,0.40206248457180943,0.0939398328438807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.28,95.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,11,Ln Landscape-Quality (visibility-adjusted MACBETH score),view,E_environment,log,"index (visibility-adjusted MACBETH landscape-quality score, 10-110)",0.0287,,2.67,,**,,,high,Natural landscape quality score; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.0287,0.010749063670411986,b (log-log),|b/t|,0.26420250696165765,0.09895224979837366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.67,95.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c1,2,1,11,Ln Resort-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"index (share of the six resorts' total infrastructure, averaged over 7 types)",0.1297,,6.19,,**,,,high,"Resort-level infrastructure index (bars, sports stores, etc.); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.7472,t,"Model 1: tourists, no geographical interaction terms (adj. R2)",elasticity,0.1297,0.020953150242326333,b (log-log),|b/t|,0.2987459739865103,0.04826267754224722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.19,391.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,11,Ln Resort-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"index (share of the six resorts' total infrastructure, averaged over 7 types)",-0.026,,-0.54,,,,,high,"Resort-level infrastructure index (bars, sports stores, etc.); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,-0.026,0.04814814814814814,b (log-log),|b/t|,-0.02729879626926644,0.050553326424567474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.54,391.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c3,2,3,11,Ln Resort-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"index (share of the six resorts' total infrastructure, averaged over 7 types)",0.1962,,0.82,,,,,high,"Resort-level infrastructure index (bars, sports stores, etc.); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8517,t,"Model 3: residents, no geographical interaction terms (adj. R2)",elasticity,0.1962,0.23926829268292685,b (log-log),|b/t|,0.08383406365746704,0.10223666299691103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.82,95.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,True
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,11,Ln Resort-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"index (share of the six resorts' total infrastructure, averaged over 7 types)",0.0156,,0.58,,,,,high,"Resort-level infrastructure index (bars, sports stores, etc.); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.0156,0.02689655172413793,b (log-log),|b/t|,0.05940166515019167,0.10241666405205462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.58,95.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c1,2,1,11,Ln Distances-to-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"distance (sum of distances to ropeways, grocery stores and resort centre; unit not printed)",0.0428,,2.68,,**,,,high,Sum of distances to the resort's main infrastructures; positive sign = rents higher away from centre; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.7472,t,"Model 1: tourists, no geographical interaction terms (adj. R2)",elasticity,0.0428,0.01597014925373134,b (log-log),|b/t|,0.1343054895033587,0.050113988620656226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.68,391.0,distance,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,11,Ln Distances-to-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"distance (sum of distances to ropeways, grocery stores and resort centre; unit not printed)",0.0001,,0.01,,,,,high,Sum of distances to the resort's main infrastructures; positive sign = rents higher away from centre; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.0001,0.01,b (log-log),|b/t|,0.0005057216727538872,0.05057216727538871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01,391.0,distance,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c3,2,3,11,Ln Distances-to-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"distance (sum of distances to ropeways, grocery stores and resort centre; unit not printed)",0.1393,,2.63,,**,,,high,Sum of distances to the resort's main infrastructures; positive sign = rents higher away from centre; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8517,t,"Model 3: residents, no geographical interaction terms (adj. R2)",elasticity,0.1393,0.052965779467680614,b (log-log),|b/t|,0.26051494695599614,0.09905511291102516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.63,95.0,distance,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,11,Ln Distances-to-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"distance (sum of distances to ropeways, grocery stores and resort centre; unit not printed)",0.1509,,2.91,,**,,,high,Sum of distances to the resort's main infrastructures; positive sign = rents higher away from centre; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.1509,0.051855670103092784,b (log-log),|b/t|,0.2860814852915927,0.09830978875999749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.91,95.0,distance,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c1,2,1,11,Ln Average-Ski-Runs,other,other,log,km per lift (ratio of run length in km to number of ski lifts),0.3234,,10.45,,**,,,high,Resort ski-run quality (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.7472,t,"Model 1: tourists, no geographical interaction terms (adj. R2)",elasticity,0.3234,0.030947368421052637,b (log-log),|b/t|,0.46724360004646104,0.04471230622454173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.45,391.0,km per lift,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,11,Ln Average-Ski-Runs,other,other,log,km per lift (ratio of run length in km to number of ski lifts),0.1678,,2.4,,*,,,high,Resort ski-run quality (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.1678,0.06991666666666667,b (log-log),|b/t|,0.12048897252132196,0.05020373855055081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,391.0,km per lift,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c3,2,3,11,Ln Average-Ski-Runs,other,other,log,km per lift (ratio of run length in km to number of ski lifts),-0.038,,-0.53,,,,,high,Resort ski-run quality (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8517,t,"Model 3: residents, no geographical interaction terms (adj. R2)",elasticity,-0.038,0.07169811320754717,b (log-log),|b/t|,-0.05429663861819049,0.10244648795885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.53,95.0,km per lift,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,11,Ln Average-Ski-Runs,other,other,log,km per lift (ratio of run length in km to number of ski lifts),-0.1663,,-2.22,,*,,,high,Resort ski-run quality (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,-0.1663,0.07490990990990991,b (log-log),|b/t|,-0.2220795187040929,0.10003581923607788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.22,95.0,km per lift,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c1,2,1,11,Ln Flat-Equipment,other,other,log,"count (1 + number of amenities: TV-cable, fireplace, balcony, dishwasher, security system)",0.1545,,3.32,,**,,,high,Composite equipment count (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.7472,t,"Model 1: tourists, no geographical interaction terms (adj. R2)",elasticity,0.1545,0.04653614457831325,b (log-log),|b/t|,0.16558193663004392,0.049874077298206004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.32,391.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c2,2,2,11,Ln Flat-Equipment,other,other,log,"count (1 + number of amenities: TV-cable, fireplace, balcony, dishwasher, security system)",0.1914,,4.44,,**,,,high,Composite equipment count (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,401.0,0.8151,t,"Model 2: tourists, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.1914,0.0431081081081081,b (log-log),|b/t|,0.21908539561380944,0.0493435575706778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.44,391.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c3,2,3,11,Ln Flat-Equipment,other,other,log,"count (1 + number of amenities: TV-cable, fireplace, balcony, dishwasher, security system)",0.1027,,2.75,,**,,,high,Composite equipment count (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8517,t,"Model 3: residents, no geographical interaction terms (adj. R2)",elasticity,0.1027,0.03734545454545454,b (log-log),|b/t|,0.27154286423622187,0.09874285972226249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,95.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T2-c4,2,4,11,Ln Flat-Equipment,other,other,log,"count (1 + number of amenities: TV-cable, fireplace, balcony, dishwasher, security system)",0.1091,,2.15,,*,,,high,Composite equipment count (other); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,105.0,0.8677,t,"Model 4: residents, Ln Rooms interacted with resort dummies (adj. R2)",elasticity,0.1091,0.05074418604651163,b (log-log),|b/t|,0.21540696508304633,0.10018928608513783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.15,95.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Rooms,bedrooms,A_structural,log,count (rooms excluding kitchen and bathrooms),0.577,,25.76,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",elasticity,0.577,0.022399068322981365,b (log-log),|b/t|,0.7564773863306632,0.029366358165010223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.76,496.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Verbier × R,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.5995,,10.66,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.5995,0.05623827392120075,not harmonised,|b/t|,0.43173975896152356,0.04050091547481458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.66,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Verbier × T,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.8857,,20.46,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.8857,0.04328934506353861,not harmonised,|b/t|,0.6765303381556038,0.03306599893233644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.46,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Anzere × R,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.4432,,16.66,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.4432,0.026602641056422568,not harmonised,|b/t|,0.5990033204820983,0.03595458106135043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.66,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Anzere × T,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.6499,,16.03,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.6499,0.04054273237679351,not harmonised,|b/t|,0.5841808416150693,0.0364429720283886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.03,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Ovronnaz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.5267,,12.55,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.5267,0.041968127490039836,not harmonised,|b/t|,0.490930595898338,0.03911797576879187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.55,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Champery,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.5102,,14.11,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.5102,0.036158752657689586,not harmonised,|b/t|,0.535187347297709,0.03792964899345918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.11,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Grimentz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.4905,,15.77,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.4905,0.031103360811667723,not harmonised,|b/t|,0.5778872154435177,0.03664471879794025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.77,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Rooms × Haute-Nendaz,bedrooms,A_structural,other,"count (log rooms, interacted with resort dummy and/or R=resident, T=tourist)",0.4122,,11.5,,**,,,high,"Interaction term (resort-specific, and tenant-type-specific where R/T); robust (Huber-White) t-value","ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.4122,0.03584347826086957,not harmonised,|b/t|,0.45880864427913803,0.03989640385035983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.5,496.0,count,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Landscape-Quality,view,E_environment,log,"index (visibility-adjusted MACBETH score, 10-110)",0.0417,,7.58,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",elasticity,0.0417,0.00550131926121372,b (log-log),|b/t|,0.3222014560565706,0.042506788397964464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.58,496.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Landscape-Quality,view,E_environment,log,"index (visibility-adjusted MACBETH score, 10-110)",0.0249,,5.31,,**,,,high,95% CI 0.0157 to 0.0341 reported in text (p. 670); robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",elasticity,0.0249,0.004689265536723164,b (log-log),|b/t|,0.23192503368016262,0.043677030824889385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.31,496.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Resort-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,index (resort infrastructure share),0.1138,,6.39,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",elasticity,0.1138,0.01780907668231612,b (log-log),|b/t|,0.2757919717646572,0.04315993298351443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.39,496.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Resort-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,index (resort infrastructure share),-0.006,,-0.19,,,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",elasticity,-0.006,0.031578947368421054,b (log-log),|b/t|,-0.008530941401331125,0.044899691585953284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.19,496.0,index,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Distances-to-Infrastructure,neighbourhood_amenities,F_neighbourhood,log,"distance (sum of distances to ropeways, grocery, centre; unit not printed)",0.047,,3.11,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",elasticity,0.047,0.015112540192926046,b (log-log),|b/t|,0.1383011822965704,0.044469833535874734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.11,496.0,distance,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,True,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Distances × R,neighbourhood_amenities,F_neighbourhood,other,distance (log sum of distances x resident dummy),0.132,,2.69,,**,,,high,Interaction with resident dummy; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.132,0.04907063197026023,not harmonised,|b/t|,0.1199130324865576,0.04457733549686156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.69,496.0,distance,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Distances × T,neighbourhood_amenities,F_neighbourhood,other,distance (log sum of distances x tourist dummy),0.002,,0.13,,,,,high,Interaction with tourist dummy; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.002,0.015384615384615384,not harmonised,|b/t|,0.005837072874649105,0.04490056057422389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13,496.0,distance,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,True,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Average-Ski-Runs × R,other,other,other,km per lift x resident dummy,-0.0592,,-1.01,,,,,high,Interaction with resident dummy; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",other,-0.0592,0.05861386138613862,not harmonised,|b/t|,-0.045303775612399295,0.04485522337861317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.01,496.0,km per lift x resident dummy,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Average-Ski-Runs × T,other,other,other,km per lift x tourist dummy,0.3251,,10.89,,**,,,high,Interaction with tourist dummy; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",other,0.3251,0.029853076216712578,not harmonised,|b/t|,0.4392728815665064,0.04033727103457359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.89,496.0,km per lift x tourist dummy,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Average-Ski-Runs × R,other,other,other,km per lift x resident dummy,-0.0777,,-1.19,,,,,high,Interaction with resident dummy; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,-0.0777,0.06529411764705884,not harmonised,|b/t|,-0.053356464212367734,0.04483736488434264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.19,496.0,km per lift x resident dummy,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Average-Ski-Runs × T,other,other,other,km per lift x tourist dummy,0.1695,,2.72,,**,,,high,Interaction with tourist dummy; robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",other,0.1695,0.062316176470588236,not harmonised,|b/t|,0.12123080489434003,0.04457014885821324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.72,496.0,km per lift x tourist dummy,nonstandard_x_transform,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c1,4,1,17,Ln Flat-Equipment,other,other,log,count (1 + number of amenities),0.1324,,3.93,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.827,t,"Model 5: joint tourists+residents, no geographical interaction terms; t-values in separate column; adj. pseudo-R2 0.8270 (R=1) / 0.7422 (T=1)",elasticity,0.1324,0.03368956743002544,b (log-log),|b/t|,0.173777331602557,0.044218150535001784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.93,496.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df,9ba5bc20c4b8dd4891df1e9ed1d47b53aa5ee9df:T4-c2,4,2,17,Ln Flat-Equipment,other,other,log,count (1 + number of amenities),0.1672,,5.26,,**,,,high,robust (Huber-White) t-value,"ln monthly rent (CHF, incl. bills)",log,OLS,506.0,0.8587,t,"Model 6: joint, with resort interactions for Ln Rooms; adj. pseudo-R2 0.8587 (R=1) / 0.8087 (T=1)",elasticity,0.1672,0.03178707224334601,b (log-log),|b/t|,0.22985708351943523,0.04369906530787742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.26,496.0,count,,2008.0,Switzerland,Europe,High income,2003.0,2003.0,rental,rent,journal,False,False,robust,,2003.0,ols,journal,False,False,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Bedrooms,bedrooms,A_structural,level,count,8664822.596,,0.738,0.461,,,,high,,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,8664822.596,11740952.027100272,"b (currency per unit, as reported)",|b/t|,0.04700115990698336,0.06368720854604791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.738,246.0,count,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Bathrooms,bathrooms,A_structural,level,count,-20051336.87,,-1.448,0.149,,,,high,,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,-20051336.87,13847608.33563536,"b (currency per unit, as reported)",|b/t|,-0.09193017027732676,0.06348768665561241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.448,246.0,count,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Property type,dwelling_type,A_structural,other,index,8076944.769,,1.144,0.254,,,,high,"Ordinal construction-style code 1-6 (duplex=1, detached=2, semi-detached=3, terrace=4, flat=5, others=6) entered as a single numeric regressor",Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),other,8076944.769,7060266.406468532,not harmonised,|b/t|,0.07274552718950003,0.06358874754326926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.144,246.0,index,nonstandard_x_transform,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Boys' quarters,other,other,level,count,102816020.589,,15.151,0.0,,,,high,Printed name: Boys' quarters = number of servants' (BQ) rooms,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,102816020.589,6786088.08586892,"b (currency per unit, as reported)",|b/t|,0.6947713897855547,0.045856470845855375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.151,246.0,count,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,False,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Parking,parking,A_structural,level,count,9526774.351,,2.615,0.009,,,,high,Number of parking lots,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,9526774.351,3643125.946845124,"b (currency per unit, as reported)",|b/t|,0.1644562286944658,0.06288957120247257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.615,246.0,count,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Age,age,A_structural,level,years,-1635743.326,,-1.068,0.286,,,,high,Property age in years,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,-1635743.326,1531594.874531835,"b (currency per unit, as reported)",|b/t|,-0.06793587654141382,0.06361037129345863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.068,246.0,years,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Floors,storeys,A_structural,level,count,4539570.089,,1.455,0.147,,,,high,Number of floors,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,4539570.089,3119979.4426116836,"b (currency per unit, as reported)",|b/t|,0.09237080122709744,0.06348508675401886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.455,246.0,count,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Security fence,other,other,binary,binary,-10789691.84,,-0.229,0.819,,,,high,Printed name: Security fence (1 if available),Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,-10789691.84,47116558.253275104,"b (currency per unit, as reported)",|b/t|,-0.014598950747890818,0.06375087662834418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.229,246.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,False,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Sea view,view,E_environment,binary,binary,154767562.522,,4.832,0.0,,,,high,1 if sea view present,Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),monetary,154767562.522,32029710.786837753,"b (currency per unit, as reported)",|b/t|,0.2944217348920593,0.0609316504329593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.832,246.0,binary,,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,True,False
9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88,9dd3ef8baea1541e7438928532ebd498c89ffa88:T4-c1,4,1,10,Location,other,other,other,index,-26885403.503,,-6.176,0.0,,,,high,"Printed name: Location = neighbourhood code 1-5 (Ikoyi, Victoria Island, Lekki Phase 1, Lekki-Epe corridor...) entered as a single numeric regressor",Sale price in Naira (CPI-deflated),level,OLS,256.0,0.766,t,Training set (80% of 321 transactions),other,-26885403.503,4353206.525744818,not harmonised,|b/t|,-0.3663859615998244,0.059324151813443055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.176,246.0,index,nonstandard_x_transform,2018.0,Nigeria,Africa,Lower middle income,2010.0,2016.0,mixed,transaction,journal,False,False,OLS,,2013.0,ols,journal,False,False,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Area,living_area,A_structural,level,sqft,5e-05,,10.579,,,,,medium,"p printed as '< 0.0001'; Area of house in square feet; SE printed as '< 0.0001' (not recorded); Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",semi_elasticity,0.05381955208354861,0.005087395035783024,100*b/0.092903 (X per sqft->m2),|b/t|,0.41625249627793215,0.03934705513545062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.579,534.0,sqft,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Bedrooms,bedrooms,A_structural,level,count,0.029,0.014,2.046,0.041,,,,medium,"Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",semi_elasticity,2.9000000000000004,1.4000000000000001,100*b/1 (per unit count),reported,0.088194069902095,0.04310560601275416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.046,534.0,count,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Bathrooms,bathrooms,A_structural,level,count,0.262,0.02,8.121,,,,,medium,"p printed as '< 0.0001'; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",semi_elasticity,26.200000000000003,2.0,100*b/1 (per unit count),reported,0.33155207453189395,0.04082650837728038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.121,534.0,count,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Stories,storeys,A_structural,level,count,0.09,0.012,7.264,,,,,medium,"p printed as '< 0.0001'; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",semi_elasticity,9.0,1.2,100*b/1 (per unit count),reported,0.2998772210509405,0.041282657083003924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.264,534.0,count,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Main Road,other,other,binary,binary,0.118,0.027,4.312,,,,,medium,"p printed as '< 0.0001'; House accessible from the main road (yes/no); Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,12.48340346297745,3.0381591006918245,100*(exp(b - se^2/2) - 1),reported,0.18343234892672386,0.042539969602672506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.312,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,False,False,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Guestroom,other,other,binary,binary,0.071,0.025,2.812,0.005,,,,medium,"House has a guest room; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,7.324578415077165,2.683953064670894,100*(exp(b - se^2/2) - 1),reported,0.12079606802782973,0.042957349938773016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.812,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,False,False,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Basement,basement,A_structural,binary,binary,0.091,0.021,4.293,,,,,medium,"p printed as '< 0.0001'; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,9.502752506697544,2.3000649110427775,100*(exp(b - se^2/2) - 1),reported,0.1826511118644158,0.04254626411936077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.293,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Hot Water Heating,heating,A_structural,binary,binary,0.164,0.043,3.814,0.0002,,,,medium,"Hot water heating system; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,17.712555931332474,5.066321554898705,100*(exp(b - se^2/2) - 1),reported,0.16284481176980894,0.0426965945909305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.814,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Airconditioning,air_conditioning,A_structural,binary,binary,0.173,0.021,8.298,,,,,medium,"p printed as '< 0.0001'; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,18.860398900722064,2.4966188206764675,100*(exp(b - se^2/2) - 1),reported,0.3379608136949462,0.04072798429681202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.298,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Parking,parking,A_structural,level,count,0.044,0.011,3.872,,,,,medium,"p printed as '< 0.0001'; Number of parking spaces; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",semi_elasticity,4.3999999999999995,1.0999999999999999,100*b/1 (per unit count),reported,0.16525407928059754,0.04267925601255102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.872,534.0,count,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,True,True,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Prefer area,other,other,binary,binary,0.123,0.022,5.5,,,,,medium,"p printed as '< 0.0001'; House located in a preferred area (yes/no); Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,13.061078002950666,2.4879457260844764,100*(exp(b - se^2/2) - 1),reported,0.23154046738576292,0.04209826679741144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,False,False,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Semi-furnished,other,other,dummy,binary,0.021,0.023,0.92,0.358,,,,medium,"Furnishing status dummy, reference = fully furnished; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,2.0951974124196138,2.3488107187663156,100*(exp(b - se^2/2) - 1),reported,0.039780779503980214,0.04323997772171762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.92,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,False,False,False
a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7,a072fb43e67fcc553e457777f0c68324ace19eb7:T2-c1,2,1,4,Unfurnished,other,other,dummy,binary,-0.108,0.024,-4.416,,,,,medium,"p printed as '< 0.0001'; Furnishing status dummy, reference = fully furnished; Kaggle toy dataset of unverifiable provenance (compiled 'from various websites', published 2023; country/period unknown, paper labels it US)",log housing price (currency not stated; dataset labelled 'US'),log,OLS,544.0,0.697,none,"Full Kaggle 'Housing Price Prediction' dataset after removing 1 outlier (545 -> 544); adjusted R2 0.6896, RSE 0.2059; SE, t, p, VIF printed in columns",pct_premium,-10.263088309449886,2.154306231433044,100*(exp(b - se^2/2) - 1),reported,-0.18770240527106322,0.04250507365739656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.416,534.0,binary,,2024.0,United States,North America,High income,,,unknown,listing,conference,False,False,OLS,"random_forest (prediction comparison only, RMSE/MAPE; no marginal effects)",2021.0,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.21734,,7.79,,***,,,high,Linear term of the school-quality polynomial,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,21.734,2.789987163029525,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.03959537620453998,0.005082846752829265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.79,38646.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.22192,,7.13,,***,,,high,Linear term of the school-quality polynomial,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,22.192,3.112482468443198,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.03624530814694234,0.005083493428743666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.13,38646.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.31693,,7.7,,***,,,high,Linear term of the school-quality polynomial,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,31.692999999999998,4.115974025974026,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.03913862483174416,0.005082938289836905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.7,38646.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,test math sq,school_quality,F_neighbourhood,squared,standard deviations squared,0.03002,,1.48,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",other,0.03002,0.020283783783783784,not harmonised,|b/t|,0.007528303746669005,0.0050866917207223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,38646.0,standard deviations sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,test math sq,school_quality,F_neighbourhood,squared,standard deviations squared,0.01555,,0.76,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",other,0.01555,0.020460526315789474,not harmonised,|b/t|,0.0038659663725297555,0.005086797858591783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.76,38646.0,standard deviations sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,test math cb,school_quality,F_neighbourhood,other,standard deviations cubed,-0.03606,,-2.6,,**,,,high,Cubic term,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",other,-0.03606,0.013869230769230769,not harmonised,|b/t|,-0.013224616685369872,0.005086391032834566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6,38646.0,standard deviations cubed,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house bedrooms,bedrooms,A_structural,level,count,0.01062,,1.09,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,1.0619999999999998,0.9743119266055044,100*b/1 (per unit count),|b/t|,0.005544565872283473,0.005086757680994012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.09,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house bedrooms,bedrooms,A_structural,level,count,0.01502,,1.52,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,1.502,0.9881578947368421,100*b/1 (per unit count),|b/t|,0.007731759412213918,0.0050866838238249465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.52,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house bedrooms,bedrooms,A_structural,level,count,0.01575,,1.62,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,1.575,0.9722222222222222,100*b/1 (per unit count),|b/t|,0.008240394319930455,0.005086663160450898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.62,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house bathrooms,bathrooms,A_structural,level,count,0.14086,,4.75,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,14.086000000000002,2.9654736842105267,100*b/1 (per unit count),|b/t|,0.02415542015154239,0.00508535161085103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house bathrooms,bathrooms,A_structural,level,count,0.14413,,4.93,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,14.413,2.923529411764706,100*b/1 (per unit count),|b/t|,0.025070218617591785,0.005085237042107868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.93,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house bathrooms,bathrooms,A_structural,level,count,0.13458,,4.44,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,13.458,3.031081081081081,100*b/1 (per unit count),|b/t|,0.022579792948770522,0.005085538952425793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.44,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house baths sq,bathrooms,A_structural,squared,count squared,-0.00612,,-1.14,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",other,-0.00612,0.005368421052631579,not harmonised,|b/t|,-0.005798895391217863,0.005086750343173564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.14,38646.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house baths sq,bathrooms,A_structural,squared,count squared,-0.0074,,-1.37,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",other,-0.0074,0.005401459854014599,not harmonised,|b/t|,-0.006968795921610066,0.005086712351540194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.37,38646.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house baths sq,bathrooms,A_structural,squared,count squared,-0.00501,,-0.89,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",other,-0.00501,0.005629213483146067,not harmonised,|b/t|,-0.0045272375304067245,0.005086783742030028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.89,38646.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house age,age,A_structural,level,years,0.00065,,0.37,,,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,0.065,0.17567567567567566,100*b/1 (per year),|b/t|,0.0018821259389689919,0.005086826862078357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37,38646.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house age,age,A_structural,level,years,0.00057,,0.31,,,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,0.056999999999999995,0.18387096774193548,100*b/1 (per year),|b/t|,0.0015769171596462103,0.00508682954724584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.31,38646.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house age,age,A_structural,level,years,0.00123,,0.67,,,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,0.123,0.1835820895522388,100*b/1 (per year),|b/t|,0.0034081602401521173,0.005086806328585249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.67,38646.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house age sq,age,A_structural,squared,years squared,2e-05,,1.35,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",other,2e-05,1.4814814814814815e-05,not harmonised,|b/t|,0.006867066507555604,0.00508671593152267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.35,38646.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house age sq,age,A_structural,squared,years squared,2e-05,,1.31,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",other,2e-05,1.5267175572519083e-05,not harmonised,|b/t|,0.006663607042975479,0.005086722933569068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.31,38646.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house age sq,age,A_structural,squared,years squared,2e-05,,1.03,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",other,2e-05,1.9417475728155343e-05,not harmonised,|b/t|,0.005239369033633134,0.005086766052071004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.03,38646.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house lotarea,lot_size,A_structural,level,1000 sqft,0.00123,,4.21,,***,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,0.0013239609812552957,0.00031448004305351443,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.021410669827542913,0.005085669792765538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.21,38646.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house lotarea,lot_size,A_structural,level,1000 sqft,0.0012,,4.27,,***,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,0.0012916692500051666,0.0003024986533969945,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.02171566713059185,0.00508563633034938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.27,38646.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house lotarea,lot_size,A_structural,level,1000 sqft,0.00119,,4.17,,***,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,0.001280905339588457,0.000307171544265817,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.021207334965855248,0.005085691838334592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.17,38646.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house livingarea,living_area,A_structural,level,1000 sqft,0.45365,,20.02,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,0.48830479605403654,0.024390848953748077,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.10131443968767803,0.005060661323060841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.02,38646.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house livingarea,living_area,A_structural,level,1000 sqft,0.44475,,17.35,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,0.47872491578316484,0.027592214166176642,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.08791487205472512,0.005067139599695972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.35,38646.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house livingarea,living_area,A_structural,level,1000 sqft,0.43526,,19.05,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,0.4685099647977073,0.024593698939512198,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.09645241678338391,0.0050631189912537485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.05,38646.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house stories,storeys,A_structural,level,count,0.39693,,11.29,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,39.693,3.515766164747564,100*b/1 (per unit count),|b/t|,0.057335900815049176,0.005078467742697004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.29,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,True
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house stories,storeys,A_structural,level,count,0.38775,,10.58,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,38.775,3.6649338374291114,100*b/1 (per unit count),|b/t|,0.0537409506775155,0.005079484941164036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.58,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house stories,storeys,A_structural,level,count,0.37835,,10.87,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,37.835,3.4806807727690896,100*b/1 (per unit count),|b/t|,0.05520957102610711,0.005079077371307003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.87,38646.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,house rooms,other,other,level,count,0.07484,,10.1,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,7.484,0.740990099009901,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.05130936881717225,0.005080135526452698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.1,38646.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,house rooms,other,other,level,count,0.07421,,10.21,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,7.420999999999999,0.7268364348677766,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.05186668850379747,0.005079989079705923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.21,38646.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,house rooms,other,other,level,count,0.07245,,10.11,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,7.245,0.7166172106824926,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.051360036232012636,0.005080122278141704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.11,38646.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,cenbl fem,demographics,F_neighbourhood,level,"percent (share of female population, census block)",-0.00061,,-0.88,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,-0.061,0.06931818181818182,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.00447637071806207,0.0050867849068887165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.88,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,cenbl fem,demographics,F_neighbourhood,level,"percent (share of female population, census block)",-0.0005,,-0.73,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,-0.05,0.0684931506849315,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.0037133645842771005,0.0050868008003795906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.73,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,cenbl fem,demographics,F_neighbourhood,level,"percent (share of female population, census block)",-0.00053,,-0.79,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,-0.053,0.0670886075949367,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.00401856789069503,0.005086794798348139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.79,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,cenbl nonwhite,demographics,F_neighbourhood,level,"percent (share of nonwhite population, census block)",-0.00221,,-3.62,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,-0.22100000000000003,0.06104972375690608,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.01841122460690421,0.00508597364831608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.62,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,cenbl nonwhite,demographics,F_neighbourhood,level,"percent (share of nonwhite population, census block)",-0.00277,,-5.06,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,-0.27699999999999997,0.05474308300395257,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.02573086736513962,0.0050851516531896495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.06,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,cenbl nonwhite,demographics,F_neighbourhood,level,"percent (share of nonwhite population, census block)",-0.00257,,-4.57,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,-0.257,0.056236323851203494,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.02324056100223337,0.005085461926090452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.57,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,cenbl p5 14,demographics,F_neighbourhood,level,"percent (share of population aged 5-14, census block)",-0.00017,,-0.19,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,-0.017,0.08947368421052632,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.0009664983642455622,0.005086833496029275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.19,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,cenbl p5 14,demographics,F_neighbourhood,level,"percent (share of population aged 5-14, census block)",-0.00033,,-0.38,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,-0.033,0.0868421052631579,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.0019329940200231053,0.005086826368481855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.38,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,cenbl p5 14,demographics,F_neighbourhood,level,"percent (share of population aged 5-14, census block)",-0.00021,,-0.24,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,-0.021,0.08750000000000001,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.0012208396994513968,0.005086832081047487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.24,38646.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c1,3,1,25,tax,public_services,F_neighbourhood,level,"property tax rate ($ per $1,000 assessed value, school district)",-0.04636,,-1.65,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.697,t,"Full sample, linear in test score, neighbourhood (census block) controls and tax rate, no boundary FE",semi_elasticity,-4.636,2.8096969696969696,100*b (unit as reported: property tax rate; no harmonised unit defined for this characteristic),|b/t|,-0.008392983562991457,0.005086656704843308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.65,38646.0,property tax rate,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c2,3,2,25,tax,public_services,F_neighbourhood,level,"property tax rate ($ per $1,000 assessed value, school district)",-0.04457,,-1.51,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.699,t,"Full sample, quadratic in test score",semi_elasticity,-4.457,2.9516556291390725,100*b (unit as reported: property tax rate; no harmonised unit defined for this characteristic),|b/t|,-0.007680895584859966,0.005086685817788057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.51,38646.0,property tax rate,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T3-c3,3,3,25,tax,public_services,F_neighbourhood,level,"property tax rate ($ per $1,000 assessed value, school district)",-0.03562,,-1.28,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,38656.0,0.702,t,"Full sample, cubic in test score",semi_elasticity,-3.562,2.7828125,100*b (unit as reported: property tax rate; no harmonised unit defined for this characteristic),|b/t|,-0.006511011900062528,0.00508672804692385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.28,38646.0,property tax rate,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.06437,,2.58,,**,,,high,Linear term of the school-quality polynomial; boundary fixed effects,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,6.436999999999999,2.4949612403100776,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.025562532483143805,0.009907958326799924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.58,10180.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.06274,,2.9,,***,,,high,Linear term of the school-quality polynomial; boundary fixed effects,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,6.274,2.163448275862069,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.028730606313594153,0.009907105625377294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9,10180.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.04659,,1.64,,,,,high,Linear term of the school-quality polynomial; boundary fixed effects,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,4.659,2.8408536585365853,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.016252216363134925,0.009909888026301784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.64,10180.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.03227,,1.78,,*,,,high,Linear term of the school-quality polynomial; boundary fixed effects,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,3.227,1.8129213483146067,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.017646118832358414,0.009913549905819332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.78,10172.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.03579,,1.93,,*,,,high,Linear term of the school-quality polynomial; boundary fixed effects,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,3.579,1.8544041450777204,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.019132628124827706,0.009913278821154253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.93,10172.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,test math,school_quality,F_neighbourhood,level,"standard deviations (school-level Math MAP index, 4th grade, averaged 1998-2001)",0.03172,,1.2,,,,,high,Linear term of the school-quality polynomial; boundary fixed effects,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,3.1719999999999997,2.643333333333333,100*b (unit as reported: standard deviations; no harmonised unit defined for this characteristic),|b/t|,0.011897270391903235,0.009914391993252697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,10172.0,standard deviations,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,test math sq,school_quality,F_neighbourhood,squared,standard deviations squared,0.02656,,2.47,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",other,0.02656,0.010753036437246963,not harmonised,|b/t|,0.02447332436746086,0.00990822848885055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.47,10180.0,standard deviations sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,test math sq,school_quality,F_neighbourhood,squared,standard deviations squared,0.02909,,2.47,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",other,0.02909,0.011777327935222673,not harmonised,|b/t|,0.02447332436746086,0.00990822848885055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.47,10180.0,standard deviations sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,test math sq,school_quality,F_neighbourhood,squared,standard deviations squared,0.02209,,2.48,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",other,0.02209,0.008907258064516128,not harmonised,|b/t|,0.02458200195086377,0.009912097560832164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.48,10172.0,standard deviations sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,test math sq,school_quality,F_neighbourhood,squared,standard deviations squared,0.02284,,2.4,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",other,0.02284,0.009516666666666666,not harmonised,|b/t|,0.02378949039323981,0.00991228766384992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,10172.0,standard deviations sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,test math cb,school_quality,F_neighbourhood,other,standard deviations cubed,0.00514,,0.73,,,,,high,Cubic term,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",other,0.00514,0.007041095890410958,not harmonised,|b/t|,0.007234984486884795,0.009910937653266843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.73,10180.0,standard deviations cubed,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,test math cb,school_quality,F_neighbourhood,other,standard deviations cubed,0.00137,,0.21,,,,,high,Cubic term,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",other,0.00137,0.006523809523809524,not harmonised,|b/t|,0.0020821651706600802,0.009915072241238476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21,10172.0,standard deviations cubed,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house bedrooms,bedrooms,A_structural,level,count,0.03726,,3.88,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,3.726,0.9603092783505155,100*b/1 (per unit count),|b/t|,0.038427041717471064,0.009903876731306976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.88,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house bedrooms,bedrooms,A_structural,level,count,0.0373,,3.89,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,3.73,0.9588688946015423,100*b/1 (per unit count),|b/t|,0.038525933675398645,0.00990383899110505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.89,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house bedrooms,bedrooms,A_structural,level,count,0.03749,,3.9,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,3.749,0.9612820512820512,100*b/1 (per unit count),|b/t|,0.038624824501352135,0.009903801154192855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house bedrooms,bedrooms,A_structural,level,count,0.03816,,4.02,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,3.816,0.9492537313432837,100*b/1 (per unit count),|b/t|,0.03982705246703697,0.009907226981849995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.02,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house bedrooms,bedrooms,A_structural,level,count,0.03805,,4.01,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,3.805,0.9488778054862843,100*b/1 (per unit count),|b/t|,0.03972813676010828,0.009907266024964658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.01,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house bedrooms,bedrooms,A_structural,level,count,0.03809,,4.0,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,3.8089999999999997,0.9522499999999999,100*b/1 (per unit count),|b/t|,0.039629219885184266,0.009907304971296067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house bathrooms,bathrooms,A_structural,level,count,0.10834,,5.78,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,10.834000000000001,1.8743944636678203,100*b/1 (per unit count),|b/t|,0.05719294886246734,0.009894973851637949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.78,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house bathrooms,bathrooms,A_structural,level,count,0.10785,,5.8,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,10.785,1.8594827586206897,100*b/1 (per unit count),|b/t|,0.05739019765101756,0.009894861663968545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house bathrooms,bathrooms,A_structural,level,count,0.10792,,5.82,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,10.792,1.854295532646048,100*b/1 (per unit count),|b/t|,0.057587439719005186,0.009894749092612575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.82,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house bathrooms,bathrooms,A_structural,level,count,0.10349,,5.81,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,10.349,1.781239242685026,100*b/1 (per unit count),|b/t|,0.05751134703252018,0.00989868279389332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.81,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house bathrooms,bathrooms,A_structural,level,count,0.10318,,5.82,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,10.318,1.7728522336769759,100*b/1 (per unit count),|b/t|,0.05761000561312009,0.009898626394006887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.82,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house bathrooms,bathrooms,A_structural,level,count,0.1032,,5.83,,***,,,high,Quadratic in bathrooms (baths sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,10.32,1.7701543739279588,100*b/1 (per unit count),|b/t|,0.0577086625059034,0.009898569898096638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.83,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house baths sq,bathrooms,A_structural,squared,count squared,-0.00529,,-1.68,,*,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",other,-0.00529,0.003148809523809524,not harmonised,|b/t|,-0.016648503321807142,0.009909823405837584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.68,10180.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house baths sq,bathrooms,A_structural,squared,count squared,-0.00533,,-1.7,,*,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",other,-0.00533,0.0031352941176470586,not harmonised,|b/t|,-0.01684664387069095,0.009909790512171147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7,10180.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house baths sq,bathrooms,A_structural,squared,count squared,-0.00535,,-1.71,,*,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",other,-0.00535,0.0031286549707602337,not harmonised,|b/t|,-0.01694571340231834,0.009909773919484407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.71,10180.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house baths sq,bathrooms,A_structural,squared,count squared,-0.00488,,-1.58,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",other,-0.00488,0.0030886075949367086,not harmonised,|b/t|,-0.015663926107112633,0.009913877282982677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.58,10172.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house baths sq,bathrooms,A_structural,squared,count squared,-0.00491,,-1.6,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",other,-0.00491,0.00306875,not harmonised,|b/t|,-0.015862154076292868,0.009913846297683042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,10172.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house baths sq,bathrooms,A_structural,squared,count squared,-0.00491,,-1.6,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",other,-0.00491,0.00306875,not harmonised,|b/t|,-0.015862154076292868,0.009913846297683042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,10172.0,count sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house age,age,A_structural,level,years,-0.00408,,-2.73,,***,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,-0.40800000000000003,0.14945054945054947,100*b/1 (per year),|b/t|,-0.02704766881713674,0.009907570995288184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.73,10180.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house age,age,A_structural,level,years,-0.00411,,-2.75,,***,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,-0.411,0.14945454545454545,100*b/1 (per year),|b/t|,-0.0272456736784359,0.009907517701249417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.75,10180.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house age,age,A_structural,level,years,-0.00412,,-2.76,,***,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,-0.41200000000000003,0.14927536231884062,100*b/1 (per year),|b/t|,-0.027344674907948674,0.009907490908677057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.76,10180.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house age,age,A_structural,level,years,-0.00453,,-3.11,,***,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,-0.453,0.14565916398713827,100*b/1 (per year),|b/t|,-0.03082129170109757,0.00991038318363266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.11,10172.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house age,age,A_structural,level,years,-0.00454,,-3.13,,***,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,-0.45399999999999996,0.1450479233226837,100*b/1 (per year),|b/t|,-0.03101930925857279,0.009910322446828367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.13,10172.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house age,age,A_structural,level,years,-0.00454,,-3.14,,***,,,high,Quadratic in age (age sq also included),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,-0.45399999999999996,0.1445859872611465,100*b/1 (per year),|b/t|,-0.031118316669135608,0.009910291932845735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.14,10172.0,years,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house age sq,age,A_structural,squared,years squared,4e-05,,2.89,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",other,4e-05,1.384083044982699e-05,not harmonised,|b/t|,0.028631616613163182,0.009907133776181033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.89,10180.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house age sq,age,A_structural,squared,years squared,4e-05,,2.91,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",other,4e-05,1.3745704467353953e-05,not harmonised,|b/t|,0.028829595168744355,0.00990707737757538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.91,10180.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house age sq,age,A_structural,squared,years squared,4e-05,,2.92,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",other,4e-05,1.3698630136986303e-05,not harmonised,|b/t|,0.028928583175711103,0.009907049032777776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.92,10180.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house age sq,age,A_structural,squared,years squared,4e-05,,3.15,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",other,4e-05,1.26984126984127e-05,not harmonised,|b/t|,0.03121732316371104,0.00991026132181303,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.15,10172.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house age sq,age,A_structural,squared,years squared,4e-05,,3.16,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",other,4e-05,1.2658227848101267e-05,not harmonised,|b/t|,0.03131632873939614,0.009910230613732954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.16,10172.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house age sq,age,A_structural,squared,years squared,4e-05,,3.17,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",other,4e-05,1.2618296529968456e-05,not harmonised,|b/t|,0.03141533339328806,0.00991019980860822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.17,10172.0,years sqd,nonstandard_x_transform,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house lotarea,lot_size,A_structural,level,1000 sqft,0.00089,,2.41,,**,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,0.0009579880270871652,0.00039750540543035897,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.023879173864643388,0.009908370898192277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.41,10180.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house lotarea,lot_size,A_structural,level,1000 sqft,0.00089,,2.41,,**,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,0.0009579880270871652,0.00039750540543035897,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.023879173864643388,0.009908370898192277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.41,10180.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house lotarea,lot_size,A_structural,level,1000 sqft,0.00089,,2.41,,**,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,0.0009579880270871652,0.00039750540543035897,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.023879173864643388,0.009908370898192277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.41,10180.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house lotarea,lot_size,A_structural,level,1000 sqft,0.00088,,2.39,,**,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,0.0009472241166704556,0.0003963280822888935,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.02369042326426138,0.009912310989230703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.39,10172.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house lotarea,lot_size,A_structural,level,1000 sqft,0.00088,,2.4,,**,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,0.0009472241166704556,0.00039467671527935647,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.02378949039323981,0.00991228766384992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,10172.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house lotarea,lot_size,A_structural,level,1000 sqft,0.00088,,2.39,,**,,,high,Lot area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,0.0009472241166704556,0.0003963280822888935,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.02369042326426138,0.009912310989230703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.39,10172.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house livingarea,living_area,A_structural,level,1000 sqft,0.35315,,15.43,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,0.3801274963661039,0.024635612207783784,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.15117221619598556,0.00979729204121747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.43,10180.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house livingarea,living_area,A_structural,level,1000 sqft,0.35228,,15.29,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,0.3791910361598501,0.02479993696271093,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.14983152527233068,0.00979931492951803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.29,10180.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house livingarea,living_area,A_structural,level,1000 sqft,0.35236,,15.29,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,0.3792771474431838,0.024805568832124508,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.14983152527233068,0.00979931492951803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.29,10180.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house livingarea,living_area,A_structural,level,1000 sqft,0.34332,,15.52,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,0.3695465724264782,0.023810990491396792,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.15209204009072286,0.00979974485120637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.52,10172.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house livingarea,living_area,A_structural,level,1000 sqft,0.34297,,15.49,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,0.3691698355618933,0.02383278473608091,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.15180482912157642,0.009800182641806094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.49,10172.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house livingarea,living_area,A_structural,level,1000 sqft,0.34301,,15.49,,***,,,high,Internal living area in thousands of square feet (Table 1),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,0.3692128912035601,0.023835564312689485,100*b/92.903 (X per 1000sqft->m2),|b/t|,0.15180482912157642,0.009800182641806094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.49,10172.0,1000sqft,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house stories,storeys,A_structural,level,count,0.27574,,9.3,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,27.573999999999998,2.9649462365591397,100*b/1 (per unit count),|b/t|,0.09178505115932917,0.009869360339712812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.3,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house stories,storeys,A_structural,level,count,0.27559,,9.3,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,27.559,2.963333333333333,100*b/1 (per unit count),|b/t|,0.09178505115932917,0.009869360339712812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.3,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house stories,storeys,A_structural,level,count,0.27558,,9.31,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,27.558,2.9600429645542428,100*b/1 (per unit count),|b/t|,0.09188291198869973,0.009869270890300721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.31,10180.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house stories,storeys,A_structural,level,count,0.26621,,9.55,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,26.621,2.7875392670157066,100*b/1 (per unit count),|b/t|,0.09426748545715484,0.00987094088556595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.55,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house stories,storeys,A_structural,level,count,0.26625,,9.57,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,26.625,2.782131661442006,100*b/1 (per unit count),|b/t|,0.09446314447474644,0.009870756998406104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.57,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house stories,storeys,A_structural,level,count,0.26626,,9.57,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,26.626,2.7822361546499477,100*b/1 (per unit count),|b/t|,0.09446314447474644,0.009870756998406104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.57,10172.0,count,,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,True,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c1,5,1,27,house rooms,other,other,level,count,0.05974,,7.38,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.769,t,"Boundary sample (houses within 0.1 mile of elementary attendance-zone boundaries), linear, boundary FE, no neighbourhood controls",semi_elasticity,5.974,0.8094850948509485,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.07294974900737446,0.009884789838397624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.38,10180.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c2,5,2,27,house rooms,other,other,level,count,0.05952,,7.33,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, quadratic, boundary FE, no neighbourhood controls",semi_elasticity,5.952,0.812005457025921,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.07245811316414875,0.009885145042857946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.33,10180.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c3,5,3,27,house rooms,other,other,level,count,0.05945,,7.31,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10190.0,0.77,t,"Boundary sample, cubic, boundary FE, no neighbourhood controls",semi_elasticity,5.945,0.8132694938440493,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.07226144401659683,0.009885286459178773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.31,10180.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,house rooms,other,other,level,count,0.05902,,7.43,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,5.902,0.7943472409152087,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.07347004997258401,0.009888297439109558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.43,10172.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,house rooms,other,other,level,count,0.05893,,7.4,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,5.893000000000001,0.7963513513513513,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.07317499268215033,0.00988851252461491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.4,10172.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,house rooms,other,other,level,count,0.05891,,7.39,,***,,,high,Total number of rooms (bedrooms entered separately),log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,5.891,0.7971583220568336,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.07307663597810199,0.009888584029513125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.39,10172.0,count,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,False,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,cenbl fem,demographics,F_neighbourhood,level,"percent (share of female population, census block)",-0.00044,,-0.66,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,-0.044000000000000004,0.06666666666666667,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.006543821751648633,0.009914881441891869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.66,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,cenbl fem,demographics,F_neighbourhood,level,"percent (share of female population, census block)",-0.00039,,-0.59,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,-0.039,0.06610169491525424,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.005849805209836236,0.009914924084468197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,cenbl fem,demographics,F_neighbourhood,level,"percent (share of female population, census block)",-0.00039,,-0.59,,,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,-0.039,0.06610169491525424,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.005849805209836236,0.009914924084468197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.59,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,cenbl nonwhite,demographics,F_neighbourhood,level,"percent (share of nonwhite population, census block)",-0.00219,,-3.5,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,-0.219,0.06257142857142857,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.03468195085486535,0.009909128815675815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,cenbl nonwhite,demographics,F_neighbourhood,level,"percent (share of nonwhite population, census block)",-0.00223,,-3.56,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,-0.22300000000000003,0.06264044943820225,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.03527576496774865,0.009908922743749621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.56,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,cenbl nonwhite,demographics,F_neighbourhood,level,"percent (share of nonwhite population, census block)",-0.00222,,-3.55,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,-0.22200000000000003,0.06253521126760564,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,-0.035176798526511205,0.009908957331411608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.55,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,cenbl p5 14,demographics,F_neighbourhood,level,"percent (share of population aged 5-14, census block)",0.00154,,2.25,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,0.154,0.06844444444444445,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.02230341150299165,0.009912627334662956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,cenbl p5 14,demographics,F_neighbourhood,level,"percent (share of population aged 5-14, census block)",0.00153,,2.24,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,0.153,0.06830357142857141,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.02220433421115151,0.009912649201406923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.24,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,cenbl p5 14,demographics,F_neighbourhood,level,"percent (share of population aged 5-14, census block)",0.00154,,2.25,,**,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,0.154,0.06844444444444445,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.02230341150299165,0.009912627334662956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,10172.0,percent,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c4,5,4,27,tax,public_services,F_neighbourhood,level,"property tax rate ($ per $1,000 assessed value, school district)",-0.06787,,-3.21,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, linear, boundary FE, with neighbourhood controls and tax",semi_elasticity,-6.787,2.114330218068536,100*b (unit as reported: property tax rate; no harmonised unit defined for this characteristic),|b/t|,-0.03181134273287215,0.00991007561771718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.21,10172.0,property tax rate,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c5,5,5,27,tax,public_services,F_neighbourhood,level,"property tax rate ($ per $1,000 assessed value, school district)",-0.05526,,-2.88,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, quadratic, boundary FE, with neighbourhood controls",semi_elasticity,-5.526,1.91875,100*b (unit as reported: property tax rate; no harmonised unit defined for this characteristic),|b/t|,-0.028543834792494862,0.00991105374739405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.88,10172.0,property tax rate,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636,a2a7539a9c4a1cf05650ab56eedb83ae824bb636:T5-c6,5,6,27,tax,public_services,F_neighbourhood,level,"property tax rate ($ per $1,000 assessed value, school district)",-0.05465,,-2.73,,***,,,high,,log adj price: log of sale price deflated to 1998 US$ (OFHEO St. Louis index),log,OLS,10182.0,0.772,t,"Boundary sample, cubic, boundary FE, with neighbourhood controls",semi_elasticity,-5.465,2.001831501831502,100*b (unit as reported: property tax rate; no harmonised unit defined for this characteristic),|b/t|,-0.02705829506866539,0.009911463395115526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.73,10172.0,property tax rate,unit_not_harmonisable,2003.0,United States,North America,High income,1998.0,2001.0,single_family,transaction,working_paper,False,True,clustered,,1999.5,ols,other,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T2-c1,2,1,13,Treatment effect,flood_risk,E_environment,other,binary,-0.0042,0.0098,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"houses in flood-risk zones (treated) vs houses 0.5-3 km outside (control), Germany 2014-2022; zip-code FE + year-month FE (Eq. 1, main)",other,-0.0042,0.0098,not harmonised,reported,-0.0003539014173194909,0.0008257699737454788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42857142857142855,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T2-c2,2,2,13,Treatment effect,flood_risk,E_environment,other,binary,-0.0042,0.0098,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"houses in flood-risk zones (treated) vs houses 0.5-3 km outside (control), Germany 2014-2022; zip-code FE + year FE + month FE (no year-month FE)",other,-0.0042,0.0098,not harmonised,reported,-0.0003539014173194909,0.0008257699737454788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42857142857142855,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T2-c3,2,3,13,Treatment effect,flood_risk,E_environment,other,binary,0.0035,0.0104,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,"houses in flood-risk zones (treated) vs houses 0.5-3 km outside (control), Germany 2014-2022; county FE + year-month FE",other,0.0035,0.0104,not harmonised,reported,0.00027770857146664537,0.0008251911837866033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.33653846153846156,1468557.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T2-c4,2,4,13,Treatment effect,flood_risk,E_environment,other,binary,-0.0066,0.0083,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,"houses in flood-risk zones (treated) vs houses 0.5-3 km outside (control), Germany 2014-2022; 1 km2 grid FE + year-month FE",other,-0.0066,0.0083,not harmonised,reported,-0.0006586679517089084,0.0008283248483612032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7951807228915663,1457466.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T2-c5,2,5,13,Treatment effect,flood_risk,E_environment,other,binary,-0.0071,0.01,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"houses in flood-risk zones (treated) vs houses 0.5-3 km outside (control), Germany 2014-2022; zip-code FE + year-month FE + county x year linear trends",other,-0.0071,0.01,not harmonised,reported,-0.0005862966173070959,0.000825769883531121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7100000000000001,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c1,3 (Panel A),1,15,Treatment effect,flood_risk,E_environment,other,binary,-0.0592,0.0165,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here evaluated at distance 0 km to the flooded area",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"as Table 2 column 1 (zip-code FE + year-month FE (Eq. 1, main)); treatment effect interacted with continuous distance to the 2021 flooded area",other,-0.0592,0.0165,not harmonised,reported,-0.0029627497545804773,0.0008257664011921939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.587878787878788,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c1,3 (Panel A),1,15,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to nearest potentially flooded area of the July 2021 flood,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"as Table 2 column 1 (zip-code FE + year-month FE (Eq. 1, main)); treatment effect interacted with continuous distance to the 2021 flooded area",other,0.0003,0.0001,not harmonised,reported,0.0024773024747015353,0.0008257674915671786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1466499.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c1,3 (Panel A),1,15,Distance to flooded area (km),flood_risk,E_environment,level,km,-0.0006,0.0002,,,**,,,high,"uninteracted continuous distance from the house to the nearest potentially flooded area (July 2021 Western European flood), in km",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"as Table 2 column 1 (zip-code FE + year-month FE (Eq. 1, main)); treatment effect interacted with continuous distance to the 2021 flooded area",monetary,-0.0006,0.0002,"b (currency per unit, as reported)",reported,-0.0024773024747015353,0.0008257674915671786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,1466499.0,km,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c1,3 (Panel B),1,15,Treatment effect,flood_risk,E_environment,other,binary,0.0134,0.0122,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here for counties with below-median climate change beliefs",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"as Table 2 column 1 (zip-code FE + year-month FE (Eq. 1, main)); treatment effect interacted with indicator for counties with above-median belief in human-made climate change",other,0.0134,0.0122,not harmonised,reported,0.0009069929335874996,0.0008257696858035447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0983606557377048,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c1,3 (Panel B),1,15,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0422,0.0178,,,**,,,high,triple interaction: flood-risk zone x post-flood x county above-median share believing in human-made climate change,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"as Table 2 column 1 (zip-code FE + year-month FE (Eq. 1, main)); treatment effect interacted with indicator for counties with above-median belief in human-made climate change",other,-0.0422,0.0178,not harmonised,reported,-0.001957720690717637,0.0008257684430041217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.370786516853933,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c1,3 (Panel B),1,15,High climate change beliefs,other,other,dummy,binary,0.0252,0.0143,,,*,,,high,county-level indicator (above-median share of population believing in human-made climate change); not a property characteristic,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"as Table 2 column 1 (zip-code FE + year-month FE (Eq. 1, main)); treatment effect interacted with indicator for counties with above-median belief in human-made climate change",monetary,0.0252,0.0143,"b (currency per unit, as reported)",reported,0.00145520158100736,0.0008257691511271924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7622377622377623,1466499.0,binary,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,False,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c2,3 (Panel A),2,15,Treatment effect,flood_risk,E_environment,other,binary,-0.0589,0.0165,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here evaluated at distance 0 km to the flooded area",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 2 (zip-code FE + year FE + month FE (no year-month FE)); treatment effect interacted with continuous distance to the 2021 flooded area,other,-0.0589,0.0165,not harmonised,reported,-0.002947735950803868,0.0008257664378313044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5696969696969694,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c2,3 (Panel A),2,15,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to nearest potentially flooded area of the July 2021 flood,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 2 (zip-code FE + year FE + month FE (no year-month FE)); treatment effect interacted with continuous distance to the 2021 flooded area,other,0.0003,0.0001,not harmonised,reported,0.0024773024747015353,0.0008257674915671786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1466499.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c2,3 (Panel A),2,15,Distance to flooded area (km),flood_risk,E_environment,level,km,-0.0006,0.0002,,,**,,,high,"uninteracted continuous distance from the house to the nearest potentially flooded area (July 2021 Western European flood), in km",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 2 (zip-code FE + year FE + month FE (no year-month FE)); treatment effect interacted with continuous distance to the 2021 flooded area,monetary,-0.0006,0.0002,"b (currency per unit, as reported)",reported,-0.0024773024747015353,0.0008257674915671786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,1466499.0,km,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c2,3 (Panel B),2,15,Treatment effect,flood_risk,E_environment,other,binary,0.013,0.0122,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here for counties with below-median climate change beliefs",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 2 (zip-code FE + year FE + month FE (no year-month FE)); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,0.013,0.0122,not harmonised,reported,0.0008799185389445613,0.0008257697057787423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0655737704918031,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c2,3 (Panel B),2,15,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.041,0.0179,,,**,,,high,triple interaction: flood-risk zone x post-flood x county above-median share believing in human-made climate change,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 2 (zip-code FE + year FE + month FE (no year-month FE)); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,-0.041,0.0179,not harmonised,reported,-0.0018914251666455992,0.0008257685483647859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2905027932960897,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c2,3 (Panel B),2,15,High climate change beliefs,other,other,dummy,binary,0.0254,0.0144,,,*,,,high,county-level indicator (above-median share of population believing in human-made climate change); not a property characteristic,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 2 (zip-code FE + year FE + month FE (no year-month FE)); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,monetary,0.0254,0.0144,"b (currency per unit, as reported)",reported,0.001456565027569149,0.0008257691494880214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7638888888888888,1466499.0,binary,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,False,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c3,3 (Panel A),3,15,Treatment effect,flood_risk,E_environment,other,binary,-0.0361,0.0179,,,**,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here evaluated at distance 0 km to the flooded area",log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,as Table 2 column 3 (county FE + year-month FE); treatment effect interacted with continuous distance to the 2021 flooded area,other,-0.0361,0.0179,not harmonised,reported,-0.0016642101469877635,0.0008251900728831292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.016759776536313,1468557.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c3,3 (Panel A),3,15,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0002,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to nearest potentially flooded area of the July 2021 flood,log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,as Table 2 column 3 (county FE + year-month FE); treatment effect interacted with continuous distance to the 2021 flooded area,other,0.0002,0.0001,not harmonised,reported,0.0016503801835936226,0.0008251900917968113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1468557.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c3,3 (Panel A),3,15,Distance to flooded area (km),flood_risk,E_environment,level,km,-0.0029,0.0005,,,***,,,high,"uninteracted continuous distance from the house to the nearest potentially flooded area (July 2021 Western European flood), in km",log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,as Table 2 column 3 (county FE + year-month FE); treatment effect interacted with continuous distance to the 2021 flooded area,monetary,-0.0029,0.0005,"b (currency per unit, as reported)",reported,-0.004786054234144579,0.000825181764507686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8,1468557.0,km,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c3,3 (Panel B),3,15,Treatment effect,flood_risk,E_environment,other,binary,0.0165,0.0131,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here for counties with below-median climate change beliefs",log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,as Table 2 column 3 (county FE + year-month FE); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,0.0165,0.0131,not harmonised,reported,0.001039362420090831,0.0008251907698902961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2595419847328244,1468557.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c3,3 (Panel B),3,15,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0313,0.0187,,,*,,,high,triple interaction: flood-risk zone x post-flood x county above-median share believing in human-made climate change,log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,as Table 2 column 3 (county FE + year-month FE); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,-0.0313,0.0187,not harmonised,reported,-0.0013812010915384412,0.0008251904284910175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6737967914438503,1468557.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c3,3 (Panel B),3,15,High climate change beliefs,other,other,dummy,binary,0.0915,0.0204,,,***,,,high,county-level indicator (above-median share of population believing in human-made climate change); not a property characteristic,log listing price per m2 (EUR),per_m2,FE_panel,1468567.0,,se,as Table 2 column 3 (county FE + year-month FE); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,monetary,0.0915,0.0204,"b (currency per unit, as reported)",reported,0.0037011999538858,0.0008251855634892932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.485294117647058,1468557.0,binary,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,False,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c4,3 (Panel A),4,15,Treatment effect,flood_risk,E_environment,other,binary,-0.0421,0.0127,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here evaluated at distance 0 km to the flooded area",log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,as Table 2 column 4 (1 km2 grid FE + year-month FE); treatment effect interacted with continuous distance to the 2021 flooded area,other,-0.0421,0.0127,not harmonised,reported,-0.002745854505199288,0.0008283219053689064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3149606299212597,1457466.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c4,3 (Panel A),4,15,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0002,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to nearest potentially flooded area of the July 2021 flood,log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,as Table 2 column 4 (1 km2 grid FE + year-month FE); treatment effect interacted with continuous distance to the 2021 flooded area,other,0.0002,0.0001,not harmonised,reported,0.0016566477827612634,0.0008283238913806318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,1457466.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c4,3 (Panel A),4,15,Distance to flooded area (km),flood_risk,E_environment,level,km,-0.0012,0.0045,,,,,,high,"uninteracted continuous distance from the house to the nearest potentially flooded area (July 2021 Western European flood), in km",log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,as Table 2 column 4 (1 km2 grid FE + year-month FE); treatment effect interacted with continuous distance to the 2021 flooded area,monetary,-0.0012,0.0045,"b (currency per unit, as reported)",reported,-0.0002208866687561479,0.0008283250078355547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.26666666666666666,1457466.0,km,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c4,3 (Panel B),4,15,Treatment effect,flood_risk,E_environment,other,binary,0.0029,0.0109,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here for counties with below-median climate change beliefs",log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,as Table 2 column 4 (1 km2 grid FE + year-month FE); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,0.0029,0.0109,not harmonised,reported,0.00022038004798088196,0.0008283250079281427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2660550458715596,1457466.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c4,3 (Panel B),4,15,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0218,0.0149,,,,,,high,triple interaction: flood-risk zone x post-flood x county above-median share believing in human-made climate change,log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,as Table 2 column 4 (1 km2 grid FE + year-month FE); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,-0.0218,0.0149,not harmonised,reported,-0.0012119108960109634,0.0008283244197506127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4630872483221475,1457466.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c4,3 (Panel B),4,15,High climate change beliefs,other,other,dummy,binary,-0.0125,0.0148,,,,,,high,county-level indicator (above-median share of population believing in human-made climate change); not a property characteristic,log listing price per m2 (EUR),per_m2,FE_panel,1457476.0,,se,as Table 2 column 4 (1 km2 grid FE + year-month FE); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,monetary,-0.0125,0.0148,"b (currency per unit, as reported)",reported,-0.0006995986700472161,0.0008283248253359039,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8445945945945946,1457466.0,binary,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,False,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c5,3 (Panel A),5,15,Treatment effect,flood_risk,E_environment,other,binary,-0.0585,0.0166,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here evaluated at distance 0 km to the flooded area",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 5 (zip-code FE + year-month FE + county x year linear trends); treatment effect interacted with continuous distance to the 2021 flooded area,other,-0.0585,0.0166,not harmonised,reported,-0.002910080839814142,0.0008257665289045258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.524096385542169,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c5,3 (Panel A),5,15,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to nearest potentially flooded area of the July 2021 flood,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 5 (zip-code FE + year-month FE + county x year linear trends); treatment effect interacted with continuous distance to the 2021 flooded area,other,0.0003,0.0001,not harmonised,reported,0.0024773024747015353,0.0008257674915671786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1466499.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3A-c5,3 (Panel A),5,15,Distance to flooded area (km),flood_risk,E_environment,level,km,-0.003,0.0007,,,***,,,high,"uninteracted continuous distance from the house to the nearest potentially flooded area (July 2021 Western European flood), in km",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 5 (zip-code FE + year-month FE + county x year linear trends); treatment effect interacted with continuous distance to the 2021 flooded area,monetary,-0.003,0.0007,"b (currency per unit, as reported)",reported,-0.003538992232616829,0.00082576485427726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.285714285714286,1466499.0,km,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c5,3 (Panel B),5,15,Treatment effect,flood_risk,E_environment,other,binary,0.0088,0.0129,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; here for counties with below-median climate change beliefs",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 5 (zip-code FE + year-month FE + county x year linear trends); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,0.0088,0.0129,not harmonised,reported,0.000563315896981712,0.0008257698944391004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6821705426356589,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c5,3 (Panel B),5,15,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0377,0.018,,,**,,,high,triple interaction: flood-risk zone x post-flood x county above-median share believing in human-made climate change,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 5 (zip-code FE + year-month FE + county x year linear trends); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,other,-0.0377,0.018,not harmonised,reported,-0.0017295268554679902,0.00082576879040912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0944444444444446,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T3B-c5,3 (Panel B),5,15,High climate change beliefs,other,other,dummy,binary,0.0227,0.015,,,,,,high,county-level indicator (above-median share of population believing in human-made climate change); not a property characteristic,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,as Table 2 column 5 (zip-code FE + year-month FE + county x year linear trends); treatment effect interacted with indicator for counties with above-median belief in human-made climate change,monetary,0.0227,0.015,"b (currency per unit, as reported)",reported,0.0012496643294156247,0.0008257693806711175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5133333333333334,1466499.0,binary,,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,False,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c1,4 (Average effect),1,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0063,0.0103,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1050981.0,,se,"robustness: Spillovers: 1 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE",other,-0.0063,0.0103,not harmonised,reported,-0.0005966338387422536,0.0009754489744516208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6116504854368932,1050971.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c1,4 (Distance heterogeneity),1,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0629,0.0168,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1050981.0,,se,"robustness: Spillovers: 1 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.0629,0.0168,not harmonised,reported,-0.003652103704455361,0.0009754426428434033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.744047619047619,1050971.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c1,4 (Distance heterogeneity),1,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1050981.0,,se,"robustness: Spillovers: 1 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0003,0.0001,not harmonised,reported,0.0029263349143822965,0.0009754449714607658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1050971.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c1,4 (Climate change belief heterogeneity),1,21,Treatment effect,flood_risk,E_environment,other,binary,0.0108,0.0129,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1050981.0,,se,"robustness: Spillovers: 1 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0108,0.0129,not harmonised,reported,0.0008166548283840662,0.0009754488227920791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8372093023255814,1050971.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c1,4 (Climate change belief heterogeneity),1,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0401,0.0179,,,**,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1050981.0,,se,"robustness: Spillovers: 1 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.0401,0.0179,not harmonised,reported,-0.0021852188517084204,0.0009754468190917888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2402234636871508,1050971.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c2,4 (Average effect),2,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0156,0.0106,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,831395.0,,se,"robustness: Spillovers: 1.5 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE",other,-0.0156,0.0106,not harmonised,reported,-0.00161405033789497,0.0010967265116465822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.471698113207547,831385.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c2,4 (Distance heterogeneity),2,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0729,0.0156,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,831395.0,,se,"robustness: Spillovers: 1.5 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.0729,0.0156,not harmonised,reported,-0.005125026720305025,0.0010967135368553963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.673076923076923,831385.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c2,4 (Distance heterogeneity),2,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0004,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,831395.0,,se,"robustness: Spillovers: 1.5 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0004,0.0001,not harmonised,reported,0.004386869548452541,0.0010967173871131353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,831385.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c2,4 (Climate change belief heterogeneity),2,21,Treatment effect,flood_risk,E_environment,other,binary,0.0064,0.0134,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,831395.0,,se,"robustness: Spillovers: 1.5 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0064,0.0134,not harmonised,reported,0.0005238102876484662,0.0010967277897639763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47761194029850745,831385.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c2,4 (Climate change belief heterogeneity),2,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.049,0.0172,,,***,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,831395.0,,se,"robustness: Spillovers: 1.5 km buffer excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.049,0.0172,not harmonised,reported,-0.003124384114706812,0.0010967225872032076,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8488372093023258,831385.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c3,4 (Average effect),3,21,Treatment effect,flood_risk,E_environment,other,binary,0.0026,0.0103,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1024595.0,,se,"robustness: Control group: distance to nearest treated house <= 3 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE",other,0.0026,0.0103,not harmonised,reported,0.0002493802827552113,0.0009879295816841063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2524271844660194,1024585.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c3,4 (Distance heterogeneity),3,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0428,0.0166,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1024595.0,,se,"robustness: Control group: distance to nearest treated house <= 3 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.0428,0.0166,not harmonised,reported,-0.0025471837494153537,0.0009879264074835253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.578313253012048,1024585.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c3,4 (Distance heterogeneity),3,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1024595.0,,se,"robustness: Control group: distance to nearest treated house <= 3 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0003,0.0001,not harmonised,reported,0.0029637758202716896,0.0009879252734238968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1024585.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c3,4 (Climate change belief heterogeneity),3,21,Treatment effect,flood_risk,E_environment,other,binary,0.0152,0.0131,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1024595.0,,se,"robustness: Control group: distance to nearest treated house <= 3 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0152,0.0131,not harmonised,reported,0.0011462992551651118,0.0009879289633330898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1603053435114503,1024585.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c3,4 (Climate change belief heterogeneity),3,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0297,0.0179,,,*,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1024595.0,,se,"robustness: Control group: distance to nearest treated house <= 3 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.0297,0.0179,not harmonised,reported,-0.001639188272009063,0.0009879282851502434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6592178770949721,1024585.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c4,4 (Average effect),4,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0021,0.0102,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1125285.0,,se,"robustness: Control group: distance to flood risk area <= 2 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE",other,-0.0021,0.0102,not harmonised,reported,-0.0001940840202684542,0.000942693812732492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.20588235294117643,1125275.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c4,4 (Distance heterogeneity),4,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0503,0.0165,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1125285.0,,se,"robustness: Control group: distance to flood risk area <= 2 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.0503,0.0165,not harmonised,reported,-0.0028737759922610917,0.0009426899378192449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0484848484848484,1125275.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c4,4 (Distance heterogeneity),4,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1125285.0,,se,"robustness: Control group: distance to flood risk area <= 2 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0003,0.0001,not harmonised,reported,0.002828070181968855,0.000942690060656285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1125275.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c4,4 (Climate change belief heterogeneity),4,21,Treatment effect,flood_risk,E_environment,other,binary,0.0125,0.0132,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1125285.0,,se,"robustness: Control group: distance to flood risk area <= 2 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0125,0.0132,not harmonised,reported,0.0008927021352869139,0.000942693454862981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.946969696969697,1125275.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c4,4 (Climate change belief heterogeneity),4,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0341,0.0181,,,*,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1125285.0,,se,"robustness: Control group: distance to flood risk area <= 2 km; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.0341,0.0181,not harmonised,reported,-0.0017760115426560458,0.0009426923437558486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.883977900552486,1125275.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c5,4 (Average effect),5,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0053,0.0107,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1263985.0,,se,"robustness: Exclusion of flooded areas: only certainly flooded area excluded (state-level trends); full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE",other,-0.0053,0.0107,not harmonised,reported,-0.00044057800056537393,0.0008894687935942454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4953271028037384,1263975.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c5,4 (Distance heterogeneity),5,21,Treatment effect,flood_risk,E_environment,other,binary,-0.069,0.0197,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1263985.0,,se,"robustness: Exclusion of flooded areas: only certainly flooded area excluded (state-level trends); full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.069,0.0197,not harmonised,reported,-0.003115383496487007,0.0008894645634897686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5025380710659904,1263975.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c5,4 (Distance heterogeneity),5,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0004,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1263985.0,,se,"robustness: Exclusion of flooded areas: only certainly flooded area excluded (state-level trends); full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0004,0.0001,not harmonised,reported,0.003557853001253693,0.0008894632503134233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,1263975.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c5,4 (Climate change belief heterogeneity),5,21,Treatment effect,flood_risk,E_environment,other,binary,0.0141,0.0137,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1263985.0,,se,"robustness: Exclusion of flooded areas: only certainly flooded area excluded (state-level trends); full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0141,0.0137,not harmonised,reported,0.0009154383906439742,0.0008894685072214502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0291970802919708,1263975.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c5,4 (Climate change belief heterogeneity),5,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.046,0.0192,,,**,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1263985.0,,se,"robustness: Exclusion of flooded areas: only certainly flooded area excluded (state-level trends); full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.046,0.0192,not harmonised,reported,-0.002131014352756571,0.0008894668602810036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3958333333333335,1263975.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c6,4 (Average effect),6,21,Treatment effect,flood_risk,E_environment,other,binary,-0.008,0.0097,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1559132.0,,se,"robustness: Exclusion of flooded areas: potentially flooded counties excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE",other,-0.008,0.0097,not harmonised,reported,-0.0006605080395901461,0.0008008659980030523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8247422680412371,1559122.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c6,4 (Distance heterogeneity),6,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0734,0.0163,,,***,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1559132.0,,se,"robustness: Exclusion of flooded areas: potentially flooded counties excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.0734,0.0163,not harmonised,reported,-0.003606330970333786,0.0008008609648016444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.503067484662577,1559122.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c6,4 (Distance heterogeneity),6,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0003,0.0001,,,***,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1559132.0,,se,"robustness: Exclusion of flooded areas: potentially flooded counties excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0003,0.0001,not harmonised,reported,0.002402591583655595,0.0008008638612185318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1559122.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c6,4 (Climate change belief heterogeneity),6,21,Treatment effect,flood_risk,E_environment,other,binary,0.0141,0.0122,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1559132.0,,se,"robustness: Exclusion of flooded areas: potentially flooded counties excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0141,0.0122,not harmonised,reported,0.0009255908358976029,0.0008008658296418976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1557377049180326,1559122.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c6,4 (Climate change belief heterogeneity),6,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0503,0.0172,,,***,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1559132.0,,se,"robustness: Exclusion of flooded areas: potentially flooded counties excluded; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.0503,0.0172,not harmonised,reported,-0.0023420615118553423,0.0008008639762209123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9244186046511627,1559122.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c7,4 (Average effect),7,21,Treatment effect,flood_risk,E_environment,other,binary,0.0047,0.0071,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2019; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date",other,0.0047,0.0071,not harmonised,reported,0.0005466364140549566,0.0008257699020830196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6619718309859155,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c7,4 (Distance heterogeneity),7,21,Treatment effect,flood_risk,E_environment,other,binary,-0.007,0.0108,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2019; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.007,0.0108,not harmonised,reported,-0.0005352212361364712,0.0008257699071819841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6481481481481481,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c7,4 (Distance heterogeneity),7,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0001,0.0001,,,,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2019; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0001,0.0001,not harmonised,reported,0.0008257697439132144,0.0008257697439132144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1466499.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c7,4 (Climate change belief heterogeneity),7,21,Treatment effect,flood_risk,E_environment,other,binary,0.0029,0.0096,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2019; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0029,0.0096,not harmonised,reported,0.0002494513540958555,0.0008257699997655907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3020833333333333,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c7,4 (Climate change belief heterogeneity),7,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,0.0042,0.0123,,,,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2019; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0042,0.0123,not harmonised,reported,0.000281970241385999,0.0008257699926304258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3414634146341463,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4avg-c8,4 (Average effect),8,21,Treatment effect,flood_risk,E_environment,other,binary,0.0049,0.0074,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2020; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date",other,0.0049,0.0074,not harmonised,reported,0.0005467935837647447,0.0008257699020120636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6621621621621621,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c8,4 (Distance heterogeneity),8,21,Treatment effect,flood_risk,E_environment,other,binary,-0.0092,0.0112,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; evaluated at distance 0 km",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2020; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted distance to flooded area (coefficient not printed)",other,-0.0092,0.0112,not harmonised,reported,-0.0006783109362928799,0.0008257698354869842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8214285714285714,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4dist-c8,4 (Distance heterogeneity),8,21,Treatment effect × distance to flooded area (km),flood_risk,E_environment,other,km,0.0001,0.0001,,,,,,high,triple interaction: flood-risk zone x post-flood x distance (km) to flooded area,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2020; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted distance to flooded area (coefficient not printed)",other,0.0001,0.0001,not harmonised,reported,0.0008257697439132144,0.0008257697439132144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1466499.0,km,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c8,4 (Climate change belief heterogeneity),8,21,Treatment effect,flood_risk,E_environment,other,binary,0.0068,0.0101,,,,,,high,"DiD treatment effect = flood-risk-zone indicator x post-July-2021-flood indicator (interaction); treatment group dummy and post dummy included but not printed; property characteristics (construction year categories, living space, floors, rooms, cellar, condition) included but not printed; for low-belief counties",log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2020; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted high-belief indicator (coefficient not printed)",other,0.0068,0.0101,not harmonised,reported,0.0005559638916128355,0.0008257698978367115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6732673267326733,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4,a34939ebd1d20ba654297f426501ad47056b65a4:T4belief-c8,4 (Climate change belief heterogeneity),8,21,Treatment effect × high climate change beliefs,flood_risk,E_environment,other,binary,-0.0044,0.0127,,,,,,high,triple interaction: flood-risk zone x post-flood x high climate change belief county,log listing price per m2 (EUR),per_m2,FE_panel,1466509.0,,se,"robustness: Placebo flood July 2020; full controls, county x year linear trends (state-level in col 5), year-month FE, zip-code FE; PLACEBO treatment date; includes uninteracted high-belief indicator (coefficient not printed)",other,-0.0044,0.0127,not harmonised,reported,-0.0002860935404187903,0.0008257699916633265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3464566929133859,1466499.0,binary,nonstandard_x_transform,2025.0,Germany,Europe,High income,2014.0,2022.0,single_family,listing,journal,True,True,clustered,,2018.0,panel,journal,False,True,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m1,3,Model (1),13,Grant for renovations (dummy),renovation,A_structural,dummy,binary,0.0386,,,,,,,medium,"1 if the owner received a public grant for renovations between sale and resale; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.575,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; base model: quarter dummies + renovation grant dummy",pct_premium,3.935465862724663,,100*(exp(b) - 1),,,,,,734.0,binary,,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m2,3,Model (2),13,Grant for renovations (dummy),renovation,A_structural,dummy,binary,0.0231,,,,,,,medium,"1 if the owner received a public grant for renovations between sale and resale; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.586,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds network-distance buffers to d'Auteuil Street (reference > 600 m)",pct_premium,2.33688713176754,,100*(exp(b) - 1),,,,,,734.0,binary,,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m3,3,Model (3),13,Grant for renovations (dummy),renovation,A_structural,dummy,binary,0.0202,,,,,,,medium,"1 if the owner received a public grant for renovations between sale and resale; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.585,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds Euclidean-distance buffers to d'Auteuil Street (reference > 600 m)",pct_premium,2.04054007001484,,100*(exp(b) - 1),,,,,,734.0,binary,,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m2,3,Model (2),13,"Network dist., 0 to 150 m (Buffer)",other,other,other,binary,-0.0405,,,,,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.586,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds network-distance buffers to d'Auteuil Street (reference > 600 m)",other,-0.0405,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m2,3,Model (2),13,"Network dist., 150 to 300 m (Buffer)",other,other,other,binary,-0.2952,,,,**,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.586,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds network-distance buffers to d'Auteuil Street (reference > 600 m)",other,-0.2952,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m2,3,Model (2),13,"Network dist., 300 to 450 m (Buffer)",other,other,other,binary,-0.2376,,,,*,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.586,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds network-distance buffers to d'Auteuil Street (reference > 600 m)",other,-0.2376,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m2,3,Model (2),13,"Network dist., 450 to 600 m (Buffer)",other,other,other,binary,0.1353,,,,,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.586,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds network-distance buffers to d'Auteuil Street (reference > 600 m)",other,0.1353,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m3,3,Model (3),13,"Euclidean dist., 0 to 150 m (Buffer)",other,other,other,binary,0.0102,,,,,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.585,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds Euclidean-distance buffers to d'Auteuil Street (reference > 600 m)",other,0.0102,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m3,3,Model (3),13,"Euclidean dist., 150 to 300 m (Buffer)",other,other,other,binary,-0.2476,,,,**,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.585,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds Euclidean-distance buffers to d'Auteuil Street (reference > 600 m)",other,-0.2476,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m3,3,Model (3),13,"Euclidean dist., 300 to 450 m (Buffer)",other,other,other,binary,0.0883,,,,,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.585,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds Euclidean-distance buffers to d'Auteuil Street (reference > 600 m)",other,0.0883,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212,a5c3a8b4f02ea9b02009cba2eba334012eb7b212:T3-m3,3,Model (3),13,"Euclidean dist., 450 to 600 m (Buffer)",other,other,other,binary,0.0795,,,,,,,medium,"Policy impact dummy: distance buffer to the reorganised/landscaped d'Auteuil Street (Old Quebec) for units sold before and resold after the 2005 works; coefficient = change in willingness-to-pay by proximity (DiD), reference > 600 m; only significance stars printed (legend * p<0.05, ** p<0.01, *** p<0.001)","change in (log) condominium price between sale and resale (repeat-sales first difference), CAD",log,OLS,744.0,0.585,none,"Repeat-sales DiD first-difference equation, no constant; quarterly time dummies (1997-Q2 reference) not extracted; adds Euclidean-distance buffers to d'Auteuil Street (reference > 600 m)",other,0.0795,,not harmonised,,,,,,734.0,binary,nonstandard_x_transform,2018.0,Canada,North America,High income,1997.0,2011.0,condo_apartment,transaction,journal,True,False,unknown,,2004.0,ols,journal,False,False,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Log (size),living_area,A_structural,log,m2,0.664,,,2e-09,,,,high,natural log of structure size in m2; elasticity; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,elasticity,0.664,0.11070712984571564,b (log-log),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,m2,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Log (size),living_area,A_structural,log,m2,0.626,,,2e-09,,,,high,natural log of structure size in m2; elasticity; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,elasticity,0.626,0.10437148084852106,b (log-log),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,m2,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Log (size),living_area,A_structural,log,m2,0.558,,,2e-09,,,,high,natural log of structure size in m2; elasticity; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,elasticity,0.558,0.09303400369564657,b (log-log),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,m2,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Log (size),living_area,A_structural,log,m2,0.479,,,2e-09,,,,high,natural log of structure size in m2; elasticity; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,elasticity,0.479,0.07986252288568943,b (log-log),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,m2,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Log (size),living_area,A_structural,log,m2,0.512,,,2e-09,,,,high,natural log of structure size in m2; elasticity; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,elasticity,0.512,0.08536453385693735,b (log-log),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,m2,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Bedrooms,bedrooms,A_structural,level,count,0.003,,,0.313,,,,high,number of bedrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,semi_elasticity,0.3,0.2973396691663207,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.0015658293383743241,0.0015519439248104679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0089471103574519,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Bedrooms,bedrooms,A_structural,level,count,0.017,,,2e-09,,,,high,number of bedrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,semi_elasticity,1.7000000000000002,0.28343692882186233,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Bedrooms,bedrooms,A_structural,level,count,0.021,,,2e-09,,,,high,number of bedrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,semi_elasticity,2.1,0.3501279708975946,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Bedrooms,bedrooms,A_structural,level,count,0.034,,,2e-09,,,,high,number of bedrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,semi_elasticity,3.4000000000000004,0.5668738576437247,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Bedrooms,bedrooms,A_structural,level,count,0.025,,,2e-09,,,,high,number of bedrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,semi_elasticity,2.5,0.4168190129733269,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879795,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.997807015007687,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Bathrooms,bathrooms,A_structural,level,count,0.111,,,2e-09,,,,high,number of bathrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,semi_elasticity,11.1,1.8506764176015715,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Bathrooms,bathrooms,A_structural,level,count,0.096,,,2e-09,,,,high,number of bathrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,semi_elasticity,9.6,1.6005850098175753,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Bathrooms,bathrooms,A_structural,level,count,0.112,,,2e-09,,,,high,number of bathrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,semi_elasticity,11.200000000000001,1.8673491781205045,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Bathrooms,bathrooms,A_structural,level,count,0.117,,,2e-09,,,,high,number of bathrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,semi_elasticity,11.700000000000001,1.9507129807151702,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Bathrooms,bathrooms,A_structural,level,count,0.112,,,2e-09,,,,high,number of bathrooms; only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,semi_elasticity,11.200000000000001,1.8673491781205045,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,count,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Cluster,dwelling_type,A_structural,dummy,binary,0.09,,,2e-09,,,,high,cluster home vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,pct_premium,9.405110600133915,1.6418615218372898,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Cluster,dwelling_type,A_structural,dummy,binary,0.136,,,2e-09,,,,high,cluster home vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,14.53874032418323,2.5978284586187463,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Cluster,dwelling_type,A_structural,dummy,binary,0.146,,,2e-09,,,,high,cluster home vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,15.68533939010026,2.816873617851803,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Cluster,dwelling_type,A_structural,dummy,binary,0.187,,,2e-09,,,,high,cluster home vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,20.504144943169187,3.7589122447622967,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Cluster,dwelling_type,A_structural,dummy,binary,0.187,,,2e-09,,,,high,cluster home vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,pct_premium,20.504144943169187,3.7589122447622967,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Duplex,dwelling_type,A_structural,dummy,binary,0.003,,,0.874,,,,high,duplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,pct_premium,0.28250389122423947,1.8974767570741184,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0002461071424419526,0.001551945780359029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15857972975384352,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Duplex,dwelling_type,A_structural,dummy,binary,0.025,,,0.104,,,,high,duplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,2.5193902547093305,1.5766671970944113,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.002523088672537373,0.0015519408875255647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6257633862332344,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Duplex,dwelling_type,A_structural,dummy,binary,0.035,,,0.024,,,,high,duplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,3.5495209348074486,1.6058756890652666,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.003502920821377062,0.0015519363057893512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2571292444862254,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Duplex,dwelling_type,A_structural,dummy,binary,0.086,,,2e-09,,,,high,duplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,8.969430485742592,1.5626268734510285,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Duplex,dwelling_type,A_structural,dummy,binary,0.079,,,2e-09,,,,high,duplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,pct_premium,8.211045146048402,1.4254233460601817,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,House,dwelling_type,A_structural,dummy,binary,0.027,,,0.0003,,,,high,house vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,pct_premium,2.733915249184826,0.7672649744553345,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.005610661447841738,0.0015519213999101614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.615300006924662,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,House,dwelling_type,A_structural,dummy,binary,0.063,,,2e-09,,,,high,house vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,6.4968088307346905,1.118687058514607,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879795,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.997807015007687,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,House,dwelling_type,A_structural,dummy,binary,0.103,,,2e-09,,,,high,house vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,10.832796853518945,1.9036060154843515,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.997807015007686,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,House,dwelling_type,A_structural,dummy,binary,0.158,,,2e-09,,,,high,house vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,17.07598988560639,3.0851986117725168,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,House,dwelling_type,A_structural,dummy,binary,0.141,,,2e-09,,,,high,house vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,pct_premium,15.11065222712662,2.70683726501395,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Simplex,dwelling_type,A_structural,dummy,binary,0.061,,,4e-05,,,,high,"simplex vs apartment (reference); p printed as '4-e5' (typo), read as 4e-5; only p-value printed","listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,pct_premium,6.27817089953473,1.5785065105075968,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.006374456398092487,0.0015519142963921974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.10747965458625,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Simplex,dwelling_type,A_structural,dummy,binary,0.068,,,5e-07,,,,high,simplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,7.0267359265588025,1.4480762210905056,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.007800327916161132,0.0015518986124788757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.026312836056685,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Simplex,dwelling_type,A_structural,dummy,binary,0.078,,,2e-09,,,,high,simplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,8.103124085412894,1.4059733360086846,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Simplex,dwelling_type,A_structural,dummy,binary,0.117,,,2e-09,,,,high,simplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,12.390557051317996,2.1928343633714644,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Simplex,dwelling_type,A_structural,dummy,binary,0.087,,,2e-09,,,,high,simplex vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,pct_premium,9.078192136446184,1.582378540990261,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2013,4,2013,9,Townhouse,dwelling_type,A_structural,dummy,binary,0.05,,,0.064,,,,high,townhouse vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2013 (training split 70%); gamma GLM log link; deviance explained 0.89,pct_premium,5.0888113546968095,2.8379292955779576,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0028744709279834167,0.0015519394158047815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8521798587690466,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2014,4,2014,9,Townhouse,dwelling_type,A_structural,dummy,binary,0.063,,,2e-09,,,,high,townhouse vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2014 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,6.4968088307346905,1.118687058514607,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879795,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.997807015007687,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2015,4,2015,9,Townhouse,dwelling_type,A_structural,dummy,binary,0.077,,,2e-09,,,,high,townhouse vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2015 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,7.995307654566819,1.3865607825350088,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2016,4,2016,9,Townhouse,dwelling_type,A_structural,dummy,binary,0.09,,,2e-09,,,,high,townhouse vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2016 (training split 70%); gamma GLM log link; deviance explained 0.87,pct_premium,9.405110600133915,1.6418615218372898,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9978070150076865,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4,a7a83b631f85316bae926a6c965b325fb26c01c4:T4-2017,4,2017,9,Townhouse,dwelling_type,A_structural,dummy,binary,0.099,,,2e-09,,,,high,townhouse vs apartment (reference); only p-value printed,"listing price (ZAR), gamma GLM log link",log,other,415200.0,,p,Listings of year 2017 (training split 70%); gamma GLM log link; deviance explained 0.88,pct_premium,10.391590885529256,1.822375467947417,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009307868343879793,0.0015518785983926603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.997807015007686,415190.0,binary,,2019.0,South Africa,Africa,Upper middle income,2013.0,2017.0,mixed,listing,journal,False,True,unknown,autoencoder (H2O) used only for anomaly detection in data cleaning,2015.0,other,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c1,2,1,7,South,sunlight,E_environment,binary,binary,0.08,0.003,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,pct_premium,8.328219289412232,0.3249861203024876,100*(exp(b - se^2/2) - 1),reported,0.13145844875624849,0.004929691828359318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.666666666666668,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c2,2,2,7,South,sunlight,E_environment,binary,binary,0.066,0.003,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,pct_premium,6.822191015658174,0.320468015149798,100*(exp(b - se^2/2) - 1),reported,0.10875375357194118,0.004943352435088236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c3,2,3,7,South,sunlight,E_environment,binary,binary,0.067,0.003,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.234,se,,pct_premium,6.929066635577508,0.32078864345238006,100*(exp(b - se^2/2) - 1),reported,0.11038160897875766,0.004942460103526462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.333333333333336,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,South,sunlight,E_environment,binary,binary,0.071,0.006,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,pct_premium,7.356190158021092,0.6441487355210145,100*(exp(b - se^2/2) - 1),reported,0.05874374510847941,0.004964260150012345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.833333333333332,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,South,sunlight,E_environment,binary,binary,0.077,0.009,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,pct_premium,7.999833557426395,0.9720378687533403,100*(exp(b - se^2/2) - 1),reported,0.04250702153776645,0.004968353166751923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.555555555555555,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,South,sunlight,E_environment,binary,binary,0.072,0.002,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,pct_premium,7.465319475527488,0.21493106881276272,100*(exp(b - se^2/2) - 1),reported,0.1762209324446923,0.004895025901241453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.0,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,South,sunlight,E_environment,binary,binary,0.071,0.002,,,***,,,medium,indicator for south-facing orientation (access to sunlight); reference = other orientations; SE printed as negative in table (sign dropped),log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,pct_premium,7.35790787080528,0.2147162451736715,100*(exp(b - se^2/2) - 1),reported,0.17384789586618524,0.004897123827216487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.49999999999999,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c1,2,1,7,Rheight,floor_level,A_structural,level,share,2.771,0.078,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,semi_elasticity,277.09999999999997,7.8,100*b (unit as reported: share; count expected),reported,0.17396966412438566,0.004897016889823921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.52564102564102,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c2,2,2,7,Rheight,floor_level,A_structural,level,share,2.732,0.072,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,semi_elasticity,273.20000000000005,7.199999999999999,100*b (unit as reported: share; count expected),reported,0.1854199111222995,0.004886615520060601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.94444444444445,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c3,2,3,7,Rheight,floor_level,A_structural,level,share,2.932,0.073,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.234,se,,semi_elasticity,293.2,7.3,100*b (unit as reported: share; count expected),reported,0.19586282949119035,0.0048765302022022155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.16438356164384,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Rheight,floor_level,A_structural,level,share,2.551,0.092,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,255.10000000000002,9.2,100*b (unit as reported: share; count expected),reported,0.13659597074702234,0.004926236498912605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.72826086956522,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Rheight,floor_level,A_structural,level,share,2.441,0.081,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,244.1,8.1,100*b (unit as reported: share; count expected),reported,0.14820577798044665,0.004917930363136493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.1358024691358,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Rheight,floor_level,A_structural,level,share,2.516,0.057,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,251.6,5.7,100*b (unit as reported: share; count expected),reported,0.2143989557338731,0.004857210046435123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.14035087719298,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Rheight,floor_level,A_structural,level,share,2.519,0.075,,,***,,,medium,relative height = floor / total storeys of building (0-1); SE printed as negative in table (sign dropped),log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,251.9,7.5,100*b (unit as reported: share; count expected),reported,0.16473938949082578,0.004904904411199655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.58666666666667,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c1,2,1,7,Rheight2,floor_level,A_structural,squared,share,-1.251,0.164,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,other,-1.251,0.164,not harmonised,reported,-0.037905863496220375,0.004969273871606829,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.628048780487804,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c2,2,2,7,Rheight2,floor_level,A_structural,squared,share,-1.207,0.181,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,other,-1.207,0.181,not harmonised,reported,-0.033143258126670884,0.004970115758846256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.668508287292818,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c3,2,3,7,Rheight2,floor_level,A_structural,squared,share,-1.133,0.157,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.234,se,,other,-1.133,0.157,not harmonised,reported,-0.03586377050300608,0.004969648692826084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.2165605095541405,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Rheight2,floor_level,A_structural,squared,share,-1.35,0.164,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,other,-1.35,0.164,not harmonised,reported,-0.04090077361157401,0.004968686572072694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.231707317073171,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Rheight2,floor_level,A_structural,squared,share,-1.523,0.167,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,other,-1.523,0.167,not harmonised,reported,-0.04530461495959747,0.004967741758537609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.119760479041915,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Rheight2,floor_level,A_structural,squared,share,-1.521,0.15,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,other,-1.521,0.15,not harmonised,reported,-0.050360692431263974,0.00496653771511479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.14,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Rheight2,floor_level,A_structural,squared,share,-1.516,0.154,,,***,,,medium,square of relative height; SE printed as negative in table (sign dropped),log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,other,-1.516,0.154,not harmonised,reported,-0.048894936654950795,0.004966899897666506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.844155844155845,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c1,2,1,7,Age,age,A_structural,level,years,0.072,0.006,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,semi_elasticity,7.199999999999999,0.6,100*b/1 (per year),reported,0.0595682062769542,0.004964017189746185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.999999999999998,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c2,2,2,7,Age,age,A_structural,level,years,0.067,0.006,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.239,se,,semi_elasticity,6.7,0.6,100*b/1 (per year),reported,0.05544471485666451,0.004965198345372941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.166666666666668,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c3,2,3,7,Age,age,A_structural,level,years,0.081,0.006,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.234,se,,semi_elasticity,8.1,0.6,100*b/1 (per year),reported,0.06698267270274079,0.004961679459462281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.5,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,True
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Age,age,A_structural,level,years,0.075,0.006,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,7.5,0.6,100*b/1 (per year),reported,0.06204085181566509,0.004963268145253208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,True
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Age,age,A_structural,level,years,0.049,0.006,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,4.9,0.6,100*b/1 (per year),reported,0.04057814124215719,0.0049687519888355755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.166666666666666,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Age,age,A_structural,level,years,0.025,0.005,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,2.5,0.5,100*b/1 (per year),reported,0.02485655657397901,0.004971311314795801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Age,age,A_structural,level,years,0.026,0.005,,,***,,,medium,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,2.6,0.5,100*b/1 (per year),reported,0.02585016720858074,0.0049711860016501425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.199999999999999,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Area,living_area,A_structural,other,m2,1.02,0.015,,,***,,,medium,"total floor area; transformation not stated, magnitude (1.02) suggests log; only in column (7); SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,other,1.02,0.015,not harmonised,reported,0.3203344752350963,0.004710801106398475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),68.0,40438.0,m2,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c3,2,3,7,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.023,0.007,,,***,,,medium,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.234,se,,semi_elasticity,-2.3,0.7000000000000001,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.016337176348573495,0.004972184106087586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2857142857142856,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.023,0.007,,,***,,,medium,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,-2.3,0.7000000000000001,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.016337176348573495,0.004972184106087586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2857142857142856,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.108,0.008,,,***,,,medium,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,-10.8,0.8,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.06698267270274079,0.004961679459462281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.5,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.109,0.013,,,***,,,medium,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,-10.9,1.3,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.041659219333133,0.004968530746153477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.384615384615385,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.112,0.014,,,***,,,medium,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); SE printed as negative in table (sign dropped),log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,-11.200000000000001,1.4000000000000001,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.039751338097592494,0.004968917262199063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c3,2,3,7,Dust,air_pollution,E_environment,level,tons/km2,-0.354,0.06,,,***,,,medium,"dust pollution, district-level 6-month geometric mean, tons per km2; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.234,se,,semi_elasticity,-35.4,6.0,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.029327181877298852,0.004970708792762518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8999999999999995,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Dust,air_pollution,E_environment,level,tons/km2,-0.353,0.06,,,***,,,medium,"dust pollution, district-level 6-month geometric mean, tons per km2; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,-35.3,6.0,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.02924440768312427,0.0049707208526556836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.883333333333333,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Dust,air_pollution,E_environment,level,tons/km2,-0.332,0.068,,,***,,,medium,"dust pollution, district-level 6-month geometric mean, tons per km2; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,-33.2,6.800000000000001,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.02427204512805155,0.004971382737070801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.88235294117647,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Dust,air_pollution,E_environment,level,tons/km2,-0.321,0.061,,,***,,,medium,"dust pollution, district-level 6-month geometric mean, tons per km2; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,-32.1,6.1,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.026159636978985507,0.004971145967969208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.262295081967213,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Dust,air_pollution,E_environment,level,tons/km2,-0.319,0.021,,,***,,,medium,"dust pollution, district-level 6-month geometric mean, tons per km2; SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,-31.900000000000002,2.1,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.07532531790500521,0.0049587199874768315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.19047619047619,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.91,0.137,,,***,,,medium,"district-level population density (mean 2.66, scale unit not stated); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,91.0,13.700000000000001,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.033013319719248625,0.004970137144546222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.642335766423358,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.935,0.143,,,***,,,medium,"district-level population density (mean 2.66, scale unit not stated); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,93.5,14.299999999999999,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.032497600115543965,0.004970221194142018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.538461538461539,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.833,0.138,,,***,,,medium,"district-level population density (mean 2.66, scale unit not stated); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,83.3,13.8,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.030003748169856292,0.004970608940504405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.03623188405797,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.831,0.136,,,***,,,medium,"district-level population density (mean 2.66, scale unit not stated); SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,83.1,13.600000000000001,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.030371545047919543,0.004970553702186593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.110294117647058,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Factory,hazard_site,E_environment,level,count,-0.06,0.029,,,**,,,medium,"number of factories in district (count, not distance); yearly district data; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,-6.0,2.9000000000000004,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.01028810607388635,0.004972584602378404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.068965517241379,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Factory,hazard_site,E_environment,level,count,-0.022,0.021,,,,,,medium,"number of factories in district (count, not distance); yearly district data; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,-2.1999999999999997,2.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.005209579365985588,0.004972780303895335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0476190476190474,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Factory,hazard_site,E_environment,level,count,-0.113,0.05,,,**,,,medium,"number of factories in district (count, not distance); yearly district data; SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,-11.3,5.0,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.011237926302912274,0.004972533762350565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.26,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Factory,hazard_site,E_environment,level,count,-0.121,0.05,,,**,,,medium,"number of factories in district (count, not distance); yearly district data; SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,-12.1,5.0,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.012033420306631,0.00497248773001281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.42,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,School,school_distance,D_location,level,count,0.754,0.161,,,***,,,medium,"number of schools in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,75.4,16.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.023282675868298786,0.004971499754371492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.683229813664596,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,School,school_distance,D_location,level,count,1.362,0.163,,,***,,,medium,"number of schools in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,136.20000000000002,16.3,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.04151643637726699,0.004968560300656769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.355828220858896,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,School,school_distance,D_location,level,count,1.216,0.156,,,***,,,medium,"number of schools in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,121.6,15.6,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.038733622304961614,0.004969116019386523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.794871794871795,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,School,school_distance,D_location,level,count,1.218,0.161,,,***,,,medium,"number of schools in district (count, not distance); SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,121.8,16.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.037594080219665565,0.004969332442829356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.565217391304348,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Shopping,commercial_distance,D_location,level,count,0.573,0.139,,,***,,,medium,"number of shopping malls in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,57.3,13.900000000000002,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.020495275216270165,0.004971803237454718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.1223021582733805,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Shopping,commercial_distance,D_location,level,count,1.123,0.139,,,***,,,medium,"number of shopping malls in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,112.3,13.900000000000002,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.040143931170660625,0.004968839209903676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.07913669064748,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Shopping,commercial_distance,D_location,level,count,0.234,0.134,,,*,,,medium,"number of shopping malls in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,23.400000000000002,13.4,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.008683600810264824,0.004972660293057634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7462686567164178,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Shopping,commercial_distance,D_location,level,count,0.241,0.122,,,*,,,medium,"number of shopping malls in district (count, not distance); SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,24.099999999999998,12.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.009822938485572547,0.004972607864065772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9754098360655739,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Hospital,hospital_distance,D_location,level,count,2.014,0.46,,,***,,,medium,"number of hospitals in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,201.39999999999998,46.0,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.021767266218551372,0.0049716695434625785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.378260869565216,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Hospital,hospital_distance,D_location,level,count,0.284,0.479,,,,,,medium,"number of hospitals in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,28.4,47.9,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0029483979800270592,0.004972826170538598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5929018789144049,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Hospital,hospital_distance,D_location,level,count,3.411,0.452,,,***,,,medium,"number of hospitals in district (count, not distance); SE printed as negative in table (sign dropped)","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,341.1,45.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03750100063345212,0.004969349834746514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.54646017699115,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Hospital,hospital_distance,D_location,level,count,3.419,0.422,,,***,,,medium,"number of hospitals in district (count, not distance); SE printed as negative in table (sign dropped)",log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,341.9,42.199999999999996,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.04025683418895558,0.004968816621158015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.101895734597157,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c4,2,4,7,Greenspace,green_space,E_environment,level,m2 per capita,0.004,0.001,,,***,,,medium,green space per capita in district (mean 3654); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.372,se,,semi_elasticity,0.4,0.1,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.01988745712024159,0.004971864280060396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c5,2,5,7,Greenspace,green_space,E_environment,level,m2 per capita,0.005,0.001,,,**,,,medium,green space per capita in district (mean 3654); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.355,se,,semi_elasticity,0.5,0.1,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.02485655657397901,0.004971311314795801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c6,2,6,7,Greenspace,green_space,E_environment,level,m2 per capita,0.006,0.002,,,***,,,medium,green space per capita in district (mean 3654); SE printed as negative in table (sign dropped),"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.479,se,,semi_elasticity,0.6,0.2,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.014916883475245026,0.004972294491748342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:T2-c7,2,7,7,Greenspace,green_space,E_environment,level,m2 per capita,0.006,0.002,,,***,,,medium,green space per capita in district (mean 3654); SE printed as negative in table (sign dropped),log price (column 7 adds Area as control; table header still says unit price but note implies total price - ambiguous),log,OLS,40448.0,0.421,se,,semi_elasticity,0.6,0.2,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.014916883475245026,0.004972294491748342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,South,sunlight,E_environment,binary,binary,0.067,0.003,,,***,,,high,indicator for south-facing orientation (access to sunlight); reference = other orientations; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,pct_premium,6.929066635577508,0.32078864345238006,100*(exp(b - se^2/2) - 1),reported,0.11038160897875766,0.004942460103526462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.333333333333336,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,South,sunlight,E_environment,binary,binary,0.071,0.006,,,***,,,high,indicator for south-facing orientation (access to sunlight); reference = other orientations; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,pct_premium,7.356190158021092,0.6441487355210145,100*(exp(b - se^2/2) - 1),reported,0.05874374510847941,0.004964260150012345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.833333333333332,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,South,sunlight,E_environment,binary,binary,0.077,0.009,,,***,,,high,indicator for south-facing orientation (access to sunlight); reference = other orientations; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,pct_premium,7.999833557426395,0.9720378687533403,100*(exp(b - se^2/2) - 1),reported,0.04250702153776645,0.004968353166751923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.555555555555555,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,South,sunlight,E_environment,binary,binary,0.072,0.002,,,***,,,high,indicator for south-facing orientation (access to sunlight); reference = other orientations; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,pct_premium,7.465319475527488,0.21493106881276272,100*(exp(b - se^2/2) - 1),reported,0.1762209324446923,0.004895025901241453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.0,40438.0,binary,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,Rheight,floor_level,A_structural,level,share,1.932,0.073,,,***,,,high,relative height = floor / total storeys of building (0-1); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,193.2,7.3,100*b (unit as reported: share; count expected),reported,0.13048493485313747,0.004930331389378383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.465753424657535,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,Rheight,floor_level,A_structural,level,share,1.552,0.092,,,***,,,high,relative height = floor / total storeys of building (0-1); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,155.20000000000002,9.2,100*b (unit as reported: share; count expected),reported,0.08359614201796516,0.00495544140828144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.869565217391305,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,Rheight,floor_level,A_structural,level,share,1.433,0.076,,,***,,,high,relative height = floor / total storeys of building (0-1); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,143.3,7.6,100*b (unit as reported: share; count expected),reported,0.09335487512408769,0.004951130850963477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.855263157894736,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,Rheight,floor_level,A_structural,level,share,1.522,0.061,,,***,,,high,relative height = floor / total storeys of building (0-1); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,152.2,6.1,100*b (unit as reported: share; count expected),reported,0.12313243590601952,0.004935005644065171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.95081967213115,40438.0,share,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,Rheight2,floor_level,A_structural,squared,share,-1.133,0.157,,,***,,,high,square of relative height; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,other,-1.133,0.157,not harmonised,reported,-0.03586377050300608,0.004969648692826084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.2165605095541405,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,Rheight2,floor_level,A_structural,squared,share,-1.35,0.164,,,***,,,high,square of relative height; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,other,-1.35,0.164,not harmonised,reported,-0.04090077361157401,0.004968686572072694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.231707317073171,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,Rheight2,floor_level,A_structural,squared,share,-1.531,0.167,,,***,,,high,square of relative height; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,other,-1.531,0.167,not harmonised,reported,-0.04554209831841611,0.004967688059552902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.16766467065868,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,Rheight2,floor_level,A_structural,squared,share,-1.532,0.15,,,***,,,high,square of relative height; main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,other,-1.532,0.15,not harmonised,reported,-0.050723971461898505,0.004966446291961342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.213333333333335,40438.0,share,nonstandard_x_transform,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,Age,age,A_structural,level,years,0.081,0.006,,,***,,,high,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,8.1,0.6,100*b/1 (per year),reported,0.06698267270274079,0.004961679459462281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.5,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,True
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,Age,age,A_structural,level,years,0.072,0.006,,,***,,,high,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,7.199999999999999,0.6,100*b/1 (per year),reported,0.0595682062769542,0.004964017189746185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.999999999999998,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,Age,age,A_structural,level,years,0.047,0.006,,,***,,,high,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,4.7,0.6,100*b/1 (per year),reported,0.03892445329349993,0.004969079143851055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.833333333333333,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,Age,age,A_structural,level,years,0.026,0.005,,,***,,,high,"age of unit in years (mean 17.5); transformation not stated in paper, positive sign unusual; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,2.6,0.5,100*b/1 (per year),reported,0.02585016720858074,0.0049711860016501425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.199999999999999,40438.0,years,,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,True,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.023,0.006,,,***,,,high,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,-2.3,0.6,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.019059120619686003,0.004971944509483305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.833333333333333,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.108,0.007,,,***,,,high,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,-10.8,0.7000000000000001,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.07649910924212057,0.004958275599026334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.428571428571429,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.111,0.008,,,***,,,high,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,-11.1,0.8,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.06883460504491729,0.004961052615849894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.875,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,PM25,air_pollution,E_environment,level,mg/m3 (as printed),-0.121,0.013,,,***,,,high,district-level geometric mean of previous 6 months; paper states unit mg/m3 (mean 50.7); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,-12.1,1.3,100*b (unit as reported: mg/m3; pollution unit not ug/m3),reported,-0.04623623602584945,0.004967529490380518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.307692307692308,40438.0,mg/m3,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,Dust,air_pollution,E_environment,level,tons/km2,-0.354,0.06,,,***,,,high,"dust pollution, district-level 6-month geometric mean, tons per km2; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,-35.4,6.0,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.029327181877298852,0.004970708792762518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8999999999999995,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,Dust,air_pollution,E_environment,level,tons/km2,-0.353,0.06,,,***,,,high,"dust pollution, district-level 6-month geometric mean, tons per km2; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,-35.3,6.0,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.02924440768312427,0.0049707208526556836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.883333333333333,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,Dust,air_pollution,E_environment,level,tons/km2,-0.331,0.068,,,***,,,high,"dust pollution, district-level 6-month geometric mean, tons per km2; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,-33.1,6.800000000000001,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.024198979434712584,0.004971391545499866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.867647058823529,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,Dust,air_pollution,E_environment,level,tons/km2,-0.323,0.061,,,***,,,high,"dust pollution, district-level 6-month geometric mean, tons per km2; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,-32.300000000000004,6.1,100*b (unit as reported: tons/km2; pollution unit not ug/m3),reported,-0.026322512790237153,0.004971124706515375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.295081967213115,40438.0,tons/km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,11,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.807,0.124,,,***,,,high,"district-level population density (mean 2.66, scale unit not stated); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,80.7,12.4,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.03234667863575687,0.00497024554006673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.508064516129033,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,11,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.914,0.137,,,***,,,high,"district-level population density (mean 2.66, scale unit not stated); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,91.4,13.700000000000001,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.03315827401316121,0.004970113282060269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.671532846715328,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,11,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.932,0.143,,,***,,,high,"district-level population density (mean 2.66, scale unit not stated); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,93.2,14.299999999999999,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.03239343933129711,0.004970238008986573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.517482517482518,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,11,Pop density,density,F_neighbourhood,level,per_km2 (scale not stated; mean 2.66),0.831,0.138,,,***,,,high,"district-level population density (mean 2.66, scale unit not stated); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,83.1,13.8,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,0.029931774976197523,0.004970619671137495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.021739130434782,40438.0,per_km2,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,12,Factory,hazard_site,E_environment,level,count,-0.03,0.009,,,***,,,high,"number of factories in district (count, not distance); yearly district data; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,-3.0,0.8999999999999999,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.016573882445349887,0.004972164733604966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,12,Factory,hazard_site,E_environment,level,count,-0.061,0.029,,,**,,,high,"number of factories in district (count, not distance); yearly district data; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,-6.1,2.9000000000000004,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.010459555903154442,0.004972575757237358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.103448275862069,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,12,Factory,hazard_site,E_environment,level,count,-0.024,0.021,,,,,,high,"number of factories in district (count, not distance); yearly district data; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,-2.4,2.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.005683162831065444,0.004972767477182264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1428571428571428,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,12,Factory,hazard_site,E_environment,level,count,-0.121,0.05,,,**,,,high,"number of factories in district (count, not distance); yearly district data; main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,-12.1,5.0,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.012033420306631,0.00497248773001281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.42,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,12,School,school_distance,D_location,level,count,0.643,0.034,,,***,,,high,"number of schools in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,64.3,3.4000000000000004,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.09363217277954534,0.0049510013600381686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.91176470588235,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,12,School,school_distance,D_location,level,count,0.751,0.161,,,***,,,high,"number of schools in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,75.1,16.1,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.023190089144517628,0.004971510455748785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6645962732919255,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,12,School,school_distance,D_location,level,count,1.359,0.163,,,***,,,high,"number of schools in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,135.9,16.3,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.04142514758014612,0.0049685791431669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.337423312883436,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,12,School,school_distance,D_location,level,count,1.221,0.156,,,***,,,high,"number of schools in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,122.10000000000001,15.6,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03889264842393524,0.004969085302320964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8269230769230775,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,12,Shopping,commercial_distance,D_location,level,count,0.235,0.131,,,***,,,high,"number of shopping malls in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,23.5,13.100000000000001,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.008920402541885007,0.004972649927603983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7938931297709921,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,12,Shopping,commercial_distance,D_location,level,count,0.573,0.141,,,***,,,high,"number of shopping malls in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,57.3,14.099999999999998,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.02020468162365117,0.0049718326508461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0638297872340425,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,12,Shopping,commercial_distance,D_location,level,count,1.132,0.139,,,***,,,high,"number of shopping malls in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,113.19999999999999,13.900000000000002,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.040465129865002655,0.004968774780243259,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.14388489208633,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,12,Shopping,commercial_distance,D_location,level,count,0.241,0.134,,,*,,,high,"number of shopping malls in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,24.099999999999998,13.4,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.008943346171220683,0.00497264890847955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.798507462686567,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,12,Hospital,hospital_distance,D_location,level,count,1.743,0.455,,,***,,,high,"number of hospitals in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,174.3,45.5,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.019046376670298088,0.0049719457171460875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.830769230769231,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,12,Hospital,hospital_distance,D_location,level,count,2.014,0.46,,,***,,,high,"number of hospitals in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,201.39999999999998,46.0,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.021767266218551372,0.0049716695434625785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.378260869565216,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,12,Hospital,hospital_distance,D_location,level,count,0.256,0.479,,,,,,high,"number of hospitals in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,25.6,47.9,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.0026577130207725507,0.004972830222461139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5344467640918581,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,12,Hospital,hospital_distance,D_location,level,count,3.411,0.452,,,***,,,high,"number of hospitals in district (count, not distance); main effect in a model with interaction terms","log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,341.1,45.2,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.03750100063345212,0.004969349834746514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.54646017699115,40438.0,count,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c1,A2,1,12,Greenspace,green_space,E_environment,level,m2 per capita,0.003,0.002,,,*,,,high,green space per capita in district (mean 3654); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.644,se,,semi_elasticity,0.3,0.2,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.007459064166775624,0.0049727094445170825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c2,A2,2,12,Greenspace,green_space,E_environment,level,m2 per capita,0.004,0.001,,,***,,,high,green space per capita in district (mean 3654); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.643,se,,semi_elasticity,0.4,0.1,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.01988745712024159,0.004971864280060396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c3,A2,3,12,Greenspace,green_space,E_environment,level,m2 per capita,0.005,0.001,,,**,,,high,green space per capita in district (mean 3654); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.641,se,,semi_elasticity,0.5,0.1,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.02485655657397901,0.004971311314795801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579,a7fe404df2e413395450e192cebe2f68aa921579:TA2-c4,A2,4,12,Greenspace,green_space,E_environment,level,m2 per capita,0.006,0.002,,,***,,,high,green space per capita in district (mean 3654); main effect in a model with interaction terms,"log(unit price per m2, 10,000 CNY/m2)",log,OLS,40448.0,0.642,se,,semi_elasticity,0.6,0.2,"100*b (unit as reported: m2 per capita; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.014916883475245026,0.004972294491748342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,40438.0,m2 per capita,unit_not_harmonisable,2022.0,China,Asia,Upper middle income,2019.0,2021.0,condo_apartment,transaction,journal,True,True,clustered,,2020.0,ols,journal,False,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Municipality,other,other,binary,binary,0.236,0.01,,,***,,,high,Located in a municipality under central-government control (urban status indicator),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,26.6111003034063,1.2661743101668799,100*(exp(b - se^2/2) - 1),reported,0.03791073137372327,0.001606386922615393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.599999999999998,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Municipality,other,other,binary,binary,0.184,0.089,,,**,,,high,Located in a municipality under central-government control (urban status indicator),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,19.726465414164494,10.697940825557081,100*(exp(b - se^2/2) - 1),reported,0.003323440373866347,0.001607533659098396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.067415730337079,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Municipality,other,other,binary,binary,0.236,0.01,,,***,,,high,Located in a municipality under central-government control (urban status indicator),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,26.6111003034063,1.2661743101668799,100*(exp(b - se^2/2) - 1),reported,0.03791073137372327,0.001606386922615393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.599999999999998,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Municipality,other,other,binary,binary,0.184,0.089,,,**,,,high,Located in a municipality under central-government control (urban status indicator),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,19.726465414164494,10.697940825557081,100*(exp(b - se^2/2) - 1),reported,0.003323440373866347,0.001607533659098396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.067415730337079,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,ln (house area),living_area,A_structural,log,ping,0.904,0.003,,,***,,,high,Log of transaction area in pings (1 ping = 3.306 m2); elasticity,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",elasticity,0.904,0.003,b (log-log),reported,0.4359511610810052,0.0014467405788086455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),301.3333333333333,386968.0,pyeong,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,ln (house area),living_area,A_structural,log,ping,0.905,0.003,,,***,,,high,Log of transaction area in pings (1 ping = 3.306 m2); elasticity,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",elasticity,0.905,0.003,b (log-log),reported,0.43634163210041366,0.0014464363495041335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),301.6666666666667,386968.0,pyeong,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,ln (house area),living_area,A_structural,log,ping,0.904,0.003,,,***,,,high,Log of transaction area in pings (1 ping = 3.306 m2); elasticity,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",elasticity,0.904,0.003,b (log-log),reported,0.4359511610810052,0.0014467405788086455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),301.3333333333333,386968.0,pyeong,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,ln (house area),living_area,A_structural,log,ping,0.905,0.003,,,***,,,high,Log of transaction area in pings (1 ping = 3.306 m2); elasticity,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",elasticity,0.905,0.003,b (log-log),reported,0.43634163210041366,0.0014464363495041335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),301.6666666666667,386968.0,pyeong,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Hall,other,other,level,count,0.0798,0.002,,,***,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",semi_elasticity,7.9799999999999995,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06400941305315158,0.0016042459411817439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.9,386968.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Hall,other,other,level,count,0.08,0.002,,,***,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",semi_elasticity,8.0,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06416917789191351,0.0016042294472978376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.0,386968.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Hall,other,other,level,count,0.0798,0.002,,,***,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",semi_elasticity,7.9799999999999995,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06400941305315158,0.0016042459411817439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.9,386968.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Hall,other,other,level,count,0.08,0.002,,,***,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,8.0,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06416917789191351,0.0016042294472978376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.0,386968.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,wall,other,other,binary,binary,-0.21,0.007,,,***,,,high,Partition walls present = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,-18.943561310056456,0.567408972179131,100*(exp(b - se^2/2) - 1),reported,-0.048170292042775696,0.0016056764014258564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,wall,other,other,binary,binary,-0.21,0.007,,,***,,,high,Partition walls present = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,-18.943561310056456,0.567408972179131,100*(exp(b - se^2/2) - 1),reported,-0.048170292042775696,0.0016056764014258564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,wall,other,other,binary,binary,-0.21,0.007,,,***,,,high,Partition walls present = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,-18.943561310056456,0.567408972179131,100*(exp(b - se^2/2) - 1),reported,-0.048170292042775696,0.0016056764014258564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,wall,other,other,binary,binary,-0.211,0.007,,,***,,,high,Partition walls present = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,-19.02457723403308,0.5668418468168934,100*(exp(b - se^2/2) - 1),reported,-0.0483991383333756,0.001605658617694925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.142857142857142,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Age,age,A_structural,level,years,-0.00631,,,,***,,,high,"Years between construction and transaction; SE printed as (0.000), rounded, recorded as null",ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",semi_elasticity,-0.631,,100*b/1 (per year),,,,,,386968.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Age,age,A_structural,level,years,-0.0063,,,,***,,,high,"Years between construction and transaction; SE printed as (0.000), rounded, recorded as null",ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",semi_elasticity,-0.63,,100*b/1 (per year),,,,,,386968.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Age,age,A_structural,level,years,-0.00631,,,,***,,,high,"Years between construction and transaction; SE printed as (0.000), rounded, recorded as null",ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",semi_elasticity,-0.631,,100*b/1 (per year),,,,,,386968.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Age,age,A_structural,level,years,-0.0063,,,,***,,,high,"Years between construction and transaction; SE printed as (0.000), rounded, recorded as null",ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,-0.63,,100*b/1 (per year),,,,,,386968.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Residential,other,other,binary,binary,-0.156,0.002,,,***,,,high,Unit designated for residential use = 1 (vs other use),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,-14.444252074565112,0.1711118380742037,100*(exp(b - se^2/2) - 1),reported,-0.12441409862250008,0.0015950525464423085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-78.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Residential,other,other,binary,binary,-0.156,0.002,,,***,,,high,Unit designated for residential use = 1 (vs other use),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,-14.444252074565112,0.1711118380742037,100*(exp(b - se^2/2) - 1),reported,-0.12441409862250008,0.0015950525464423085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-78.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Residential,other,other,binary,binary,-0.156,0.002,,,***,,,high,Unit designated for residential use = 1 (vs other use),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,-14.444252074565112,0.1711118380742037,100*(exp(b - se^2/2) - 1),reported,-0.12441409862250008,0.0015950525464423085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-78.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Residential,other,other,binary,binary,-0.156,0.002,,,***,,,high,Unit designated for residential use = 1 (vs other use),ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,-14.444252074565112,0.1711118380742037,100*(exp(b - se^2/2) - 1),reported,-0.12441409862250008,0.0015950525464423085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-78.0,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Steel reinforced concrete,roof_materials,A_structural,binary,binary,0.0978,0.012,,,***,,,high,Exterior structure is steel reinforced concrete = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,10.266282002805859,1.3232906575311254,100*(exp(b - se^2/2) - 1),reported,0.013100347396765036,0.001607404588560127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.15,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Steel reinforced concrete,roof_materials,A_structural,binary,binary,0.0967,0.012,,,***,,,high,Exterior structure is steel reinforced concrete = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,10.145055779249379,1.3218358381052198,100*(exp(b - se^2/2) - 1),reported,0.01295302683848081,0.0016074076738549093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.058333333333334,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Steel reinforced concrete,roof_materials,A_structural,binary,binary,0.0978,0.012,,,***,,,high,Exterior structure is steel reinforced concrete = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,10.266282002805859,1.3232906575311254,100*(exp(b - se^2/2) - 1),reported,0.013100347396765036,0.001607404588560127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.15,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Steel reinforced concrete,roof_materials,A_structural,binary,binary,0.0967,0.012,,,***,,,high,Exterior structure is steel reinforced concrete = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,10.145055779249379,1.3218358381052198,100*(exp(b - se^2/2) - 1),reported,0.01295302683848081,0.0016074076738549093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.058333333333334,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Apartment,dwelling_type,A_structural,binary,binary,-0.209,0.003,,,***,,,high,Apartment-type unit = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,-18.860841562693352,0.24341857069302344,100*(exp(b - se^2/2) - 1),reported,-0.11129635230575156,0.0015975552962548074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-69.66666666666666,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Apartment,dwelling_type,A_structural,binary,binary,-0.209,0.003,,,***,,,high,Apartment-type unit = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,-18.860841562693352,0.24341857069302344,100*(exp(b - se^2/2) - 1),reported,-0.11129635230575156,0.0015975552962548074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-69.66666666666666,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Apartment,dwelling_type,A_structural,binary,binary,-0.209,0.003,,,***,,,high,Apartment-type unit = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,-18.860841562693352,0.24341857069302344,100*(exp(b - se^2/2) - 1),reported,-0.11129635230575156,0.0015975552962548074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-69.66666666666666,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Apartment,dwelling_type,A_structural,binary,binary,-0.209,0.003,,,***,,,high,Apartment-type unit = 1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,-18.860841562693352,0.24341857069302344,100*(exp(b - se^2/2) - 1),reported,-0.11129635230575156,0.0015975552962548074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-69.66666666666666,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Quarter 2,transaction_timing,G_market,dummy,binary,0.0206,0.007,,,***,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,2.0788634875824608,0.7145695511526222,100*(exp(b - se^2/2) - 1),reported,0.004730715100605997,0.0016075245487496108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9428571428571426,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Quarter 2,transaction_timing,G_market,dummy,binary,0.02,0.007,,,***,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,2.0176345400110485,0.714140938018729,100*(exp(b - se^2/2) - 1),reported,0.004592930232375474,0.001607525581331416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.857142857142857,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Quarter 2,transaction_timing,G_market,dummy,binary,0.0206,0.007,,,***,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,2.0788634875824608,0.7145695511526222,100*(exp(b - se^2/2) - 1),reported,0.004730715100605997,0.0016075245487496108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9428571428571426,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Quarter 2,transaction_timing,G_market,dummy,binary,0.02,0.007,,,***,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,2.0176345400110485,0.714140938018729,100*(exp(b - se^2/2) - 1),reported,0.004592930232375474,0.001607525581331416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.857142857142857,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Quarter 3,transaction_timing,G_market,dummy,binary,0.0148,0.006,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,1.489179408699659,0.6089460373822223,100*(exp(b - se^2/2) - 1),reported,0.003965240417856927,0.0016075298991311867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.466666666666667,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Quarter 3,transaction_timing,G_market,dummy,binary,0.0143,0.006,,,**,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,1.4384475030286614,0.608641640469101,100*(exp(b - se^2/2) - 1),reported,0.0038312815936792197,0.0016075307386066659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3833333333333333,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Quarter 3,transaction_timing,G_market,dummy,binary,0.0148,0.006,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,1.489179408699659,0.6089460373822223,100*(exp(b - se^2/2) - 1),reported,0.003965240417856927,0.0016075298991311867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.466666666666667,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Quarter 3,transaction_timing,G_market,dummy,binary,0.0143,0.006,,,**,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,1.4384475030286614,0.608641640469101,100*(exp(b - se^2/2) - 1),reported,0.0038312815936792197,0.0016075307386066659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3833333333333333,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c1,2,1,24,Quarter 4,transaction_timing,G_market,dummy,binary,0.0475,0.006,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county + year FE",pct_premium,4.862732565954464,0.6291877206727712,100*(exp(b - se^2/2) - 1),reported,0.012725347956318255,0.0016074123734296742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.916666666666667,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c2,2,2,24,Quarter 4,transaction_timing,G_market,dummy,binary,0.0468,0.006,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy, county-by-year FE",pct_premium,4.78935433853418,0.6287474433833294,100*(exp(b - se^2/2) - 1),reported,0.012537846212673094,0.0016074161811119353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c3,2,3,24,Quarter 4,transaction_timing,G_market,dummy,binary,0.0475,0.006,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county + year FE",pct_premium,4.862732565954464,0.6291877206727712,100*(exp(b - se^2/2) - 1),reported,0.012725347956318255,0.0016074123734296742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.916666666666667,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T2-c4,2,4,24,Quarter 4,transaction_timing,G_market,dummy,binary,0.0468,0.006,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,OLS,386978.0,0.6,se,"Houses transacted only once; tax dummy x effective tax rate, county-by-year FE",pct_premium,4.78935433853418,0.6287474433833294,100*(exp(b - se^2/2) - 1),reported,0.012537846212673094,0.0016074161811119353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8,386968.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,ols,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,26,Hall,other,other,level,count,-0.0095,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",semi_elasticity,-0.95,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.004837261435447387,0.006110224971091437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7916666666666666,26784.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,26,Hall,other,other,level,count,-0.00931,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",semi_elasticity,-0.931,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.004740518403031291,0.006110227801973735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7758333333333334,26784.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,26,Hall,other,other,level,count,-0.0083,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",semi_elasticity,-0.83,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.00422625064084803,0.006110241890382694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6916666666666667,26784.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,26,Hall,other,other,level,count,-0.00824,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,-0.8240000000000001,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.004195699971174549,0.006110242676467789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6866666666666668,26784.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,26,Hall,other,other,level,count,-0.00517,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",semi_elasticity,-0.517,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0025180216992925627,0.005844537793328966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43083333333333335,29275.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,26,Hall,other,other,level,count,-0.00443,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",semi_elasticity,-0.443,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0021576103533292898,0.005844542717822004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36916666666666664,29275.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,26,Hall,other,other,level,count,-0.00408,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",semi_elasticity,-0.40800000000000003,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.001987145226057483,0.005844544782522008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34,29275.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,26,Hall,other,other,level,count,-0.00347,0.012,,,,,,high,Number of living and dining rooms (not bedrooms),ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,-0.347,1.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.001690048456122904,0.005844547975064797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2891666666666667,29275.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,26,Wall,other,other,binary,binary,0.146,0.043,,,***,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",pct_premium,15.6126854550517,4.975943608618423,100*(exp(b - se^2/2) - 1),reported,0.020742124535977843,0.006108981883883884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3953488372093026,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,26,Wall,other,other,binary,binary,0.12,0.044,,,***,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",pct_premium,12.640596270244565,4.960986146949253,100*(exp(b - se^2/2) - 1),reported,0.016662131481412948,0.006109448209851413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7272727272727275,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,26,Wall,other,other,binary,binary,0.146,0.043,,,***,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",pct_premium,15.6126854550517,4.975943608618423,100*(exp(b - se^2/2) - 1),reported,0.020742124535977843,0.006108981883883884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3953488372093026,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,26,Wall,other,other,binary,binary,0.121,0.044,,,***,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",pct_premium,12.753293205591065,4.9659496144163136,100*(exp(b - se^2/2) - 1),reported,0.01680094354526825,0.006109434016461182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,26,Wall,other,other,binary,binary,0.127,0.041,,,***,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,13.446310071784428,4.655209772904095,100*(exp(b - se^2/2) - 1),reported,0.018100903538965176,0.005843598780295845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.097560975609756,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,26,Wall,other,other,binary,binary,0.101,0.041,,,**,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,10.53472068957404,4.535734231230037,100*(exp(b - se^2/2) - 1),reported,0.014396073570412131,0.005843950657296014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4634146341463414,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,26,Wall,other,other,binary,binary,0.128,0.041,,,***,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,13.559813123923693,4.659867311057948,100*(exp(b - se^2/2) - 1),reported,0.018243383087804228,0.005843583645312291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1219512195121952,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,26,Wall,other,other,binary,binary,0.102,0.041,,,**,,,high,Partition walls present = 1,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,10.645310696051013,4.540272234084527,100*(exp(b - se^2/2) - 1),reported,0.014538578972208132,0.005843938606475819,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.48780487804878,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,26,Age,age,A_structural,level,years,-0.00353,0.007,,,,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",semi_elasticity,-0.35300000000000004,0.7000000000000001,100*b/1 (per year),reported,-0.0030813205864771293,0.006110267451937649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5042857142857143,26784.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,26,Age,age,A_structural,level,years,0.000662,0.006,,,,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",semi_elasticity,0.06620000000000001,0.6,100*b/1 (per year),reported,0.0006741692227945397,0.006110295070645374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11033333333333334,26784.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,26,Age,age,A_structural,level,years,-0.00273,0.006,,,,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",semi_elasticity,-0.27299999999999996,0.6,100*b/1 (per year),reported,-0.00278017414438929,0.006110272844811627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45499999999999996,26784.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,26,Age,age,A_structural,level,years,0.00119,0.006,,,,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,0.11900000000000001,0.6,100*b/1 (per year),reported,0.001211874574507864,0.006110291972308559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.19833333333333333,26784.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,26,Age,age,A_structural,level,years,-0.00685,0.004,,,*,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",semi_elasticity,-0.685,0.4,100*b/1 (per year),reported,-0.010008301417293374,0.005844263601339197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7125,29275.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,26,Age,age,A_structural,level,years,-0.00368,0.004,,,,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",semi_elasticity,-0.368,0.4,100*b/1 (per year),reported,-0.005376914087900153,0.005844471834674079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.92,29275.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,26,Age,age,A_structural,level,years,-0.00624,0.003,,,*,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",semi_elasticity,-0.624,0.3,100*b/1 (per year),reported,-0.012155778962991573,0.0058441245014382555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08,29275.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,26,Age,age,A_structural,level,years,-0.00332,0.003,,,,,,high,Years between construction and transaction; house fixed-effects model,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,-0.332,0.3,100*b/1 (per year),reported,-0.006467840374171696,0.005844434073046713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1066666666666667,29275.0,years,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,26,Residential,other,other,binary,binary,0.227,0.144,,,,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",pct_premium,24.188700361946314,18.06955009834002,100*(exp(b - se^2/2) - 1),reported,0.009631756642471932,0.006110013024299374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.576388888888889,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,26,Residential,other,other,binary,binary,0.277,0.114,,,**,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",pct_premium,31.062221797327936,15.03849662979853,100*(exp(b - se^2/2) - 1),reported,0.014845312314203647,0.006109623118480923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.429824561403509,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,26,Residential,other,other,binary,binary,0.218,0.125,,,*,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",pct_premium,23.390939628008777,15.544838345238274,100*(exp(b - se^2/2) - 1),reported,0.010655752019409211,0.006109949552413539,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.744,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,26,Residential,other,other,binary,binary,0.266,0.1,,,***,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",pct_premium,29.822766543368996,13.04735058686928,100*(exp(b - se^2/2) - 1),reported,0.01625124215650567,0.006109489532520927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.66,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,26,Residential,other,other,binary,binary,0.229,0.105,,,**,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,25.043000980365203,13.20209140960331,100*(exp(b - se^2/2) - 1),reported,0.012745663620735898,0.005844081572826502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.180952380952381,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,26,Residential,other,other,binary,binary,0.264,0.099,,,***,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,29.576272680539084,12.891069143378854,100*(exp(b - se^2/2) - 1),reported,0.015583590955928105,0.005843846608473041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6666666666666665,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,26,Residential,other,other,binary,binary,0.243,0.104,,,**,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,26.819166505708214,13.260713690831981,100*(exp(b - se^2/2) - 1),reported,0.01365475747746835,0.005844011430686042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3365384615384617,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,26,Residential,other,other,binary,binary,0.265,0.096,,,***,,,high,Unit designated for residential use = 1,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,29.74385829184989,12.512937367472341,100*(exp(b - se^2/2) - 1),reported,0.016131311431902592,0.005843795839481692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.760416666666667,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,27,Apartment,dwelling_type,A_structural,binary,binary,0.0277,0.058,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",pct_premium,2.6359422776782937,5.962905829537807,100*(exp(b - se^2/2) - 1),reported,0.0029181808836016895,0.006110270442198484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4775862068965517,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,27,Apartment,dwelling_type,A_structural,binary,binary,0.0361,0.058,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",pct_premium,3.5017153489705466,6.013205200102345,100*(exp(b - se^2/2) - 1),reported,0.003803105292557623,0.0061102522705911945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6224137931034482,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,27,Apartment,dwelling_type,A_structural,binary,binary,0.0292,0.059,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",pct_premium,2.783998674482091,6.074819949904953,100*(exp(b - se^2/2) - 1),reported,0.0030240650979948677,0.006110268519921137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49491525423728816,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,27,Apartment,dwelling_type,A_structural,binary,binary,0.0374,0.059,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",pct_premium,3.6302925263442365,6.124838268330722,100*(exp(b - se^2/2) - 1),reported,0.0038732775147455154,0.006110250624865919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6338983050847459,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,27,Apartment,dwelling_type,A_structural,binary,binary,0.0459,0.056,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,4.532934662770827,5.863030369005819,100*(exp(b - se^2/2) - 1),reported,0.004790393876765919,0.0058444892614137565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8196428571428572,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,27,Apartment,dwelling_type,A_structural,binary,binary,0.0488,0.058,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,4.8245694774039505,6.0900599005027445,100*(exp(b - se^2/2) - 1),reported,0.004917429311834449,0.005844485657508157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8413793103448276,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,27,Apartment,dwelling_type,A_structural,binary,binary,0.0479,0.056,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,4.7422097374126615,5.874768163625852,100*(exp(b - se^2/2) - 1),reported,0.004999120528544204,0.005844483290156063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8553571428571428,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,27,Apartment,dwelling_type,A_structural,binary,binary,0.0513,0.058,,,,,,high,Apartment-type unit = 1,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,5.086958751028425,6.105304097560638,100*(exp(b - se^2/2) - 1),reported,0.005169340229338115,0.005844478232000208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8844827586206896,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.0156,0.031,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",pct_premium,-1.5951899005369308,3.052015254144373,100*(exp(b - se^2/2) - 1),reported,-0.003074844327432304,0.006110267573743683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5032258064516129,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.0126,0.031,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",pct_premium,-1.2995322054391356,3.061185047719825,100*(exp(b - se^2/2) - 1),reported,-0.002483532192003624,0.006110277615247012,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4064516129032258,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.0156,0.031,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",pct_premium,-1.5951899005369308,3.052015254144373,100*(exp(b - se^2/2) - 1),reported,-0.003074844327432304,0.006110267573743683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5032258064516129,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.0124,0.031,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",pct_premium,-1.2797901377392673,3.0617973459571517,100*(exp(b - se^2/2) - 1),reported,-0.00244411128348721,0.006110278208718025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39999999999999997,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.0106,0.03,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,-1.0989173002265318,2.9683679460667562,100*(exp(b - se^2/2) - 1),reported,-0.0020650721637720435,0.0058445438597321975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35333333333333333,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.00312,0.03,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,-0.35636351264523025,2.9906545864261735,100*(exp(b - se^2/2) - 1),reported,-0.000607833745188551,0.005844555242197605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.104,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.0104,0.03,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,-1.0791351055330467,2.9689616790272866,100*(exp(b - se^2/2) - 1),reported,-0.002026108699528693,0.005844544325563538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3466666666666667,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,27,Quarter 2,transaction_timing,G_market,dummy,binary,-0.00231,0.03,,,,,,high,Transaction in Q2 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,-0.27561947016795374,2.9930779979903623,100*(exp(b - se^2/2) - 1),reported,-0.0004500307912119783,0.005844555730025692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.077,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.101,0.025,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",pct_premium,10.59309843246603,2.765691604408559,100*(exp(b - se^2/2) - 1),reported,0.024678079691939706,0.006108435567311808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.04,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.0976,0.025,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",pct_premium,10.217720402061437,2.7563042205493087,100*(exp(b - se^2/2) - 1),reported,0.023847813140349478,0.006108558693737059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.904,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.1,0.025,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",pct_premium,10.4825606121552,2.7629272951891193,100*(exp(b - se^2/2) - 1),reported,0.024433888871261047,0.006108472217815261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.0967,0.025,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",pct_premium,10.118569078487894,2.753824662719208,100*(exp(b - se^2/2) - 1),reported,0.02362802836949712,0.006108590581565957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8679999999999994,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.104,0.024,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,10.928093599749955,2.663041091797398,100*(exp(b - se^2/2) - 1),reported,0.025318292110667162,0.005842682794769346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.333333333333333,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.102,0.024,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,10.706459120907464,2.6577203321470404,100*(exp(b - se^2/2) - 1),reported,0.024831705042410345,0.005842754127625964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.103,0.025,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,10.814505963059041,2.771228522690018,100*(exp(b - se^2/2) - 1),reported,0.02407259410200236,0.005842862646117079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.119999999999999,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,27,Quarter 3,transaction_timing,G_market,dummy,binary,0.102,0.024,,,***,,,high,Transaction in Q3 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,10.706459120907464,2.6577203321470404,100*(exp(b - se^2/2) - 1),reported,0.024831705042410345,0.005842754127625964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c1,3,1,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.131,0.027,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.402,se,"Houses transacted more than once, short-term transactions only; tax dummy, county + year FE",pct_premium,13.95523387747042,3.077913009542374,100*(exp(b - se^2/2) - 1),reported,0.02963323372169805,0.006107613057143873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.851851851851852,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c2,3,2,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.131,0.027,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.444,se,"Houses transacted more than once, short-term transactions only; tax dummy, county-by-year FE",pct_premium,13.95523387747042,3.077913009542374,100*(exp(b - se^2/2) - 1),reported,0.02963323372169805,0.006107613057143873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.851851851851852,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c3,3,3,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.13,0.028,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.394,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county + year FE",pct_premium,13.838205008538896,3.188719473308941,100*(exp(b - se^2/2) - 1),reported,0.028357824477013918,0.006107839118126074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.642857142857143,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c4,3,4,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.131,0.027,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,26794.0,0.437,se,"Houses transacted more than once, short-term transactions only; tax dummy x effective tax rate, county-by-year FE",pct_premium,13.95523387747042,3.077913009542374,100*(exp(b - se^2/2) - 1),reported,0.02963323372169805,0.006107613057143873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.851851851851852,26784.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.133,0.026,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,14.18639830717121,2.969849995660325,100*(exp(b - se^2/2) - 1),reported,0.029883800810680667,0.00584194602314058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.115384615384616,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.139,0.026,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,14.873576169067594,2.9877226600093736,100*(exp(b - se^2/2) - 1),reported,0.031230655638697993,0.0058417053712672504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.346153846153847,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.133,0.027,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,14.183372407709417,3.0840749954934146,100*(exp(b - se^2/2) - 1),reported,0.028777927609201483,0.005842135680063459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9259259259259265,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,27,Quarter 4,transaction_timing,G_market,dummy,binary,0.14,0.026,,,***,,,high,Transaction in Q4 vs Q1,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,14.988507201175128,2.990711877028791,100*(exp(b - se^2/2) - 1),reported,0.03145511510287215,0.005841664233390541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.384615384615385,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,True,True,True
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c5,3,5,26,Days 730,other,other,binary,binary,0.232,0.033,,,***,,,high,Holding period under 730 days (short-term resale) = 1; transaction characteristic,ln(transaction price),log,FE_panel,29285.0,0.413,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county + year FE",pct_premium,26.04332360506021,4.161695105133487,100*(exp(b - se^2/2) - 1),reported,0.04105436047228128,0.005839628860281389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.03030303030303,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c6,3,6,26,Days 730,other,other,binary,binary,0.206,0.034,,,***,,,high,Holding period under 730 days (short-term resale) = 1; transaction characteristic,ln(transaction price),log,FE_panel,29285.0,0.453,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy, county-by-year FE",pct_premium,22.80431898572557,4.177760893584061,100*(exp(b - se^2/2) - 1),reported,0.03538895434994292,0.00584089537814592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.058823529411764,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c7,3,7,26,Days 730,other,other,binary,binary,0.232,0.033,,,***,,,high,Holding period under 730 days (short-term resale) = 1; transaction characteristic,ln(transaction price),log,FE_panel,29285.0,0.406,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county + year FE",pct_premium,26.04332360506021,4.161695105133487,100*(exp(b - se^2/2) - 1),reported,0.04105436047228128,0.005839628860281389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.03030303030303,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T3-c8,3,8,26,Days 730,other,other,binary,binary,0.206,0.034,,,***,,,high,Holding period under 730 days (short-term resale) = 1; transaction characteristic,ln(transaction price),log,FE_panel,29285.0,0.446,se,"Houses transacted more than once, whole (short- and long-term) transactions; tax dummy x effective tax rate, county-by-year FE",pct_premium,22.80431898572557,4.177760893584061,100*(exp(b - se^2/2) - 1),reported,0.03538895434994292,0.00584089537814592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.058823529411764,29275.0,binary,,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c1,7,1,35,N_tran_1 month,liquidity,G_market,level,count,5.09e-05,,,,,,,high,"Total number of short-term housing transactions in the market over a one-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28168.0,0.403,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy, county + year FE",semi_elasticity,0.00509,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28158.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c2,7,2,35,N_tran_1 month,liquidity,G_market,level,count,5.02e-05,,,,,,,high,"Total number of short-term housing transactions in the market over a one-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28168.0,0.445,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy, county-by-year FE",semi_elasticity,0.00502,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28158.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c3,7,3,35,N_tran_1 month,liquidity,G_market,level,count,5.33e-05,,,,*,,,high,"Total number of short-term housing transactions in the market over a one-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28168.0,0.395,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy x effective tax rate, county + year FE",semi_elasticity,0.0053300000000000005,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28158.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c4,7,4,35,N_tran_1 month,liquidity,G_market,level,count,5.41e-05,,,,*,,,high,"Total number of short-term housing transactions in the market over a one-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28168.0,0.438,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,0.00541,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28158.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c5,7,5,35,N_tran_2 months,liquidity,G_market,level,count,5.35e-05,,,,**,,,high,"Total number of short-term housing transactions in the market over a two-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28167.0,0.404,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy, county + year FE",semi_elasticity,0.00535,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28157.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c6,7,6,35,N_tran_2 months,liquidity,G_market,level,count,4.97e-05,,,,**,,,high,"Total number of short-term housing transactions in the market over a two-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28167.0,0.447,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy, county-by-year FE",semi_elasticity,0.0049700000000000005,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28157.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c7,7,7,35,N_tran_2 months,liquidity,G_market,level,count,5.53e-05,,,,**,,,high,"Total number of short-term housing transactions in the market over a two-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28167.0,0.397,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy x effective tax rate, county + year FE",semi_elasticity,0.00553,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28157.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c8,7,8,35,N_tran_2 months,liquidity,G_market,level,count,5.21e-05,,,,**,,,high,"Total number of short-term housing transactions in the market over a two-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28167.0,0.439,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,0.00521,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28157.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c9,7,9,35,N_tran_3 months,liquidity,G_market,level,count,4.91e-05,,,,**,,,high,"Total number of short-term housing transactions in the market over a three-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28166.0,0.406,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy, county + year FE",semi_elasticity,0.00491,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28156.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c10,7,10,35,N_tran_3 months,liquidity,G_market,level,count,4.84e-05,,,,***,,,high,"Total number of short-term housing transactions in the market over a three-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28166.0,0.448,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy, county-by-year FE",semi_elasticity,0.00484,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28156.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c11,7,11,35,N_tran_3 months,liquidity,G_market,level,count,5.12e-05,,,,***,,,high,"Total number of short-term housing transactions in the market over a three-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28166.0,0.399,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy x effective tax rate, county + year FE",semi_elasticity,0.0051199999999999996,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28156.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3,a842ed7c689fc0985af5ca73c3f97f4279427db3:T7-c12,7,12,35,N_tran_3 months,liquidity,G_market,level,count,5.05e-05,,,,***,,,high,"Total number of short-term housing transactions in the market over a three-month window (sales volume); SE printed as (0.000), recorded as null",ln(transaction price),log,FE_panel,28166.0,0.441,se,"Short-term transactions within 2 years (units transacted once and more than once), house FE; tax dummy x effective tax rate, county-by-year FE",semi_elasticity,0.00505,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,28156.0,count,unit_not_harmonisable,2025.0,Taiwan,Asia,High income,2012.0,2015.0,mixed,transaction,journal,True,True,unknown,,2013.5,panel,journal,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-B1-wb,3,Taranto B1 / model with bargaining,11,Location,other,other,level,ordinal,9603.85,,,0.001,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,62.0,0.9601,p,"Taranto OMI zone B1, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,9603.85,2918.636067620002,not harmonised,|b/z(p)| (two-sided normal),0.41513591275954975,0.12616093003788817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,52.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-B1-wb,3,Taranto B1 / model with bargaining,11,State of building,condition,A_structural,level,ordinal,8340.49,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,62.0,0.9601,p,"Taranto OMI zone B1, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,8340.49,,not harmonised,,,,,,52.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-B1-nb,3,Taranto B1 / model without bargaining,11,Location,other,other,level,ordinal,12766.47,,,0.002,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,62.0,0.9132,p,"Taranto OMI zone B1, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,12766.47,4131.233103258718,not harmonised,|b/z(p)| (two-sided normal),0.39389343077118993,0.12746401944766925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,52.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-B1-nb,3,Taranto B1 / model without bargaining,11,State of building,condition,A_structural,level,ordinal,10216.55,,,0.002,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,62.0,0.9132,p,"Taranto OMI zone B1, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,10216.55,3306.0783099085224,not harmonised,|b/z(p)| (two-sided normal),0.39389343077118993,0.12746401944766925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,52.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-C1-wb,3,Taranto C1 / model with bargaining,11,Location,other,other,level,ordinal,11463.41,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,89.0,0.9163,p,"Taranto OMI zone C1, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,11463.41,,not harmonised,,,,,,79.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-C1-wb,3,Taranto C1 / model with bargaining,11,State of building,condition,A_structural,level,ordinal,6959.62,,,0.248,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed. Flagged (*) as not significant at 10%.,Selling price (EUR); marginal prices reported,other,OLS,89.0,0.9163,p,"Taranto OMI zone C1, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,6959.62,6024.49308321545,not harmonised,|b/z(p)| (two-sided normal),0.12888841254485264,0.11157036589398331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1552208466119518,79.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-C1-nb,3,Taranto C1 / model without bargaining,11,Location,other,other,level,ordinal,17416.52,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,89.0,0.8555,p,"Taranto OMI zone C1, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,17416.52,,not harmonised,,,,,,79.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-C1-nb,3,Taranto C1 / model without bargaining,11,State of building,condition,A_structural,level,ordinal,12507.79,,,0.108,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed. Flagged (*) as not significant at 10%.,Selling price (EUR); marginal prices reported,other,OLS,89.0,0.8555,p,"Taranto OMI zone C1, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,12507.79,7782.116288989052,not harmonised,|b/z(p)| (two-sided normal),0.17794360096653025,0.11071322716507098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.607247891900218,79.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-D1-wb,3,Taranto D1 / model with bargaining,11,Location,other,other,level,ordinal,20538.28,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,63.0,0.9363,p,"Taranto OMI zone D1, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,20538.28,,not harmonised,,,,,,53.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-D1-wb,3,Taranto D1 / model with bargaining,11,State of building,condition,A_structural,level,ordinal,15764.46,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,63.0,0.9363,p,"Taranto OMI zone D1, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,15764.46,,not harmonised,,,,,,53.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-D1-nb,3,Taranto D1 / model without bargaining,11,Location,other,other,level,ordinal,29548.34,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,63.0,0.8499,p,"Taranto OMI zone D1, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,29548.34,,not harmonised,,,,,,53.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-D1-nb,3,Taranto D1 / model without bargaining,11,State of building,condition,A_structural,level,ordinal,17403.33,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,63.0,0.8499,p,"Taranto OMI zone D1, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,17403.33,,not harmonised,,,,,,53.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-E2-wb,3,Taranto E2 / model with bargaining,11,Location,other,other,level,ordinal,5311.7,,,0.113,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed. Flagged (*) as not significant at 10%.,Selling price (EUR); marginal prices reported,other,OLS,65.0,0.8555,p,"Taranto OMI zone E2, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,5311.7,3351.5435658325664,not harmonised,|b/z(p)| (two-sided normal),0.20898271056736878,0.13186261629465415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5848518438340828,55.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-E2-wb,3,Taranto E2 / model with bargaining,11,State of building,condition,A_structural,level,ordinal,14109.49,,,0.008,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,65.0,0.8555,p,"Taranto OMI zone E2, 2009-2010, hedonic model with bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,14109.49,5320.180471101813,not harmonised,|b/z(p)| (two-sided normal),0.33672231522858015,0.12696585671511024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.652069807902196,55.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-E2-nb,3,Taranto E2 / model without bargaining,11,Location,other,other,level,ordinal,7352.9,,,0.06,,,,low,Marginal price in EUR per unit of the ordinal 'Location' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,65.0,0.7994,p,"Taranto OMI zone E2, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,7352.9,3909.4667102934395,not harmonised,|b/z(p)| (two-sided normal),0.24582411418344194,0.13070233390737526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.880793608151251,55.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb,a855c4aba93669899f6285712fd4b23cfb3500bb:T3-E2-nb,3,Taranto E2 / model without bargaining,11,State of building,condition,A_structural,level,ordinal,28117.67,,,0.0,,,,low,Marginal price in EUR per unit of the ordinal 'State of building' score (scale not stated); functional form (linear vs log-linear) not stated; p-value printed.,Selling price (EUR); marginal prices reported,other,OLS,65.0,0.7994,p,"Taranto OMI zone E2, 2009-2010, hedonic model without bargaining seller/buyer bargaining dummies; adj. R2 from Table 2",other,28117.67,,not harmonised,,,,,,55.0,ordinal,,2013.0,Italy,Europe,High income,2009.0,2010.0,unknown,transaction,conference,False,False,unknown,,2009.5,ols,other,False,False,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,Z.age,age,A_structural,other,years,-0.1495,0.0254,,,***,,,high,"Mean (global) effect of a spatially varying coefficient; Z.age = age / SD(age), SD of age = 27 years (Table 2), so beta per year = beta/27; quadratic in Z.age2",log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,other,-0.1495,0.0254,not harmonised,reported,-0.14702428850849136,0.024979377445589836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.8858267716535435,1568.0,years,nonstandard_x_transform,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,False,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,Z.age2,age,A_structural,squared,years,0.0114,0.0062,,,.,,,high,Square of standardized age (Z.age^2); mean effect of SVC; '.' = p<0.1,log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,other,0.0114,0.0062,not harmonised,reported,0.04638445234213402,0.025226631975546566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.838709677419355,1568.0,years,nonstandard_x_transform,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,False,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,log (volume),living_area,A_structural,log,m3,0.5132,0.0222,,,***,,,high,"Building VOLUME in m3 (SIA Zurich 2003), not floor area; elasticity",log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,elasticity,0.5132,0.0222,b (log-log),reported,0.5041687060009393,0.02180932438273744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.117117117117115,1568.0,m3,,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,True,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,log (plotsize),lot_size,A_structural,log,m2,0.172,0.0129,,,***,,,high,"Plot size m2, elasticity",log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,elasticity,0.172,0.0129,b (log-log),reported,0.31911282313630135,0.023933461735222605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.333333333333332,1568.0,m2,,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,True,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,renov,renovation,A_structural,level,index,0.0274,0.0059,,,***,,,high,"Need-for-renovation score 0-4 (difference between actual and theoretical building condition, higher = better)",log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,semi_elasticity,2.74,0.59,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.11648207061856601,0.02508190571713648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6440677966101696,1568.0,index,unit_not_harmonisable,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,False,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,standard,condition,A_structural,level,index,0.0911,0.0082,,,***,,,high,Building standard/quality rating 2-5 (higher = better),log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,semi_elasticity,9.11,0.8200000000000001,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.27013318313391643,0.024314951720067122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.109756097560975,1568.0,index,unit_not_harmonisable,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,False,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,micro,other,other,level,index,0.0385,0.0075,,,***,,,high,Micro-location quality rating 2-5 (higher = better),log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,semi_elasticity,3.85,0.75,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.12856047895476275,0.025044249147031704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.133333333333334,1568.0,index,unit_not_harmonisable,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,False,False,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,SFHtype 2,dwelling_type,A_structural,dummy,binary,-0.0062,0.012,,,n.s.,,,high,Semi-detached vs detached (reference),log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,pct_premium,-0.6252372064908585,1.1925830164081903,100*(exp(b - se^2/2) - 1),reported,-0.01304669317940572,0.02525166421820462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5166666666666666,1568.0,binary,,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,True,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,SFHtype3,dwelling_type,A_structural,dummy,binary,-0.0274,0.0165,,,.,,,high,Row house vs detached (reference); '.' = p<0.1,log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,pct_premium,-2.71604619096526,1.6054037585623042,100*(exp(b - se^2/2) - 1),reported,-0.041899807918440266,0.025231636155265125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6606060606060606,1568.0,binary,,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,True,True,False
a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7,a90efa2e2b4a4c7fde54cbffdb07fb2a1d2e8ed7:T3-SVC,3,Mean mu_j,8,energy2,energy_efficiency,A_structural,dummy,binary,0.0119,0.0281,,,n.s.,,,high,Enhanced energy efficiency standard vs insulated shell (reference); only 40 obs,log transaction price (CHF),log,other,1578.0,,se,Full sample; error variance tau2 = 0.0242,pct_premium,1.1571634310588719,2.843638753619649,100*(exp(b - se^2/2) - 1),reported,0.01069406396107189,0.025252369521522697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4234875444839858,1568.0,binary,,2022.0,Switzerland,Europe,High income,2018.0,2019.0,single_family,transaction,journal,True,False,unknown,Gaussian process SVC,2018.5,other,journal,True,True,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Surface area (sqm),living_area,A_structural,level,m2,-36.59,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-36.59,,"b (currency per unit, as reported)",,,,,,13529.0,m2,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Surface area (sqm),living_area,A_structural,level,m2,10.36,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,10.36,,"b (currency per unit, as reported)",,,,,,1389.0,m2,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Year of construction,age,A_structural,level,years,29.02,,,,***,,,high,Year built (higher = newer); sign opposite to age Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,29.02,,"b (currency per unit, as reported)",,,,,,13529.0,years,year_built_not_age,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Year of construction,age,A_structural,level,years,36.24,,,,***,,,high,Year built (higher = newer); sign opposite to age Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,36.24,,"b (currency per unit, as reported)",,,,,,1389.0,years,year_built_not_age,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Number of rooms,bedrooms,A_structural,level,count,-270.41,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-270.41,,"b (currency per unit, as reported)",,,,,,13529.0,count,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Number of rooms,bedrooms,A_structural,level,count,-858.58,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-858.58,,"b (currency per unit, as reported)",,,,,,1389.0,count,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Floor,floor_level,A_structural,level,count,21.19,,,,**,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,21.19,,"b (currency per unit, as reported)",,,,,,13529.0,count,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Floor,floor_level,A_structural,level,count,-99.0,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-99.0,,"b (currency per unit, as reported)",,,,,,1389.0,count,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Floors in building,storeys,A_structural,level,count,-49.85,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-49.85,,"b (currency per unit, as reported)",,,,,,13529.0,count,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Floors in building,storeys,A_structural,level,count,-78.01,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-78.01,,"b (currency per unit, as reported)",,,,,,1389.0,count,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Market type (secondary),other,other,binary,binary,1120.63,,,,***,,,high,Secondary (resale) vs primary (developer) market Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,1120.63,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Market type (secondary),other,other,binary,binary,1533.71,,,,***,,,high,Secondary (resale) vs primary (developer) market Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,1533.71,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Air conditioning,air_conditioning,A_structural,binary,binary,709.32,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,709.32,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Air conditioning,air_conditioning,A_structural,binary,binary,949.89,,,,**,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,949.89,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Garage,garage,A_structural,binary,binary,477.42,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,477.42,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Garage,garage,A_structural,binary,binary,-181.82,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-181.82,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Elevator,accessibility,B_interior,binary,binary,310.65,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,310.65,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Elevator,accessibility,B_interior,binary,binary,638.83,,,,*,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,638.83,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Balcony,terrace_balcony,C_exterior,binary,binary,-221.8,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-221.8,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Balcony,terrace_balcony,C_exterior,binary,binary,-363.79,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-363.79,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Garden,garden,C_exterior,binary,binary,45.04,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,45.04,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Garden,garden,C_exterior,binary,binary,755.41,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,755.41,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Terrace,terrace_balcony,C_exterior,binary,binary,99.67,,,,,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,99.67,,"b (currency per unit, as reported)",,,,,,13529.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Terrace,terrace_balcony,C_exterior,binary,binary,777.06,,,,**,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,777.06,,"b (currency per unit, as reported)",,,,,,1389.0,binary,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Distance to city centre,cbd_distance,D_location,level,km,-366.55,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-366.55,,"b (currency per unit, as reported)",,,,,,13529.0,km,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Distance to city centre,cbd_distance,D_location,level,km,-1641.29,,,,***,,,high,Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-1641.29,,"b (currency per unit, as reported)",,,,,,1389.0,km,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Transit time,transit_distance,D_location,level,minutes,-14.63,,,,**,,,high,"Public-transport travel time to centre, minutes Stars only; no SE printed.",Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-14.63,,"b (currency per unit, as reported)",,,,,,13529.0,minutes,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Transit time,transit_distance,D_location,level,minutes,99.83,,,,**,,,high,"Public-transport travel time to centre, minutes Stars only; no SE printed.",Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,99.83,,"b (currency per unit, as reported)",,,,,,1389.0,minutes,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Mean PM10,air_pollution,E_environment,level,ug/m3,-60.07,,,,***,,,high,Yearly mean PM10 concentration Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-60.07,,"b (currency per unit, as reported)",,,,,,13529.0,ug/m3,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Mean PM10,air_pollution,E_environment,level,ug/m3,-89.05,,,,,,,high,Yearly mean PM10 concentration Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-89.05,,"b (currency per unit, as reported)",,,,,,1389.0,ug/m3,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Mean PM2.5,air_pollution,E_environment,level,ug/m3,92.58,,,,**,,,high,Yearly mean PM2.5 concentration Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,92.58,,"b (currency per unit, as reported)",,,,,,13529.0,ug/m3,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Mean PM2.5,air_pollution,E_environment,level,ug/m3,-421.3,,,,,,,high,Yearly mean PM2.5 concentration Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,-421.3,,"b (currency per unit, as reported)",,,,,,1389.0,ug/m3,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c1,3,1 (Standard flats),14,Mean PM1,air_pollution,E_environment,level,ug/m3,-12.31,,,,,,,high,Yearly mean PM1 concentration Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,13539.0,,none,standard flats; lambda=0.485,monetary,-12.31,,"b (currency per unit, as reported)",,,,,,13529.0,ug/m3,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a,aa81dc840f8ab6c12b272e830117706e34b5a89a:T3-c2,3,2 (Exclusive flats),14,Mean PM1,air_pollution,E_environment,level,ug/m3,811.13,,,,**,,,high,Yearly mean PM1 concentration Stars only; no SE printed.,Price per square metre of a flat (PLN/m2),per_m2,spatial_error,1399.0,,none,exclusive (luxury) flats; lambda=0.382,monetary,811.13,,"b (currency per unit, as reported)",,,,,,1389.0,ug/m3,,2024.0,Poland,Europe,High income,2021.0,2022.0,condo_apartment,listing,journal,False,True,unknown,,2021.5,spatial,journal,False,False,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Floor Area,living_area,A_structural,level,m2,0.007,,34.316,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),semi_elasticity,0.7000000000000001,0.020398647861056066,100*b/1 (X per m2->m2),|b/t|,0.6671685489008392,0.01944190898999997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.316,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Floor Area,living_area,A_structural,level,m2,0.007,,33.664,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),semi_elasticity,0.7000000000000001,0.020793726235741442,100*b/1 (X per m2->m2),|b/t|,0.6600448214568297,0.019606844743845938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.664,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Terr,dwelling_type,A_structural,dummy,binary,-0.408,,-16.6,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,-33.52219455502006,1.634405919033373,100*(exp(b - se^2/2) - 1),|b/t|,-0.39754806094162787,0.02394867836997758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.6,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Terr,dwelling_type,A_structural,dummy,binary,-0.404,,-16.274,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-33.25615730187623,1.6574181208839085,100*(exp(b - se^2/2) - 1),|b/t|,-0.39094412763243164,0.024022620599264573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.274,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Detach,dwelling_type,A_structural,dummy,binary,0.102,,3.97,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,10.701803204751958,2.845166602424413,100*(exp(b - se^2/2) - 1),|b/t|,0.10306436826049548,0.02596079805050264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.97,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Detach,dwelling_type,A_structural,dummy,binary,0.103,,3.973,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,10.811896018590916,2.873763280514189,100*(exp(b - se^2/2) - 1),|b/t|,0.10314142243930326,0.025960589589555313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.973,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Apt,dwelling_type,A_structural,dummy,binary,-0.092,,-2.453,,***,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,-8.853612316137017,3.4208591014328706,100*(exp(b - se^2/2) - 1),|b/t|,-0.06389196885094872,0.026046461007317053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.453,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Apt,dwelling_type,A_structural,dummy,binary,-0.087,,-2.176,,***,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-8.405527945112734,3.6650233143421067,100*(exp(b - se^2/2) - 1),|b/t|,-0.05670176667077233,0.02605779718325934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.176,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Pre1919,age,A_structural,dummy,construction period dummy,0.06,,1.732,,*,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,6.119959835337241,3.6784175977321922,100*(exp(b - se^2/2) - 1),|b/t|,0.045158715187530514,0.026073161193724315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.732,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Pre1919,age,A_structural,dummy,construction period dummy,0.064,,1.758,,*,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,6.538617362646337,3.8811099841146945,100*(exp(b - se^2/2) - 1),|b/t|,0.045835203706662296,0.026072357057259556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.758,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Early Modern,age,A_structural,dummy,construction period dummy,-0.029,,-1.279,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,-2.883321024319363,2.2025861977741625,100*(exp(b - se^2/2) - 1),|b/t|,-0.03336304467768454,0.026085257762067666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.279,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Early Modern,age,A_structural,dummy,construction period dummy,-0.025,,-0.978,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-2.500868667666445,2.4931234970049405,100*(exp(b - se^2/2) - 1),|b/t|,-0.02551728060933998,0.026091288966605296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.978,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Inter War,age,A_structural,dummy,construction period dummy,0.037,,1.442,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,3.735148180284842,2.662596516713718,100*(exp(b - se^2/2) - 1),|b/t|,0.037609267050190014,0.02608132250359918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.442,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Inter War,age,A_structural,dummy,construction period dummy,0.041,,1.439,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,4.142930643282083,2.9684458879336106,100*(exp(b - se^2/2) - 1),|b/t|,0.03753113341075769,0.02608139917356337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.439,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,No Garage,garage,A_structural,binary,binary,-0.019,,-0.971,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,-1.9008459376579245,1.9199184225142443,100*(exp(b - se^2/2) - 1),|b/t|,-0.0253347592357099,0.02609141012946437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.971,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,No Garage,garage,A_structural,binary,binary,-0.028,,-1.134,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-2.790800270782734,2.4009589303734495,100*(exp(b - se^2/2) - 1),|b/t|,-0.029584204156082466,0.02608836345333551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.134,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,Gas Heat,heating,A_structural,binary,binary,0.132,,6.358,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),pct_premium,14.08624201930424,2.369083015779727,100*(exp(b - se^2/2) - 1),|b/t|,0.16370380846815985,0.02574768928407673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.358,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,Gas Heat,heating,A_structural,binary,binary,0.138,,6.048,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,14.76767501838092,2.6193886563795745,100*(exp(b - se^2/2) - 1),|b/t|,0.15592091868937397,0.02578057518012136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.048,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c1,6,Model 1,30,EPC score,energy_efficiency,A_structural,level,index (EPC SAP score points),0.001,,2.049,,**,,,high,Continuous EPC energy-efficiency score,ln(transaction price),log,OLS,1478.0,0.674,t,Parsimonious OLS with continuous EPC score; spatial and temporal dummies included (not printed),semi_elasticity,0.1,0.04880429477794046,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.05340215556256895,0.02606254541853048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.049,1468.0,index,unit_not_harmonisable,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,False,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,EPC Band G,energy_efficiency,A_structural,dummy,binary,-0.119,,-0.945,,,,,high,EPC band G dummy; reference band D,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-11.920350190981367,11.179801935520853,100*(exp(b - se^2/2) - 1),|b/t|,-0.02465680066675898,0.026091852557416913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.945,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,EPC Band F,energy_efficiency,A_structural,dummy,binary,-0.035,,-0.956,,,,,high,EPC band F dummy; reference band D,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-3.5041494662738093,3.535166272909501,100*(exp(b - se^2/2) - 1),|b/t|,-0.024943633498107083,0.026091666839024145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.956,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,EPC Band E,energy_efficiency,A_structural,dummy,binary,-0.003,,-0.114,,,,,high,EPC band E dummy; reference band D,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,-0.3340667898234617,2.62369604079835,100*(exp(b - se^2/2) - 1),|b/t|,-0.002975362610333008,0.02609967202046498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.114,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,EPC Band C,energy_efficiency,A_structural,dummy,binary,0.045,,1.145,,,,,high,EPC band C dummy; reference band D,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,4.522032813580901,4.111026523658713,100*(exp(b - se^2/2) - 1),|b/t|,0.02987092132588901,0.026088140895972927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.145,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T6-c2,6,Model 2,30,EPC Band B,energy_efficiency,A_structural,dummy,binary,0.088,,3.008,,***,,,high,EPC band B dummy; reference band D,ln(transaction price),log,OLS,1478.0,0.674,t,OLS with EPC band dummies (band D reference); spatial and temporal dummies included (not printed),pct_premium,9.1520920045437,3.194646101678237,100*(exp(b - se^2/2) - 1),|b/t|,0.07826732989837015,0.02601972403536242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.008,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c1,7,Model 3,31,Floor Area,living_area,A_structural,level,m2,0.007,,34.486,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property type x EPC interactions (interactions not extracted); main effects only,semi_elasticity,0.7000000000000001,0.020298091979353942,100*b/1 (X per m2->m2),|b/t|,0.6689964628689895,0.019399073910253133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.486,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c1,7,Model 3,31,Terr,dwelling_type,A_structural,dummy,binary,-0.405,,-16.586,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property type x EPC interactions (interactions not extracted); main effects only,pct_premium,-33.32220012029181,1.6286362498352958,100*(exp(b - se^2/2) - 1),|b/t|,-0.39726571239091635,0.02395186979325433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.586,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c1,7,Model 3,31,Detach,dwelling_type,A_structural,dummy,binary,0.103,,3.99,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property type x EPC interactions (interactions not extracted); main effects only,pct_premium,10.812212664714483,2.861519176311497,100*(exp(b - se^2/2) - 1),|b/t|,0.10357802767269904,0.02595940543175414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.99,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c1,7,Model 3,31,Apt,dwelling_type,A_structural,dummy,binary,-0.081,,-2.173,,**,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property type x EPC interactions (interactions not extracted); main effects only,pct_premium,-7.844676836936538,3.437537460055833,100*(exp(b - se^2/2) - 1),|b/t|,-0.05662384409588073,0.026057912607400237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.173,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c1,7,Model 3,31,Gas Heat,heating,A_structural,binary,binary,0.131,,6.277,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property type x EPC interactions (interactions not extracted); main effects only,pct_premium,13.97195516779217,2.3790947801795563,100*(exp(b - se^2/2) - 1),|b/t|,0.16167310089142775,0.025756428372061137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.277,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c1,7,Model 3,31,No Garage,garage,A_structural,binary,binary,-0.018,,-0.95,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property type x EPC interactions (interactions not extracted); main effects only,pct_premium,-1.8015251085306994,1.8609366928894118,100*(exp(b - se^2/2) - 1),|b/t|,-0.02478717998561971,0.026091768405915487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.95,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c2,7,Model 4,31,Floor Area,living_area,A_structural,level,m2,0.007,,34.61,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property age x EPC interactions (interactions not extracted); main effects only,semi_elasticity,0.7000000000000001,0.020225368390638543,100*b/1 (X per m2->m2),|b/t|,0.6703221550212739,0.01936787503673141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.61,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c2,7,Model 4,31,Pre1919,age,A_structural,dummy,construction period dummy,0.058,,1.661,,,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property age x EPC interactions (interactions not extracted); main effects only,pct_premium,5.906912821606425,3.7003895094037738,100*(exp(b - se^2/2) - 1),|b/t|,0.04331106737585808,0.026075296433388368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.661,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c2,7,Model 4,31,Early Modern,age,A_structural,dummy,construction period dummy,-0.029,,-1.285,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property age x EPC interactions (interactions not extracted); main effects only,pct_premium,-2.8830884390155354,2.1923017486016763,100*(exp(b - se^2/2) - 1),|b/t|,-0.033519380785964635,0.026085121234213724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.285,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c2,7,Model 4,31,Inter War,age,A_structural,dummy,construction period dummy,0.039,,1.503,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property age x EPC interactions (interactions not extracted); main effects only,pct_premium,3.942050170466138,2.6980072430044015,100*(exp(b - se^2/2) - 1),|b/t|,0.039197832791135125,0.026079729069284848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.503,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c2,7,Model 4,31,Gas Heat,heating,A_structural,binary,binary,0.131,,6.276,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property age x EPC interactions (interactions not extracted); main effects only,pct_premium,13.971947257582752,2.379473858379075,100*(exp(b - se^2/2) - 1),|b/t|,0.16164801753284716,0.025756535617088457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.276,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T7-c2,7,Model 4,31,No Garage,garage,A_structural,binary,binary,-0.018,,-0.947,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,OLS,1478.0,0.673,t,OLS with property age x EPC interactions (interactions not extracted); main effects only,pct_premium,-1.801636964953679,1.8668319516842042,100*(exp(b - se^2/2) - 1),|b/t|,-0.02470895254565685,0.026091818950007235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.947,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Floor Area,living_area,A_structural,level,m2,0.007,,33.83,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,semi_elasticity,0.7000000000000001,0.02069169376293231,100*b/1 (X per m2->m2),|b/t|,0.6618756946190412,0.019564755974550437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.83,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Terr,dwelling_type,A_structural,dummy,binary,-0.407,,-16.47,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,-33.45590208187753,1.6449130871446214,100*(exp(b - se^2/2) - 1),|b/t|,-0.39492191319528946,0.023978258238936827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.47,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Detach,dwelling_type,A_structural,dummy,binary,0.103,,3.987,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,10.81215708010539,2.863672313389233,100*(exp(b - se^2/2) - 1),|b/t|,0.10350098403369504,0.025959614756382005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.987,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Apt,dwelling_type,A_structural,dummy,binary,-0.091,,-2.337,,**,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,-8.767419965302537,3.5551823573741195,100*(exp(b - se^2/2) - 1),|b/t|,-0.06088205539134681,0.026051371583802652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.337,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Pre1919,age,A_structural,dummy,construction period dummy,0.063,,1.731,,*,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,6.4321701895483585,3.8761808706858596,100*(exp(b - se^2/2) - 1),|b/t|,0.04513269515163727,0.026073191884250297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.731,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Early Modern,age,A_structural,dummy,construction period dummy,-0.027,,-1.027,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,-2.6975080767235315,2.558982816081509,100*(exp(b - se^2/2) - 1),|b/t|,-0.026794857752785266,0.02609041650709374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.027,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Inter War,age,A_structural,dummy,construction period dummy,0.04,,1.385,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,4.037679224839819,3.00595169441845,100*(exp(b - se^2/2) - 1),|b/t|,0.03612461157532241,0.02608275203994398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.385,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,Gas Heat,heating,A_structural,binary,binary,0.129,,6.282,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,pct_premium,13.74502785598153,2.3362309139983983,100*(exp(b - se^2/2) - 1),|b/t|,0.16179851298323525,0.02575589191073468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.282,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c1,8,Model 5,33,EPC,energy_efficiency,A_structural,level,index (EPC SAP score points),0.001,,2.037,,**,,,high,Continuous EPC energy-efficiency score,ln(transaction price),log,OLS,1478.0,0.674,t,Partial regression model: predictor set A + EPC,semi_elasticity,0.1,0.049091801669121256,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.05309028911874147,0.026062979439735626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.037,1468.0,index,unit_not_harmonisable,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,False,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c2,8,Model 6,33,Floor Area,living_area,A_structural,level,m2,0.007,,35.798,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.662,t,Partial regression model: floor area + type x EPC interactions (interactions not extracted),semi_elasticity,0.7000000000000001,0.01955416503715291,100*b/1 (X per m2->m2),|b/t|,0.6827035389247089,0.019070996673688723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.798,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c3,8,Model 7,33,Floor Area,living_area,A_structural,level,m2,0.007,,35.511,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.671,t,Partial regression model: floor area + type x EPC interactions + gas heat (interactions not extracted),semi_elasticity,0.7000000000000001,0.019712201852946972,100*b/1 (X per m2->m2),|b/t|,0.6797647785253957,0.019142372181166278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.511,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c3,8,Model 7,33,Gas Heat,heating,A_structural,binary,binary,0.132,,6.428,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.671,t,Partial regression model: floor area + type x EPC interactions + gas heat (interactions not extracted),pct_premium,14.08677460820571,2.343284040810128,100*(exp(b - se^2/2) - 1),|b/t|,0.16545706721258585,0.025740054015648082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.428,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c4,8,Model 8,33,Floor Area,living_area,A_structural,level,m2,0.008,,45.47,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.586,t,Partial regression model: floor area + age x EPC interactions + gas heat (interactions not extracted),semi_elasticity,0.8,0.017594018033868484,100*b/1 (X per m2->m2),|b/t|,0.764712606581649,0.01681795923865513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.47,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c4,8,Model 8,33,Gas Heat,heating,A_structural,binary,binary,0.079,,3.698,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.586,t,Partial regression model: floor area + age x EPC interactions + gas heat (interactions not extracted),pct_premium,8.195740558402886,2.3119021482843394,100*(exp(b - se^2/2) - 1),|b/t|,0.096070577277132,0.02597906362280476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.698,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Floor Area,living_area,A_structural,level,m2,0.007,,34.19,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",semi_elasticity,0.7000000000000001,0.02047382275519158,100*b/1 (X per m2->m2),|b/t|,0.6658059178151129,0.019473703358148957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.19,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Terr,dwelling_type,A_structural,dummy,binary,-0.408,,-16.597,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,-33.52220181460363,1.6347013469876481,100*(exp(b - se^2/2) - 1),|b/t|,-0.39748756718278133,0.023949362365655317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.597,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Detach,dwelling_type,A_structural,dummy,binary,0.101,,3.916,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,10.590875098129416,2.853267130186562,100*(exp(b - se^2/2) - 1),|b/t|,0.10167707703172993,0.025964524267551057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.916,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Apt,dwelling_type,A_structural,dummy,binary,-0.094,,-2.513,,***,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,-9.035383558187638,3.404957407506252,100*(exp(b - se^2/2) - 1),|b/t|,-0.06544814195394888,0.026043828871448023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.513,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Pre1919,age,A_structural,dummy,construction period dummy,0.134,,6.409,,***,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,14.3142930692155,2.390616911099746,100*(exp(b - se^2/2) - 1),|b/t|,0.1649813370540278,0.02574213403869992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.409,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Early Modern,age,A_structural,dummy,construction period dummy,0.059,,1.699,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,6.013583030426672,3.683680941946402,100*(exp(b - se^2/2) - 1),|b/t|,0.0443000057888295,0.02607416467853414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.699,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Inter War,age,A_structural,dummy,construction period dummy,-0.029,,-1.288,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,-2.882973363168617,2.1871954557089706,100*(exp(b - se^2/2) - 1),|b/t|,-0.03359754791814423,0.02608505273147844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.288,1468.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,Gas Heat,heating,A_structural,binary,binary,0.036,,1.984,,**,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",pct_premium,3.648520268665467,1.8810287537133694,100*(exp(b - se^2/2) - 1),|b/t|,0.05171269439052915,0.02606486612425864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.984,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c5,8,Model 9,33,EPC,energy_efficiency,A_structural,level,index (EPC SAP score points),0.001,,2.155,,*,,,high,Continuous EPC energy-efficiency score,ln(transaction price),log,OLS,1478.0,0.675,t,"Partial regression model: set A + EPC + coordinate polynomials (X*Y, Y*Y, X*X not extracted)",semi_elasticity,0.1,0.046403712296983764,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.05615628696537368,0.0260586018400806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.155,1468.0,index,unit_not_harmonisable,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,False,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c6,8,Model 10,33,Floor Area,living_area,A_structural,level,m2,0.007,,34.573,,***,,,high,,ln(transaction price),log,OLS,1478.0,0.673,t,Partial regression model: floor area + type/age x EPC interactions + gas heat + coordinate polynomials (interactions not extracted),semi_elasticity,0.7000000000000001,0.020247013565499086,100*b/1 (X per m2->m2),|b/t|,0.669927255455896,0.019377180327304427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.573,1468.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T8-c6,8,Model 10,33,Gas Heat,heating,A_structural,binary,binary,0.128,,6.249,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,1478.0,0.673,t,Partial regression model: floor area + type/age x EPC interactions + gas heat + coordinate polynomials (interactions not extracted),pct_premium,13.631459897301701,2.3280330348091494,100*(exp(b - se^2/2) - 1),|b/t|,0.16097064856407728,0.025759425278296895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.249,1468.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Floor Area,living_area,A_structural,level,m2,0.006611,,33.88551,,***,,,high,,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,semi_elasticity,0.6611,0.019509814076872387,100*b/1 (X per m2->m2),|b/t|,0.7395784415791967,0.021825802284787706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.88551,951.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Floor Area,living_area,A_structural,level,m2,0.006795,,28.6727,,***,,,high,,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,semi_elasticity,0.6795,0.023698500664395054,100*b/1 (X per m2->m2),|b/t|,0.6809246719881037,0.023748188066980223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.6727,951.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Floor Area,living_area,A_structural,level,m2,0.006796,,28.72439,,***,,,high,,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",semi_elasticity,0.6796,0.023659336194780813,100*b/1 (X per m2->m2),|b/t|,0.6815822299801891,0.023728344796188502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.72439,951.0,m2,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Terr,dwelling_type,A_structural,dummy,binary,-0.398965,,-15.9981,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,-32.91944388571055,1.6733935585640018,100*(exp(b - se^2/2) - 1),|b/t|,-0.4604958387003762,0.02878440806723149,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.9981,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Terr,dwelling_type,A_structural,dummy,binary,-0.36092,,-11.5667,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,-30.330449241180023,2.1749832296546723,100*(exp(b - se^2/2) - 1),|b/t|,-0.3511857825749567,0.030361795721766505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.5667,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Terr,dwelling_type,A_structural,dummy,binary,-0.363045,,-11.64686,,***,,,high,Terraced =1; reference semi-detached,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,-30.47826980190932,2.168119245808803,100*(exp(b - se^2/2) - 1),|b/t|,-0.35331668194654064,0.03033578852553741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.64686,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Detach,dwelling_type,A_structural,dummy,binary,0.099087,,3.837041,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,10.379425309650014,2.8513673822650816,100*(exp(b - se^2/2) - 1),|b/t|,0.12347248207864552,0.032179088542094156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.837041,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Detach,dwelling_type,A_structural,dummy,binary,0.100533,,3.242625,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,10.522881823322194,3.428252828142477,100*(exp(b - se^2/2) - 1),|b/t|,0.10457281075330473,0.03224943086336062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.242625,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Detach,dwelling_type,A_structural,dummy,binary,0.102709,,3.316137,,***,,,high,Detached =1; reference semi-detached,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,10.763748215534608,3.4322743001881197,100*(exp(b - se^2/2) - 1),|b/t|,0.10691672778887205,0.032241348228035226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.316137,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Apt,dwelling_type,A_structural,dummy,binary,-0.079251,,-1.934257,,*,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,-7.696706778070849,3.785055933917746,100*(exp(b - se^2/2) - 1),|b/t|,-0.06259956437848929,0.032363623023460325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.934257,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Apt,dwelling_type,A_structural,dummy,binary,-0.056519,,-1.173586,,,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,-5.604676739429659,4.5512810971485935,100*(exp(b - se^2/2) - 1),|b/t|,-0.03802860560448492,0.032403765556580366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.173586,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Apt,dwelling_type,A_structural,dummy,binary,-0.057789,,-1.201785,,,,,high,Apartment =1; reference semi-detached,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,-5.724150344008605,4.538590047491906,100*(exp(b - se^2/2) - 1),|b/t|,-0.03894099000469658,0.032402626097593645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.201785,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Pre1919,age,A_structural,dummy,construction period dummy,0.054449,,1.484746,,,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,5.524880416924471,3.872439525887735,100*(exp(b - se^2/2) - 1),|b/t|,0.048090481856863286,0.03238970292350563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.484746,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Pre1919,age,A_structural,dummy,construction period dummy,0.030529,,0.710688,,,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,3.004897489335745,4.428862249882903,100*(exp(b - se^2/2) - 1),|b/t|,0.023039520026955248,0.03241861411330323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.710688,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Pre1919,age,A_structural,dummy,construction period dummy,0.035264,,0.821004,,,,,high,"Built pre-1919 =1 (construction-period dummy, not age in years)",ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,3.493802996127471,4.449398052693368,100*(exp(b - se^2/2) - 1),|b/t|,0.026613448932192252,0.03241573601613665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.821004,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Early Modern,age,A_structural,dummy,construction period dummy,-0.020627,,-0.790323,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,-2.074929920293511,2.5566616398398128,100*(exp(b - se^2/2) - 1),|b/t|,-0.02561956718084233,0.032416578007779515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.790323,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Early Modern,age,A_structural,dummy,construction period dummy,0.007467,,0.226569,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,0.6947948873100351,3.3203857427320207,100*(exp(b - se^2/2) - 1),|b/t|,0.0073468049241798405,0.032426346606022186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.226569,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Early Modern,age,A_structural,dummy,construction period dummy,0.007792,,0.236813,,,,,high,"Early-modern construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,0.7277026386634189,3.316098536084502,100*(exp(b - se^2/2) - 1),|b/t|,0.007678961255229048,0.03242626568317216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.236813,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),35,Inter War,age,A_structural,dummy,construction period dummy,0.04319,,1.496247,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,4.370135474935544,3.013957264491826,100*(exp(b - se^2/2) - 1),|b/t|,0.04846212430681989,0.03238912045058061,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.496247,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),35,Inter War,age,A_structural,dummy,construction period dummy,0.030309,,0.850356,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,3.0118452739940293,3.6739552214770463,100*(exp(b - se^2/2) - 1),|b/t|,0.02756420517254363,0.032414900550526635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.850356,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),35,Inter War,age,A_structural,dummy,construction period dummy,0.024715,,0.6924,,,,,high,"Inter-war construction period =1 (construction-period dummy, not age in years)",ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,2.4370159030260075,3.658787138041321,100*(exp(b - se^2/2) - 1),|b/t|,0.022446951082698563,0.03241905124595402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6924,951.0,construction period dummy,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),36,Gas Heat,heating,A_structural,binary,binary,0.128549,,6.05882,,***,,,high,Gas central heating =1,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,13.6921217876375,2.4127302738207947,100*(exp(b - se^2/2) - 1),|b/t|,0.19278510614246533,0.03181891954909791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.05882,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),36,Gas Heat,heating,A_structural,binary,binary,0.108793,,4.202728,,,,,high,Gas central heating =1 printed without stars despite t=4.20,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,11.455804062755547,2.8861431564274143,100*(exp(b - se^2/2) - 1),|b/t|,0.13503456356415935,0.032130217221804355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.202728,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),36,Gas Heat,heating,A_structural,binary,binary,0.10717,,4.14469,,***,,,high,Gas central heating =1,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,11.275141784307197,2.8782235152456073,100*(exp(b - se^2/2) - 1),|b/t|,0.1332031043088718,0.03213825504654674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.14469,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),36,No Garage,garage,A_structural,binary,binary,-0.017509,,-0.907136,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,pct_premium,-1.7539630198191158,1.8966398806391354,100*(exp(b - se^2/2) - 1),|b/t|,-0.029403181769984247,0.03241320129504754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.907136,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),36,No Garage,garage,A_structural,binary,binary,0.000841,,0.035559,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,pct_premium,0.05614765986872161,2.367073254295825,100*(exp(b - se^2/2) - 1),|b/t|,0.0011530788118691976,0.03242720019880192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.035559,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),36,No Garage,garage,A_structural,binary,binary,0.001888,,0.079952,,,,,high,Reverse-coded: no garage =1,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",pct_premium,0.16104809567958345,2.3658794164601242,100*(exp(b - se^2/2) - 1),|b/t|,0.0025926125205102605,0.032427112774042685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.079952,951.0,binary,,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,True,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c1,11,Model 13 (Base No lag),36,EPC,energy_efficiency,A_structural,level,index (EPC SAP score points),0.001248,,2.05993,,**,,,high,Continuous EPC energy-efficiency score,ln(transaction price),log,OLS,961.0,0.614722,t,Base model on 961-observation subsample used for spatial lag models,semi_elasticity,0.1248,0.06058458297126602,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.06664927963128894,0.03235511868427031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.05993,951.0,index,unit_not_harmonisable,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,ols,journal,False,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c2,11,Model 14 (Base + Lag),36,EPC,energy_efficiency,A_structural,level,index (EPC SAP score points),0.001204,,1.671947,,*,,,high,Continuous EPC energy-efficiency score,ln(transaction price),log,spatial_lag,961.0,0.673818,t,Spatial lag of price (inverse exponential distance weights) added; W*P coefficient 0.027939 (t 1.195744) not extracted,semi_elasticity,0.1204,0.07201185205033413,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.054137088175885105,0.03237966764250608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.671947,951.0,index,unit_not_harmonisable,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,False,True,False
aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0,aa82db73a0ef7b24a4266aa846327f66030956a0:T11-c3,11,Model 15 (Base+Lag + EPC),36,EPC,energy_efficiency,A_structural,level,index (EPC SAP score points),0.036022,,2.081373,,**,,,high,Continuous EPC energy-efficiency score Model 15 also includes W*P*EPC interaction; EPC main effect conditional on it,ln(transaction price),log,spatial_lag,961.0,0.675209,t,"Spatial lag of price and spatially lagged price x EPC term added (W*P 0.184266 t 2.273**, W*P*EPC -0.002952 t -2.0135** not extracted)",semi_elasticity,3.6022,1.7306845048917225,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.06733994046394545,0.03235361488015145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.081373,951.0,index,unit_not_harmonisable,2019.0,United Kingdom,Europe,High income,2013.0,2014.0,mixed,transaction,journal,True,True,OLS,,2013.5,spatial,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: Apartment,dwelling_type,A_structural,dummy,binary,0.91,,,,,0.9,0.92,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.91,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9271181892189675,0.005198111786460983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),178.35672399999987,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: Detached House,dwelling_type,A_structural,dummy,binary,1.16,,,,,1.15,1.18,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.16,0.007653201793502336,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9030438217655129,0.00595791085891985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),151.5705493333332,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: Duplex,dwelling_type,A_structural,dummy,binary,0.88,,,,,0.86,0.9,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.88,0.010204269058003115,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.7672060892190777,0.008896337906056605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),86.23841599999993,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: End of Terrace House,dwelling_type,A_structural,dummy,binary,1.01,,,,,1.0,1.03,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.01,0.007653201793502336,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8775794274493232,0.00664979450306526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),131.97090933333322,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: Semi-Detached House,dwelling_type,A_structural,dummy,binary,1.11,,,,,1.1,1.13,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.11,0.00765320179350228,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8954657384037381,0.006174036031685887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),145.03733600000095,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: Terraced House,dwelling_type,A_structural,dummy,binary,1.0,,,,,0.99,1.01,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.0,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9385170374196092,0.004788440182674833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),195.99639999999982,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Type: Townhouse,dwelling_type,A_structural,dummy,binary,0.96,,,,,0.96,0.96,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.96,0.0,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),,,,,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,False,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: A,energy_efficiency,A_structural,dummy,binary,1.08,,,,,1.05,1.11,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.08,0.015306403587004672,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.699440412405272,0.009912886330866741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),70.55870399999995,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: B,energy_efficiency,A_structural,dummy,binary,1.04,,,,,1.03,1.06,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.04,0.007653201793502336,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8833710512558515,0.006500593186345508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),135.89083733333322,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: C,energy_efficiency,A_structural,dummy,binary,1.0,,,,,0.99,1.01,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.0,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9385170374196092,0.004788440182674833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),195.99639999999982,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: D,energy_efficiency,A_structural,dummy,binary,0.99,,,,,0.98,1.0,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.99,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9373835103280657,0.00483096643935506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),194.03643599999984,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: E,energy_efficiency,A_structural,dummy,binary,0.99,,,,,0.97,1.0,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.99,0.007653201793502336,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8734919413875375,0.006752535446905996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),129.3576239999999,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: F,energy_efficiency,A_structural,dummy,binary,0.98,,,,,0.97,0.99,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.98,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9362193947332018,0.0048742013271318475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),192.07647199999982,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: G,energy_efficiency,A_structural,dummy,binary,0.94,,,,,0.93,0.96,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.94,0.007653201793502308,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8624017820904062,0.007021420069589238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),122.824410666667,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,BER: Exempt,energy_efficiency,A_structural,dummy,binary,0.98,,,,,0.98,0.98,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); BER exempt (properties < 50 m2)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.98,0.0,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),,,,,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,False,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Attic Conversion,storage,B_interior,binary,binary,1.0,,,,,0.98,1.02,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); text-extracted binary attribute",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.0,0.010204269058003115,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8054954183148554,0.008219491973473554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),97.99819999999991,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Car Space,parking,A_structural,binary,binary,1.0,,,,,0.99,1.01,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.0,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9385170374196092,0.004788440182674833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),195.99639999999982,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Car Space.CC,parking,A_structural,other,binary,1.04,,,,,1.01,1.06,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); interaction: car space x city-centre location",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",other,1.04,0.012755336322503894,not harmonised,(ci_hi-ci_lo)/(2*1.96),0.7491311409060721,0.00918790351145962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),81.53450239999992,5198.0,binary,nonstandard_x_transform,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Development Potential,other,other,binary,binary,1.04,,,,,1.03,1.06,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); listing text mentions development potential (text-extracted binary attribute)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.04,0.007653201793502336,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.8833710512558515,0.006500593186345508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),135.89083733333322,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,False,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Fireplace,fireplace,B_interior,binary,binary,1.03,,,,,1.02,1.04,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.03,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9417441560945682,0.0046649566760150795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),201.87629199999984,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Garage,garage,A_structural,binary,binary,1.07,,,,,1.06,1.08,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.07,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9456765750175604,0.004509316922116846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),209.71614799999983,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Hot Press,storage,B_interior,binary,binary,1.01,,,,,1.0,1.02,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); hot press = airing/hot-water cupboard (text-extracted attribute)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.01,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9396210032740432,0.004746606697999585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),197.95636399999984,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Open Plan,other,other,binary,binary,1.01,,,,,1.0,1.02,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); open-plan layout (text-extracted)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.01,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9396210032740432,0.004746606697999585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),197.95636399999984,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,False,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Refurbished,renovation,A_structural,binary,binary,1.04,,,,,1.03,1.05,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.04,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9427651989227702,0.004625110455927772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),203.83625599999985,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,South Facing,sunlight,E_environment,binary,binary,1.02,,,,,1.01,1.03,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.02,0.005102134529001557,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.9406963939314185,0.004705450541945825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),199.91632799999985,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Ground Floor Apartment,floor_level,A_structural,dummy,binary,0.97,,,,,0.95,0.99,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); apartment floor-position dummy",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.97,0.010204269058003115,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.7967558469288436,0.00838176395422584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),95.05825399999992,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,First Floor Apartment,floor_level,A_structural,dummy,binary,0.98,,,,,0.96,1.01,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.98,0.012755336322503894,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.7292128213707877,0.009491178354353421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),76.83058879999993,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Second Floor Apartment,floor_level,A_structural,dummy,binary,0.98,,,,,0.94,1.02,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean)",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,0.98,0.02040853811600623,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.5543352815077438,0.01154405379765085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.019117999999956,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb,afc63bb9b7bf51c6364ea26f57de3de63b12d8bb:T13-GAM3,13,Estimate,35,Penthouse Apartment,floor_level,A_structural,dummy,binary,1.14,,,,,1.08,1.19,high,"relative scaling exp(coefficient) on log price per m2, 95% CI printed; categorical levels constrained to sum to zero (relative to grand mean); apartment floor-position dummy",log price per m2 (EUR/m2) of sold property,per_m2,other,5208.0,0.87,none,"GAM 3 (geo-additive model, mgcv): linear terms + cubic regression splines for beds, baths, size (IFSC distance excluded in GAM 3) + Gaussian-process spatial surface (100 knots), with given postcode; Dublin sales Jan-Nov 2018; linear coefficients reported as relative scalings exp(beta) with 95% CI (sum-to-zero constraint: relative to grand mean of the categorical variable, not to a base category); R2 from 5-fold CV",monetary,1.14,0.02806173990950851,"b (currency per unit, as reported)",(ci_hi-ci_lo)/(2*1.96),0.4909041975080009,0.012083882378031156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.62470836363641,5198.0,binary,,2022.0,Ireland,Europe,High income,2018.0,2018.0,mixed,transaction,journal,False,True,unknown,random forest;decision tree;text-extracted attributes from listing descriptions,2018.0,other,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Square Feet,living_area,A_structural,level,sqft,19.903,,4.866,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,19.903,4.090217838060008,"b (currency per unit, as reported)",|b/t|,0.01148396562027151,0.002360042256529287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.866,179516.0,sqft,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Square Feet,living_area,A_structural,level,sqft,32.911,,4.582,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,32.911,7.1826713225665655,"b (currency per unit, as reported)",|b/t|,0.010541888569677214,0.0023007177149011815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.582,188897.0,sqft,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,(Square Feet)2,living_area,A_structural,squared,sqft,0.004,,5.393,,,,,high,Square of living area,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,0.004,0.0007417022065640646,not harmonised,|b/t|,0.012727516258536287,0.002360006723259093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.393,179516.0,sqft,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,(Square Feet)2,living_area,A_structural,squared,sqft,0.007,,6.423,,,,,high,Square of living area,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,0.007,0.001089833411178577,not harmonised,|b/t|,0.014776717551457538,0.0023005943564467597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.423,188897.0,sqft,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Age,age,A_structural,level,years,-455.16,,-41.319,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-455.16,11.01575546358818,"b (currency per unit, as reported)",|b/t|,-0.09706056905167955,0.0023490541651946936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-41.319,179516.0,years,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Age,age,A_structural,level,years,-614.98,,-16.648,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-614.98,36.940172993753,"b (currency per unit, as reported)",|b/t|,-0.03827640708168397,0.0022991594835225834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.648,188897.0,years,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,(Age)2,age,A_structural,squared,years,0.415,,7.327,,,,,high,Square of age,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,0.415,0.05663982530367135,not harmonised,|b/t|,0.01729058476184728,0.0023598450609863897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.327,179516.0,years,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,(Age)2,age,A_structural,squared,years,2.002,,4.641,,,,,high,Square of age,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,2.002,0.4313725490196078,not harmonised,|b/t|,0.010677615537033159,0.002300714401429252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.641,188897.0,years,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Rooms/Square Feet,other,other,other,rooms per sqft,1093897.0,,2.406,,,,,high,Number of rooms divided by square footage,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,1093897.0,454653.7822111388,not harmonised,|b/t|,0.0056785445779711785,0.0023601598412182783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.406,179516.0,rooms per sqft,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Rooms/Square Feet,other,other,other,rooms per sqft,5350646.0,,5.39,,,,,high,Number of rooms divided by square footage,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,5350646.0,992698.7012987013,not harmonised,|b/t|,0.012400604042483646,0.0023006686535220126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.39,188897.0,rooms per sqft,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Bedrooms/Rooms,bedrooms,A_structural,other,share,-3887.943,,-2.947,,,,,high,"Share of rooms that are bedrooms (ratio), not bedroom count",assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,-3887.943,1319.2884289107567,not harmonised,|b/t|,-0.006955334951969199,0.0023601408048758736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.947,179516.0,share,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Bedrooms/Rooms,bedrooms,A_structural,other,share,-1082.821,,-0.388,,,,,high,"Share of rooms that are bedrooms (ratio), not bedroom count",assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,-1082.821,2790.775773195876,not harmonised,|b/t|,-0.0008927277240914932,0.0023008446497203433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.388,188897.0,share,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Bathrooms,bathrooms,A_structural,level,count,896.266,,1.683,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,896.266,532.5407011289363,"b (currency per unit, as reported)",|b/t|,0.003972181719652166,0.002360179274897306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.683,179516.0,count,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Bathrooms,bathrooms,A_structural,level,count,10706.93,,10.576,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,10706.93,1012.3799167927383,"b (currency per unit, as reported)",|b/t|,0.024326541527742655,0.0023001646679030496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.576,188897.0,count,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Year Remodeled,renovation,A_structural,other,years (calendar year),1.056,,11.9,,,,,high,"Most recent year the house was remodeled (calendar year, level), not a renovation dummy",assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,1.056,0.08873949579831933,not harmonised,|b/t|,0.02807528363138028,0.0023592675320487626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.9,179516.0,years,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Year Remodeled,renovation,A_structural,other,years (calendar year),0.343,,2.256,,,,,high,"Most recent year the house was remodeled (calendar year, level), not a renovation dummy",assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,other,0.343,0.1520390070921986,not harmonised,|b/t|,0.0051906376718096475,0.0023008145708376102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.256,188897.0,years,nonstandard_x_transform,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Garage Square Feet,garage,A_structural,level,sqft,12.673,,27.806,,,,,high,Garage area in sq ft,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,12.673,0.4557649428180968,"b (currency per unit, as reported)",|b/t|,0.06548678871109018,0.0023551315799140537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.806,179516.0,sqft,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Garage Square Feet,garage,A_structural,level,sqft,16.726,,27.861,,,,,high,Garage area in sq ft,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,16.726,0.6003373891820106,"b (currency per unit, as reported)",|b/t|,0.06397255173421644,0.002296132649015342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.861,188897.0,sqft,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Carport Dummy Variable (Yes=1),garage,A_structural,binary,binary,939.558,,1.047,,,,,high,Carport present =1,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,939.558,897.381088825215,"b (currency per unit, as reported)",|b/t|,0.0024711196510010408,0.002360190688635187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.047,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Carport Dummy Variable (Yes=1),garage,A_structural,binary,binary,9104.345,,7.346,,,,,high,Carport present =1,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,9104.345,1239.360876667574,"b (currency per unit, as reported)",|b/t|,0.016899597782765025,0.0023005169864912913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.346,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Porch Square Feet,terrace_balcony,C_exterior,level,sqft,8.835,,10.099,,,,,high,Porch area in sq ft,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,8.835,0.874839092979503,"b (currency per unit, as reported)",|b/t|,0.02382887046175108,0.002359527721729981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.099,179516.0,sqft,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Porch Square Feet,terrace_balcony,C_exterior,level,sqft,11.136,,7.49,,,,,high,Porch area in sq ft,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,11.136,1.4867823765020025,"b (currency per unit, as reported)",|b/t|,0.017230774818764658,0.002300503981143479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.49,188897.0,sqft,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Number of Stories,storeys,A_structural,level,count,-7284.721,,-15.187,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-7284.721,479.6682030684138,"b (currency per unit, as reported)",|b/t|,-0.03582132091857365,0.002358683144700971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.187,179516.0,count,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Number of Stories,storeys,A_structural,level,count,-3603.818,,-5.259,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-3603.818,685.2667807567979,"b (currency per unit, as reported)",|b/t|,-0.012099261119075546,0.0023006771475709347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.259,188897.0,count,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Foundation is a Slab,roof_materials,A_structural,binary,binary,-413.565,,-1.772,,,,,high,Slab foundation =1 (other foundation = 0); construction-type dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-413.565,233.38882618510158,"b (currency per unit, as reported)",|b/t|,-0.004182234093277881,0.0023601772535428223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.772,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Foundation is a Slab,roof_materials,A_structural,binary,binary,-5099.968,,-15.189,,,,,high,Slab foundation =1 (other foundation = 0); construction-type dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-5099.968,335.7671999473303,"b (currency per unit, as reported)",|b/t|,-0.0349262215839996,0.0022994418055171245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.189,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Roof Type: Gable,roof_materials,A_structural,binary,binary,4664.23,,24.293,,,,,high,Gable roof =1,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,4664.23,191.99892973284483,"b (currency per unit, as reported)",|b/t|,0.057242274109015424,0.0023563279178782127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.293,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Roof Type: Gable,roof_materials,A_structural,binary,binary,-2862.608,,-15.282,,,,,high,Gable roof =1,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2862.608,187.31893731187017,"b (currency per unit, as reported)",|b/t|,-0.03513980641350895,0.002299424578818803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.282,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Roof Type: Flat,roof_materials,A_structural,binary,binary,-1520.997,,-12.607,,,,,high,Flat roof =1,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-1520.997,120.6470214959943,"b (currency per unit, as reported)",|b/t|,-0.029741851636755932,0.002359153774629645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.607,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Roof Type: Flat,roof_materials,A_structural,binary,binary,-16895.01,,-8.113,,,,,high,Flat roof =1,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-16895.01,2082.461481572784,"b (currency per unit, as reported)",|b/t|,-0.018663508734235248,0.0023004448088543386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.113,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Roof Cover: Composition Shingle,roof_materials,A_structural,binary,binary,-2118.425,,-1.369,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2118.425,1547.4251278305335,"b (currency per unit, as reported)",|b/t|,-0.003231094051644156,0.00236018557461224,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.369,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Roof Cover: Composition Shingle,roof_materials,A_structural,binary,binary,-7863.394,,-7.362,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-7863.394,1068.1056778049444,"b (currency per unit, as reported)",|b/t|,-0.0169363955078791,0.002300515553909141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.362,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Roof Cover: Wood Shake,roof_materials,A_structural,binary,binary,1055.74,,1.524,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,1055.74,692.7427821522309,"b (currency per unit, as reported)",|b/t|,0.003596918323367747,0.0023601826268817238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.524,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Roof Cover: Wood Shake,roof_materials,A_structural,binary,binary,-2663.464,,-1.931,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2663.464,1379.3184878301397,"b (currency per unit, as reported)",|b/t|,-0.004442888938712539,0.0023008228579557426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.931,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Exterior: Frame Masonry Veneer,roof_materials,A_structural,binary,binary,199.077,,0.224,,,,,high,,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,199.077,888.7366071428571,"b (currency per unit, as reported)",|b/t|,0.0005286842545587825,0.0023601975649945647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.224,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Exterior: Frame Masonry Veneer,roof_materials,A_structural,binary,binary,-6341.225,,-9.878,,,,,high,,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-6341.225,641.9543429844098,"b (currency per unit, as reported)",|b/t|,-0.022721884761049534,0.0023002515449533846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.878,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,True,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Crown Heights,other,other,binary,binary,8855.167,,4.957,,,,,high,Historic district dummy (located in Crown Heights Historic District =1),assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,8855.167,1786.3964091184184,"b (currency per unit, as reported)",|b/t|,0.011698700342879552,0.0023600363814564357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.957,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Crown Heights,other,other,binary,binary,61823.22,,47.073,,,,,high,Historic district dummy (located in Crown Heights Historic District =1),assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,61823.22,1313.3477789815818,"b (currency per unit, as reported)",|b/t|,0.10767798326606617,0.0022874680446554538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.073,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Edgemere Park,other,other,binary,binary,6854.99,,3.201,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,6854.99,2141.5151515151515,"b (currency per unit, as reported)",|b/t|,0.00755477785893455,0.0023601305401232584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.201,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Edgemere Park,other,other,binary,binary,41013.14,,27.463,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,41013.14,1493.3962058041727,"b (currency per unit, as reported)",|b/t|,0.06306235146478206,0.002296265938345485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.463,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Jefferson Park,other,other,binary,binary,6562.413,,8.427,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,6562.413,778.7365610537557,"b (currency per unit, as reported)",|b/t|,0.01988545482764612,0.0023597312006225367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.427,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Jefferson Park,other,other,binary,binary,-2802.856,,-2.708,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2802.856,1035.0280649926144,"b (currency per unit, as reported)",|b/t|,-0.006230568855431082,0.0023008009067323047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.708,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Shepherd,other,other,binary,binary,-837.9,,-1.205,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-837.9,695.3526970954356,"b (currency per unit, as reported)",|b/t|,-0.002844026961272188,0.0023601883496034756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.205,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Shepherd,other,other,binary,binary,8740.323,,12.206,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,8740.323,716.0677535638212,"b (currency per unit, as reported)",|b/t|,0.02807305230882256,0.0022999387439638346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.206,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Paseo,other,other,binary,binary,-2434.624,,-1.444,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2434.624,1686.027700831025,"b (currency per unit, as reported)",|b/t|,-0.003408105967084493,0.0023601841877316436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.444,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Paseo,other,other,binary,binary,1289.545,,0.712,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,1289.545,1811.1587078651687,"b (currency per unit, as reported)",|b/t|,0.0016381998451722006,0.0023008424791744393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.712,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Heritage Hills,other,other,binary,binary,-11390.75,,-1.917,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-11390.75,5941.9666145018255,"b (currency per unit, as reported)",|b/t|,-0.004524453054393328,0.0023601737372943806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.917,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Heritage Hills,other,other,binary,binary,33808.32,,3.031,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,33808.32,11154.180138568128,"b (currency per unit, as reported)",|b/t|,0.006973693332354947,0.0023007896180649774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.031,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Putnam Heights,other,other,binary,binary,-6365.538,,-1.747,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-6365.538,3643.696622781911,"b (currency per unit, as reported)",|b/t|,-0.004123230672252927,0.002360177831856283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.747,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Putnam Heights,other,other,binary,binary,-17303.18,,-4.523,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-17303.18,3825.5980543886803,"b (currency per unit, as reported)",|b/t|,-0.010406161019652022,0.0023007209859942567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.523,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Mesta Park,other,other,binary,binary,-1409.864,,-1.365,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-1409.864,1032.8673992673994,"b (currency per unit, as reported)",|b/t|,-0.0032216534074752177,0.002360185646501991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.365,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Mesta Park,other,other,binary,binary,2171.24,,1.362,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,2171.24,1594.1556534508074,"b (currency per unit, as reported)",|b/t|,0.0031337362744278314,0.0023008342690365867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.362,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Gatewood East,other,other,binary,binary,1752.864,,1.29,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,1752.864,1358.8093023255815,"b (currency per unit, as reported)",|b/t|,0.0030446411725781374,0.0023601869554869285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.29,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Gatewood East,other,other,binary,binary,6523.245,,5.043,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,6523.245,1293.5246876859012,"b (currency per unit, as reported)",|b/t|,0.01160238318421233,0.00230069069684956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.043,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Gatewood West,other,other,binary,binary,-2490.629,,-3.251,,,,,high,Historic district dummy,assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2490.629,766.1116579513996,"b (currency per unit, as reported)",|b/t|,-0.0076727774921383956,0.0023601284196057815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.251,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Gatewood West,other,other,binary,binary,9928.553,,9.769,,,,,high,Historic district dummy,assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,9928.553,1016.3325826594329,"b (currency per unit, as reported)",|b/t|,0.02247128465558778,0.002300264577294276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.769,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c1,1,1,5,Capitol-Lincoln,other,other,binary,binary,-2446.37,,-2.37,,,,,high,Historic district dummy (Capitol-Lincoln Terrace),assessed property value ($),level,OLS,179526.0,0.877,t,Year 2000 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,-2446.37,1032.223628691983,"b (currency per unit, as reported)",|b/t|,-0.0055935815022988295,0.002360160971434105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.37,179516.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69,b0f7dc6f984405958850264589ee0ef2b7e42b69:T1-c2,1,2,5,Capitol-Lincoln,other,other,binary,binary,15207.58,,10.762,,,,,high,Historic district dummy (Capitol-Lincoln Terrace),assessed property value ($),level,OLS,188907.0,0.852,t,Year 2003 sample; Huber-White t-statistics; condition/quality dummies and neighbourhood FE included,monetary,15207.58,1413.0812116706932,"b (currency per unit, as reported)",|b/t|,0.024754112259974762,0.0023001405184886418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.762,188897.0,binary,,2009.0,United States,North America,High income,2000.0,2003.0,single_family,appraisal,journal,False,True,robust,,2001.5,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,11,Income,other,other,level,BDT per month,3.9e-06,6.3e-07,6.14,0.0,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Tenant household monthly income (not a property characteristic)",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,0.00039,6.3e-05,100*b (unit as reported: bdt per month; no harmonised unit defined for this characteristic),reported,0.3982484972324989,0.06486131876750797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.14,200.0,bdt per month,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Income,other,other,level,BDT per month,1.1e-06,3e-07,3.22,0.002,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Tenant household monthly income (not a property characteristic)",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,0.00011,2.9999999999999997e-05,100*b (unit as reported: bdt per month; no harmonised unit defined for this characteristic),reported,0.3214085097866553,0.09981630738716002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.22,90.0,bdt per month,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Income,other,other,level,BDT per month,1.04e-06,5e-07,1.85,0.068,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Tenant household monthly income (not a property characteristic)",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,0.000104,4.9999999999999996e-05,100*b (unit as reported: bdt per month; no harmonised unit defined for this characteristic),reported,0.1819131996109812,0.09833145924917902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.85,100.0,bdt per month,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,11,Age of house,age,A_structural,level,years,-0.005,0.002,-2.99,0.003,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,-0.5,0.2,100*b/1 (per year),reported,-0.20685226904721918,0.06918136088535758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.99,200.0,years,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Age of house,age,A_structural,level,years,-0.0044,0.0016,-2.68,0.009,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,-0.44,0.16,100*b/1 (per year),reported,-0.2718572903961926,0.10143928746126589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.68,90.0,years,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Age of house,age,A_structural,level,years,-8e-05,0.0019,-0.05,0.964,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,-0.008,0.19,100*b/1 (per year),reported,-0.004999937501171852,0.099998750023437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05,100.0,years,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Room Number,bedrooms,A_structural,level,count,0.0102,0.0032,3.13,0.002,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Defined in Table 1 as number of bedrooms",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,1.02,0.32,100*b/1 (per unit count),reported,0.2160950427408833,0.0690399497574707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.13,200.0,count,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Room Number,bedrooms,A_structural,level,count,0.0039,0.0017,2.23,0.028,**,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Defined in Table 1 as number of bedrooms",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,0.38999999999999996,0.16999999999999998,100*b/1 (per unit count),reported,0.22882581262987028,0.10261247203133196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.23,90.0,count,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Room Number,bedrooms,A_structural,level,count,0.0202,0.011,1.83,0.07,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Defined in Table 1 as number of bedrooms",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,2.02,1.0999999999999999,100*b/1 (per unit count),reported,0.18001063356783711,0.09836646643051208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.83,100.0,count,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Garage,garage,A_structural,binary,binary,0.011,0.0139,0.8,0.427,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,pct_premium,1.0963053641351195,1.40537440419816,100*(exp(b - se^2/2) - 1),reported,0.056478249472490506,0.07059781184061313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,200.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Garage,garage,A_structural,binary,binary,0.0137,0.0043,3.16,0.002,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,pct_premium,1.3784902545930011,0.4359315382631917,100*(exp(b - se^2/2) - 1),reported,0.31602275445751093,0.10000720077769333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.16,90.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Garage,garage,A_structural,binary,binary,0.0081,0.0079,1.02,0.309,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,pct_premium,0.8101435457033457,0.7964249860649905,100*(exp(b - se^2/2) - 1),reported,0.10147350073033704,0.09948382424542847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.02,100.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Work place distance,employment_access,D_location,level,km,-0.0066,0.0029,-2.28,0.024,**,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Distance from house to tenant's workplace",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,-0.66,0.29,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.15916510567878167,0.06980925687665863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.28,200.0,km,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Work place distance,employment_access,D_location,level,km,-0.01,0.003,-3.33,0.001,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Distance from house to tenant's workplace",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,-1.0,0.3,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.33120165488017816,0.09945995642047392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.33,90.0,km,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Work place distance,employment_access,D_location,level,km,-0.005,0.0029,-1.73,0.087,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Distance from house to tenant's workplace",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,-0.5,0.29,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.17046784057157932,0.09853632402981465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.73,100.0,km,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,School distance,school_distance,D_location,level,km,-0.0039,0.0099,-0.4,0.688,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,-0.38999999999999996,0.9900000000000001,100*b/1 (X per km->km),reported,-0.0282729643226657,0.07068241080666425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,200.0,km,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,School distance,school_distance,D_location,level,km,-0.0217,0.0165,-1.32,0.192,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,-2.17,1.6500000000000001,100*b/1 (X per km->km),reported,-0.13781258614401581,0.10440347435152711,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.32,90.0,km,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,School distance,school_distance,D_location,level,km,-0.0035,0.0265,-0.14,0.893,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes).",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,-0.35000000000000003,2.65,100*b/1 (X per km->km),reported,-0.013998628201651065,0.09999020144036475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14,100.0,km,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Open space distance,green_space,E_environment,level,m,-0.0052,0.0027,-2.9,0.06,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Distance to nearest open space/playground/park in metres; printed t=-2.90 and p=0.060 inconsistent with beta/SE ratio (-1.93)",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,-520.0,270.0,100*b/0.001 (X per m->km),reported,-0.20088093498583928,0.06926928792615147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,200.0,m,implausible_magnitude,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Open space distance,green_space,E_environment,level,m,-0.0117,0.0038,-5.25,0.0,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Distance to nearest open space/playground/park in metres",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,-1170.0,380.0,100*b/0.001 (X per m->km),reported,-0.48420012470625223,0.0922285951821433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.25,90.0,m,implausible_magnitude,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Open space distance,green_space,E_environment,level,m,-0.0066,0.0032,-2.03,0.045,**,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Distance to nearest open space/playground/park in metres",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,-660.0,320.0,100*b/0.001 (X per m->km),reported,-0.19894227555455044,0.09800112096283273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.03,100.0,m,implausible_magnitude,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Ventilation,other,other,level,1-5 Likert scale,0.0121,0.0059,2.05,0.042,**,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Perceived ventilation (air and light flow) satisfaction, 5-point scale",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,1.21,0.59,100*b (unit as reported: 1-5 likert scale; no harmonised unit defined for this characteristic),reported,0.14345752492846495,0.06997928045290973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.05,200.0,1-5 likert scale,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Ventilation,other,other,level,1-5 Likert scale,0.0117,0.0027,4.27,0.0,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Perceived ventilation (air and light flow) satisfaction, 5-point scale",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,1.17,0.27,100*b (unit as reported: 1-5 likert scale; no harmonised unit defined for this characteristic),reported,0.41043862825442595,0.09612145860759391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.27,90.0,1-5 likert scale,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,14,Ventilation,other,other,level,1-5 Likert scale,0.0094,0.0079,1.18,0.24,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Perceived ventilation (air and light flow) satisfaction, 5-point scale",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,0.9400000000000001,0.79,100*b (unit as reported: 1-5 likert scale; no harmonised unit defined for this characteristic),reported,0.11718696474334096,0.09931098707062794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.18,100.0,1-5 likert scale,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,False,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Waterlogging,flood_risk,E_environment,binary,binary,-0.0151,0.0083,-3.82,0.07,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Presence of waterlogging at the house (1/0); printed t-ratio inconsistent with beta/SE ratio",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,pct_premium,-1.5020494868881462,0.8175611496676332,100*(exp(b - se^2/2) - 1),reported,-0.2607691398300077,0.06826417273036853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.82,200.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Waterlogging,flood_risk,E_environment,binary,binary,-0.0118,0.0062,-2.91,0.059,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Presence of waterlogging at the house (1/0); printed t-ratio inconsistent with beta/SE ratio",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,pct_premium,-1.174964738705775,0.6127269951196979,100*(exp(b - se^2/2) - 1),reported,-0.2932548544137552,0.1007748640597097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.91,90.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,15,Waterlogging,flood_risk,E_environment,binary,binary,-0.0104,0.0062,-1.67,0.098,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Presence of waterlogging at the house (1/0)",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,pct_premium,-1.0365127955935916,0.6135854136656392,100*(exp(b - se^2/2) - 1),reported,-0.1647188728944956,0.09863405562544647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.67,100.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Landfill,hazard_site,E_environment,binary,binary,-0.0101,0.0075,-1.36,0.176,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Presence of landfill by wastes nearby (1/0)",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,pct_premium,-1.0077008259791298,0.742463125286908,100*(exp(b - se^2/2) - 1),reported,-0.09572490892278161,0.07038596244322178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.36,200.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Landfill,hazard_site,E_environment,binary,binary,-0.0028,0.0041,-0.68,0.497,,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Presence of landfill by wastes nearby (1/0)",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,pct_premium,-0.2804465119800703,0.4088536057009961,100*(exp(b - se^2/2) - 1),reported,-0.07149486656219525,0.10513950965028712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.68,90.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,15,Landfill,hazard_site,E_environment,binary,binary,-0.0146,0.0075,-1.93,0.056,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Presence of landfill by wastes nearby (1/0)",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,pct_premium,-1.4521653769887788,0.739129547398776,100*(exp(b - se^2/2) - 1),reported,-0.18950287214271566,0.09818801665425682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.93,100.0,binary,,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,True,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T3-c1,3,1,12,Drainage/Sewage,public_services,F_neighbourhood,level,1-5 Likert scale,0.0222,0.0053,4.18,0.0,***,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Perceived drainage/sewage condition, 5-point scale",log monthly house rent (BDT/month),log,OLS,210.0,0.9543,se,Overall Rajshahi City Corporation area; robust SEs; coefficients on pp. 11-12,semi_elasticity,2.22,0.53,100*b (unit as reported: 1-5 likert scale; no harmonised unit defined for this characteristic),reported,0.28344852940455834,0.0678106529675977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.18,200.0,1-5 likert scale,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T4-c1,4,1,13,Drainage/Sewage,public_services,F_neighbourhood,level,1-5 Likert scale,0.0046,0.0022,2.05,0.043,**,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Perceived drainage/sewage condition, 5-point scale",log monthly house rent (BDT/month),log,OLS,100.0,0.9531,se,Planned residential area; robust SEs; N/R2 note printed on p. 14,semi_elasticity,0.45999999999999996,0.22,100*b (unit as reported: 1-5 likert scale; no harmonised unit defined for this characteristic),reported,0.2112139734639955,0.1030312065678027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.05,90.0,1-5 likert scale,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6,b150aa7a491222e57926fd7829879b82c8d7c8d6:T5-c1,5,1,15,Drainage/Sewage,public_services,F_neighbourhood,level,1-5 Likert scale,0.0114,0.0059,1.94,0.055,*,,,medium,"Log base of dependent variable uncertain (likely log10, see study notes). Perceived drainage/sewage condition, 5-point scale",log monthly house rent (BDT/month),log,OLS,110.0,0.8798,se,Unplanned residential area; conventional SEs; coefficients on pp. 14-15,semi_elasticity,1.1400000000000001,0.59,100*b (unit as reported: 1-5 likert scale; no harmonised unit defined for this characteristic),reported,0.19044922709911088,0.09816970469026334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.94,100.0,1-5 likert scale,unit_not_harmonisable,2025.0,Bangladesh,Asia,Lower middle income,,,rental,rent,journal,False,False,robust,,2022.0,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,MILF1,hazard_site,E_environment,other,miles,-0.0627,0.0354,,,*,,,high,"inverse of distance to the landfill in miles (1/miles); decay form; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0627,0.0354,not harmonised,reported,-0.2452962096490604,0.13849259683535464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7711864406779663,49.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,MICBD1,cbd_distance,D_location,other,miles,0.0386,0.0367,,,,,,high,"inverse of distance to the central business district in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0386,0.0367,not harmonised,reported,0.14858515074152273,0.1412713738915514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0517711171662125,49.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,MIHWY1,highway_distance,D_location,other,miles,0.0001,0.0008,,,,,,high,"inverse of distance to the nearest primary highway in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0001,0.0008,not harmonised,reported,0.017854296416882943,0.14283437133506352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.125,49.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,AGE,age,A_structural,level,years,-0.0021,0.005,,,,,,high,"age of the house in years (quadratic also included); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-0.21,0.5,100*b/1 (per year),reported,-0.05989229072794672,0.14260069220939695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42,49.0,years,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,AGE2,age,A_structural,squared,years,0.0,0.0002,,,,,,high,"age squared; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0,0.0002,not harmonised,reported,0.0,0.14285714285714285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,49.0,years,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,BDRM,bedrooms,A_structural,level,count,0.065,0.0315,,,**,,,high,"number of bedrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,6.5,3.15,100*b/1 (per unit count),reported,0.2827550552637739,0.1370274498585981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0634920634920637,49.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,OTHRM,other,other,level,count,0.0169,0.0246,,,,,,high,"number of other (non-bedroom) rooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,1.69,2.46,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.09767244258520352,0.14217408802343237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6869918699186991,49.0,count,unit_not_harmonisable,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,False,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,BTHRM,bathrooms,A_structural,level,count,0.1236,0.0345,,,***,,,high,"number of bathrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,12.36,3.45,100*b/1 (per unit count),reported,0.45559808965081044,0.12716936968408546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5826086956521737,49.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,LNLTSZ,lot_size,A_structural,log,sqft,0.1171,0.0316,,,***,,,high,"log of lot size, printed as 'in feet' (assumed square feet); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",elasticity,0.1171,0.0316,b (log-log),reported,0.4678693627600685,0.1262568049805138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.705696202531645,49.0,sqft,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,GRACE,garage,A_structural,dummy,binary,0.1107,0.0316,,,***,,,high,"garage dummy (printed GRACE in Table 3; GARAGE in Eq. 1 / Table 2); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,11.650215228127326,3.529908774166058,100*(exp(b - se^2/2) - 1),reported,0.4475369895613224,0.12775220298227447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5031645569620253,49.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,FPLACE,fireplace,B_interior,dummy,binary,0.0896,0.0414,,,**,,,high,"fireplace dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,9.279979254899985,4.528069944267964,100*(exp(b - se^2/2) - 1),reported,0.2953828675621677,0.13648270889591232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1642512077294684,49.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c1,3,1 (Pepperell),9,POOL,pool,C_exterior,dummy,binary,0.0194,0.0661,,,,,,high,"swimming pool dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5506,se,"single-family sales in the town of Pepperell, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,1.7364433938868773,6.7394859526551345,100*(exp(b - se^2/2) - 1),reported,0.04189101019077475,0.14273174090774282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2934947049924357,49.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,MILF1,hazard_site,E_environment,other,miles,-0.028,0.0486,,,,,,high,"inverse of distance to the landfill in miles (1/miles); decay form; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.028,0.0486,not harmonised,reported,-0.09429345607270676,0.16366649875476957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5761316872427984,37.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,MICBD1,cbd_distance,D_location,other,miles,0.0522,0.0193,,,***,,,high,"inverse of distance to the central business district in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0522,0.0193,not harmonised,reported,0.40629064440399476,0.15021857158998275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.704663212435233,37.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,MIHWY1,highway_distance,D_location,other,miles,-0.0017,0.0016,,,,,,high,"inverse of distance to the nearest primary highway in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0017,0.0016,not harmonised,reported,-0.17206865539620994,0.16194696978466816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0625,37.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,AGE,age,A_structural,level,years,-0.0057,0.0015,,,***,,,high,"age of the house in years (quadratic also included); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-0.5700000000000001,0.15,100*b/1 (per year),reported,-0.5298258172065025,0.1394278466332901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8000000000000003,37.0,years,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,AGE2,age,A_structural,squared,years,2e-05,1e-05,,,**,,,high,"age squared; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,2e-05,1e-05,not harmonised,reported,0.31234752377721214,0.15617376188860607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,37.0,years,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,BDRM,bedrooms,A_structural,level,count,0.1063,0.0403,,,**,,,high,"number of bedrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,10.63,4.03,100*b/1 (per unit count),reported,0.3978427709129949,0.1508284446640987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6377171215880892,37.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,OTHRM,other,other,level,count,0.0213,0.018,,,,,,high,"number of other (non-bedroom) rooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,2.13,1.7999999999999998,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.19095890486451092,0.16137372242071346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1833333333333333,37.0,count,unit_not_harmonisable,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,False,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,BTHRM,bathrooms,A_structural,level,count,0.0041,0.0471,,,,,,high,"number of bathrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,0.41000000000000003,4.71,100*b/1 (per unit count),reported,0.014309274696366414,0.16438215565825806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.08704883227176222,37.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,LNLTSZ,lot_size,A_structural,log,sqft,0.1639,0.037,,,***,,,high,"log of lot size, printed as 'in feet' (assumed square feet); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",elasticity,0.1639,0.037,b (log-log),reported,0.5886846182455134,0.1328940260834899,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.42972972972973,37.0,sqft,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,GRACE,garage,A_structural,dummy,binary,0.0397,0.0427,,,,,,high,"garage dummy (printed GRACE in Table 3; GARAGE in Eq. 1 / Table 2); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,3.9550444712037613,4.44292892717155,100*(exp(b - se^2/2) - 1),reported,0.1510939142249798,0.1625115903628876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9297423887587821,37.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,FPLACE,fireplace,B_interior,dummy,binary,0.0624,0.0284,,,**,,,high,"fireplace dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,6.395885493726627,3.022861962015706,100*(exp(b - se^2/2) - 1),reported,0.3397306312692407,0.1546209924366416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.197183098591549,37.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c2,3,2 (Hudson),9,POOL,pool,C_exterior,dummy,binary,0.0901,0.0522,,,*,,,high,"swimming pool dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,47.0,0.7335,se,"single-family sales in the town of Hudson, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,9.279384773429822,5.7121609484761935,100*(exp(b - se^2/2) - 1),reported,0.27298381952294376,0.15815488767034033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7260536398467432,37.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,MILF1,hazard_site,E_environment,other,miles,-0.0065,0.0271,,,,,,high,"inverse of distance to the landfill in miles (1/miles); decay form; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0065,0.0271,not harmonised,reported,-0.025135419333495192,0.1047953636827261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23985239852398524,91.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,MICBD1,cbd_distance,D_location,other,miles,-0.0085,0.0265,,,***,,,medium,"inverse of distance to the central business district in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level; printed stars inconsistent with printed SE (|b/se|<1.65) - as printed",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0085,0.0265,not harmonised,reported,-0.03360523912330773,0.10476927491384173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3207547169811321,91.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,MIHWY1,highway_distance,D_location,other,miles,-0.0008,0.001,,,,,,high,"inverse of distance to the nearest primary highway in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0008,0.001,not harmonised,reported,-0.08356943129654698,0.10446178912068371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,91.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,AGE,age,A_structural,level,years,-0.0045,0.0014,,,***,,,high,"age of the house in years (quadratic also included); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-0.44999999999999996,0.13999999999999999,100*b/1 (per year),reported,-0.31930958700902373,0.0993407604028074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.214285714285714,91.0,years,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,AGE2,age,A_structural,squared,years,0.000216,7.15e-05,,,***,,,high,"age squared; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.000216,7.15e-05,not harmonised,reported,0.30190728631767577,0.09993690264682324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.020979020979021,91.0,years,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,BDRM,bedrooms,A_structural,level,count,0.073,0.0246,,,***,,,high,"number of bedrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,7.3,2.46,100*b/1 (per unit count),reported,0.2970363283029813,0.10009717364730605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.967479674796748,91.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,OTHRM,other,other,level,count,0.0843,0.0191,,,***,,,high,"number of other (non-bedroom) rooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,8.43,1.91,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.4199063304040399,0.09513891946283702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.4136125654450264,91.0,count,unit_not_harmonisable,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,False,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,BTHRM,bathrooms,A_structural,level,count,0.1191,0.0381,,,***,,,high,"number of bathrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,11.91,3.81,100*b/1 (per unit count),reported,0.3113990807289997,0.09961633061104021,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1259842519685037,91.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,LNLTSZ,lot_size,A_structural,log,sqft,0.0601,0.0265,,,**,,,high,"log of lot size, printed as 'in feet' (assumed square feet); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",elasticity,0.0601,0.0265,b (log-log),reported,0.2312962946308943,0.1019858869836722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.267924528301887,91.0,sqft,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,GRACE,garage,A_structural,dummy,binary,0.0183,0.0351,,,,,,high,"garage dummy (printed GRACE in Table 3; GARAGE in Eq. 1 / Table 2); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,1.7841282629535105,3.57482433357724,100*(exp(b - se^2/2) - 1),reported,0.05457272099126674,0.1046722681307903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5213675213675214,91.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,FPLACE,fireplace,B_interior,dummy,binary,0.0008,0.033,,,,,,high,"fireplace dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,0.025553264290500977,3.3026410562816566,100*(exp(b - se^2/2) - 1),reported,0.002541288367825194,0.10482814517278927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.024242424242424242,91.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c3,3,3 (Belchertown),9,POOL,pool,C_exterior,dummy,binary,0.066,0.0599,,,,,,high,"swimming pool dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,101.0,0.7034,se,"single-family sales in the town of Belchertown, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,6.631203108599171,6.3986780358243,100*(exp(b - se^2/2) - 1),reported,0.11474098673985264,0.10413613796541173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1018363939899833,91.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,MILF1,hazard_site,E_environment,other,miles,-0.0099,0.0207,,,,,,high,"inverse of distance to the landfill in miles (1/miles); decay form; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0099,0.0207,not harmonised,reported,-0.06816407064459441,0.14252487498415192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.47826086956521746,49.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,MICBD1,cbd_distance,D_location,other,miles,0.0298,0.0167,,,***,,,high,"inverse of distance to the central business district in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0298,0.0167,not harmonised,reported,0.24701896680654797,0.13843009213655538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.784431137724551,49.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,MIHWY1,highway_distance,D_location,other,miles,-0.0012,0.0022,,,,,,high,"inverse of distance to the nearest primary highway in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0012,0.0022,not harmonised,reported,-0.07768658419976379,0.14242540436623363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5454545454545454,49.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,AGE,age,A_structural,level,years,-0.0083,0.0047,,,***,,,high,"age of the house in years (quadratic also included); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-0.83,0.47000000000000003,100*b/1 (per year),reported,-0.24461542537618627,0.1385171685865151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7659574468085106,49.0,years,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,AGE2,age,A_structural,squared,years,5.3e-05,3.9e-05,,,,,,high,"age squared; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,5.3e-05,3.9e-05,not harmonised,reported,0.1905809121410454,0.14023878440567492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.358974358974359,49.0,years,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,BDRM,bedrooms,A_structural,level,count,0.0576,0.0323,,,***,,,high,"number of bedrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,5.76,3.2300000000000004,100*b/1 (per unit count),reported,0.24686955466197005,0.13843553152051447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7832817337461297,49.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,OTHRM,other,other,level,count,0.1069,0.0348,,,*,,,high,"number of other (non-bedroom) rooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,10.69,3.4799999999999995,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.4018440064811296,0.13081544832126576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.07183908045977,49.0,count,unit_not_harmonisable,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,False,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,BTHRM,bathrooms,A_structural,level,count,-0.036,0.0611,,,,,,high,"number of bathrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-3.5999999999999996,6.11,100*b/1 (per unit count),reported,-0.08387455591779644,0.14235376018270451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5891980360065466,49.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,LNLTSZ,lot_size,A_structural,log,sqft,0.0255,0.05122,,,,,,high,"log of lot size, printed as 'in feet' (assumed square feet); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",elasticity,0.0255,0.05122,b (log-log),reported,0.07094257331722399,0.14249720020816523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49785240140570086,49.0,sqft,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,GRACE,garage,A_structural,dummy,binary,0.1298,0.0552,,,**,,,high,"garage dummy (printed GRACE in Table 3; GARAGE in Eq. 1 / Table 2); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,13.686728031498197,6.285075535134994,100*(exp(b - se^2/2) - 1),reported,0.3184348553997904,0.13542067810530375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.351449275362319,49.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,FPLACE,fireplace,B_interior,dummy,binary,0.0842,0.0625,,,,,,high,"fireplace dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,8.572381481310476,6.7990402583851,100*(exp(b - se^2/2) - 1),reported,0.1889889151666458,0.14028274581847222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3472,49.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c4,3,4 (Clinton),9,POOL,pool,C_exterior,dummy,binary,-0.016,0.104,,,,,,high,"swimming pool dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,59.0,0.5466,se,"single-family sales in the town of Clinton, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,-2.1180475276815103,10.234924128574965,100*(exp(b - se^2/2) - 1),reported,-0.021972715840335277,0.14282265296217928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15384615384615385,49.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,MILF1,hazard_site,E_environment,other,miles,0.2558,0.1588,,,,,,high,"inverse of distance to the landfill in miles (1/miles); decay form; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.2558,0.1588,not harmonised,reported,0.2592900029111618,0.16096658507542022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.610831234256927,36.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,MICBD1,cbd_distance,D_location,other,miles,-0.0082,0.0238,,,,,,high,"inverse of distance to the central business district in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0082,0.0238,not harmonised,reported,-0.05732852950310889,0.16639256124073068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3445378151260504,36.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,MIHWY1,highway_distance,D_location,other,miles,-0.0038,0.0018,,,**,,,high,"inverse of distance to the nearest primary highway in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0038,0.0018,not harmonised,reported,-0.3319062115909338,0.1572187318062318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.111111111111111,36.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,AGE,age,A_structural,level,years,-0.0086,0.0032,,,**,,,high,"age of the house in years (quadratic also included); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-0.86,0.32,100*b/1 (per year),reported,-0.4087829245940621,0.152105274267558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6875,36.0,years,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,AGE2,age,A_structural,squared,years,0.0004,0.0002,,,*,,,high,"age squared; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0004,0.0002,not harmonised,reported,0.31622776601683794,0.15811388300841897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,36.0,years,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,BDRM,bedrooms,A_structural,level,count,-0.0178,0.0234,,,,,,high,"number of bedrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-1.78,2.34,100*b/1 (per unit count),reported,-0.12577385403802577,0.16534315643201142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7606837606837606,36.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,OTHRM,other,other,level,count,0.0078,0.0314,,,,,,high,"number of other (non-bedroom) rooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,0.7799999999999999,3.1399999999999997,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04136583718728085,0.16652401124110497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2484076433121019,36.0,count,unit_not_harmonisable,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,False,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,BTHRM,bathrooms,A_structural,level,count,0.161,0.0819,,,,,,high,"number of bathrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,16.1,8.19,100*b/1 (per unit count),reported,0.3113502848840519,0.15838253622362639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.965811965811966,36.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,LNLTSZ,lot_size,A_structural,log,sqft,0.0056,0.0419,,,,,,high,"log of lot size, printed as 'in feet' (assumed square feet); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",elasticity,0.0056,0.0419,b (log-log),reported,0.02226973425937893,0.16662533311928165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.13365155131264916,36.0,sqft,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,GRACE,garage,A_structural,dummy,binary,0.0629,0.0679,,,,,,high,"garage dummy (printed GRACE in Table 3; GARAGE in Eq. 1 / Table 2); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,6.246830948015392,7.2308091213131815,100*(exp(b - se^2/2) - 1),reported,0.1525858009242068,0.16471503788161596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.926362297496318,36.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,FPLACE,fireplace,B_interior,dummy,binary,0.0935,0.0534,,,*,,,high,"fireplace dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,9.644619729770865,5.86337662184254,100*(exp(b - se^2/2) - 1),reported,0.28013805872740055,0.1599932870165047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.750936329588015,36.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c5,3,5 (Leicester),9,POOL,pool,C_exterior,dummy,binary,0.2151,0.1118,,,*,,,high,"swimming pool dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,46.0,0.4127,se,"single-family sales in the town of Leicester, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,23.226061390373264,13.86304224299197,100*(exp(b - se^2/2) - 1),reported,0.30534741089903433,0.1587068365342261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9239713774597498,36.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,MILF1,hazard_site,E_environment,other,miles,0.0073,0.0309,,,,,,high,"inverse of distance to the landfill in miles (1/miles); decay form; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,0.0073,0.0309,not harmonised,reported,0.03127623264153803,0.13238843679774318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23624595469255663,57.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,MICBD1,cbd_distance,D_location,other,miles,-0.0139,0.0249,,,,,,high,"inverse of distance to the central business district in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0139,0.0249,not harmonised,reported,-0.07373846549771339,0.1320926468268391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5582329317269076,57.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,MIHWY1,highway_distance,D_location,other,miles,-0.0012,0.0022,,,,,,high,"inverse of distance to the nearest primary highway in miles (1/miles); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,-0.0012,0.0022,not harmonised,reported,-0.07205940143025914,0.13210890262214178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5454545454545454,57.0,miles,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,AGE,age,A_structural,level,years,-0.0028,0.0018,,,,,,high,"age of the house in years (quadratic also included); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,-0.27999999999999997,0.18,100*b/1 (per year),reported,-0.20179950882471162,0.1297282556730289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5555555555555556,57.0,years,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,AGE2,age,A_structural,squared,years,4.86e-05,0.0001,,,,,,high,"age squared; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",other,4.86e-05,0.0001,not harmonised,reported,0.06423931305538982,0.13217965649257166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.486,57.0,years,nonstandard_x_transform,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,BDRM,bedrooms,A_structural,level,count,0.092,0.0326,,,***,,,high,"number of bedrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,9.2,3.26,100*b/1 (per unit count),reported,0.35013319301668644,0.12406893578634758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8220858895705523,57.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,OTHRM,other,other,level,count,0.0445,0.0244,,,*,,,high,"number of other (non-bedroom) rooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,4.45,2.44,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.23481044960873845,0.1287499993360274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8237704918032784,57.0,count,unit_not_harmonisable,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,False,False,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,BTHRM,bathrooms,A_structural,level,count,0.0235,0.0406,,,,,,high,"number of bathrooms; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",semi_elasticity,2.35,4.06,100*b/1 (per unit count),reported,0.07644195872792679,0.13206568188739692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5788177339901478,57.0,count,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,LNLTSZ,lot_size,A_structural,log,sqft,0.01066,0.0305,,,***,,,medium,"log of lot size, printed as 'in feet' (assumed square feet); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level; printed stars inconsistent with printed SE (|b/se|<1.65) - as printed",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",elasticity,0.01066,0.0305,b (log-log),reported,0.046243965652913194,0.13231153399754714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34950819672131145,57.0,sqft,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,GRACE,garage,A_structural,dummy,binary,0.1602,0.0547,,,***,,,high,"garage dummy (printed GRACE in Table 3; GARAGE in Eq. 1 / Table 2); White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,17.199093326162075,6.420388413608715,100*(exp(b - se^2/2) - 1),reported,0.36165829840749475,0.12348757130393236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9287020109689217,57.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,FPLACE,fireplace,B_interior,dummy,binary,0.0633,0.0477,,,,,,high,"fireplace dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,6.413509835784037,5.081702305171141,100*(exp(b - se^2/2) - 1),reported,0.17311734084788044,0.13045335163418478,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3270440251572326,57.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd,b4d90ac556c12775841d148b92b32c2089b062dd:T3-c6,3,6 (Ware),9,POOL,pool,C_exterior,dummy,binary,0.0564,0.0781,,,,,,high,"swimming pool dummy; White-corrected SE in parentheses; *** 99%, ** 95%, * 90% level",log of sale price (1995 dollars),log,OLS,67.0,0.6215,se,"single-family sales in the town of Ware, Massachusetts, Jan 1992-Aug 1995 (separate regression per town; Chow test rejected pooling); r2 = adjusted R2; year-of-sale dummies SOLD92-94 included, not extracted",pct_premium,5.479896577227716,8.263142506022074,100*(exp(b - se^2/2) - 1),reported,0.09521666377057163,0.131851443980171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7221510883482714,57.0,binary,,2000.0,United States,North America,High income,1992.0,1995.0,single_family,transaction,journal,True,False,robust,,1993.5,ols,journal,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,lnarea_total,living_area,A_structural,log,m2,0.41,0.017,23.988,,***,,,high,Log total floor area excl. basement,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",elasticity,0.41,0.017,b (log-log),reported,0.3594974180725158,0.01498655236253609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.988,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,lnarea_total,living_area,A_structural,log,m2,0.409,0.018,22.825,,***,,,high,Log total floor area excl. basement,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",elasticity,0.409,0.018,b (log-log),reported,0.3441788113005924,0.015079027877353445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.825,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,lnarea_total,living_area,A_structural,log,m2,0.411,0.017,23.864,,***,,,high,Log total floor area excl. basement,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,elasticity,0.411,0.017,b (log-log),reported,0.35787763291872227,0.01499654847966486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.864,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,lnarea_plot,lot_size,A_structural,log,m2,0.111,0.012,8.945,,***,,,high,Log plot space,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",elasticity,0.111,0.012,b (log-log),reported,0.14219902053917793,0.015897039747252983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.945,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,lnarea_plot,lot_size,A_structural,log,m2,0.111,0.013,8.855,,***,,,high,Log plot space,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",elasticity,0.111,0.013,b (log-log),reported,0.14079679069581127,0.01590025868953261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.855,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,lnarea_plot,lot_size,A_structural,log,m2,0.111,0.012,8.891,,***,,,high,Log plot space,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,elasticity,0.111,0.012,b (log-log),reported,0.14135778480775987,0.0158989747843617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.891,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,cond_house_1,condition,A_structural,binary,binary,-0.052,0.014,-3.742,,***,,,high,1 = poor condition,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,-5.0764162827532715,1.3290604135800455,100*(exp(b - se^2/2) - 1),reported,-0.059989193691741646,0.016031318463853993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.742,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,cond_house_1,condition,A_structural,binary,binary,-0.045,0.014,-3.33,,**,,,high,1 = poor condition,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,-4.409620132956982,1.33839647456634,100*(exp(b - se^2/2) - 1),reported,-0.05340428935294871,0.01603732413001463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.33,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,cond_house_1,condition,A_structural,binary,binary,-0.044,0.014,-3.229,,**,,,high,1 = poor condition,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,-4.313981941964284,1.3397355404622655,100*(exp(b - se^2/2) - 1),reported,-0.051788931745568825,0.016038690537494217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.229,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,heat_1,heating,A_structural,binary,binary,-0.102,0.02,-5.018,,***,,,high,1 = poor quality heating system,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,-9.715103618206989,1.8060591033377535,100*(exp(b - se^2/2) - 1),reported,-0.08032985621513399,0.016008341214654043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.018,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,heat_1,heating,A_structural,binary,binary,-0.107,0.023,-4.602,,***,,,high,1 = poor quality heating system,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,-10.171195562243096,2.066609047877703,100*(exp(b - se^2/2) - 1),reported,-0.0737081920515675,0.01601655629108377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.602,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,heat_1,heating,A_structural,binary,binary,-0.105,0.021,-5.005,,***,,,high,1 = poor quality heating system,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,-9.987397708557156,1.8906814974311579,100*(exp(b - se^2/2) - 1),reported,-0.08012308549879814,0.01600860849126836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.005,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,bath_1,bathroom_quality,B_interior,binary,binary,-0.076,0.019,-3.91,,***,,,high,1 = poor quality bathroom/toilet,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,-7.3351068668858215,1.760950792462826,100*(exp(b - se^2/2) - 1),reported,-0.06267210302994967,0.01602867085164953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.91,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,bath_1,bathroom_quality,B_interior,binary,binary,-0.074,0.022,-3.339,,***,,,high,1 = poor quality bathroom/toilet,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,-7.155301751745135,2.0430777264508317,100*(exp(b - se^2/2) - 1),reported,-0.053548211955903466,0.01603720034618253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.339,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,bath_1,bathroom_quality,B_interior,binary,binary,-0.075,0.019,-3.985,,***,,,high,1 = poor quality bathroom/toilet,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,-7.2423956258581335,1.7627126240242503,100*(exp(b - se^2/2) - 1),reported,-0.06386939537512255,0.016027451787985585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.985,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,attic_1,storage,B_interior,binary,binary,-0.024,0.012,-2.018,,*,,,high,1 = traditional attic exists,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,-2.3784580282721457,1.1715428517094912,100*(exp(b - se^2/2) - 1),reported,-0.032392561646013805,0.01605181449257374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.018,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,attic_1,storage,B_interior,binary,binary,-0.024,0.011,-2.077,,*,,,high,1 = traditional attic exists,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,-2.377335374084222,1.0739142807337003,100*(exp(b - se^2/2) - 1),reported,-0.033338581018466075,0.016051314886117513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.077,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,attic_1,storage,B_interior,binary,binary,-0.024,0.012,-2.02,,*,,,high,1 = traditional attic exists,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,-2.3784580282721457,1.1715428517094912,100*(exp(b - se^2/2) - 1),reported,-0.03242463153932409,0.0160517977917446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.02,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,basement_1,basement,A_structural,binary,binary,0.132,0.013,9.936,,***,,,high,,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,14.101189968804162,1.4834408150474054,100*(exp(b - se^2/2) - 1),reported,0.1575808591046172,0.015859587268983212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.936,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,basement_1,basement,A_structural,binary,binary,0.126,0.013,9.597,,***,,,high,,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,13.418632548922016,1.4745668187679326,100*(exp(b - se^2/2) - 1),reported,0.1523313681024883,0.01587281109747716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.597,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,basement_1,basement,A_structural,binary,binary,0.124,0.014,9.069,,***,,,high,,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,13.190493887957988,1.5848222193988453,100*(exp(b - se^2/2) - 1),reported,0.1441295785946081,0.015892554702239284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.069,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,garage_1,garage,A_structural,binary,binary,-0.082,0.012,-6.899,,***,,,high,1 = poor quality garage,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,-7.879437049679783,1.1055263504356185,100*(exp(b - se^2/2) - 1),reported,-0.11012569326006429,0.015962558814330233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.899,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,garage_1,garage,A_structural,binary,binary,-0.084,0.012,-7.162,,***,,,high,1 = poor quality garage,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,-8.063494057220556,1.1033175073141497,100*(exp(b - se^2/2) - 1),reported,-0.11427002209765391,0.015955043576885496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.162,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,True
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,garage_1,garage,A_structural,binary,binary,-0.084,0.012,-7.064,,***,,,high,1 = poor quality garage,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,-8.063494057220556,1.1033175073141497,100*(exp(b - se^2/2) - 1),reported,-0.11272643285568539,0.015957875545821825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.064,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,True
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,terr_1,terrace_balcony,C_exterior,binary,binary,0.07,0.012,5.893,,***,,,high,,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",pct_premium,7.243096344504063,1.28700981750506,100*(exp(b - se^2/2) - 1),reported,0.09422196285414136,0.015988793968121733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.893,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,terr_1,terrace_balcony,C_exterior,binary,binary,0.066,0.012,5.673,,***,,,high,,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",pct_premium,6.814980761113465,1.281872060599192,100*(exp(b - se^2/2) - 1),reported,0.09073394351603599,0.01599399674176555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.673,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,terr_1,terrace_balcony,C_exterior,binary,binary,0.066,0.012,5.574,,***,,,high,,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,pct_premium,6.814980761113465,1.281872060599192,100*(exp(b - se^2/2) - 1),reported,0.08916323693018595,0.015996275014385712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.574,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,age,age,A_structural,level,years,-0.006,0.0,-16.976,,***,,,high,SE printed as 0.000 (rounded); S2SLS t printed without stars,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,-0.6,0.0353440150801131,100*b/1 (per year),|b/t|,-0.2630378468142042,0.015494689374069526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.976,3877.0,years,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,age,age,A_structural,level,years,-0.006,0.0,-17.617,,***,,,high,SE printed as 0.000 (rounded); S2SLS t printed without stars,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",semi_elasticity,-0.6,0.034058012147357666,100*b/1 (per year),|b/t|,-0.2722462286250993,0.015453608935976573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.617,3877.0,years,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,age,age,A_structural,level,years,-0.006,0.0,-16.434,,,,,high,SE printed as 0.000 (rounded); S2SLS t printed without stars,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,semi_elasticity,-0.6,0.03650967506389193,100*b/1 (per year),|b/t|,-0.25519506213420917,0.015528481327382816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.434,3877.0,years,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,muni_pp_ind,income,F_neighbourhood,level,index,0.005,0.001,5.8,,***,,,high,Municipal purchasing power index (federal state = 100),"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,0.5,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.09274789773584584,0.015991016851007903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,muni_pp_ind,income,F_neighbourhood,level,index,0.005,0.001,5.89,,***,,,high,Municipal purchasing power index (federal state = 100),"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",semi_elasticity,0.5,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.0941744219849046,0.015988866211359015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.89,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,muni_pp_ind,income,F_neighbourhood,level,index,0.005,0.001,5.709,,***,,,high,Municipal purchasing power index (federal state = 100),"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,semi_elasticity,0.5,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.09130494233200177,0.015993158579786615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.709,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,muni_acad,education_level,F_neighbourhood,level,share,0.011,0.002,5.695,,***,,,high,Proportion of academics in municipality (%),"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,1.0999999999999999,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09108289790659997,0.01599348514602282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.695,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,muni_acad,education_level,F_neighbourhood,level,share,0.009,0.002,4.384,,***,,,high,Proportion of academics in municipality (%),"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",semi_elasticity,0.8999999999999999,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07023423236977314,0.016020582201134382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.384,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,muni_acad,education_level,F_neighbourhood,level,share,0.008,0.002,3.692,,***,,,high,Proportion of academics in municipality (%),"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,semi_elasticity,0.8,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.059190455011299554,0.0160320842392469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.692,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,muni_unempl,unemployment,F_neighbourhood,level,share,-1.454,0.649,-2.241,,*,,,high,Municipal unemployment rate (OLS only),"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,-145.4,64.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03596771562551345,0.016049850792286234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.241,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,muni_age_ind,demographics,F_neighbourhood,level,index,-0.02,0.004,-5.538,,***,,,high,Population age index,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,-2.0,0.4,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.08859190530218161,0.01599709377070813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.538,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,muni_age_ind,demographics,F_neighbourhood,level,index,-0.017,0.004,-4.788,,***,,,high,Population age index,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",semi_elasticity,-1.7000000000000002,0.4,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.07667009780835626,0.01601296946707524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.788,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,muni_age_ind,demographics,F_neighbourhood,level,index,-0.016,0.004,-4.255,,***,,,high,Population age index,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,semi_elasticity,-1.6,0.4,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.06817732830306095,0.01602287386675933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.255,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,ln_muni_popd,density,F_neighbourhood,log,per_km2,0.033,0.007,4.518,,***,,,high,Log municipal population density,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",elasticity,0.033,0.007,b (log-log),reported,0.07236991283280168,0.016018130330412057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.518,3877.0,per_km2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,ln_muni_popd,density,F_neighbourhood,log,per_km2,0.02,0.006,3.153,,**,,,high,Log municipal population density,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",elasticity,0.02,0.006,b (log-log),reported,0.05057314635458123,0.016039691200311207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.153,3877.0,per_km2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,ln_muni_popd,density,F_neighbourhood,log,per_km2,0.02,0.006,3.245,,,,,high,Log municipal population density,"lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,elasticity,0.02,0.006,b (log-log),reported,0.052044857407595395,0.016038476859043267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.245,3877.0,per_km2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,enumd_acad_dmuni,education_level,F_neighbourhood,level,share,0.008,0.001,6.072,,***,,,high,"Enumeration-district proportion of academics, deviation from municipal mean","lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,0.8,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09705739035301289,0.01598441870108908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.072,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-SAR,2,SAR,14,enumd_acad_dmuni,education_level,F_neighbourhood,level,share,0.006,0.001,4.997,,***,,,high,"Enumeration-district proportion of academics, deviation from municipal mean","lnp (log purchase price, EUR)",log,spatial_lag,3887.0,0.486,se,"Spatial autoregressive (lag) model, k=5 nearest neighbours, ML; z-values",semi_elasticity,0.6,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07999583683815062,0.01600877263120885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.997,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-S2SLS,2,S2SLS,14,enumd_acad_dmuni,education_level,F_neighbourhood,level,share,0.005,0.001,4.232,,**,,,high,"Enumeration-district proportion of academics, deviation from municipal mean","lnp (log purchase price, EUR)",log,spatial_lag,3887.0,,se,Spatial two-stage least squares with SHAC standard errors,semi_elasticity,0.5,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.06781050136800347,0.016023275370511215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.232,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,enumd_unempl_dmuni,unemployment,F_neighbourhood,level,share,1.758,0.607,2.898,,**,,,high,"Enumeration-district unemployment, deviation from municipal mean (OLS only)","lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,175.8,60.699999999999996,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04649225406464916,0.016042875798705714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.898,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T2-OLS,2,OLS,14,enumd_age_ind_dmuni,demographics,F_neighbourhood,level,index,0.004,0.003,1.568,,***,,,high,Enumeration-district age index deviation (OLS only); stars as printed although t = 1.568,"lnp (log purchase price, EUR)",log,OLS,3887.0,0.473,se,"Global model, stepwise AIC selection; HC (White) standard errors; federal state dummies",semi_elasticity,0.4,0.3,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.02517447928098069,0.016055152602666257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.568,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,ols,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,lnarea_total,living_area,A_structural,log,m2,0.422,0.024,17.453,,***,,,medium,Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",elasticity,0.422,0.024,b (log-log),reported,0.2698972569963901,0.015464232911040513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.453,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,lnarea_plot,lot_size,A_structural,log,m2,0.077,0.018,4.268,,***,,,medium,Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",elasticity,0.077,0.018,b (log-log),reported,0.06838465304548642,0.01602264598066692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.268,3877.0,m2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,heat_1,heating,A_structural,binary,binary,-0.111,0.031,-3.544,,***,,,medium,1 = poor heating; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,-10.549116584453344,2.774310121679996,100*(exp(b - se^2/2) - 1),reported,-0.056825527928064716,0.016034291176090495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.544,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,Age,age,A_structural,level,years,-0.005,0.0,-11.441,,***,,,medium,SE printed 0.000; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",semi_elasticity,-0.5,0.043702473560003494,100*b/1 (per year),|b/t|,-0.1807198001911136,0.015795804579242515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.441,3877.0,years,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,enumd_unempl,unemployment,F_neighbourhood,level,share,-2.103,0.587,3.581,,***,,,medium,Enumeration-district unemployment rate; t printed positive while coefficient negative (as printed); Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",semi_elasticity,-210.3,58.699999999999996,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.05741685094664771,0.016033747820901344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.581,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,muni_pp_ind,income,F_neighbourhood,level,index,0.004,0.001,4.442,,***,,,medium,Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",semi_elasticity,0.4,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.07115875166539642,0.016019529866140574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.442,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,muni_acad,education_level,F_neighbourhood,level,share,0.012,0.003,4.567,,***,,,medium,Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",semi_elasticity,1.2,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07315062368209505,0.016017215608078617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.567,3877.0,share,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,muni_age_ind,demographics,F_neighbourhood,level,index,-0.038,0.005,-8.26,,***,,,medium,Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",semi_elasticity,-3.8,0.5,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.1315055300131859,0.015920766345422024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.26,3877.0,index,unit_not_harmonisable,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,False,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,ln_muni_popd,density,F_neighbourhood,log,per_km2,0.067,0.008,8.623,,***,,,medium,Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",elasticity,0.067,0.008,b (log-log),reported,0.13717826296868457,0.015908415049134244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.623,3877.0,per_km2,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,cond_house_1,condition,A_structural,binary,binary,-0.038,0.02,-1.953,,.,,,medium,Spatially stationary in MGWR; '.' = p<0.10; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,-3.74795824440034,1.925425881782399,100*(exp(b - se^2/2) - 1),reported,-0.03135023621236665,0.016052348291022346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.953,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,bath_1,bathroom_quality,B_interior,binary,binary,-0.064,0.031,-2.029,,*,,,medium,Spatially stationary in MGWR; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,-6.244560360547191,2.9078154985452613,100*(exp(b - se^2/2) - 1),reported,-0.032568944818451885,0.0160517224339339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.029,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,attic_1,storage,B_interior,binary,binary,-0.028,0.016,-1.674,,.,,,medium,Spatially stationary in MGWR; '.' = p<0.10; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,-2.773609094423801,1.555821386881995,100*(exp(b - se^2/2) - 1),reported,-0.02687513515039033,0.016054441547425528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.674,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,basement_1,basement,A_structural,binary,binary,0.144,0.019,7.665,,***,,,medium,Spatially stationary in MGWR; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,15.467567075539957,2.194279806197336,100*(exp(b - se^2/2) - 1),reported,0.12217948335950372,0.01593991955114204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.665,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,garage_1,garage,A_structural,binary,binary,-0.068,0.017,-4.06,,***,,,medium,Spatially stationary in MGWR; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,-6.587451730789207,1.5882428050812631,100*(exp(b - se^2/2) - 1),reported,-0.06506641198679952,0.0160262098489654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.06,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6,b51ae7fa8ad6ec948d8787f76cdf0f315d38c6f6:T3-global,3,Global coeff.,16,terr_1,terrace_balcony,C_exterior,binary,binary,0.076,0.017,4.587,,***,,,medium,Spatially stationary in MGWR; Global column of the semi-local (MGWR) table; exact estimator of the global counterparts for locally varying covariates is not stated,"lnp (log purchase price, EUR)",log,GWR,3887.0,,se,"Mixed GWR on 50% random subsample; 'global' column = spatially stationary estimates for cond_house_1, bath_1, attic_1, basement_1, garage_1, terr_1 and global counterparts for the locally varying covariates (local quantiles not extracted)",pct_premium,7.880667532927976,1.8342363760673828,100*(exp(b - se^2/2) - 1),reported,0.07346924261165404,0.016016839461882284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.587,3877.0,binary,,2014.0,Austria,Europe,High income,1998.0,2009.0,single_family,transaction,working_paper,False,True,robust,,2003.5,spatial,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,Log(living area sqft),living_area,A_structural,log,sqft,0.458,0.032,,,***,,,high,,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",elasticity,0.458,0.032,b (log-log),reported,0.028141307710993487,0.0019662049055715974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.3125,258463.0,sqft,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,Log(living area sqft),living_area,A_structural,log,sqft,0.323,0.01,,,***,,,high,,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",elasticity,0.323,0.01,b (log-log),reported,0.041350615272622404,0.0012802048072019323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.3,609113.0,sqft,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,Log(lotsize),lot_size,A_structural,log,acres,0.0921,0.011,,,***,,,high,Lot size in acres (Table 1),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",elasticity,0.0921,0.011,b (log-log),reported,0.01646678692954335,0.0019667172228553403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.372727272727273,258463.0,acre,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,Log(lotsize),lot_size,A_structural,log,acres,0.063,0.005,,,***,,,high,Lot size in acres (Table 1),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",elasticity,0.063,0.005,b (log-log),reported,0.016142285370328788,0.001281133759549904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.6,609113.0,acre,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,2 bathrooms+,bathrooms,A_structural,dummy,binary,0.0436,0.007,,,***,,,high,2 (or 2.5 in CT) bathrooms; ref. 1 bathroom,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,4.453885401851321,0.731195111873752,100*(exp(b - se^2/2) - 1),reported,0.012250580476166919,0.001966836314980927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.228571428571429,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,2 bathrooms+,bathrooms,A_structural,dummy,binary,0.051,0.003,,,***,,,high,2 (or 2.5 in CT) bathrooms; ref. 1 bathroom,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,5.231815784083893,0.31569686798496116,100*(exp(b - se^2/2) - 1),reported,0.021776946479713705,0.001280996851747865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,3 bathrooms+,bathrooms,A_structural,dummy,binary,0.122,0.009,,,***,,,high,3 (or 3.5 in CT) bathrooms; ref. 1 bathroom,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,12.97083476657237,1.0167786916022867,100*(exp(b - se^2/2) - 1),reported,0.026654086684282586,0.001966285083266748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.555555555555557,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,3 bathrooms+,bathrooms,A_structural,dummy,binary,0.117,0.008,,,***,,,high,3 (or 3.5 in CT) bathrooms; ref. 1 bathroom,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,12.408345844432978,0.8992955437524295,100*(exp(b - se^2/2) - 1),reported,0.01873573359086643,0.001281075801084884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.625,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,4 or more bathrooms,bathrooms,A_structural,dummy,binary,0.282,0.018,,,***,,,high,ref. 1 bathroom,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,32.55639612300831,2.3864016959762253,100*(exp(b - se^2/2) - 1),reported,0.03080145987152973,0.0019660506300976425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.666666666666666,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,4 or more bathrooms,bathrooms,A_structural,dummy,binary,0.07,0.012,,,***,,,high,ref. 1 bathroom,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,7.243096344504063,1.28700981750506,100*(exp(b - se^2/2) - 1),reported,0.007474045359410974,0.001281264918756167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.833333333333334,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,Age>=10 and < 30,age,A_structural,dummy,years,0.166,0.012,,,***,,,high,Age category 10-29 years; ref. age < 10,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,18.048810351992394,1.4166877220679315,100*(exp(b - se^2/2) - 1),reported,0.027199876949598543,0.00196625616503122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.833333333333334,258463.0,years,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,Age>=10 and < 30,age,A_structural,dummy,years,-0.033,0.006,,,***,,,high,Age category 10-29 years; ref. age < 10,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,-3.2478855948300245,0.5805231357534192,100*(exp(b - se^2/2) - 1),reported,-0.007046978904994657,0.0012812688918172103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,609113.0,years,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,Age>=30 and <=50,age,A_structural,dummy,years,0.0588,0.008,,,***,,,high,Age category 30-50 years; ref. age < 10,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,6.0529169429683005,0.8484504855248812,100*(exp(b - se^2/2) - 1),reported,0.014455821151528898,0.0019667783879631154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.35,258463.0,years,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,Age>=30 and <=50,age,A_structural,dummy,years,-0.127,0.007,,,***,,,high,Age category 30-50 years; ref. age < 10,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,-11.928790511349362,0.6165135708180098,100*(exp(b - se^2/2) - 1),reported,-0.02324017706044849,0.0012809546411270824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.142857142857142,609113.0,years,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,Age>=50,age,A_structural,dummy,years,-0.0248,0.013,,,,,,high,Age category > 50 years; ref. age < 10,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,-2.4577433171138585,1.268156491573708,100*(exp(b - se^2/2) - 1),reported,-0.0037523736755291307,0.001966970071849947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9076923076923078,258463.0,years,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,Age>=50,age,A_structural,dummy,years,-0.177,0.011,,,***,,,high,Age category > 50 years; ref. age < 10,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,-16.22708996207589,0.9215577629752931,100*(exp(b - se^2/2) - 1),reported,-0.020612912663507132,0.0012810284706134376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.09090909090909,609113.0,years,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,True
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,2 bedrooms,bedrooms,A_structural,dummy,binary,0.0357,0.03,,,**,,,medium,Bedroom-count dummy (reference not stated); printed stars inconsistent with printed SE (as printed),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,3.5878646092636135,3.1090346891454743,100*(exp(b - se^2/2) - 1),reported,0.0023407044522585968,0.0019669785313097448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1900000000000002,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,2 bedrooms,bedrooms,A_structural,dummy,binary,0.01,0.013,,,,,,high,Bedroom-count dummy (reference not stated),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,0.9964821450953343,1.3130652172094182,100*(exp(b - se^2/2) - 1),reported,0.0009856154496154165,0.0012813000845000415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7692307692307693,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,3 bedrooms,bedrooms,A_structural,dummy,binary,0.05,0.03,,,***,,,medium,Bedroom-count dummy (reference not stated); printed stars inconsistent with printed SE (as printed),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,5.079813080788886,3.1538132891280726,100*(exp(b - se^2/2) - 1),reported,0.0032782889166280515,0.001966973349976831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,3 bedrooms,bedrooms,A_structural,dummy,binary,0.019,0.014,,,,,,high,Bedroom-count dummy (reference not stated),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,1.9081773709793914,1.4268543080643714,100*(exp(b - se^2/2) - 1),reported,0.0017389054731061148,0.0012812987696571374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.357142857142857,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,4 bedrooms,bedrooms,A_structural,dummy,binary,0.0356,0.028,,,**,,,medium,Bedroom-count dummy (reference not stated); printed stars inconsistent with printed SE (as printed),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,3.583514010313249,2.9014755478064678,100*(exp(b - se^2/2) - 1),reported,0.0025008717344223194,0.001966977768646768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2714285714285714,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,4 bedrooms,bedrooms,A_structural,dummy,binary,0.032,0.014,,,**,,,high,Bedroom-count dummy (reference not stated),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,3.2416323547585613,1.4455245074271657,100*(exp(b - se^2/2) - 1),reported,0.0029286747700462577,0.0012812952118952377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2857142857142856,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,5 or more bedrooms,bedrooms,A_structural,dummy,binary,0.0566,0.026,,,***,,,medium,Bedroom-count dummy (reference not stated); printed stars inconsistent with printed SE (as printed),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,5.787481063178834,2.75140432515654,100*(exp(b - se^2/2) - 1),reported,0.004281933431815004,0.0019669658874061856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1769230769230767,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,5 or more bedrooms,bedrooms,A_structural,dummy,binary,0.006,0.014,,,,,,high,Bedroom-count dummy (reference not stated),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,0.5919451117272434,1.408425250475691,100*(exp(b - se^2/2) - 1),reported,0.0005491287915723828,0.0012813005136688932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42857142857142855,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,5 total rooms,other,other,dummy,binary,-0.0167,0.009,,,*,,,high,Total-rooms dummy (reference not stated),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,-1.660115647349325,0.8850948047875713,100*(exp(b - se^2/2) - 1),reported,-0.0036498236297340587,0.001966970818419553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8555555555555556,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,5 total rooms,other,other,dummy,binary,0.024,0.005,,,***,,,high,Total-rooms dummy (reference not stated),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,2.4277514341670248,0.5121451589453108,100*(exp(b - se^2/2) - 1),reported,0.006150127078193522,0.0012812764746236505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,6 total rooms,other,other,dummy,binary,-0.021,0.014,,,,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,-2.0876994187012388,1.3709065503972435,100*(exp(b - se^2/2) - 1),reported,-0.0029504630373362335,0.0019669753582241558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,6 total rooms,other,other,dummy,binary,0.026,0.007,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,2.6315803428232476,0.7184386639314094,100*(exp(b - se^2/2) - 1),reported,0.004759063017000145,0.0012812861968846544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.714285714285714,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,7 total rooms,other,other,dummy,binary,-0.00341,0.018,,,,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,-0.356562799718918,1.7938724534045876,100*(exp(b - se^2/2) - 1),reported,-0.0003726341500404328,0.0019669837832046308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18944444444444444,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,7 total rooms,other,other,dummy,binary,0.034,0.008,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,3.4551500550404235,0.8276676853824944,100*(exp(b - se^2/2) - 1),reported,0.005445447265685617,0.0012812817095730863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,8 total rooms,other,other,dummy,binary,0.0107,0.022,,,,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,1.0512876012948924,2.2236663893865725,100*(exp(b - se^2/2) - 1),reported,0.0009566690141079517,0.001966983019661209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4863636363636364,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,8 total rooms,other,other,dummy,binary,0.064,0.008,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,6.605828435057837,0.8528739190092042,100*(exp(b - se^2/2) - 1),reported,0.010249867188006169,0.0012812333985007713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,9 total rooms,other,other,dummy,binary,0.0293,0.025,,,,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,2.9411726748123312,2.574333670455532,100*(exp(b - se^2/2) - 1),reported,0.0023052990282996463,0.0019669786930884355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.172,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,9 total rooms,other,other,dummy,binary,0.106,0.009,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,11.177684863235292,1.0006396888558777,100*(exp(b - se^2/2) - 1),reported,0.01508915692875967,0.0012811548335739341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.777777777777779,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,10 to 14 total rooms,other,other,dummy,binary,0.0577,0.026,,,**,,,medium,Total-rooms dummy; printed stars inconsistent with printed SE (as printed),"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,5.903911317248012,2.75443253512435,100*(exp(b - se^2/2) - 1),reported,0.004365149648797641,0.0019669651797008434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2192307692307693,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,10 to 14 total rooms,other,other,dummy,binary,0.13,0.011,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,13.875948629158707,1.2527112216570837,100*(exp(b - se^2/2) - 1),reported,0.015140908913990093,0.001281153831183777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.81818181818182,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c1,2,1,16,15 or more rooms,other,other,dummy,binary,0.196,0.05,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,258473.0,0.844,se,"Connecticut, 50 municipalities, 1994-2013; town FE + year dummies; clustered SE",pct_premium,21.500719671841196,6.0826345266715816,100*(exp(b - se^2/2) - 1),reported,0.007710347767257617,0.001966925450831025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.92,258463.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,15 or more rooms,other,other,dummy,binary,0.107,0.023,,,***,,,high,Total-rooms dummy,"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,11.263992229616292,2.559748785302449,100*(exp(b - se^2/2) - 1),reported,0.005960727827206833,0.001281277944165955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6521739130434785,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,False,False,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,1 half bathroom,bathrooms,A_structural,dummy,binary,0.073,0.002,,,***,,,high,Half bathrooms (MA only),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,7.572838545578109,0.2151461073829407,100*(exp(b - se^2/2) - 1),reported,0.04671641470880892,0.00127990177284408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.5,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d,b633c49b2f5879640f919bdc54ae4390ee31d91d:T2-c2,2,2,16,2 or more half bathrooms,bathrooms,A_structural,dummy,binary,0.112,0.007,,,***,,,high,Half bathrooms (MA only),"logprice (log sale price, USD)",log,FE_panel,609123.0,0.7,se,"Massachusetts (Greater Boston, 145 towns), 1987-2012; town FE + year dummies; clustered SE",pct_premium,11.848545741565042,0.7829590024515317,100*(exp(b - se^2/2) - 1),reported,0.020496504593168305,0.001281031537073019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,609113.0,binary,,2019.0,United States,North America,High income,1987.0,2013.0,single_family,transaction,working_paper,True,True,clustered,,2000.0,panel,other,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Age,age,A_structural,level,years,-0.005,0.001,,,***,,,high,"building age in years; age-category dummies (construction periods, ref post-2000) also included","log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",semi_elasticity,-0.5,0.1,100*b/1 (per year),reported,-0.03950502160492692,0.007901004320985386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,15994.0,years,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Age,age,A_structural,level,years,-0.008,0.001,,,***,,,high,"building age in years; age-category dummies (construction periods, ref post-2000) also included","log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",semi_elasticity,-0.8,0.1,100*b/1 (per year),reported,-0.06356417261637282,0.007945521577046602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,15776.0,years,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Age,age,A_structural,level,years,-0.005,0.001,,,***,,,high,"building age in years; age-category dummies (construction periods, ref post-2000) also included","log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",semi_elasticity,-0.5,0.1,100*b/1 (per year),reported,-0.031396284457030986,0.0062792568914061975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,25337.0,years,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Age2,age,A_structural,squared,years,0.0,,,,***,,,low,age squared; coefficient and SE printed as 0.000 (rounded); SE printed as (0.000),"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",other,0.0,,not harmonised,,,,,,15994.0,years,nonstandard_x_transform,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Age2,age,A_structural,squared,years,0.0,,,,***,,,low,age squared; coefficient and SE printed as 0.000 (rounded); SE printed as (0.000),"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",other,0.0,,not harmonised,,,,,,15776.0,years,nonstandard_x_transform,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Age2,age,A_structural,squared,years,0.0,,,,***,,,low,age squared; coefficient and SE printed as 0.000 (rounded); SE printed as (0.000),"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",other,0.0,,not harmonised,,,,,,25337.0,years,nonstandard_x_transform,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Floor space (log),living_area,A_structural,log,m2,-0.106,0.008,,,***,,,high,"dependent variable is per m2, so this is a size (dis)count on price/rent per m2","log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",elasticity,-0.106,0.008,b (log-log),reported,-0.10419976598072,0.007864133281563775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.25,15994.0,m2,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,True
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Floor space (log),living_area,A_structural,log,m2,0.097,0.014,,,***,,,high,"dependent variable is per m2, so this is a size (dis)count on price/rent per m2","log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",elasticity,0.097,0.014,b (log-log),reported,0.055078929578326996,0.007949536227799772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.928571428571429,15776.0,m2,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Floor space (log),living_area,A_structural,log,m2,0.671,0.013,,,***,,,high,"dependent variable is per m2, so this is a size (dis)count on price/rent per m2","log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",elasticity,0.671,0.013,b (log-log),reported,0.3084545990071641,0.005976020547083655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),51.61538461538462,25337.0,m2,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,No of rooms,bedrooms,A_structural,level,count,0.014,0.003,,,***,,,high,number of rooms (not bedrooms),"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",semi_elasticity,1.4000000000000001,0.3,100*b/1 (per unit count),reported,0.03687506239428105,0.007901799084488797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.666666666666667,15994.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,No of rooms,bedrooms,A_structural,level,count,0.007,0.004,,,,,,high,number of rooms (not bedrooms),"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",semi_elasticity,0.7000000000000001,0.4,100*b/1 (per unit count),reported,0.013931486244953401,0.007960849282830515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.75,15776.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,No of rooms,bedrooms,A_structural,level,count,-0.014,0.002,,,***,,,high,number of rooms (not bedrooms),"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",semi_elasticity,-1.4000000000000001,0.2,100*b/1 (per unit count),reported,-0.04393401582816633,0.006276287975452332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,25337.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Multi-family housing: Floor level,floor_level,A_structural,level,count,-0.008,0.001,,,***,,,high,floor level of apartment in multi-family building,"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",semi_elasticity,-0.8,0.1,100*b/1 (per unit count),reported,-0.06313123135698485,0.007891403919623105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,15994.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Multi-family housing: Floor level,floor_level,A_structural,level,count,-0.018,0.002,,,***,,,high,floor level of apartment in multi-family building,"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",semi_elasticity,-1.7999999999999998,0.2,100*b/1 (per unit count),reported,-0.07147135179576962,0.00794126131064107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,15776.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Good state of house,condition,A_structural,binary,binary,0.071,0.004,,,***,,,high,,"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",pct_premium,7.357263725290486,0.429432490347343,100*(exp(b - se^2/2) - 1),reported,0.13899009727387757,0.007830428015429724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.749999999999996,15994.0,binary,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Good state of house,condition,A_structural,binary,binary,0.083,0.006,,,***,,,high,,"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",pct_premium,8.652225097170296,0.6519250851289429,100*(exp(b - se^2/2) - 1),reported,0.10947381891083018,0.00791377004174676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.833333333333334,15776.0,binary,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Good state of house,condition,A_structural,binary,binary,0.038,0.006,,,***,,,high,,"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",pct_premium,3.8712535884579458,0.6232387397270986,100*(exp(b - se^2/2) - 1),reported,0.03975678485815569,0.006277387082866688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.333333333333333,25337.0,binary,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Bad state of house,condition,A_structural,binary,binary,-0.023,0.004,,,***,,,high,,"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",pct_premium,-2.2745334295320774,0.39090499350931085,100*(exp(b - se^2/2) - 1),reported,-0.04541934631035977,0.007899016749627785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.75,15994.0,binary,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Bad state of house,condition,A_structural,binary,binary,-0.105,0.006,,,***,,,high,,"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",pct_premium,-9.969168310928922,0.5401947135517594,100*(exp(b - se^2/2) - 1),reported,-0.13799541096850645,0.007885452055343226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.5,15776.0,binary,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Bad state of house,condition,A_structural,binary,binary,-0.085,0.005,,,***,,,high,,"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",pct_premium,-8.149919693033924,0.4592561422007287,100*(exp(b - se^2/2) - 1),reported,-0.10619608598571659,0.006246828587395093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.0,25337.0,binary,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Duration of offer,time_on_market,G_market,level,days,0.0,,,,***,,,low,coefficient and SE printed as 0.000 (rounded); SE printed as (0.000),"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",semi_elasticity,0.0,,100*b (unit as reported: days; no harmonised unit defined for this characteristic),,,,,,15994.0,days,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Duration of offer,time_on_market,G_market,level,days,-0.0,,,,,,,low,coefficient and SE printed as 0.000 (rounded); SE printed as (0.000),"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",semi_elasticity,-0.0,,100*b (unit as reported: days; no harmonised unit defined for this characteristic),,,,,,15776.0,days,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Duration of offer,time_on_market,G_market,level,days,0.0,,,,,,,low,coefficient and SE printed as 0.000 (rounded); SE printed as (0.000),"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",semi_elasticity,0.0,,100*b (unit as reported: days; no harmonised unit defined for this characteristic),,,,,,25337.0,days,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Population (log),density,F_neighbourhood,log,count,0.009,0.007,,,,,,high,"annual population of the 5-digit postcode district (log), not density per km2","log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",elasticity,0.009,0.007,b (log-log),reported,0.010165844948541782,0.007906768293310275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2857142857142856,15994.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Population (log),density,F_neighbourhood,log,count,0.033,0.017,,,**,,,high,"annual population of the 5-digit postcode district (log), not density per km2","log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",elasticity,0.033,0.017,b (log-log),reported,0.015453067774063012,0.007960671277547612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9411764705882353,15776.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Population (log),density,F_neighbourhood,log,count,0.059,0.014,,,***,,,high,"annual population of the 5-digit postcode district (log), not density per km2","log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",elasticity,0.059,0.014,b (log-log),reported,0.026466360405839055,0.006280153316639775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.214285714285714,25337.0,count,,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,True,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Mortality (deaths per 100 inhabitants),demographics,F_neighbourhood,level,deaths per 100 inhabitants,-0.013,0.006,,,**,,,high,district-level mortality as proxy for local ageing,"log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",semi_elasticity,-1.3,0.6,100*b (unit as reported: deaths per 100 inhabitants; no harmonised unit defined for this characteristic),reported,-0.017129702874921767,0.007906016711502355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1666666666666665,15994.0,deaths per 100 inhabitants,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Mortality (deaths per 100 inhabitants),demographics,F_neighbourhood,level,deaths per 100 inhabitants,-0.036,0.011,,,***,,,high,district-level mortality as proxy for local ageing,"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",semi_elasticity,-3.5999999999999996,1.0999999999999999,100*b (unit as reported: deaths per 100 inhabitants; no harmonised unit defined for this characteristic),reported,-0.02604737663590979,0.007958920638750215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2727272727272725,15776.0,deaths per 100 inhabitants,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Mortality (deaths per 100 inhabitants),demographics,F_neighbourhood,level,deaths per 100 inhabitants,0.006,0.012,,,,,,high,district-level mortality as proxy for local ageing,"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",semi_elasticity,0.6,1.2,100*b (unit as reported: deaths per 100 inhabitants; no harmonised unit defined for this characteristic),reported,0.003141161501522023,0.006282323003044046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,25337.0,deaths per 100 inhabitants,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c1,3,1,14,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.002,,,,***,,,medium,"share of foreign nationals in the 5-digit postcode district; SE printed as (0.000), rounded","log(asking rent, EUR per m2)",log,SAC,16004.0,,se,"Rental apartments, Dortmund 2007-2016 (30% random sample); lambda 1.271***, rho 1.176***",semi_elasticity,-0.2,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,15994.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,False,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c2,3,2,14,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.006,0.001,,,***,,,high,share of foreign nationals in the 5-digit postcode district,"log(asking price, EUR per m2)",log,SAC,15786.0,,se,"Apartments for sale, Dortmund 2007-2016; lambda 0.743***, rho 1.392***",semi_elasticity,-0.6,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.047715320747639206,0.007952553457939868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,15776.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T3-c3,3,3,14,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.002,0.002,,,,,,high,share of foreign nationals in the 5-digit postcode district,"log(asking price, EUR per m2)",log,SAC,25347.0,,se,"1-/2-family homes for sale, Dortmund 2008-2015; lambda 0.244***, rho 1.447***",semi_elasticity,-0.2,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.006282230024570618,0.006282230024570618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,25337.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c1,4,"Community 1 (City Centre), col 1 (Rent)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),-0.003,0.003,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1839.0,,se,"Rental apartments, residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.3,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02337622911060922,0.02337622911060922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1829.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c1,4,"Community 1 (City Centre), col 1 (Rent)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.003,0.001,,,**,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1839.0,,se,"Rental apartments, residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.3,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.06997590133914913,0.023325300446383042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1829.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c1,4,"Community 1 (City Centre), col 1 (Rent)",18,New stock,liquidity,G_market,level,share,0.185,0.159,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking rent, EUR per m2)",log,SAC,1839.0,,se,"Rental apartments, residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,18.5,15.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.027196128432035838,0.023373969841587558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1635220125786163,1829.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c2,4,"Community 1 (City Centre), col 2 (Purchase: Apartments)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.0,0.006,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,2118.0,,se,"Apartments for sale, residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.0,0.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0,0.021780342093451605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,2108.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c2,4,"Community 1 (City Centre), col 2 (Purchase: Apartments)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.009,0.003,,,***,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,2118.0,,se,"Apartments for sale, residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.8999999999999999,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0652019862276467,0.02173399540921557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,2108.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c2,4,"Community 1 (City Centre), col 2 (Purchase: Apartments)",18,New stock,liquidity,G_market,level,share,0.209,0.061,,,***,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,2118.0,,se,"Apartments for sale, residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,20.9,6.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07441752996242951,0.02171994893640287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4262295081967213,2108.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c3,4,"Community 1 (City Centre), col 3 (1-/2-Family Homes)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.027,0.008,,,***,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,670.0,,se,"1-/2-family homes for sale (2008-2015), residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,2.7,0.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.13025252194171758,0.03859333983458298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.375,660.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c3,4,"Community 1 (City Centre), col 3 (1-/2-Family Homes)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.002,0.005,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,670.0,,se,"1-/2-family homes for sale (2008-2015), residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.2,0.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.015568091956133519,0.0389202298903338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,660.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C1-c3,4,"Community 1 (City Centre), col 3 (1-/2-Family Homes)",18,New stock,liquidity,G_market,level,share,0.451,0.644,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,670.0,,se,"1-/2-family homes for sale (2008-2015), residential community 1 (City Centre) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,45.1,64.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0272494291228597,0.03891049302687726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7003105590062112,660.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c1,4,"Community 2 (Nordstadt), col 1 (Rent)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.001,0.004,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1149.0,,se,"Rental apartments, residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.1,0.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007407407407407408,0.02962962962962963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,1139.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c1,4,"Community 2 (Nordstadt), col 1 (Rent)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.0,0.002,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1149.0,,se,"Rental apartments, residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.0,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0,0.029630442547297988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,1139.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c1,4,"Community 2 (Nordstadt), col 1 (Rent)",18,New stock,liquidity,G_market,level,share,-0.36,0.254,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking rent, EUR per m2)",log,SAC,1149.0,,se,"Rental apartments, residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-36.0,25.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.04195891857644352,0.02960434810671293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4173228346456692,1139.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c2,4,"Community 2 (Nordstadt), col 2 (Purchase: Apartments)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.003,0.014,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,820.0,,se,"Apartments for sale, residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.3,1.4000000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007529019119663405,0.035135422558429226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21428571428571427,810.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c2,4,"Community 2 (Nordstadt), col 2 (Purchase: Apartments)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.002,0.005,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,820.0,,se,"Apartments for sale, residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.2,0.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.014053179478717433,0.03513294869679358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,810.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c2,4,"Community 2 (Nordstadt), col 2 (Purchase: Apartments)",18,New stock,liquidity,G_market,level,share,0.158,0.182,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,820.0,,se,"Apartments for sale, residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,15.8,18.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03048886391888258,0.03512008375466222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8681318681318682,810.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c3,4,"Community 2 (Nordstadt), col 3 (1-/2-Family Homes)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.011,0.023,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,404.0,,se,"1-/2-family homes for sale (2008-2015), residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,1.0999999999999999,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.024087443654561558,0.05036465491408326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47826086956521735,394.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c3,4,"Community 2 (Nordstadt), col 3 (1-/2-Family Homes)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.004,0.008,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,404.0,,se,"1-/2-family homes for sale (2008-2015), residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.4,0.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.025181648258293953,0.050363296516587906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,394.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C2-c3,4,"Community 2 (Nordstadt), col 3 (1-/2-Family Homes)",18,New stock,liquidity,G_market,level,share,0.292,0.204,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,404.0,,se,"1-/2-family homes for sale (2008-2015), residential community 2 (Nordstadt) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,29.2,20.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07192474289857673,0.05024879298393716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4313725490196079,394.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c1,4,"Community 3 (Dortmund West), col 1 (Rent)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.006,0.002,,,***,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,3997.0,,se,"Rental apartments, residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.6,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04745789978762495,0.015819299929208316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,3987.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c1,4,"Community 3 (Dortmund West), col 1 (Rent)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),0.003,0.001,,,***,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,3997.0,,se,"Rental apartments, residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.3,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04745789978762495,0.015819299929208316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,3987.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c1,4,"Community 3 (Dortmund West), col 1 (Rent)",18,New stock,liquidity,G_market,level,share,-0.631,0.094,,,***,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking rent, EUR per m2)",log,SAC,3997.0,,se,"Rental apartments, residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-63.1,9.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.10571532432877884,0.015748400137726166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.712765957446808,3987.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c2,4,"Community 3 (Dortmund West), col 2 (Purchase: Apartments)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.0,0.002,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,2767.0,,se,"Apartments for sale, residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.0,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0,0.019045028063583717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,2757.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c2,4,"Community 3 (Dortmund West), col 2 (Purchase: Apartments)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.003,0.003,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,2767.0,,se,"Apartments for sale, residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.3,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.019041575062365312,0.019041575062365312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,2757.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c2,4,"Community 3 (Dortmund West), col 2 (Purchase: Apartments)",18,New stock,liquidity,G_market,level,share,0.047,0.118,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,2767.0,,se,"Apartments for sale, residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,4.7,11.799999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007585513272171901,0.01904448013013371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39830508474576276,2757.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c3,4,"Community 3 (Dortmund West), col 3 (1-/2-Family Homes)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.003,0.002,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,5279.0,,se,"1-/2-family homes for sale (2008-2015), residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.3,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02066019670207803,0.01377346446805202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,5269.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c3,4,"Community 3 (Dortmund West), col 3 (1-/2-Family Homes)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.002,0.002,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,5279.0,,se,"1-/2-family homes for sale (2008-2015), residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.2,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.013775097846589401,0.013775097846589401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5269.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C3-c3,4,"Community 3 (Dortmund West), col 3 (1-/2-Family Homes)",18,New stock,liquidity,G_market,level,share,0.156,0.058,,,***,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,5279.0,,se,"1-/2-family homes for sale (2008-2015), residential community 3 (Dortmund West) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,15.6,5.800000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03702836794722644,0.013766957313712394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.689655172413793,5269.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c1,4,"Community 4 (Dortmund North), col 1 (Rent)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.003,0.002,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1935.0,,se,"Rental apartments, residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.3,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03416821033480479,0.02277880688986986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,1925.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c1,4,"Community 4 (Dortmund North), col 1 (Rent)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.001,0.001,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1935.0,,se,"Rental apartments, residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.1,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.022786197567488776,0.022786197567488776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1925.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c1,4,"Community 4 (Dortmund North), col 1 (Rent)",18,New stock,liquidity,G_market,level,share,-0.091,0.074,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking rent, EUR per m2)",log,SAC,1935.0,,se,"Rental apartments, residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-9.1,7.3999999999999995,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.028017139132049403,0.02278316808540281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2297297297297298,1925.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c2,4,"Community 4 (Dortmund North), col 2 (Purchase: Apartments)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.009,0.004,,,**,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,687.0,,se,"Apartments for sale, residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.8999999999999999,0.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.08615300533381941,0.038290224592808626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.25,677.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c2,4,"Community 4 (Dortmund North), col 2 (Purchase: Apartments)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),0.001,0.002,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,687.0,,se,"Apartments for sale, residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.1,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0192130139234745,0.038426027846949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,677.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c2,4,"Community 4 (Dortmund North), col 2 (Purchase: Apartments)",18,New stock,liquidity,G_market,level,share,0.176,0.167,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,687.0,,se,"Apartments for sale, residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,17.599999999999998,16.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04047118321837294,0.038401634076524326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0538922155688621,677.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c3,4,"Community 4 (Dortmund North), col 3 (1-/2-Family Homes)",18,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.002,0.003,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,3321.0,,se,"1-/2-family homes for sale (2008-2015), residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.2,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.011585105799036174,0.01737765869855426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,3311.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c3,4,"Community 4 (Dortmund North), col 3 (1-/2-Family Homes)",18,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.003,0.002,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,3321.0,,se,"1-/2-family homes for sale (2008-2015), residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.3,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.026059384613740254,0.017372923075826836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,3311.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C4-c3,4,"Community 4 (Dortmund North), col 3 (1-/2-Family Homes)",18,New stock,liquidity,G_market,level,share,0.073,0.055,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,3321.0,,se,"1-/2-family homes for sale (2008-2015), residential community 4 (Dortmund North) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,7.3,5.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02306030650848738,0.017374203533791863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3272727272727272,3311.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c1,4,"Community 5 (Dortmund East), col 1 (Rent)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.001,0.001,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,3205.0,,se,"Rental apartments, residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.1,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.017688728441930625,0.017688728441930625,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,3195.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c1,4,"Community 5 (Dortmund East), col 1 (Rent)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.002,0.001,,,*,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,3205.0,,se,"Rental apartments, residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.2,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03536086462614385,0.017680432313071925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,3195.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c1,4,"Community 5 (Dortmund East), col 1 (Rent)",19,New stock,liquidity,G_market,level,share,-0.354,0.94,,,***,,,medium,"annual offerings in relation to the existing stock of residential buildings (footnote a); SE printed as (0.94), probably a typo for (0.094)","log(asking rent, EUR per m2)",log,SAC,3205.0,,se,"Rental apartments, residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-35.4,94.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0066623943982927454,0.01769110376947791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.37659574468085105,3195.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c2,4,"Community 5 (Dortmund East), col 2 (Purchase: Apartments)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),-0.0,0.002,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,3288.0,,se,"Apartments for sale, residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.0,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0,0.017466083231604194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,3278.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c2,4,"Community 5 (Dortmund East), col 2 (Purchase: Apartments)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.006,0.003,,,**,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,3288.0,,se,"Apartments for sale, residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.6,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03491087285168191,0.017455436425840954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,3278.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c2,4,"Community 5 (Dortmund East), col 2 (Purchase: Apartments)",19,New stock,liquidity,G_market,level,share,0.025,0.096,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,3288.0,,se,"Apartments for sale, residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,2.5,9.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.004548412125271659,0.01746590256104317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2604166666666667,3278.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c3,4,"Community 5 (Dortmund East), col 3 (1-/2-Family Homes)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.003,0.001,,,***,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,6806.0,,se,"1-/2-family homes for sale (2008-2015), residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.3,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03636697600054138,0.012122325333513794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,6796.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c3,4,"Community 5 (Dortmund East), col 3 (1-/2-Family Homes)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.0,0.001,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,6806.0,,se,"1-/2-family homes for sale (2008-2015), residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.0,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0,0.012130349526500852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,6796.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C5-c3,4,"Community 5 (Dortmund East), col 3 (1-/2-Family Homes)",19,New stock,liquidity,G_market,level,share,0.009,0.02,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,6806.0,,se,"1-/2-family homes for sale (2008-2015), residential community 5 (Dortmund East) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.8999999999999999,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0054585759631026525,0.012130168806894786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.44999999999999996,6796.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c1,4,"Community 6 (Dortmund South), col 1 (Rent)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.001,0.001,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1594.0,,se,"Rental apartments, residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.1,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025118017965166415,0.02511801796516641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1584.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c1,4,"Community 6 (Dortmund South), col 1 (Rent)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.0,0.001,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,1594.0,,se,"Rental apartments, residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.0,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0,0.025125945381480302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,1584.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c1,4,"Community 6 (Dortmund South), col 1 (Rent)",19,New stock,liquidity,G_market,level,share,0.06,0.125,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking rent, EUR per m2)",log,SAC,1594.0,,se,"Rental apartments, residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,6.0,12.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.012059576754873694,0.025124118239320193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48,1584.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c2,4,"Community 6 (Dortmund South), col 2 (Purchase: Apartments)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.003,0.003,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,2433.0,,se,"Apartments for sale, residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.3,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02031111159255969,0.02031111159255969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,2423.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c2,4,"Community 6 (Dortmund South), col 2 (Purchase: Apartments)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.003,0.003,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,2433.0,,se,"Apartments for sale, residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.3,0.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02031111159255969,0.02031111159255969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,2423.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c2,4,"Community 6 (Dortmund South), col 2 (Purchase: Apartments)",19,New stock,liquidity,G_market,level,share,-0.083,0.076,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,2433.0,,se,"Apartments for sale, residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-8.3,7.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.022180990258464338,0.020310304333051678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.092105263157895,2423.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c3,4,"Community 6 (Dortmund South), col 3 (1-/2-Family Homes)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.007,0.002,,,***,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,4957.0,,se,"1-/2-family homes for sale (2008-2015), residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.7000000000000001,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04970041823077913,0.014200119494508322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,4947.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c3,4,"Community 6 (Dortmund South), col 3 (1-/2-Family Homes)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),0.0,0.002,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,4957.0,,se,"1-/2-family homes for sale (2008-2015), residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.0,0.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0,0.014217690134308215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,4947.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C6-c3,4,"Community 6 (Dortmund South), col 3 (1-/2-Family Homes)",19,New stock,liquidity,G_market,level,share,0.05,0.076,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,4957.0,,se,"1-/2-family homes for sale (2008-2015), residential community 6 (Dortmund South) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,5.0,7.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.009353334344977727,0.014217068204366143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6578947368421053,4947.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c1,4,"Community 7 (Hörde), col 1 (Rent)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),-0.003,0.006,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,626.0,,se,"Rental apartments, residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.3,0.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02014148736276902,0.040282974725538045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,616.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c1,4,"Community 7 (Hörde), col 1 (Rent)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.004,0.006,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking rent, EUR per m2)",log,SAC,626.0,,se,"Rental apartments, residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-0.4,0.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.026851080677052736,0.040276621015579106,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,616.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c1,4,"Community 7 (Hörde), col 1 (Rent)",20,New offers,liquidity,G_market,level,share,-0.143,0.226,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking rent, EUR per m2)",log,SAC,626.0,,se,"Rental apartments, residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-14.299999999999999,22.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02548567584719671,0.04027806112913607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6327433628318584,616.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c2,4,"Community 7 (Hörde), col 2 (Purchase: Apartments)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),-0.012,0.013,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,646.0,,se,"Apartments for sale, residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-1.2,1.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.036577886755958446,0.03962604398562165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9230769230769231,636.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c2,4,"Community 7 (Hörde), col 2 (Purchase: Apartments)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),-0.014,0.009,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,646.0,,se,"Apartments for sale, residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-1.4000000000000001,0.8999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.06156478516390055,0.03957736189107892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5555555555555558,636.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c2,4,"Community 7 (Hörde), col 2 (Purchase: Apartments)",20,New offers,liquidity,G_market,level,share,-0.377,0.278,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,646.0,,se,"Apartments for sale, residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-37.7,27.800000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05369588476427252,0.039595373911055075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3561151079136688,636.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c3,4,"Community 7 (Hörde), col 3 (1-/2-Family Homes)",19,Population aged >60 (in %),demographics,F_neighbourhood,level,share (%),0.018,0.016,,,,,,high,share of population over 60 in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,475.0,,se,"1-/2-family homes for sale (2008-2015), residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,1.7999999999999998,1.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.05209977204469979,0.04631090848417759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.125,465.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c3,4,"Community 7 (Hörde), col 3 (1-/2-Family Homes)",19,Foreigners (in %),demographics,F_neighbourhood,level,share (%),0.0,0.008,,,,,,high,share of foreign nationals in 1 km grid cell,"log(asking price, EUR per m2)",log,SAC,475.0,,se,"1-/2-family homes for sale (2008-2015), residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,0.0,0.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0,0.046373889576016826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,465.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf,b79aa4750ced5d8cf3b4aca71bb4a545f71e34bf:T4-C7-c3,4,"Community 7 (Hörde), col 3 (1-/2-Family Homes)",20,New offers,liquidity,G_market,level,share,-0.796,0.574,,,,,,high,annual offerings in relation to the existing stock of residential buildings (footnote a),"log(asking price, EUR per m2)",log,SAC,475.0,,se,"1-/2-family homes for sale (2008-2015), residential community 7 (Hörde) only, 2007-2016; 1 km grid demographics; housing characteristics and year FE included (not printed)",semi_elasticity,-79.60000000000001,57.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.064176864404056,0.046278291668251426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3867595818815333,465.0,share,unit_not_harmonisable,2021.0,Germany,Europe,High income,2007.0,2016.0,mixed,listing,journal,True,True,robust,,2011.5,spatial,journal,False,True,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Construction Date,age,A_structural,other,ordinal category,0.114,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Construction-period ordinal category (before 1500 ... from 2001 onwards), not age in years",Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",other,0.114,,not harmonised,,,,,,31062.0,ordinal category,year_built_not_age;nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Construction Date,age,A_structural,other,ordinal category,0.118,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Construction-period ordinal category (before 1500 ... from 2001 onwards), not age in years",Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",other,0.118,,not harmonised,,,,,,31062.0,ordinal category,year_built_not_age;nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Construction Date,age,A_structural,other,ordinal category,0.147,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Construction-period ordinal category (before 1500 ... from 2001 onwards), not age in years",Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",other,0.147,,not harmonised,,,,,,31062.0,ordinal category,year_built_not_age;nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Construction Date,age,A_structural,other,ordinal category,0.054,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Construction-period ordinal category (before 1500 ... from 2001 onwards), not age in years",Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",other,0.054,,not harmonised,,,,,,31062.0,ordinal category,year_built_not_age;nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Construction Date,age,A_structural,other,ordinal category,0.144,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Construction-period ordinal category (before 1500 ... from 2001 onwards), not age in years",Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",other,0.144,,not harmonised,,,,,,31062.0,ordinal category,year_built_not_age;nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Construction Date,age,A_structural,other,ordinal category,0.08,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Construction-period ordinal category (before 1500 ... from 2001 onwards), not age in years",Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",other,0.08,,not harmonised,,,,,,31062.0,ordinal category,year_built_not_age;nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Insulation,insulation,B_interior,other,ordinal category,0.136,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Ordinal insulation level (without insulation ... fully insulated),Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",other,0.136,,not harmonised,,,,,,31062.0,ordinal category,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Insulation,insulation,B_interior,other,ordinal category,0.158,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Ordinal insulation level (without insulation ... fully insulated),Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",other,0.158,,not harmonised,,,,,,31062.0,ordinal category,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Insulation,insulation,B_interior,other,ordinal category,0.136,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Ordinal insulation level (without insulation ... fully insulated),Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",other,0.136,,not harmonised,,,,,,31062.0,ordinal category,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Insulation,insulation,B_interior,other,ordinal category,0.156,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Ordinal insulation level (without insulation ... fully insulated),Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",other,0.156,,not harmonised,,,,,,31062.0,ordinal category,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Insulation,insulation,B_interior,other,ordinal category,0.014,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Ordinal insulation level (without insulation ... fully insulated),Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",other,0.014,,not harmonised,,,,,,31062.0,ordinal category,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Insulation,insulation,B_interior,other,ordinal category,0.048,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Ordinal insulation level (without insulation ... fully insulated),Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",other,0.048,,not harmonised,,,,,,31062.0,ordinal category,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Surface area (m2),living_area,A_structural,level,m2,0.529,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.529,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Surface area (m2),living_area,A_structural,level,m2,0.006,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.006,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Surface area (m2),living_area,A_structural,level,m2,0.371,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.371,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Surface area (m2),living_area,A_structural,level,m2,0.078,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.078,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Surface area (m2),living_area,A_structural,level,m2,0.313,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.313,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Surface area (m2),living_area,A_structural,level,m2,0.036,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.036,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Number of toilets,bathrooms,A_structural,level,count,0.092,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of toilets used as bathroom count proxy,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.092,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Number of toilets,bathrooms,A_structural,level,count,0.036,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of toilets used as bathroom count proxy,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.036,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Number of toilets,bathrooms,A_structural,level,count,0.097,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of toilets used as bathroom count proxy,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.097,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Number of toilets,bathrooms,A_structural,level,count,0.032,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of toilets used as bathroom count proxy,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.032,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Number of toilets,bathrooms,A_structural,level,count,0.054,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of toilets used as bathroom count proxy,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.054,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Number of toilets,bathrooms,A_structural,level,count,0.019,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of toilets used as bathroom count proxy,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.019,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Garden area (m2),garden,C_exterior,level,m2,0.004,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.004,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Garden area (m2),garden,C_exterior,level,m2,0.006,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.006,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Garden area (m2),garden,C_exterior,level,m2,0.008,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.008,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Garden area (m2),garden,C_exterior,level,m2,0.005,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.005,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Garden area (m2),garden,C_exterior,level,m2,0.099,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.099,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Garden area (m2),garden,C_exterior,level,m2,0.091,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.091,,"b (currency per unit, as reported)",,,,,,31062.0,m2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Number of balconies,terrace_balcony,C_exterior,level,count,0.003,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.003,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Number of balconies,terrace_balcony,C_exterior,level,count,0.05,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.05,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Number of balconies,terrace_balcony,C_exterior,level,count,0.094,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.094,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Number of balconies,terrace_balcony,C_exterior,level,count,0.056,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.056,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Number of balconies,terrace_balcony,C_exterior,level,count,0.048,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.048,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Number of balconies,terrace_balcony,C_exterior,level,count,0.025,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.025,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Garage,garage,A_structural,binary,binary,0.107,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.107,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Garage,garage,A_structural,binary,binary,0.141,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.141,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Garage,garage,A_structural,binary,binary,0.243,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.243,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Garage,garage,A_structural,binary,binary,0.176,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.176,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Garage,garage,A_structural,binary,binary,0.016,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.016,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Garage,garage,A_structural,binary,binary,0.192,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.192,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Distance to nearest CBD (Km),cbd_distance,D_location,level,km,-0.542,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.542,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Distance to nearest CBD (Km),cbd_distance,D_location,level,km,-0.583,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.583,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Distance to nearest CBD (Km),cbd_distance,D_location,level,km,-0.231,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.231,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Distance to nearest CBD (Km),cbd_distance,D_location,level,km,-0.05,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.05,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Distance to nearest CBD (Km),cbd_distance,D_location,level,km,-0.205,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.205,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Distance to nearest CBD (Km),cbd_distance,D_location,level,km,-0.222,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.222,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Distance to nearest arterial road (Km),highway_distance,D_location,level,km,-0.06,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.06,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Distance to nearest arterial road (Km),highway_distance,D_location,level,km,-0.061,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.061,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Distance to nearest arterial road (Km),highway_distance,D_location,level,km,-0.067,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.067,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Distance to nearest arterial road (Km),highway_distance,D_location,level,km,-0.095,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.095,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Distance to nearest arterial road (Km),highway_distance,D_location,level,km,-0.022,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.022,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Distance to nearest arterial road (Km),highway_distance,D_location,level,km,-0.011,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.011,,"b (currency per unit, as reported)",,,,,,31062.0,km,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Distance to the built-up area,other,other,binary,binary,-0.551,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Dummy: inside vs outside built-up area (printed name 'Distance to the built-up area'),Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.551,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Distance to the built-up area,other,other,binary,binary,-0.615,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Dummy: inside vs outside built-up area (printed name 'Distance to the built-up area'),Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.615,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Distance to the built-up area,other,other,binary,binary,-0.195,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Dummy: inside vs outside built-up area (printed name 'Distance to the built-up area'),Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.195,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Distance to the built-up area,other,other,binary,binary,-0.025,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Dummy: inside vs outside built-up area (printed name 'Distance to the built-up area'),Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.025,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Distance to the built-up area,other,other,binary,binary,-0.241,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Dummy: inside vs outside built-up area (printed name 'Distance to the built-up area'),Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.241,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Distance to the built-up area,other,other,binary,binary,-0.233,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Dummy: inside vs outside built-up area (printed name 'Distance to the built-up area'),Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.233,,"b (currency per unit, as reported)",,,,,,31062.0,binary,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Available Land (ha),other,other,level,ha,0.638,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Available land in neighbourhood (hectares),Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.638,,"b (currency per unit, as reported)",,,,,,31062.0,ha,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Available Land (ha),other,other,level,ha,0.441,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Available land in neighbourhood (hectares),Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.441,,"b (currency per unit, as reported)",,,,,,31062.0,ha,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Available Land (ha),other,other,level,ha,0.064,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Available land in neighbourhood (hectares),Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.064,,"b (currency per unit, as reported)",,,,,,31062.0,ha,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Available Land (ha),other,other,level,ha,0.209,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Available land in neighbourhood (hectares),Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.209,,"b (currency per unit, as reported)",,,,,,31062.0,ha,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Available Land (ha),other,other,level,ha,0.213,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Available land in neighbourhood (hectares),Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.213,,"b (currency per unit, as reported)",,,,,,31062.0,ha,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Available Land (ha),other,other,level,ha,0.037,,,,,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Available land in neighbourhood (hectares),Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.037,,"b (currency per unit, as reported)",,,,,,31062.0,ha,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Land-use density (per km2),density,F_neighbourhood,level,per_km2,0.485,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses, offices, schools and shopping facilities per km2 (land-use density, not population)",Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.485,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Land-use density (per km2),density,F_neighbourhood,level,per_km2,-0.06,,,,,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses, offices, schools and shopping facilities per km2 (land-use density, not population)",Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.06,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Land-use density (per km2),density,F_neighbourhood,level,per_km2,0.404,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses, offices, schools and shopping facilities per km2 (land-use density, not population)",Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.404,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Land-use density (per km2),density,F_neighbourhood,level,per_km2,0.095,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses, offices, schools and shopping facilities per km2 (land-use density, not population)",Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.095,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Land-use density (per km2),density,F_neighbourhood,level,per_km2,0.309,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses, offices, schools and shopping facilities per km2 (land-use density, not population)",Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.309,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Land-use density (per km2),density,F_neighbourhood,level,per_km2,0.346,,,,***,,,medium,"STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses, offices, schools and shopping facilities per km2 (land-use density, not population)",Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.346,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Number of houses,other,other,level,count,-0.195,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.195,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Number of houses,other,other,level,count,-0.099,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.099,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Number of houses,other,other,level,count,-0.048,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.048,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Number of houses,other,other,level,count,-0.123,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.123,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Number of houses,other,other,level,count,-0.03,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.03,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Number of houses,other,other,level,count,-0.096,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of houses in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.096,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Number of business locations,neighbourhood_amenities,F_neighbourhood,level,count,0.136,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of business locations in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.136,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Number of business locations,neighbourhood_amenities,F_neighbourhood,level,count,0.112,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of business locations in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.112,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Number of business locations,neighbourhood_amenities,F_neighbourhood,level,count,0.099,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of business locations in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.099,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Number of business locations,neighbourhood_amenities,F_neighbourhood,level,count,0.077,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of business locations in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.077,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Number of business locations,neighbourhood_amenities,F_neighbourhood,level,count,0.093,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of business locations in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.093,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Number of business locations,neighbourhood_amenities,F_neighbourhood,level,count,0.167,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of business locations in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.167,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Population density (per km2),density,F_neighbourhood,level,per_km2,0.142,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.142,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Population density (per km2),density,F_neighbourhood,level,per_km2,0.158,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.158,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Population density (per km2),density,F_neighbourhood,level,per_km2,0.294,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.294,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Population density (per km2),density,F_neighbourhood,level,per_km2,0.16,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.16,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Population density (per km2),density,F_neighbourhood,level,per_km2,0.01,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.01,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Population density (per km2),density,F_neighbourhood,level,per_km2,0.041,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed.,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.041,,"b (currency per unit, as reported)",,,,,,31062.0,per_km2,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Number of Immigrants,demographics,F_neighbourhood,level,count,0.058,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number (not share) of immigrants in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.058,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Number of Immigrants,demographics,F_neighbourhood,level,count,0.035,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number (not share) of immigrants in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,0.035,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Number of Immigrants,demographics,F_neighbourhood,level,count,0.132,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number (not share) of immigrants in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.132,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Number of Immigrants,demographics,F_neighbourhood,level,count,0.097,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number (not share) of immigrants in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,0.097,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Number of Immigrants,demographics,F_neighbourhood,level,count,0.038,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number (not share) of immigrants in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.038,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Number of Immigrants,demographics,F_neighbourhood,level,count,0.127,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number (not share) of immigrants in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,0.127,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Number of cars,other,other,level,count,-0.583,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of cars in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.583,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Number of cars,other,other,level,count,-0.488,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of cars in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",monetary,-0.488,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Number of cars,other,other,level,count,-0.213,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of cars in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.213,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Number of cars,other,other,level,count,-0.088,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of cars in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",monetary,-0.088,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Number of cars,other,other,level,count,-0.274,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of cars in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.274,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Number of cars,other,other,level,count,-0.095,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. Number of cars in neighbourhood,Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",monetary,-0.095,,"b (currency per unit, as reported)",,,,,,31062.0,count,,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c1,2,1,13,Interaction (Time*Group),transit_distance,D_location,other,binary,-0.188,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. DID treatment effect: post-opening x within catchment zone of RandstadRail station (interaction term),Transaction price (euro/m2),per_m2,OLS,31072.0,0.828,none,"Berkel Westpolder station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",other,-0.188,,not harmonised,,,,,,31062.0,binary,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c2,2,2,13,Interaction (Time*Group),transit_distance,D_location,other,binary,0.332,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. DID treatment effect: post-opening x within catchment zone of RandstadRail station (interaction term),Transaction price (euro/m2),per_m2,OLS,31072.0,0.76,none,"Berkel Westpolder station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=12492 (zone N not printed)",other,0.332,,not harmonised,,,,,,31062.0,binary,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c3,2,3,13,Interaction (Time*Group),transit_distance,D_location,other,binary,-0.115,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. DID treatment effect: post-opening x within catchment zone of RandstadRail station (interaction term),Transaction price (euro/m2),per_m2,OLS,31072.0,0.698,none,"Pijnacker Zuid station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",other,-0.115,,not harmonised,,,,,,31062.0,binary,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c4,2,4,13,Interaction (Time*Group),transit_distance,D_location,other,binary,0.15,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. DID treatment effect: post-opening x within catchment zone of RandstadRail station (interaction term),Transaction price (euro/m2),per_m2,OLS,31072.0,0.724,none,"Pijnacker Zuid station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=8619 (zone N not printed)",other,0.15,,not harmonised,,,,,,31062.0,binary,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c5,2,5,13,Interaction (Time*Group),transit_distance,D_location,other,binary,-0.153,,,,**,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. DID treatment effect: post-opening x within catchment zone of RandstadRail station (interaction term),Transaction price (euro/m2),per_m2,OLS,31072.0,0.664,none,"Nootdorp station, catchment zone 1 (<=400 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",other,-0.153,,not harmonised,,,,,,31062.0,binary,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e,b827a50a5b3fe735ba3475c0514ffecf35e91d7e:T2-c6,2,6,13,Interaction (Time*Group),transit_distance,D_location,other,binary,0.258,,,,***,,,medium,STANDARDIZED beta coefficient (Table 2 legend); stars *p=.10 **p=.05 ***p=.01; no SE printed. DID treatment effect: post-opening x within catchment zone of RandstadRail station (interaction term),Transaction price (euro/m2),per_m2,OLS,31072.0,0.651,none,"Nootdorp station, catchment zone 2 (400-800 m) vs control zone 1000-1600 m; DID model; station total N=9961 (zone N not printed)",other,0.258,,not harmonised,,,,,,31062.0,binary,nonstandard_x_transform,2021.0,Netherlands,Europe,High income,1985.0,2018.0,mixed,transaction,journal,False,False,unknown,,2001.5,ols,journal,False,False,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,LotSize,lot_size,A_structural,log,m2,-0.173,,-3.0,,,,,medium,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),elasticity,-0.173,0.057666666666666665,b (log-log),|b/t|,-0.045273027347836885,0.015091009115945629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,LotSize2,lot_size,A_structural,squared,m2,0.022,,4.62,,,,,medium,Square of ln(LotSize),ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),other,0.022,0.0047619047619047615,not harmonised,|b/t|,0.06962266584582452,0.01506984109216981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.62,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,RurLotSize,lot_size,A_structural,other,m2,-0.025,,-10.28,,,,,medium,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),other,-0.025,0.0024319066147859927,not harmonised,|b/t|,-0.15345542677893018,0.014927570698339509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.28,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,Age,age,A_structural,log,years,-0.2017,,-11.75,,,,,medium,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),elasticity,-0.2017,0.01716595744680851,b (log-log),|b/t|,-0.17476950115677747,0.014874000098449148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.75,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,Age2,age,A_structural,squared,years,0.017,,5.75,,,,,medium,Square of ln(Age),ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),other,0.017,0.002956521739130435,not harmonised,|b/t|,0.08653651807975662,0.01504982923126202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.75,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,Garage,garage,A_structural,binary,binary,0.042,,7.21,,,,,medium,Garage dummy,ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),pct_premium,4.287678439562348,0.6075113468451048,100*(exp(b - se^2/2) - 1),|b/t|,0.10827749479917226,0.01501768305120281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.21,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,LivingArea,living_area,A_structural,log,m2,0.51,,42.77,,,,,medium,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),elasticity,0.51,0.011924245966799158,b (log-log),|b/t|,0.5426864083938541,0.012688482777504185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.77,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,DistCBD,cbd_distance,D_location,log,minutes,-0.056,,-3.14,,,,,medium,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),elasticity,-0.056,0.017834394904458598,b (log-log),|b/t|,-0.04738113108966141,0.015089532194159684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.14,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M0,2,M0,13,DistCBD2,cbd_distance,D_location,squared,minutes,-0.046,,-13.18,,,,,medium,Square of ln(DistCBD),ln real selling price (1998 NOK),log,OLS,4392.0,0.822,t,M0 base; year dummies 2004-2007 included (not extracted),other,-0.046,0.0034901365705614566,not harmonised,|b/t|,-0.19527077977152374,0.014815688905274944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.18,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,LotSize,lot_size,A_structural,log,m2,-0.204,,-3.62,,,,,medium,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),elasticity,-0.204,0.05635359116022099,b (log-log),|b/t|,-0.05460393898702091,0.015083961046138373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.62,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,LotSize2,lot_size,A_structural,squared,m2,0.025,,5.27,,,,,medium,Square of ln(LotSize),ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),other,0.025,0.0047438330170777995,not harmonised,|b/t|,0.0793601542204904,0.015058852793261936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.27,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,RurLotSize,lot_size,A_structural,other,m2,-0.021,,-8.83,,,,,medium,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),other,-0.021,0.0023782559456398643,not harmonised,|b/t|,-0.13221928016253168,0.014973870913083992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.83,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,Age,age,A_structural,log,years,-0.192,,-11.38,,,,,medium,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),elasticity,-0.192,0.016871704745166957,b (log-log),|b/t|,-0.1694265897727925,0.014888101034516037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.38,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,Age2,age,A_structural,squared,years,0.016,,5.23,,,,,medium,Square of ln(Age),ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),other,0.016,0.0030592734225621415,not harmonised,|b/t|,0.07876155094126754,0.015059569969649621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.23,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,Garage,garage,A_structural,binary,binary,0.043,,7.44,,,,,medium,Garage dummy,ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),pct_premium,4.392045944398593,0.6033512026690367,100*(exp(b - se^2/2) - 1),|b/t|,0.11168913222002314,0.015011980137099885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.44,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,LivingArea,living_area,A_structural,log,m2,0.505,,42.69,,,,,medium,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),elasticity,0.505,0.011829468259545561,b (log-log),|b/t|,0.5419696871778356,0.01269547170714068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.69,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,DistCBD,cbd_distance,D_location,log,minutes,-0.104,,-5.36,,,,,medium,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),elasticity,-0.104,0.019402985074626865,b (log-log),|b/t|,-0.0807066967898395,0.015057219550343188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.36,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,DistCBD2,cbd_distance,D_location,squared,minutes,-0.027,,-5.84,,,,,medium,Square of ln(DistCBD),ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),other,-0.027,0.004623287671232877,not harmonised,|b/t|,-0.08788062201232555,0.015048051714439304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.84,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M1,2,M1,13,ACCESS,employment_access,D_location,log,index,0.064,,6.48,,,,,medium,ln of gravity-based labour market accessibility measure (eq. 2),ln real selling price (1998 NOK),log,OLS,4392.0,0.824,t,M1 = M0 + gravity accessibility; year dummies 2004-2007 included (not extracted),elasticity,0.064,0.009876543209876543,b (log-log),|b/t|,0.09742443995522455,0.015034635795559342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.48,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,LotSize,lot_size,A_structural,log,m2,-0.167,,-2.91,,,,,medium,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",elasticity,-0.167,0.057388316151202746,b (log-log),|b/t|,-0.043917496562004256,0.015091923217183594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.91,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,LotSize2,lot_size,A_structural,squared,m2,0.022,,4.55,,,,,medium,Square of ln(LotSize),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",other,0.022,0.004835164835164835,not harmonised,|b/t|,0.06857277526892738,0.01507093961954448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.55,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,RurLotSize,lot_size,A_structural,other,m2,-0.027,,-10.15,,,,,medium,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",other,-0.027,0.0026600985221674877,not harmonised,|b/t|,-0.1515596972922686,0.014931989881011683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.15,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,Age,age,A_structural,log,years,-0.205,,-11.84,,,,,medium,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",elasticity,-0.205,0.01731418918918919,b (log-log),|b/t|,-0.176066816209356,0.014870508125790199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.84,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,Age2,age,A_structural,squared,years,0.018,,5.92,,,,,medium,Square of ln(Age),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",other,0.018,0.0030405405405405403,not harmonised,|b/t|,0.08907497846003733,0.015046449064195494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.92,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,Garage,garage,A_structural,binary,binary,0.04,,6.93,,,,,medium,Garage dummy,ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",pct_premium,4.079343648456901,0.6007565796204261,100*(exp(b - se^2/2) - 1),|b/t|,0.10411903899371747,0.015024392351185783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.93,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,LivingArea,living_area,A_structural,log,m2,0.51,,42.8,,,,,medium,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",elasticity,0.51,0.01191588785046729,b (log-log),|b/t|,0.5429548736775509,0.01268586153452222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.8,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,DistCBD,cbd_distance,D_location,log,minutes,-0.043,,-2.28,,,,,medium,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",elasticity,-0.043,0.018859649122807017,b (log-log),|b/t|,-0.03442240496913621,0.015097546039094831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.28,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,DistCBD2,cbd_distance,D_location,squared,minutes,-0.051,,-12.22,,,,,medium,Square of ln(DistCBD),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",other,-0.051,0.004173486088379705,not harmonised,|b/t|,-0.18153419215012281,0.014855498539289918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.22,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,IN-COM,employment_access,D_location,log,share,-0.03,,-2.06,,,,,medium,"ln of share of jobs in zone held by in-commuters (observed in-commuting), zone level",ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",elasticity,-0.03,0.014563106796116504,b (log-log),|b/t|,-0.031104329692016652,0.015099189170881868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.06,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M2,2,M2,13,OUT-COM,employment_access,D_location,log,share,-0.043,,0.03,,,,,low,"ln of share of working population in zone commuting out (observed out-commuting), zone level; printed t-value (0.03) looks inconsistent with coefficient -0.043 (possible typo in source)",ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M2 = M0 + IN-COM, OUT-COM; year dummies 2004-2007 included (not extracted)",elasticity,-0.043,1.4333333333333333,b (log-log),|b/t|,0.0004531949100124474,0.015106497000414916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,False,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,LotSize,lot_size,A_structural,log,m2,-0.203,,-3.55,,,,,medium,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.203,0.057183098591549304,b (log-log),|b/t|,-0.053551119442545876,0.015084822378181937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.55,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,LotSize2,lot_size,A_structural,squared,m2,0.025,,5.22,,,,,medium,Square of ln(LotSize),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),other,0.025,0.0047892720306513415,not harmonised,|b/t|,0.07861188678311787,0.015059748425884652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.22,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,RurLotSize,lot_size,A_structural,other,m2,-0.023,,-9.64,,,,,medium,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),other,-0.023,0.0023858921161825724,not harmonised,|b/t|,-0.14410661941842887,0.01494881944174573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.64,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,Age,age,A_structural,log,years,-0.199,,-11.72,,,,,medium,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.199,0.016979522184300343,b (log-log),|b/t|,-0.1743368600992313,0.014875158711538505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.72,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,Age2,age,A_structural,squared,years,0.017,,5.65,,,,,medium,Square of ln(Age),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),other,0.017,0.003008849557522124,not harmonised,|b/t|,0.08504251515139535,0.015051772593167319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.65,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,Garage,garage,A_structural,binary,binary,0.042,,7.16,,,,,medium,Garage dummy,ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),pct_premium,4.287653640701494,0.6117537445186041,100*(exp(b - se^2/2) - 1),|b/t|,0.10753532372030038,0.015018899960935804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.16,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,LivingArea,living_area,A_structural,log,m2,0.503,,42.66,,,,,medium,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,0.503,0.011790904828879513,b (log-log),|b/t|,0.5417006113926338,0.012698092156414294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.66,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,DistCBD,cbd_distance,D_location,log,minutes,-0.114,,-5.57,,,,,medium,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.114,0.020466786355475764,b (log-log),|b/t|,-0.08384689918499381,0.01505330326481038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.57,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,DistCBD2,cbd_distance,D_location,squared,minutes,-0.024,,-4.53,,,,,medium,Square of ln(DistCBD),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),other,-0.024,0.005298013245033113,not harmonised,|b/t|,-0.06827276452782892,0.015071250447644355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.53,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,ACCESS,employment_access,D_location,log,index,0.096,,7.79,,,,,medium,ln of gravity-based labour market accessibility measure (eq. 2),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,0.096,0.012323491655969191,b (log-log),|b/t|,0.11687314770437816,0.015002971463976658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.79,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,IN-COM,employment_access,D_location,log,share,-0.083,,-4.9,,,,,medium,"ln of share of jobs in zone held by in-commuters (observed in-commuting), zone level",ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.083,0.01693877551020408,b (log-log),|b/t|,-0.07381988100231861,0.01506528183720788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.9,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M3,2,M3,13,OUT-COM,employment_access,D_location,log,share,0.001,,2.31,,,,,medium,"ln of share of working population in zone commuting out (observed out-commuting), zone level",ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M3 = M2 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,0.001,0.0004329004329004329,b (log-log),|b/t|,0.03487478405113943,0.015097309113047375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.31,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,LotSize,lot_size,A_structural,log,m2,-0.177,,-3.1,,,,,medium,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",elasticity,-0.177,0.05709677419354838,b (log-log),|b/t|,-0.046778879099184247,0.01508996099973685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,LotSize2,lot_size,A_structural,squared,m2,0.023,,4.72,,,,,medium,Square of ln(LotSize),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",other,0.023,0.004872881355932204,not harmonised,|b/t|,0.07112210744642121,0.015068243103055344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.72,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,RurLotSize,lot_size,A_structural,other,m2,-0.025,,-10.11,,,,,medium,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",other,-0.025,0.0024727992087042534,not harmonised,|b/t|,-0.15097605826683888,0.014933339096621055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.11,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,Age,age,A_structural,log,years,-0.204,,-11.75,,,,,medium,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",elasticity,-0.204,0.017361702127659574,b (log-log),|b/t|,-0.17476950115677747,0.014874000098449148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.75,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,Age2,age,A_structural,squared,years,0.018,,5.85,,,,,medium,Square of ln(Age),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",other,0.018,0.003076923076923077,not harmonised,|b/t|,0.08802993740429929,0.01504785254774347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.85,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,Garage,garage,A_structural,binary,binary,0.042,,7.19,,,,,medium,Garage dummy,ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",pct_premium,4.287668582086024,0.609201225417692,100*(exp(b - se^2/2) - 1),|b/t|,0.10798064802167871,0.015018170795782852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.19,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,LivingArea,living_area,A_structural,log,m2,0.511,,42.99,,,,,medium,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",elasticity,0.511,0.01188648522912305,b (log-log),|b/t|,0.5446512925740695,0.012669255468110477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.99,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,DistCBD,cbd_distance,D_location,log,minutes,-0.076,,-3.9,,,,,medium,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",elasticity,-0.076,0.019487179487179488,b (log-log),|b/t|,-0.058813361658477246,0.015080349143199294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,DistCBD2,cbd_distance,D_location,squared,minutes,-0.043,,-10.86,,,,,medium,Square of ln(DistCBD),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",other,-0.043,0.0039594843462246775,not harmonised,|b/t|,-0.16189240805256902,0.014907219894343371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.86,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,MCT,employment_access,D_location,log,minutes,0.393,,2.45,,,,,medium,ln of mean commuting time of residents of the zone (minutes),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",elasticity,0.393,0.16040816326530613,b (log-log),|b/t|,0.0369855985306437,0.015096162665568855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M4,2,M4,13,MCT2,employment_access,D_location,squared,minutes,-0.081,,-2.41,,,,,medium,Square of ln(MCT),ln real selling price (1998 NOK),log,OLS,4392.0,0.823,t,"M4 = M0 + MCT, MCT2; year dummies 2004-2007 included (not extracted)",other,-0.081,0.033609958506224065,not harmonised,|b/t|,-0.03638255795423675,0.015096497076446783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.41,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,LotSize,lot_size,A_structural,log,m2,-0.221,,-3.92,,,,,medium,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.221,0.056377551020408166,b (log-log),|b/t|,-0.05911391736854921,0.015080080961364593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.92,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,True
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,LotSize2,lot_size,A_structural,squared,m2,0.026,,5.59,,,,,medium,Square of ln(LotSize),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),other,0.026,0.004651162790697674,not harmonised,|b/t|,0.08414583733192955,0.015052922599629618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.59,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,RurLotSize,lot_size,A_structural,other,m2,-0.019,,-8.36,,,,,medium,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),other,-0.019,0.0022727272727272726,not harmonised,|b/t|,-0.12529509924378046,0.014987452062653166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.36,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,Age,age,A_structural,log,years,-0.196,,-11.47,,,,,medium,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.196,0.017088055797733218,b (log-log),|b/t|,-0.17072761151925334,0.01488470893803429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.47,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,Age2,age,A_structural,squared,years,0.016,,5.38,,,,,medium,Square of ln(Age),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),other,0.016,0.002973977695167286,not harmonised,|b/t|,0.08100586873520309,0.015056852924758939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.38,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,Garage,garage,A_structural,binary,binary,0.04,,7.06,,,,,medium,Garage dummy,ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),pct_premium,4.079406910011363,0.5896944896274154,100*(exp(b - se^2/2) - 1),|b/t|,0.10605044328766768,0.015021309247545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.06,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,LivingArea,living_area,A_structural,log,m2,0.504,,42.88,,,,,medium,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,0.504,0.011753731343283582,b (log-log),|b/t|,0.5436699678778335,0.012678870519539028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.88,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,DistCBD,cbd_distance,D_location,log,minutes,-0.171,,-7.4,,,,,medium,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,-0.171,0.023108108108108108,b (log-log),|b/t|,-0.11109608411504408,0.015012984339870823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.4,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,DistCBD2,cbd_distance,D_location,squared,minutes,-0.015,,-2.85,,,,,medium,Square of ln(DistCBD),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),other,-0.015,0.005263157894736842,not harmonised,|b/t|,-0.04301367413405034,0.015092517240017665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.85,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,ACCESS,employment_access,D_location,log,index,0.114,,7.06,,,,,medium,ln of gravity-based labour market accessibility measure (eq. 2),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,0.114,0.01614730878186969,b (log-log),|b/t|,0.10605044328766768,0.015021309247545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.06,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,MCT,employment_access,D_location,log,minutes,0.399,,2.29,,,,,medium,ln of mean commuting time of residents of the zone (minutes),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),elasticity,0.399,0.17423580786026202,b (log-log),|b/t|,0.034573200361178544,0.015097467406628184,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.29,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,True,True,True
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T2-M5,2,M5,13,MCT2,employment_access,D_location,squared,minutes,-0.054,,-1.45,,,,,medium,Square of ln(MCT),ln real selling price (1998 NOK),log,OLS,4392.0,0.826,t,M5 = M4 + ACCESS; year dummies 2004-2007 included (not extracted),other,-0.054,0.03724137931034483,not harmonised,|b/t|,-0.021899169878224347,0.015102875778085759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.45,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,ols,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,Lotsize,lot_size,A_structural,log,m2,-0.201,,-6.47,,,,,high,"ln(LotSize); lot size in m2; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.201,0.031066460587326122,b (log-log),|b/t|,-0.0972755173426374,0.015034855848939318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.47,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,LotSize2,lot_size,A_structural,squared,m2,0.025,,9.69,,,,,high,"Square of ln(LotSize); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.025,0.002579979360165119,not harmonised,|b/t|,0.14483842005784997,0.01494720537232714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.69,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,RurLotsize,lot_size,A_structural,other,m2,-0.028,,-8.89,,,,,high,"Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.028,0.0031496062992125984,not harmonised,|b/t|,-0.1331018484667277,0.014972086441701653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.89,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,Age,age,A_structural,log,years,-0.196,,-11.93,,,,,high,"ln(Age) of building (all continuous variables in logarithmic form); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.196,0.016429170159262364,b (log-log),|b/t|,-0.17736321449595122,0.014866991994631283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.93,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,Age2,age,A_structural,squared,years,0.016,,5.89,,,,,high,"Square of ln(Age); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.016,0.0027164685908319186,not harmonised,|b/t|,0.08862713953818813,0.015047052553172856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.89,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,Garage,garage,A_structural,binary,binary,0.04,,6.48,,,,,high,"Garage dummy; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,pct_premium,4.07909448816981,0.6424757865385112,100*(exp(b - se^2/2) - 1),|b/t|,0.09742443995522455,0.015034635795559342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.48,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,LivingArea,living_area,A_structural,log,m2,0.503,,52.91,,,,,high,"ln(LivingArea); printed as 'Size of house' in Table A.1; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.503,0.009506709506709508,b (log-log),|b/t|,0.624354352602825,0.01180030906450246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.91,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,DistCBD,cbd_distance,D_location,log,minutes,-0.081,,-3.45,,,,,high,"ln(travel time by car to CBD, minutes); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.081,0.023478260869565216,b (log-log),|b/t|,-0.05204678253968151,0.015086023924545363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.45,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,DistCBD2,cbd_distance,D_location,squared,minutes,-0.024,,-4.2,,,,,high,"Square of ln(DistCBD); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.024,0.005714285714285714,not harmonised,|b/t|,-0.06331997277683991,0.015076183994485692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.2,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,ACCESS,employment_access,D_location,log,index,0.084,,7.23,,,,,high,"ln of gravity-based labour market accessibility measure (eq. 2); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.084,0.011618257261410789,b (log-log),|b/t|,0.10857431261633571,0.01501719399949318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.23,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,IN-COM,employment_access,D_location,log,share,-0.073,,-4.67,,,,,high,"ln of share of jobs in zone held by in-commuters (observed in-commuting), zone level; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.073,0.01563169164882227,b (log-log),|b/t|,-0.07037244627395978,0.015069046311340423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.67,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-direct,3,M3 direct,17,OUT-COM,employment_access,D_location,log,share,0.04,,2.36,,,,,high,"ln of share of working population in zone commuting out (observed out-commuting), zone level; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.04,0.01694915254237288,b (log-log),|b/t|,0.035628701415409844,0.015096907379410954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.36,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,Lotsize,lot_size,A_structural,log,m2,-0.051,,-0.7,,,,,high,"ln(LotSize); lot size in m2; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.051,0.07285714285714286,b (log-log),|b/t|,-0.01057395780702276,0.015105654010032513,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,LotSize2,lot_size,A_structural,squared,m2,0.013,,2.01,,,,,high,"Square of ln(LotSize); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.013,0.006467661691542289,not harmonised,|b/t|,0.030350074288276543,0.015099539446903752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.01,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,RurLotsize,lot_size,A_structural,other,m2,-0.021,,-8.1,,,,,high,"Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.021,0.002592592592592593,not harmonised,|b/t|,-0.12145675391264861,0.014994660976870198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.1,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,Age,age,A_structural,log,years,-0.158,,-4.13,,,,,high,"ln(Age) of building (all continuous variables in logarithmic form); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.158,0.038256658595641646,b (log-log),|b/t|,-0.062268766384938126,0.015077183144052816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.13,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,Age2,age,A_structural,squared,years,0.012,,1.84,,,,,high,"Square of ln(Age); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.012,0.006521739130434782,not harmonised,|b/t|,0.027785225763158658,0.015100666175629704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.84,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,Garage,garage,A_structural,binary,binary,0.066,,4.7,,,,,high,"Garage dummy; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,pct_premium,6.812139878227286,1.5000630496373524,100*(exp(b - se^2/2) - 1),|b/t|,0.07082225736780139,0.015068565397404553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,LivingArea,living_area,A_structural,log,m2,0.466,,21.11,,,,,high,"ln(LivingArea); printed as 'Size of house' in Table A.1; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.466,0.02207484604452866,b (log-log),|b/t|,0.30382335147776945,0.014392389932627639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.11,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,DistCBD,cbd_distance,D_location,log,minutes,-0.153,,-4.86,,,,,high,"ln(travel time by car to CBD, minutes); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.153,0.03148148148148148,b (log-log),|b/t|,-0.07322051369806601,0.015065949320589715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.86,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,DistCBD2,cbd_distance,D_location,squared,minutes,-0.016,,-1.94,,,,,high,"Square of ln(DistCBD); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.016,0.008247422680412371,not harmonised,|b/t|,-0.029294029902833696,0.015100015413831802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.94,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,ACCESS,employment_access,D_location,log,index,0.12,,6.99,,,,,high,"ln of gravity-based labour market accessibility measure (eq. 2); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.12,0.017167381974248927,b (log-log),|b/t|,0.10501060408894254,0.015022976264512523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.99,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,IN-COM,employment_access,D_location,log,share,-0.136,,-6.18,,,,,high,"ln of share of jobs in zone held by in-commuters (observed in-commuting), zone level; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.136,0.022006472491909387,b (log-log),|b/t|,-0.09295395831506333,0.01504109357848921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.18,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,True
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M3-total,3,M3 total,17,OUT-COM,employment_access,D_location,log,share,0.073,,2.72,,,,,high,"ln of share of working population in zone commuting out (observed out-commuting), zone level; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M3; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.073,0.026838235294117642,b (log-log),|b/t|,0.04105503280920673,0.015093762062208357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.72,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,Lotsize,lot_size,A_structural,log,m2,-0.173,,-4.92,,,,,high,"ln(LotSize); lot size in m2; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.173,0.03516260162601626,b (log-log),|b/t|,-0.07411953470064267,0.015064946077366395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.92,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,LotSize2,lot_size,A_structural,squared,m2,0.024,,8.73,,,,,high,"Square of ln(LotSize); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.024,0.00274914089347079,not harmonised,|b/t|,0.13074763489314686,0.014976819575389102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.73,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,RurLotsize,lot_size,A_structural,other,m2,-0.041,,-3.92,,,,,high,"Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.041,0.010459183673469388,not harmonised,|b/t|,-0.05911391736854921,0.015080080961364593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.92,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,Age,age,A_structural,log,years,-0.161,,-9.39,,,,,high,"ln(Age) of building (all continuous variables in logarithmic form); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.161,0.01714589989350373,b (log-log),|b/t|,-0.14044409064275226,0.014956772166427289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.39,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,Age2,age,A_structural,squared,years,0.009,,2.95,,,,,high,"Square of ln(Age); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.009,0.0030508474576271183,not harmonised,|b/t|,0.044519985081507694,0.015091520366612777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.95,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,Garage,garage,A_structural,binary,binary,0.039,,6.78,,,,,high,"Garage dummy; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,pct_premium,3.9753281856929945,0.5980980658164624,100*(exp(b - se^2/2) - 1),|b/t|,0.10188903227875278,0.015027880867072678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.78,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,LivingArea,living_area,A_structural,log,m2,0.487,,53.64,,,,,high,"ln(LivingArea); printed as 'Size of house' in Table A.1; SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.487,0.009079045488441462,b (log-log),|b/t|,0.6295683756682009,0.011736919755186443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),53.64,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,DistCBD,cbd_distance,D_location,log,minutes,-0.036,,0.4,,,,,high,"ln(travel time by car to CBD, minutes); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.036,0.08999999999999998,b (log-log),|b/t|,0.0060424891069786706,0.015106222767446676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,DistCBD2,cbd_distance,D_location,squared,minutes,0.014,,0.37,,,,,high,"Square of ln(DistCBD); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.014,0.03783783783783784,not harmonised,|b/t|,0.005589317155662516,0.015106262582871665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,ACCESS,employment_access,D_location,log,index,0.161,,2.0,,,,,high,"ln of gravity-based labour market accessibility measure (eq. 2); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.161,0.0805,b (log-log),|b/t|,0.030199216944456812,0.015099608472228406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-direct,3,M5 direct,17,MCT,employment_access,D_location,log,minutes,0.247,,1.8,,,,,high,"ln of mean commuting time of residents of the zone (minutes); SDM direct impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; direct impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.247,0.13722222222222222,b (log-log),|b/t|,0.027181650351384177,0.015100916861880097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,Lotsize,lot_size,A_structural,log,m2,-0.161,,-1.84,,,,,high,"ln(LotSize); lot size in m2; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.161,0.0875,b (log-log),|b/t|,-0.027785225763158658,0.015100666175629704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.84,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,LotSize2,lot_size,A_structural,squared,m2,0.02,,2.64,,,,,high,"Square of ln(LotSize); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.02,0.007575757575757576,not harmonised,|b/t|,0.03984947833804226,0.015094499370470556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.64,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,RurLotsize,lot_size,A_structural,other,m2,-0.017,,-5.24,,,,,high,"Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.017,0.0032442748091603053,not harmonised,|b/t|,-0.07891120977394453,0.015059391178233688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.24,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,Age,age,A_structural,log,years,-0.285,,-6.22,,,,,high,"ln(Age) of building (all continuous variables in logarithmic form); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.285,0.04581993569131833,b (log-log),|b/t|,-0.09355035345480538,0.01504024975157643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.22,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,Age2,age,A_structural,squared,years,0.028,,4.13,,,,,high,"Square of ln(Age); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,0.028,0.006779661016949153,not harmonised,|b/t|,0.062268766384938126,0.015077183144052816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.13,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,Garage,garage,A_structural,binary,binary,0.048,,2.86,,,,,high,"Garage dummy; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,pct_premium,4.902290244079954,1.7608458550900208,100*(exp(b - se^2/2) - 1),|b/t|,0.04316431859963904,0.015092419090782886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.86,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,LivingArea,living_area,A_structural,log,m2,0.581,,20.94,,,,,high,"ln(LivingArea); printed as 'Size of house' in Table A.1; SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.581,0.02774594078319006,b (log-log),|b/t|,0.30160002452302365,0.014403057522589477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.94,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,DistCBD,cbd_distance,D_location,log,minutes,-0.139,,-4.23,,,,,high,"ln(travel time by car to CBD, minutes); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,-0.139,0.03286052009456265,b (log-log),|b/t|,-0.06377042549798478,0.015075750708743445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.23,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,DistCBD2,cbd_distance,D_location,squared,minutes,-0.019,,-2.66,,,,,high,"Square of ln(DistCBD); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,other,-0.019,0.007142857142857143,not harmonised,|b/t|,-0.04015088347725954,0.015094317096714114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.66,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,ACCESS,employment_access,D_location,log,index,0.109,,5.22,,,,,high,"ln of gravity-based labour market accessibility measure (eq. 2); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.109,0.02088122605363985,b (log-log),|b/t|,0.07861188678311787,0.015059748425884652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.22,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:T3-M5-total,3,M5 total,17,MCT,employment_access,D_location,log,minutes,0.125,,3.43,,,,,high,"ln of mean commuting time of residents of the zone (minutes); SDM total impact, z-value",ln real selling price (1998 NOK),log,other,4392.0,,t,Spatial Durbin model (k=3 nearest neighbours) variant of M5; total impact summary (LeSage-Pace); z-values in parentheses; MCT2 not reported in this table,elasticity,0.125,0.03644314868804664,b (log-log),|b/t|,0.05174587230763722,0.015086260147999192,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.43,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,other,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,Lotsize,lot_size,A_structural,log,m2,-0.187,,-6.11,,,,,high,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.187,0.03060556464811784,b (log-log),|b/t|,-0.09191002640275243,0.015042557512725438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.11,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,LotSize2,lot_size,A_structural,squared,m2,0.024,,9.55,,,,,high,Square of ln(LotSize),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.024,0.002513089005235602,not harmonised,|b/t|,0.1427887830079615,0.014951705027011673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.55,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,RurLotsize,lot_size,A_structural,other,m2,-0.025,,-8.76,,,,,high,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.025,0.002853881278538813,not harmonised,|b/t|,-0.13118921968775998,0.014975938320520545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.76,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,Age,age,A_structural,log,years,-0.1809,,-10.71,,,,,high,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.1809,0.016890756302521008,b (log-log),|b/t|,-0.15971375325810105,0.014912582003557518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.71,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,Age2,age,A_structural,squared,years,0.013,,4.64,,,,,high,Square of ln(Age),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.013,0.0028017241379310344,not harmonised,|b/t|,0.0699225922480563,0.015069524191391447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.64,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,Garage,garage,A_structural,binary,binary,0.039,,6.71,,,,,high,Garage dummy,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",pct_premium,3.9752921083118675,0.6043375389322825,100*(exp(b - se^2/2) - 1),|b/t|,0.10084783449875753,0.015029483531856562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.71,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,Size of house,living_area,A_structural,log,m2,0.493,,52.31,,,,,high,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.493,0.009424584209520168,b (log-log),|b/t|,0.6200054043700826,0.011852521589946137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.31,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,DistCBD,cbd_distance,D_location,log,minutes,-0.0574,,-2.58,,,,,high,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.0574,0.022248062015503875,b (log-log),|b/t|,-0.03894519797915314,0.015095037976415947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.58,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M0,A.1,M0,22,DistCBD2,cbd_distance,D_location,squared,minutes,-0.047,,-11.06,,,,,high,Square of ln(DistCBD),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M0 base; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.047,0.004249547920433997,not harmonised,|b/t|,-0.16479359770339408,0.014899963625984995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.06,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,Lotsize,lot_size,A_structural,log,m2,-0.204,,-6.61,,,,,high,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.204,0.030862329803328287,b (log-log),|b/t|,-0.09935983492987666,0.015031745072598586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.61,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,LotSize2,lot_size,A_structural,squared,m2,0.026,,9.98,,,,,high,Square of ln(LotSize),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.026,0.0026052104208416833,not harmonised,|b/t|,0.1490781436397604,0.014937689743462965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.98,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,RurLotsize,lot_size,A_structural,other,m2,-0.021,,-7.26,,,,,high,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.021,0.0028925619834710746,not harmonised,|b/t|,-0.10901948487608526,0.01501645797191257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.26,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,Age,age,A_structural,log,years,-0.178,,-10.51,,,,,high,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.178,0.01693625118934348,b (log-log),|b/t|,-0.15680525090912034,0.014919624253960073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.51,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,Age2,age,A_structural,squared,years,0.012,,4.43,,,,,high,Square of ln(Age),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.012,0.0027088036117381494,not harmonised,|b/t|,0.06677243458376998,0.015072784330422119,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.43,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,Garage,garage,A_structural,binary,binary,0.04,,6.82,,,,,high,Garage dummy,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",pct_premium,4.079287269819032,0.6104462018723684,100*(exp(b - se^2/2) - 1),|b/t|,0.10248385235746159,0.01502695782367472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.82,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,Size of house,living_area,A_structural,log,m2,0.491,,52.19,,,,,high,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.491,0.009407932554129144,b (log-log),|b/t|,0.6191286854609346,0.01186297538725684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.19,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,DistCBD,cbd_distance,D_location,log,minutes,-0.101,,-4.14,,,,,high,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.101,0.024396135265700485,b (log-log),|b/t|,-0.06241895149709479,0.015077041424419032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.14,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,DistCBD2,cbd_distance,D_location,squared,minutes,-0.029,,-5.07,,,,,high,Square of ln(DistCBD),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.029,0.005719921104536489,not harmonised,|b/t|,-0.07636629206835269,0.015062385023343726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.07,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M1,A.1,M1,22,ACCESS,employment_access,D_location,log,index,0.059,,4.36,,,,,high,ln of gravity-based labour market accessibility measure (eq. 2),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M1 = M0 + gravity accessibility; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.059,0.013532110091743118,b (log-log),|b/t|,0.06572193345224697,0.015073837947763066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.36,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,Lotsize,lot_size,A_structural,log,m2,-0.183,,-5.94,,,,,high,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.183,0.030808080808080805,b (log-log),|b/t|,-0.08937350774823243,0.015046045075460004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.94,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,LotSize2,lot_size,A_structural,squared,m2,0.024,,9.39,,,,,high,Square of ln(LotSize),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.024,0.0025559105431309905,not harmonised,|b/t|,0.14044409064275226,0.014956772166427289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.39,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,RurLotsize,lot_size,A_structural,other,m2,-0.026,,-8.86,,,,,high,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.026,0.0029345372460496616,not harmonised,|b/t|,-0.1326606037586339,0.014972980108197958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.86,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,Age,age,A_structural,log,years,-0.183,,-10.78,,,,,high,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.183,0.01697588126159555,b (log-log),|b/t|,-0.16073075130319373,0.014910088247049511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.78,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,Age2,age,A_structural,squared,years,0.013,,4.75,,,,,high,Square of ln(Age),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.013,0.0027368421052631577,not harmonised,|b/t|,0.07157184638933417,0.01506775713459667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,Garage,garage,A_structural,binary,binary,0.038,,6.59,,,,,high,Garage dummy,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",pct_premium,3.8713963927840656,0.5989648990801656,100*(exp(b - se^2/2) - 1),|b/t|,0.09906215468481133,0.015032193427133739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.59,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,Size of house,living_area,A_structural,log,m2,0.493,,52.31,,,,,high,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.493,0.009424584209520168,b (log-log),|b/t|,0.6200054043700826,0.011852521589946137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.31,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,DistCBD,cbd_distance,D_location,log,minutes,-0.04,,-1.58,,,,,high,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.04,0.02531645569620253,b (log-log),|b/t|,-0.02386147180842529,0.015102197347104612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.58,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,DistCBD2,cbd_distance,D_location,squared,minutes,-0.053,,-9.17,,,,,high,Square of ln(DistCBD),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.053,0.0057797164667393675,not harmonised,|b/t|,-0.13721628398297145,0.014963607849833308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.17,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,IN-COM,employment_access,D_location,log,share,-0.033,,-1.78,,,,,high,"ln of share of jobs in zone held by in-commuters (observed in-commuting), zone level",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.033,0.01853932584269663,b (log-log),|b/t|,-0.026879851455410934,0.01510104014348929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.78,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M2,A.1,M2,22,OUT-COM,employment_access,D_location,log,share,-0.006,,-0.22,,,,,high,"ln of share of working population in zone commuting out (observed out-commuting), zone level",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M2 = M0 + IN-COM, OUT-COM; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.006,0.027272727272727275,b (log-log),|b/t|,-0.0033234113275881047,0.015106415125400476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,Lotsize,lot_size,A_structural,log,m2,-0.202,,-6.56,,,,,high,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.202,0.03079268292682927,b (log-log),|b/t|,-0.09861558446975101,0.015032863486242533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.56,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,LotSize2,lot_size,A_structural,squared,m2,0.026,,9.99,,,,,high,Square of ln(LotSize),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.026,0.0026026026026026022,not harmonised,|b/t|,0.1492241958610427,0.014937356943047318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.99,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,RurLotsize,lot_size,A_structural,other,m2,-0.023,,-7.86,,,,,high,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.023,0.002926208651399491,not harmonised,|b/t|,-0.11790881931997389,0.015001122050887264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.86,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,Age,age,A_structural,log,years,-0.182,,-10.82,,,,,high,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.182,0.016820702402957487,b (log-log),|b/t|,-0.1613116634893092,0.014908656514723586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.82,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,Age2,age,A_structural,squared,years,0.013,,4.77,,,,,high,Square of ln(Age),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.013,0.0027253668763102725,not harmonised,|b/t|,0.07187164811850111,0.015067431471383883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.77,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,Garage,garage,A_structural,binary,binary,0.039,,6.7,,,,,high,Garage dummy,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",pct_premium,3.9752868618866177,0.6052395352590471,100*(exp(b - se^2/2) - 1),|b/t|,0.10069906482419604,0.015029711167790451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.7,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,Size of house,living_area,A_structural,log,m2,0.49,,52.27,,,,,high,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.49,0.009374402142720489,b (log-log),|b/t|,0.6197134223420983,0.01185600578423758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.27,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,DistCBD,cbd_distance,D_location,log,minutes,-0.105,,-3.93,,,,,high,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.105,0.026717557251908396,b (log-log),|b/t|,-0.05926418920039903,0.01507994636142469,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.93,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,DistCBD2,cbd_distance,D_location,squared,minutes,-0.028,,-3.96,,,,,high,Square of ln(DistCBD),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.028,0.007070707070707071,not harmonised,|b/t|,-0.05971498048234071,0.015079540525843616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.96,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,ACCESS,employment_access,D_location,log,index,0.088,,5.81,,,,,high,ln of gravity-based labour market accessibility measure (eq. 2),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.088,0.015146299483648882,b (log-log),|b/t|,0.08743264033200827,0.015048647217213128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.81,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,IN-COM,employment_access,D_location,log,share,-0.082,,-4.39,,,,,high,"ln of share of jobs in zone held by in-commuters (observed in-commuting), zone level",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.082,0.018678815489749433,b (log-log),|b/t|,-0.06617217518701116,0.01507338842528728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.39,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M3,A.1,M3,22,OUT-COM,employment_access,D_location,log,share,0.034,,1.65,,,,,high,"ln of share of working population in zone commuting out (observed out-commuting), zone level",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M3 = M2 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.034,0.02060606060606061,b (log-log),|b/t|,0.024917983144904877,0.015101807966609015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.65,4382.0,share,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,Lotsize,lot_size,A_structural,log,m2,-0.189,,-6.15,,,,,high,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.189,0.03073170731707317,b (log-log),|b/t|,-0.0925065962561703,0.015041722968482975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.15,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,LotSize2,lot_size,A_structural,squared,m2,0.024,,9.58,,,,,high,Square of ln(LotSize),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.024,0.0025052192066805845,not harmonised,|b/t|,0.14322814655805266,0.014950745987270631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.58,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,RurLotsize,lot_size,A_structural,other,m2,-0.025,,-8.73,,,,,high,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.025,0.00286368843069874,not harmonised,|b/t|,-0.13074763489314686,0.014976819575389102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.73,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,Age,age,A_structural,log,years,-0.182,,-10.78,,,,,high,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.182,0.016883116883116885,b (log-log),|b/t|,-0.16073075130319373,0.014910088247049511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.78,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,Age2,age,A_structural,squared,years,0.013,,4.75,,,,,high,Square of ln(Age),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.013,0.0027368421052631577,not harmonised,|b/t|,0.07157184638933417,0.01506775713459667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,Garage,garage,A_structural,binary,binary,0.039,,6.68,,,,,high,Garage dummy,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",pct_premium,3.975276298263397,0.6070516296759902,100*(exp(b - se^2/2) - 1),|b/t|,0.10040150521982046,0.015030165452068932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.68,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,Size of house,living_area,A_structural,log,m2,0.493,,52.47,,,,,high,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.493,0.009395845244901849,b (log-log),|b/t|,0.6211707606320215,0.011838588919992786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.47,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,DistCBD,cbd_distance,D_location,log,minutes,-0.074,,-2.96,,,,,high,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.074,0.024999999999999998,b (log-log),|b/t|,-0.0446705996472286,0.015091418799739393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.96,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,DistCBD2,cbd_distance,D_location,squared,minutes,-0.045,,-9.48,,,,,high,Square of ln(DistCBD),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.045,0.004746835443037974,not harmonised,|b/t|,-0.14176327346697482,0.014953931800313798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.48,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,MCT,employment_access,D_location,log,minutes,0.315,,1.48,,,,,high,ln of mean commuting time of residents of the zone (minutes),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.315,0.21283783783783783,b (log-log),|b/t|,0.022352032076768086,0.015102724376194655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.48,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M4,A.1,M4,22,MCT2,employment_access,D_location,squared,minutes,-0.063,,-1.44,,,,,high,Square of ln(MCT),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M4 = M0 + MCT, MCT2; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.063,0.043750000000000004,not harmonised,|b/t|,-0.021748212802661898,0.015102925557404093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.44,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,Lotsize,lot_size,A_structural,log,m2,-0.214,,-6.93,,,,,high,ln(LotSize); lot size in m2,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.214,0.03088023088023088,b (log-log),|b/t|,-0.10411903899371747,0.015024392351185783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.93,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,True
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,LotSize2,lot_size,A_structural,squared,m2,0.026,,10.3,,,,,high,Square of ln(LotSize),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.026,0.002524271844660194,not harmonised,|b/t|,0.15374692768940315,0.014926886183437198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.3,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,RurLotsize,lot_size,A_structural,other,m2,-0.019,,6.47,,,,,high,Interaction of rural-zone indicator with ln(LotSize) (definition not given in text; inferred from name); printed z-value 6.47 has sign inconsistent with coefficient (likely missing minus sign),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.019,0.002936630602782071,not harmonised,|b/t|,0.0972755173426374,0.015034855848939318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.47,4382.0,m2,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,Age,age,A_structural,log,years,-0.18,,-10.67,,,,,high,ln(Age) of building (all continuous variables in logarithmic form),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.18,0.016869728209934393,b (log-log),|b/t|,-0.15913238289330806,0.014914000271162893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.67,4382.0,years,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,Age2,age,A_structural,squared,years,0.013,,4.6,,,,,high,Square of ln(Age),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,0.013,0.002826086956521739,not harmonised,|b/t|,0.06932272056198764,0.01507015664391036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,4382.0,years,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,Garage,garage,A_structural,binary,binary,0.039,,6.67,,,,,high,Garage dummy,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",pct_premium,3.9752709807822084,0.6079617520593127,100*(exp(b - se^2/2) - 1),|b/t|,0.10025271530915254,0.01503039210032272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.67,4382.0,binary,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,Size of house,living_area,A_structural,log,m2,0.491,,52.36,,,,,high,ln(LivingArea); printed as 'Size of house' in Table A.1,ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.491,0.009377387318563789,b (log-log),|b/t|,0.6203700200046256,0.011848166921402324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.36,4382.0,m2,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,DistCBD,cbd_distance,D_location,log,minutes,-0.163,,-5.71,,,,,high,"ln(travel time by car to CBD, minutes)",ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,-0.163,0.028546409807355518,b (log-log),|b/t|,-0.0859389863936631,0.015050610576823659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.71,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,DistCBD2,cbd_distance,D_location,squared,minutes,-0.018,,-2.82,,,,,high,Square of ln(DistCBD),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.018,0.006382978723404255,not harmonised,|b/t|,-0.04256172317462028,0.01509280963639017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.82,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,ACCESS,employment_access,D_location,log,index,0.109,,6.07,,,,,high,ln of gravity-based labour market accessibility measure (eq. 2),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.109,0.017957166392092255,b (log-log),|b/t|,0.09131335756578327,0.015043386748893451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.07,4382.0,index,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,MCT,employment_access,D_location,log,minutes,0.306,,1.47,,,,,high,ln of mean commuting time of residents of the zone (minutes),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",elasticity,0.306,0.20816326530612245,b (log-log),|b/t|,0.022201079525581962,0.015102775187470722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.47,4382.0,minutes,,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,True,True,False
b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159,b8c37b8ea0a12e9cbed953c9880d16a2673b2159:TA1-M5,A.1,M5,22,MCT2,employment_access,D_location,squared,minutes,-0.035,,-0.8,,,,,high,Square of ln(MCT),ln real selling price (1998 NOK),log,spatial_error,4392.0,,t,"M5 = M4 + ACCESS; spatial error model (k=3 nearest neighbours), z-values in parentheses; lambda 0.36-0.37",other,-0.035,0.043750000000000004,not harmonised,|b/t|,-0.012084316404137072,0.015105395505171341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,4382.0,minutes,nonstandard_x_transform,2020.0,Norway,Europe,High income,2003.0,2007.0,single_family,transaction,journal,True,False,OLS,,2005.0,spatial,journal,False,True,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,parcelarea,lot_size,A_structural,level,m2,-1.24e-05,,,,*,,,high,"proportional land-plot area attributed to the apartment (ideal fraction), m2; '*' = p<0.01","ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,-0.00124,,100*b/1 (X per m2->m2),,,,,,32678.0,m2,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,parcelarea,lot_size,A_structural,level,m2,-1.26e-05,,,,*,,,high,"proportional land-plot area attributed to the apartment (ideal fraction), m2; '*' = p<0.01","ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,-0.00126,,100*b/1 (X per m2->m2),,,,,,32678.0,m2,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,income,income,F_neighbourhood,level,multiples of Brazilian minimum wage,0.012,,,,*,,,high,"mean income of head of family in the census sector, in minimum wages; '*' = p<0.01","ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,1.2,,100*b (unit as reported: multiples of brazilian minimum wage; no harmonised unit defined for this characteristic),,,,,,32678.0,multiples of brazilian minimum wage,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,income,income,F_neighbourhood,level,multiples of Brazilian minimum wage,0.0118,,,,*,,,high,"mean income of head of family in the census sector, in minimum wages; '*' = p<0.01","ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,1.18,,100*b (unit as reported: multiples of brazilian minimum wage; no harmonised unit defined for this characteristic),,,,,,32678.0,multiples of brazilian minimum wage,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,vertdens,density,F_neighbourhood,level,index,0.183,,,,*,,,high,"verticalization density (concentration of units in neighbourhood), 0-1; '*' = p<0.01","ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,18.3,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,32678.0,index,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,vertdens,density,F_neighbourhood,level,index,0.1919,,,,*,,,high,"verticalization density (concentration of units in neighbourhood), 0-1; '*' = p<0.01","ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,19.189999999999998,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,32678.0,index,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,distpv,neighbourhood_amenities,F_neighbourhood,level,m,-8.4e-06,,,0.0137,,,,high,nearest distance to main urban amenities; p-value printed in parentheses,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,-0.0008399999999999999,0.0003407660001455561,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.01363499305187313,0.005531359576546733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.46503465616053,32678.0,m,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,True,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,distpv,neighbourhood_amenities,F_neighbourhood,level,m,-1.09e-05,,,0.1556,,,,high,nearest distance to main urban amenities; p-value printed in parentheses,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,-0.00109,0.0007675913716108512,100*b (unit as reported: m; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.007855164427390407,0.005531703153256302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4200263842369005,32678.0,m,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,True,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,distassp,other,other,level,m,4.88e-05,,,,*,,,high,distance to the nearest precarious settlement (informal settlement); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,0.00488,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,32678.0,m,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,distassp,other,other,level,m,3.93e-05,,,,*,,,high,distance to the nearest precarious settlement (informal settlement); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,0.00393,,100*b (unit as reported: m; no harmonised unit defined for this characteristic),,,,,,32678.0,m,unit_not_harmonisable,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,distbm,water_proximity,E_environment,level,m,-1.34e-05,,,,*,,,high,distance to the beach (north of the city); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,-1.34,,100*b/0.001 (X per m->km),,,,,,32678.0,m,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,distbm,water_proximity,E_environment,level,m,-1.49e-05,,,,*,,,high,distance to the beach (north of the city); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,-1.49,,100*b/0.001 (X per m->km),,,,,,32678.0,m,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,elev,accessibility,B_interior,binary,binary,0.0372,,,,*,,,high,presence of elevator(s); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,3.79005801975687,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,elev,accessibility,B_interior,binary,binary,0.0488,,,,*,,,high,presence of elevator(s); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,5.001032767288693,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,pool,pool,C_exterior,binary,binary,0.1085,,,,*,,,high,; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,11.460490853834116,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,pool,pool,C_exterior,binary,binary,0.0938,,,,*,,,high,; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,9.834005593772055,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,nfloors,storeys,A_structural,level,count,0.0104,,,,*,,,high,total number of floors in the building; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,1.04,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,nfloors,storeys,A_structural,level,count,0.0103,,,,*,,,high,total number of floors in the building; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,1.03,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,standard3,condition,A_structural,dummy,binary,0.0989,,,,*,,,high,finishing standard 3 (economical); ordinal 1 rustic ... 8 luxury; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,10.39558984490736,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,standard3,condition,A_structural,dummy,binary,0.1833,,,,*,,,high,finishing standard 3 (economical); ordinal 1 rustic ... 8 luxury; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,20.117470644530755,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,standard4,condition,A_structural,dummy,binary,0.3631,,,,*,,,high,finishing standard 4 (simple); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,43.77796300155525,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,standard4,condition,A_structural,dummy,binary,0.3979,,,,*,,,high,finishing standard 4 (simple); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,48.86951529482588,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,standard5,condition,A_structural,dummy,binary,0.5058,,,,*,,,high,finishing standard 5 (medium); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,65.83116392540104,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,standard5,condition,A_structural,dummy,binary,0.5163,,,,*,,,high,finishing standard 5 (medium); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,67.58156466874301,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,standard6,condition,A_structural,dummy,binary,0.689,,,,*,,,high,finishing standard 6 (superior); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,99.17228142354028,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,standard6,condition,A_structural,dummy,binary,0.6667,,,,*,,,high,finishing standard 6 (superior); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,94.77989666047975,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,standard7,condition,A_structural,dummy,binary,0.8583,,,,*,,,high,finishing standard 7 (fine); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,135.91467333594846,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,standard7,condition,A_structural,dummy,binary,0.8574,,,,*,,,high,finishing standard 7 (fine); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,135.70244564673163,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,standard8,condition,A_structural,dummy,binary,1.1789,,,,*,,,high,finishing standard 8 (luxury); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,225.0796359441155,,100*(exp(b) - 1),,,,,,32678.0,binary,implausible_magnitude,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,standard8,condition,A_structural,dummy,binary,1.1219,,,,*,,,high,finishing standard 8 (luxury); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,207.06829618463996,,100*(exp(b) - 1),,,,,,32678.0,binary,implausible_magnitude,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,bedroom,bedrooms,A_structural,level,count,0.0084,,,0.0015,,,,high,; p-value printed in parentheses,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,0.84,0.26459330283965976,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.0175592410858308,0.005531020945545029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.174683527455075,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,True,True,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,bedroom,bedrooms,A_structural,level,count,0.0163,,,,*,,,high,; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,1.63,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,garage,garage,A_structural,level,count,0.0317,,,,*,,,high,number of garage spaces; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,3.17,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,garage,garage,A_structural,level,count,0.0305,,,,*,,,high,number of garage spaces; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,3.05,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,ln_privarea,living_area,A_structural,log,m2,0.368,,,,*,,,high,private (exclusive) area of the apartment; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,elasticity,0.368,,b (log-log),,,,,,32678.0,m2,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,ln_privarea,living_area,A_structural,log,m2,0.367,,,,*,,,high,private (exclusive) area of the apartment; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,elasticity,0.367,,b (log-log),,,,,,32678.0,m2,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,ln_totalarea,other,other,log,m2,0.3021,,,,*,,,high,ln_totalarea: total area incl. share of common areas (second area measure alongside private area); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,elasticity,0.3021,,b (log-log),,,,,,32678.0,m2,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,ln_totalarea,other,other,log,m2,0.3061,,,,*,,,high,ln_totalarea: total area incl. share of common areas (second area measure alongside private area); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,elasticity,0.3061,,b (log-log),,,,,,32678.0,m2,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,lastfloor,floor_level,A_structural,dummy,binary,0.0689,,,,*,,,high,top-floor dummy; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,7.132907088443385,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,lastfloor,floor_level,A_structural,dummy,binary,0.0799,,,,*,,,high,top-floor dummy; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,8.317874438444584,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,floor,floor_level,A_structural,level,count,0.0065,,,,*,,,high,apartment floor; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,0.65,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,floor,floor_level,A_structural,level,count,0.0065,,,,*,,,high,apartment floor; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,0.65,,100*b/1 (per unit count),,,,,,32678.0,count,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,age,age,A_structural,level,years,-0.0097,,,,*,,,high,; '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,semi_elasticity,-0.97,,100*b/1 (per year),,,,,,32678.0,years,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,age,age,A_structural,level,years,-0.0098,,,,*,,,high,; '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,semi_elasticity,-0.98,,100*b/1 (per year),,,,,,32678.0,years,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,sale,other,other,binary,binary,-0.0626,,,,*,,,high,sale: effective sale dummy (transaction vs other record types); '*' = p<0.01,"ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,-6.068087379589682,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,sale,other,other,binary,binary,-0.0759,,,,*,,,high,sale: effective sale dummy (transaction vs other record types); '*' = p<0.01,"ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,-7.309110718572631,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-MRA,A1,MRA (OLS),16,listing,other,other,binary,binary,0.0528,,,,*,,,high,"listing: asking-price dummy (1 = list price, 0 = sale price); '*' = p<0.01","ln(price, BRL)",log,OLS,32688.0,0.9386,p,Training set,pct_premium,5.421878027751914,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,ols,journal,False,False,False
b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7,b922ab81201881b47779769df9e76de174e13df7:TA1-SER,A1,SER (GMM),16,listing,other,other,binary,binary,0.0407,,,,*,,,high,"listing: asking-price dummy (1 = list price, 0 = sale price); '*' = p<0.01","ln(price, BRL)",log,spatial_error,32688.0,0.938,p,Training set; lambda = 0.6389*,pct_premium,4.153959679247277,,100*(exp(b) - 1),,,,,,32678.0,binary,,2025.0,Brazil,South America,Upper middle income,2017.0,2021.0,condo_apartment,transaction_and_listing,journal,True,False,unknown,MARS;M5P;random_forest;quantile_random_forest;GBM;LightGBM;CatBoost;XGBoost;deep_learning,2019.0,spatial,journal,False,False,False
ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f:EQ1-c1,text equation p.5,1,5,AGE_YR,age,A_structural,level,years,-0.515,,,,,,,medium,"age of the building at sale in years; USD per sqft; no SE/t printed, text says variables were statistically significant",PPSQFT00: sale price per square foot adjusted to 2000 value (USD/sqft),per_m2,OLS,145.0,0.622,none,"homogeneous single-family homes, 1997-2000",monetary,-0.515,,"b (currency per unit, as reported)",,,,,,135.0,years,,2003.0,United States,North America,High income,1997.0,2000.0,single_family,transaction,conference,False,False,unknown,"ANN (Jordan-Elman recurrent network) for price prediction, compared with hedonic OLS",1998.5,ols,other,False,False,False
ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f:EQ1-c1,text equation p.5,1,5,CONST_VENEER,roof_materials,A_structural,binary,binary,16.595,,,,,,,medium,"at least 85% of exterior is brick veneer; USD per sqft; no SE/t printed, text says variables were statistically significant",PPSQFT00: sale price per square foot adjusted to 2000 value (USD/sqft),per_m2,OLS,145.0,0.622,none,"homogeneous single-family homes, 1997-2000",monetary,16.595,,"b (currency per unit, as reported)",,,,,,135.0,binary,,2003.0,United States,North America,High income,1997.0,2000.0,single_family,transaction,conference,False,False,unknown,"ANN (Jordan-Elman recurrent network) for price prediction, compared with hedonic OLS",1998.5,ols,other,False,False,False
ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f:EQ1-c1,text equation p.5,1,5,CONST_FRAME,roof_materials,A_structural,binary,binary,15.734,,,,,,,medium,"asbestos / T111 plywood siding; USD per sqft; no SE/t printed, text says variables were statistically significant",PPSQFT00: sale price per square foot adjusted to 2000 value (USD/sqft),per_m2,OLS,145.0,0.622,none,"homogeneous single-family homes, 1997-2000",monetary,15.734,,"b (currency per unit, as reported)",,,,,,135.0,binary,,2003.0,United States,North America,High income,1997.0,2000.0,single_family,transaction,conference,False,False,unknown,"ANN (Jordan-Elman recurrent network) for price prediction, compared with hedonic OLS",1998.5,ols,other,False,False,False
ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f:EQ1-c1,text equation p.5,1,5,CONST_MIX,roof_materials,A_structural,binary,binary,18.323,,,,,,,medium,"combination of two siding materials; USD per sqft; no SE/t printed, text says variables were statistically significant",PPSQFT00: sale price per square foot adjusted to 2000 value (USD/sqft),per_m2,OLS,145.0,0.622,none,"homogeneous single-family homes, 1997-2000",monetary,18.323,,"b (currency per unit, as reported)",,,,,,135.0,binary,,2003.0,United States,North America,High income,1997.0,2000.0,single_family,transaction,conference,False,False,unknown,"ANN (Jordan-Elman recurrent network) for price prediction, compared with hedonic OLS",1998.5,ols,other,False,False,False
ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f,ba6d3043dcfb0e7c6ad45ae35c0629afbc728c5f:EQ1-c1,text equation p.5,1,5,CONST_STUCO,roof_materials,A_structural,binary,binary,37.642,,,,,,,medium,"stucco-paint / sheet sidings; USD per sqft; no SE/t printed, text says variables were statistically significant",PPSQFT00: sale price per square foot adjusted to 2000 value (USD/sqft),per_m2,OLS,145.0,0.622,none,"homogeneous single-family homes, 1997-2000",monetary,37.642,,"b (currency per unit, as reported)",,,,,,135.0,binary,,2003.0,United States,North America,High income,1997.0,2000.0,single_family,transaction,conference,False,False,unknown,"ANN (Jordan-Elman recurrent network) for price prediction, compared with hedonic OLS",1998.5,ols,other,False,False,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,SIZE,living_area,A_structural,level,m2,0.001,,,0.023,**,,,high,House size in square metres; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.001,0.0004398630941889216,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.16068842802473235,0.0706809091513126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.273434650942775,195.0,m2,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,True,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,BEDROOMS,bedrooms,A_structural,level,count,0.065,,,0.0,***,,,high,Number of bedrooms; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.065,,"b (currency per unit, as reported)",,,,,,195.0,count,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,False,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,BATHROOMS,bathrooms,A_structural,level,count,0.003,,,0.64,,,,high,Number of bathrooms; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.003,0.006414384654568036,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.033473837206710357,0.07157135590274383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46769879911450823,195.0,count,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,True,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,CENTER,cbd_distance,D_location,level,km,-0.02,,,0.0,***,,,high,Distance to the city centre in kilometres; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,-0.02,,"b (currency per unit, as reported)",,,,,,195.0,km,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,False,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,TYPE,garden,C_exterior,dummy,binary,0.048,,,0.036,**,,,high,1 if the house has a garden; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.048,0.022890634808057586,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.14849915303896227,0.0708174975316821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0969274291643423,195.0,binary,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,True,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,POOL,pool,C_exterior,dummy,binary,0.002,,,0.709,,,,high,1 if the house has amenities such as a hot-tub and/or swimming pool; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.002,0.005359064597922536,"b (currency per unit, as reported)",|b/z(p)| (two-sided normal),0.02671582911642714,0.07158592701099639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.37319945737830973,195.0,binary,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,True,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,PARK,neighbourhood_amenities,F_neighbourhood,dummy,binary,0.187,,,0.0,***,,,high,"1 if local amenities (parks, squares, schools or shopping malls) nearby; p-value column P>|z|; no SE printed",house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.187,,"b (currency per unit, as reported)",,,,,,195.0,binary,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,False,False
baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2,baaee624057dd67c85185a4177e6d00e5b13c4a2:T4-c1,4,1,8,STRUCTURE,condition,A_structural,dummy,binary,0.05,,,0.0,***,,,high,1 if the structure is new and modern; p-value column P>|z|; no SE printed,house price per square metre (PRICE),per_m2,OLS,205.0,0.8559,p,"All 205 surveyed houses; OLS with White-robust inference; F(8,196)=276.08",monetary,0.05,,"b (currency per unit, as reported)",,,,,,195.0,binary,,2020.0,Vietnam,Asia,Lower middle income,2019.0,2019.0,single_family,transaction,journal,False,False,robust,,2019.0,ols,journal,False,False,False
bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478:T5-c1,5,1,11,Area (b01),living_area,A_structural,level,m2,0.0088,0.0003,31.099,0.001,***,,,high,"Transferred (living) house area in m2, grand-mean centred; Level-2 house attribute","ln(sale price, NT$10,000)",log,other,7076.0,,se,"Non-randomly varying slope model; 7,076 repeat sales nested in 3,167 houses; robust SEs",semi_elasticity,0.88,0.03,100*b/1 (X per m2->m2),reported,0.3469792832424123,0.011157248890395583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.099,7066.0,m2,,2023.0,Taiwan,Asia,High income,2012.0,2020.0,condo_apartment,transaction,journal,False,False,robust,,2016.0,other,journal,True,True,False
bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478:T5-c1,5,1,11,Type1 (b02),dwelling_type,A_structural,dummy,binary,0.2395,0.0907,2.641,0.009,***,,,high,Luxury condominium (<=10 floors with elevator) =1; reference = condominium; grand-mean centred dummy,"ln(sale price, NT$10,000)",log,other,7076.0,,se,"Non-randomly varying slope model; 7,076 repeat sales nested in 3,167 houses; robust SEs",pct_premium,26.53980731381065,11.524466119556974,100*(exp(b - se^2/2) - 1),reported,0.031402725530403554,0.011890467826733643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.641,7066.0,binary,,2023.0,Taiwan,Asia,High income,2012.0,2020.0,condo_apartment,transaction,journal,False,False,robust,,2016.0,other,journal,True,True,False
bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478:T5-c1,5,1,11,Type2 (b03),dwelling_type,A_structural,dummy,binary,0.418,0.0698,5.987,0.001,***,,,high,Apartment building (>=11 floors with elevator) =1; reference = condominium; grand-mean centred dummy,"ln(sale price, NT$10,000)",log,other,7076.0,,se,"Non-randomly varying slope model; 7,076 repeat sales nested in 3,167 houses; robust SEs",pct_premium,51.52250563882224,10.602066307327636,100*(exp(b - se^2/2) - 1),reported,0.07104339199475038,0.011866275596250272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.987,7066.0,binary,,2023.0,Taiwan,Asia,High income,2012.0,2020.0,condo_apartment,transaction,journal,False,False,robust,,2016.0,other,journal,True,True,False
bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478:T5-c1,5,1,11,Location (b04),cbd_distance,D_location,dummy,binary,0.5478,0.0571,9.591,0.001,***,,,high,"CBD (13 central districts) =1, suburbs =0; grand-mean centred; positive sign = CBD premium (not a distance)","ln(sale price, NT$10,000)",log,other,7076.0,,se,"Non-randomly varying slope model; 7,076 repeat sales nested in 3,167 houses; robust SEs",pct_premium,72.66270010939455,9.87512554049523,100*(exp(b - se^2/2) - 1),reported,0.11336224286450437,0.011819647884944676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.591,7066.0,binary,,2023.0,Taiwan,Asia,High income,2012.0,2020.0,condo_apartment,transaction,journal,False,False,robust,,2016.0,other,journal,True,True,True
bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478:T5-c1,5,1,11,Age (b30),age,A_structural,level,years,-0.0211,0.003,-7.09,0.001,***,,,high,"House age at time of sale, grand-mean centred; Level-1 variable","ln(sale price, NT$10,000)",log,other,7076.0,,se,"Non-randomly varying slope model; 7,076 repeat sales nested in 3,167 houses; robust SEs",semi_elasticity,-2.11,0.3,100*b/1 (per year),reported,-0.08404658745464616,0.011854243646635567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.09,7066.0,years,,2023.0,Taiwan,Asia,High income,2012.0,2020.0,condo_apartment,transaction,journal,False,False,robust,,2016.0,other,journal,True,True,False
bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478,bbc85c0376ef4c6974813a189124a40708684478:T5-c1,5,1,11,Age2 (b40),age,A_structural,squared,years,0.0005,7e-05,6.925,0.001,***,,,high,Square of grand-mean-centred house age,"ln(sale price, NT$10,000)",log,other,7076.0,,se,"Non-randomly varying slope model; 7,076 repeat sales nested in 3,167 houses; robust SEs",other,0.0005,7e-05,not harmonised,reported,0.08210397843274411,0.011856170170793373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.925,7066.0,years,nonstandard_x_transform,2023.0,Taiwan,Asia,High income,2012.0,2020.0,condo_apartment,transaction,journal,False,False,robust,,2016.0,other,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,date,transaction_timing,G_market,level,month number (1-12) in 2021,82.713,7.158,,,,,,high,Month of transaction in 2021 as a linear trend; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",monetary,82.713,7.158,"b (currency per unit, as reported)",reported,0.24065623360646565,0.020826439860180155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.555322715842413,2172.0,month number in 2021,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,date,transaction_timing,G_market,level,month number (1-12) in 2021,102.824,7.028,,,,,,high,Month of transaction in 2021 as a linear trend; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",monetary,102.824,7.028,"b (currency per unit, as reported)",reported,0.29951771359184404,0.02047197630050844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.630620375640296,2172.0,month number in 2021,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,area,living_area,A_structural,level,m2,-21.749,1.162,,,,,,high,Usable floor area of the dwelling in m2; dependent variable is price per m2; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",monetary,-21.749,1.162,"b (currency per unit, as reported)",reported,-0.3726773308398977,0.01991130895378919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.716867469879517,2172.0,m2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,area,living_area,A_structural,level,m2,-21.072,1.101,,,,,,high,Usable floor area of the dwelling in m2; dependent variable is price per m2; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",monetary,-21.072,1.101,"b (currency per unit, as reported)",reported,-0.37988047007984144,0.019848538228829985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.138964577656676,2172.0,m2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,market,other,other,binary,binary,715.935,73.616,,,,,,high,"Market type: primary (new) = 0, secondary = 1; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",monetary,715.935,73.616,"b (currency per unit, as reported)",reported,0.20427526395879472,0.021004599344340805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.725263529667464,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,market,other,other,binary,binary,450.83,55.709,,,,,,high,"Market type: primary (new) = 0, secondary = 1; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",monetary,450.83,55.709,"b (currency per unit, as reported)",reported,0.17108301498583225,0.021140704216324847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.092588271194959,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,floor,floor_level,A_structural,other,index,46.24,23.775,,0.052,,,,high,"Floor location favourability: unfavourable = 1, average = 2, favourable = 3 (ordinal, entered linearly)","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",other,46.24,23.775,not harmonised,reported,0.04169553459830687,0.021438393924626854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9449001051524712,2172.0,index,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,floor,floor_level,A_structural,other,index,70.138,25.655,,0.006,,,,high,"Floor location favourability: unfavourable = 1, average = 2, favourable = 3 (ordinal, entered linearly)","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",other,70.138,25.655,not harmonised,reported,0.058560597063902216,0.021420230369762632,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.73389202884428,2172.0,index,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,high,storeys,A_structural,dummy,binary,116.94,67.993,,0.085,,,,high,"Building over five storeys = 1, up to five = 0 (building height dummy)","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",monetary,116.94,67.993,"b (currency per unit, as reported)",reported,0.036878517028505566,0.021442457741740886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.719882929125057,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,high,storeys,A_structural,dummy,binary,272.109,49.65,,,,,,high,"Building over five storeys = 1, up to five = 0 (building height dummy); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",monetary,272.109,49.65,"b (currency per unit, as reported)",reported,0.1167915468609905,0.021310211355185526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.4805438066465255,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,year,age,A_structural,other,decades since 1940,214.661,15.342,,,,,,high,"Year of construction expressed in decades since 1940 (construction period, not age; higher = newer); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",other,214.661,15.342,not harmonised,reported,0.28754218600522324,0.020550878909965643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.991722070134271,2172.0,decades since 1940,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,year,age,A_structural,other,decades since 1940,207.272,10.999,,,,,,high,"Year of construction expressed in decades since 1940 (construction period, not age; higher = newer); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",other,207.272,10.999,not harmonised,reported,0.374864679118146,0.01989239552674982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.844622238385305,2172.0,decades since 1940,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-SAR,4,Error SAR,7,POI,neighbourhood_amenities,F_neighbourhood,level,count per km2,0.823,0.178,,,,,,high,"Total density of all points of interest (POI per km2, kernel density, 200 m bandwidth) at the transaction location; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model (k=5 nearest-neighbour weights), total POI density; lambda 0.453, AIC 36254; SE and p-value printed in columns",monetary,0.823,0.178,"b (currency per unit, as reported)",reported,0.0987240873128744,0.021352232735955823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.623595505617978,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T4-OLS,4,OLS,7,POI,neighbourhood_amenities,F_neighbourhood,level,count per km2,0.492,0.109,,,,,,high,"Total density of all points of interest (POI per km2, kernel density, 200 m bandwidth) at the transaction location; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,"Multiple linear regression, total POI density; AIC 36457; SE and p-value printed in columns",monetary,0.492,0.109,"b (currency per unit, as reported)",reported,0.09640094230881065,0.02135711933264301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.513761467889908,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,date,transaction_timing,G_market,level,month number (1-12) in 2021,85.124,7.13,,,,,,high,Month of transaction in 2021 as a linear trend; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,85.124,7.13,"b (currency per unit, as reported)",reported,0.24815927416525024,0.02078586091816919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.938849929873772,2172.0,month number in 2021,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,date,transaction_timing,G_market,level,month number (1-12) in 2021,104.491,7.004,,,,,,high,Month of transaction in 2021 as a linear trend; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,104.491,7.004,"b (currency per unit, as reported)",reported,0.3048730474931569,0.02043554779494952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.918760708166763,2172.0,month number in 2021,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,area,living_area,A_structural,level,m2,-21.999,1.163,,,,,,high,Usable floor area of the dwelling in m2; dependent variable is price per m2; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,-21.999,1.163,"b (currency per unit, as reported)",reported,-0.3760795326771494,0.01988183537904108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.91573516766982,2172.0,m2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,area,living_area,A_structural,level,m2,-21.548,1.126,,,,,,high,Usable floor area of the dwelling in m2; dependent variable is price per m2; p-value printed as '< 0.001',"transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,-21.548,1.126,"b (currency per unit, as reported)",reported,-0.37984315042731587,0.019848867058713464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.136767317939608,2172.0,m2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,market,other,other,binary,binary,794.159,74.517,,,,,,high,"Market type: primary (new) = 0, secondary = 1; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,794.159,74.517,"b (currency per unit, as reported)",reported,0.2229224606641361,0.020917112317948204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.657420454392957,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,market,other,other,binary,binary,597.354,58.487,,,,,,high,"Market type: primary (new) = 0, secondary = 1; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,597.354,58.487,"b (currency per unit, as reported)",reported,0.21407023909814693,0.020959642145416818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.213449142544498,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,floor,floor_level,A_structural,other,index,47.938,23.671,,0.043,,,,high,"Floor location favourability: unfavourable = 1, average = 2, favourable = 3 (ordinal, entered linearly)","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",other,47.938,23.671,not harmonised,reported,0.04341339469394636,0.02143682393509125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0251784884457775,2172.0,index,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,floor,floor_level,A_structural,other,index,72.536,25.238,,0.004,,,,high,"Floor location favourability: unfavourable = 1, average = 2, favourable = 3 (ordinal, entered linearly)","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),other,72.536,25.238,not harmonised,reported,0.06155232901622752,0.0214163681442532,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8740787701085666,2172.0,index,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,high,storeys,A_structural,dummy,binary,103.195,67.361,,0.125,,,,high,"Building over five storeys = 1, up to five = 0 (building height dummy)","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,103.195,67.361,"b (currency per unit, as reported)",reported,0.03285380761934481,0.021445470565886783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5319695372693396,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,high,storeys,A_structural,dummy,binary,252.588,51.323,,,,,,high,"Building over five storeys = 1, up to five = 0 (building height dummy); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,252.588,51.323,"b (currency per unit, as reported)",reported,0.10501772564243275,0.021338403776690006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.921536153381525,2172.0,binary,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,year,age,A_structural,other,decades since 1940,227.864,15.581,,,,,,high,"Year of construction expressed in decades since 1940 (construction period, not age; higher = newer); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",other,227.864,15.581,not harmonised,reported,0.29940325129664547,0.020472747158186602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.624478531544831,2172.0,decades since 1940,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,year,age,A_structural,other,decades since 1940,229.437,11.984,,,,,,high,"Year of construction expressed in decades since 1940 (construction period, not age; higher = newer); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),other,229.437,11.984,not harmonised,reported,0.3799876745575631,0.019847593421714178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.145277036048064,2172.0,decades since 1940,nonstandard_x_transform,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,motorisation,other,other,level,count per km2,18.132,6.297,,0.003,,,,high,"POI density per km2: car dealership, car rental, car wash, parking","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,18.132,6.297,"b (currency per unit, as reported)",reported,0.06166727437235999,0.021416215901320916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8794664125774183,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,motorisation,other,other,level,count per km2,14.906,4.288,,,,,,high,"POI density per km2: car dealership, car rental, car wash, parking; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,14.906,4.288,"b (currency per unit, as reported)",reported,0.0743826533184268,0.021397612869275065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4762126865671643,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,culture,neighbourhood_amenities,F_neighbourhood,level,count per km2,6.568,4.176,,0.116,,,,high,"POI density per km2: culture and entertainment (cinema, library, museum, theatre, monuments...)","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,6.568,4.176,"b (currency per unit, as reported)",reported,0.03372838724255724,0.021444845481869524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5727969348659003,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,culture,neighbourhood_amenities,F_neighbourhood,level,count per km2,2.854,3.334,,0.392,,,,high,"POI density per km2: culture and entertainment (cinema, library, museum, theatre, monuments...)","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,2.854,3.334,"b (currency per unit, as reported)",reported,0.018364758195048132,0.021453435116429737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8560287942411517,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,education,other,other,level,count per km2,1.299,3.202,,0.685,,,,high,"POI density per km2: education (college, kindergarten, school, university); density, not distance","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,1.299,3.202,"b (currency per unit, as reported)",reported,0.008704452500287745,0.021456240882156554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.40568394753279197,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,education,other,other,level,count per km2,-2.394,2.423,,0.323,,,,high,"POI density per km2: education (college, kindergarten, school, university); density, not distance","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,-2.394,2.423,"b (currency per unit, as reported)",reported,-0.02119547952756211,0.021452233456676267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9880313660751135,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,finances,other,other,level,count per km2,1.337,3.395,,0.694,,,,high,"POI density per km2: ATM, bank","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,1.337,3.395,"b (currency per unit, as reported)",reported,0.0084497957946661,0.021456287750853707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3938144329896907,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,finances,other,other,level,count per km2,1.991,2.509,,0.428,,,,high,"POI density per km2: ATM, bank","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,1.991,2.509,"b (currency per unit, as reported)",reported,0.017024632333071023,0.021453944009882066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7935432443204464,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,gastronomy,neighbourhood_amenities,F_neighbourhood,level,count per km2,0.585,1.426,,0.681,,,,high,"POI density per km2: bar, cafe, fast food, pub, restaurant","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,0.585,1.426,"b (currency per unit, as reported)",reported,0.008802167026144742,0.021456222528687865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4102384291725105,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,gastronomy,neighbourhood_amenities,F_neighbourhood,level,count per km2,2.14,1.216,,0.079,,,,high,"POI density per km2: bar, cafe, fast food, pub, restaurant","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,2.14,1.216,"b (currency per unit, as reported)",reported,0.03773469725934008,0.02144177190063436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7598684210526316,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,health,other,other,level,count per km2,0.703,3.022,,0.816,,,,high,"POI density per km2: health (beauty shop, dentist, doctors, hospital, pharmacy...); density, not distance","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,0.703,3.022,"b (currency per unit, as reported)",reported,0.004991436429982851,0.021456786474264827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23262739907346128,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,health,other,other,level,count per km2,0.388,2.211,,0.861,,,,high,"POI density per km2: health (beauty shop, dentist, doctors, hospital, pharmacy...); density, not distance","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,0.388,2.211,"b (currency per unit, as reported)",reported,0.0037653902505653345,0.021456901659793696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1754862053369516,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,offices,other,other,level,count per km2,61.787,18.474,,0.001,,,,high,"POI density per km2: courthouse, public building, town hall","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,61.787,18.474,"b (currency per unit, as reported)",reported,0.07157985383672562,0.021402013688634654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3445382700010824,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,offices,other,other,level,count per km2,69.045,15.823,,,,,,high,"POI density per km2: courthouse, public building, town hall; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,69.045,15.823,"b (currency per unit, as reported)",reported,0.09322194640709665,0.02136361587369817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.3635846552486885,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,recreation,neighbourhood_amenities,F_neighbourhood,level,count per km2,1.125,0.197,,,,,,high,"POI density per km2: sport and recreation (park, playground, pitch, swimming pool, viewpoint...); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,1.125,0.197,"b (currency per unit, as reported)",reported,0.1216242705542305,0.021297761154829696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.710659898477157,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,recreation,neighbourhood_amenities,F_neighbourhood,level,count per km2,0.642,0.124,,,,,,high,"POI density per km2: sport and recreation (park, playground, pitch, swimming pool, viewpoint...); p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,0.642,0.124,"b (currency per unit, as reported)",reported,0.11041292590568924,0.021325861078357423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.17741935483871,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,religion,other,other,level,count per km2,-0.287,14.493,,0.984,,,,high,"POI density per km2: graveyard, wayside cross, wayside shrine","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,-0.287,14.493,"b (currency per unit, as reported)",reported,-0.0004249067740565833,0.02145705183415353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.019802663354722968,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,religion,other,other,level,count per km2,-18.441,10.246,,0.072,,,,high,"POI density per km2: graveyard, wayside cross, wayside shrine","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,-18.441,10.246,"b (currency per unit, as reported)",reported,-0.038590160872127764,0.021441070890722908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7998243216865115,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,security,public_services,F_neighbourhood,level,count per km2,-0.644,1.552,,0.678,,,,high,"POI density per km2: camera surveillance, fire station, police, prison","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,-0.644,1.552,"b (currency per unit, as reported)",reported,-0.008903218393313907,0.021456203332955254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.41494845360824745,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,security,public_services,F_neighbourhood,level,count per km2,0.012,1.242,,0.993,,,,high,"POI density per km2: camera surveillance, fire station, police, prison","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,0.012,1.242,"b (currency per unit, as reported)",reported,0.00020731452473467647,0.021457053310039016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.00966183574879227,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,services,commercial_distance,D_location,level,count per km2,-4.025,1.552,,0.009,,,,high,"POI density per km2: retail and services (shops, supermarket, mall, hairdresser...); density, not distance","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,-4.025,1.552,"b (currency per unit, as reported)",reported,-0.055561360673932345,0.02142390851327776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5934278350515467,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,services,commercial_distance,D_location,level,count per km2,-4.798,1.169,,,,,,high,"POI density per km2: retail and services (shops, supermarket, mall, hairdresser...); density, not distance; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,-4.798,1.169,"b (currency per unit, as reported)",reported,-0.08772798401973975,0.021374325410395117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.104362703165099,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,telecom,other,other,level,count per km2,10.426,3.479,,0.003,,,,high,"POI density per km2: post box, post office","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,10.426,3.479,"b (currency per unit, as reported)",reported,0.06417078423705357,0.021412829307568514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9968381718884736,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,telecom,other,other,level,count per km2,15.023,2.523,,,,,,high,"POI density per km2: post box, post office; p-value printed as '< 0.001'","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,15.023,2.523,"b (currency per unit, as reported)",reported,0.1267340974818425,0.02128403966895352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.954419342053111,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,tourism,other,other,level,count per km2,18.128,11.529,,0.118,,,,high,"POI density per km2: campsite, castle, hostel, hotel, tourist info","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,18.128,11.529,"b (currency per unit, as reported)",reported,0.033719513865909136,0.021444851906446738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5723826871367854,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,tourism,other,other,level,count per km2,10.477,8.697,,0.228,,,,high,"POI density per km2: campsite, castle, hostel, hotel, tourist info","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,10.477,8.697,"b (currency per unit, as reported)",reported,0.02584000090371072,0.021449889076985022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2046682764171555,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,False,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-SAR,5,Error SAR,7,other,hazard_site,E_environment,level,count per km2,-4.726,2.44,,0.053,,,,high,"POI density per km2: other (recycling, toilet, tower, wastebasket, wastewater plant)","transaction price per m2 of dwelling, PLN/m2",per_m2,spatial_error,2182.0,,none,"Spatial error model with 14 POI category densities; lambda 0.416, AIC 36233",monetary,-4.726,2.44,"b (currency per unit, as reported)",reported,-0.041524005769646044,0.021438547202271757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9368852459016395,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,spatial,journal,False,True,False
bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89,bc3577b4d2c3cc062519a4199d4f0a8834e89c89:T5-OLS,5,OLS,7,other,hazard_site,E_environment,level,count per km2,-5.022,1.782,,0.005,,,,high,"POI density per km2: other (recycling, toilet, tower, wastebasket, wastewater plant)","transaction price per m2 of dwelling, PLN/m2",per_m2,OLS,2182.0,,none,OLS with 14 POI category densities; AIC 36388 (decimal commas in source converted to points),monetary,-5.022,1.782,"b (currency per unit, as reported)",reported,-0.060359623818741985,0.02141793103245683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8181818181818183,2172.0,count per km2,,2023.0,Poland,Europe,High income,2021.0,2021.0,condo_apartment,transaction,journal,False,False,unknown,,2021.0,ols,journal,False,True,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Higher degree,education_level,F_neighbourhood,level,share,0.011,,,,‡,,,medium,Ratio (%) of residents aged 15+ with a higher degree; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,1.0999999999999999,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,61142.0,share,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Higher degree,education_level,F_neighbourhood,level,share,0.011,,,,‡,,,medium,Ratio (%) of residents aged 15+ with a higher degree; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,1.0999999999999999,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,61142.0,share,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Year,age,A_structural,other,years,0.011,,,,‡,,,medium,"Construction year of the apartment (year built, not age; positive sign = newer buildings dearer); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",other,0.011,,not harmonised,,,,,,61142.0,years,nonstandard_x_transform,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Year,age,A_structural,other,years,0.011,,,,‡,,,medium,"Construction year of the apartment (year built, not age; positive sign = newer buildings dearer); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",other,0.011,,not harmonised,,,,,,61142.0,years,nonstandard_x_transform,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Parking,parking,A_structural,level,count,0.168,,,,‡,,,medium,Parking spaces per household in the complex; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,16.8,,100*b/1 (per unit count),,,,,,61142.0,count,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Parking,parking,A_structural,level,count,0.168,,,,‡,,,medium,Parking spaces per household in the complex; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,16.8,,100*b/1 (per unit count),,,,,,61142.0,count,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Area,living_area,A_structural,level,m2,-0.002,,,,‡,,,medium,Unit size in m2 (negative coefficient as printed); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,-0.2,,100*b/1 (X per m2->m2),,,,,,61142.0,m2,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Area,living_area,A_structural,level,m2,-0.002,,,,‡,,,medium,Unit size in m2 (negative coefficient as printed); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,-0.2,,100*b/1 (X per m2->m2),,,,,,61142.0,m2,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Households,other,other,level,count,5.4e-05,,,,‡,,,medium,Number of households in the apartment complex; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.005399999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,61142.0,count,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Households,other,other,level,count,5.4e-05,,,,‡,,,medium,Number of households in the apartment complex; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.005399999999999999,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,61142.0,count,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,CBD,cbd_distance,D_location,level,m,-1e-05,,,,‡,,,medium,Network distance to city hall (CBD) in metres; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,-1.0,,100*b/0.001 (X per m->km),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,CBD,cbd_distance,D_location,level,m,-1e-05,,,,‡,,,medium,Network distance to city hall (CBD) in metres; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,-1.0,,100*b/0.001 (X per m->km),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Floor,floor_level,A_structural,level,count,0.005,,,,‡,,,medium,Floor level of the unit; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.5,,100*b/1 (per unit count),,,,,,61142.0,count,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Floor,floor_level,A_structural,level,count,0.005,,,,‡,,,medium,Floor level of the unit; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.5,,100*b/1 (per unit count),,,,,,61142.0,count,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Greenspace,green_space,E_environment,log,m,0.03,,,,‡,,,medium,"log network distance (m) to nearest park, hill or mountain (positive sign as printed); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",elasticity,0.03,,b (log-log),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Greenspace,green_space,E_environment,log,m,0.031,,,,‡,,,medium,"log network distance (m) to nearest park, hill or mountain (positive sign as printed); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",elasticity,0.031,,b (log-log),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Waterfront,water_proximity,E_environment,log,m,-0.02,,,,‡,,,medium,"log network distance (m) to nearest river, stream, pond or seashore; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",elasticity,-0.02,,b (log-log),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Waterfront,water_proximity,E_environment,log,m,-0.02,,,,‡,,,medium,"log network distance (m) to nearest river, stream, pond or seashore; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",elasticity,-0.02,,b (log-log),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Subway,transit_distance,D_location,log,m,-0.016,,,,‡,,,medium,log network distance (m) to nearest subway station; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",elasticity,-0.016,,b (log-log),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Subway,transit_distance,D_location,log,m,-0.016,,,,‡,,,medium,log network distance (m) to nearest subway station; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",elasticity,-0.016,,b (log-log),,,,,,61142.0,m,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Top school,school_quality,F_neighbourhood,level,count,-0.006,,,,‡,,,medium,Number of Seoul National University entrants from high schools within 5 km; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,-0.6,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,61142.0,count,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Top school,school_quality,F_neighbourhood,level,count,-0.006,,,,‡,,,medium,Number of Seoul National University entrants from high schools within 5 km; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,-0.6,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,61142.0,count,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Spring,transaction_timing,G_market,binary,binary,-0.03,,,,‡,,,medium,Seasonal dummy (March-May); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Spring,transaction_timing,G_market,binary,binary,-0.03,,,,‡,,,medium,Seasonal dummy (March-May); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Fall,transaction_timing,G_market,binary,binary,0.021,,,,‡,,,medium,Seasonal dummy (September-November); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Fall,transaction_timing,G_market,binary,binary,0.022,,,,‡,,,medium,Seasonal dummy (September-November); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,2.2243784470438177,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Winter,transaction_timing,G_market,binary,binary,-0.026,,,,‡,,,medium,Seasonal dummy (December-February); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,-2.566491039125063,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Winter,transaction_timing,G_market,binary,binary,-0.026,,,,‡,,,medium,Seasonal dummy (December-February); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,-2.566491039125063,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Pop. density,density,F_neighbourhood,level,per_km2,2e-06,,,,‡,,,medium,People per km2; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.00019999999999999998,,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),,,,,,61142.0,per_km2,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Pop. density,density,F_neighbourhood,level,per_km2,2e-06,,,,‡,,,medium,People per km2; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones),ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.00019999999999999998,,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),,,,,,61142.0,per_km2,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Heating type,heating,A_structural,binary,binary,-0.009,,,,‡,,,medium,"1 = city gas heating, 0 otherwise; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Heating type,heating,A_structural,binary,binary,-0.007,,,,‡,,,medium,"1 = city gas heating, 0 otherwise; '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",pct_premium,-0.6975557066764893,,100*(exp(b) - 1),,,,,,61142.0,binary,,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c1,6,1,16,Bus count,transit_distance,D_location,level,count,0.00058,,,,‡,,,medium,"Number of bus stops within 400 m (count, not distance); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,OLS,61152.0,,none,"OLS hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.058,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,61142.0,count,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,ols,journal,False,False,False
bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28,bc5c2c3c86f1f45909dcce75c8f09acff914dd28:T6-c2,6,2,16,Bus count,transit_distance,D_location,level,count,0.00057,,,,‡,,,medium,"Number of bus stops within 400 m (count, not distance); '‡' indicates significance at the 1% level; no SE or t printed; coefficients rounded to 3 decimals (scientific notation for small ones)",ln transacted condominium price,log,spatial_lag,61152.0,,none,"SLR hedonic price model, Busan condominium transactions 2015 (dataset of Song et al. 2021); Table 6 reports coefficients with '‡' = significant at 1% only, no SE/t; unclear whether estimated on the full sample (61,152) or the 70% training split; seasonal dummies included (Spring/Fall/Winter)",semi_elasticity,0.056999999999999995,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,61142.0,count,unit_not_harmonisable,2025.0,South Korea,Asia,High income,2015.0,2015.0,condo_apartment,transaction,journal,False,False,unknown,random forest;deep neural network;SHAP;Gini importance;permutation importance,2015.0,spatial,journal,False,False,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,FLAREA,other,other,level,"m2 (grand floor area of urban renewal project, Level 2)",0.001,0.001,8.803,,***,,,medium,Level-2 urban renewal project scale; coefficient rounded to 3 decimals,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,0.1,0.1,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.06348969729239526,0.007212279597000482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.803,19147.0,m2,unit_not_harmonisable,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,False,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,AREA,living_area,A_structural,level,ping (1 ping = 3.306 m2),0.001,0.001,6.831,,***,,,medium,registered house area; coefficient rounded to 3 decimals,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,0.03024986387561256,0.03024986387561256,100*b/3.3058 (X per pyeong->m2),reported,0.049306634224393256,0.0072180697151798074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.831,19147.0,pyeong,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,AGE,age,A_structural,level,years,-0.012,0.001,-18.253,,***,,,high,house age; squared term AGES also included,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,-1.2,0.1,100*b/1 (per year),reported,-0.13077895420039592,0.007164792319092529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.253,19147.0,years,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,AGES,age,A_structural,squared,years squared,0.0002,0.001,18.743,,***,,,medium,"square of house age; text says 0.001, table prints 0.0002",ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,other,0.0002,0.001,not harmonised,reported,0.13422726173200653,0.007161460904444676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.743,19147.0,years sqd,nonstandard_x_transform,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,ROOM,bedrooms,A_structural,level,count,-0.016,0.003,-5.233,,***,,,high,number of rooms (bedrooms),ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,-1.6,0.3,100*b/1 (per unit count),reported,-0.03779114245418392,0.00722169739235313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.233,19147.0,count,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,LIVROOM,other,other,level,count,0.024,0.005,4.312,,***,,,high,number of living rooms,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,2.4,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.03114710003269387,0.007223353439864069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.312,19147.0,count,unit_not_harmonisable,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,False,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,BATH,bathrooms,A_structural,level,count,0.049,0.005,10.971,,***,,,high,,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,4.9,0.5,100*b/1 (per unit count),reported,0.07903784254571243,0.007204251439769612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.971,19147.0,count,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,FLOOR1,floor_level,A_structural,dummy,binary,0.167,0.008,20.975,,***,,,high,dummy = 1 if house is on the first (ground) floor,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,pct_premium,18.171644977692345,0.9454034122466892,100*(exp(b - se^2/2) - 1),reported,0.14987132787731647,0.007145236132410797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.975,19147.0,binary,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,BUILDING,dwelling_type,A_structural,dummy,binary,0.197,0.007,29.409,,***,,,high,high-rise residential building vs apartment building (reference),ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,pct_premium,21.77142063423836,0.8524208284941358,100*(exp(b - se^2/2) - 1),reported,0.20789125052232202,0.007068967000657011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.409,19147.0,binary,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,HUAXIA,dwelling_type,A_structural,dummy,binary,0.174,0.006,28.168,,***,,,high,low-rise residential building (huaxia) vs apartment building (reference),ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,pct_premium,19.003414501296568,0.7140333394922176,100*(exp(b - se^2/2) - 1),reported,0.19947509686249643,0.007081620876970195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.168,19147.0,binary,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,PARKING,parking,A_structural,binary,binary,-0.065,0.006,-10.403,,***,,,high,availability of parking lot(s),ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,pct_premium,-6.294940368513336,0.5622404780264421,100*(exp(b - se^2/2) - 1),reported,-0.07496945070489841,0.007206522224829222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.403,19147.0,binary,,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,True,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,DISURBA,cbd_distance,D_location,level,m,-0.001,0.001,-6.099,,***,,,medium,distance to city centre (DISTURBA in Table 2); coefficient rounded to 3 decimals,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.04403386549987425,0.007219850057365839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.099,19147.0,m,implausible_magnitude,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,PRIM,school_distance,D_location,level,m,0.001,0.001,10.814,,***,,,medium,distance to nearest elementary school; rounded,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.07791369079915518,0.007204890956089807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.814,19147.0,m,implausible_magnitude,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,JUNI,school_distance,D_location,level,m,-0.001,0.001,-1.775,,*,,,medium,distance to nearest junior high school; rounded,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.012826620936135803,0.007226265316132846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.775,19147.0,m,implausible_magnitude,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,MRT,transit_distance,D_location,level,m,-0.001,0.001,-39.312,,***,,,medium,distance to nearest MRT station; rounded,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,-100.0,100.0,100*b/0.001 (X per m->km),reported,-0.27328724130963045,0.006951751152564879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.312,19147.0,m,implausible_magnitude,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,True,False
bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827,bd42cea5779904b39c488f5e59235573696a2827:T4-c1,4,1,10,TRAIN,transit_distance,D_location,level,m,0.001,0.001,5.638,,***,,,medium,distance to nearest railway station; rounded,ln housing transaction price,log,other,19157.0,,none,Full sample within 800 m of urban renewal projects; HLM random intercept; SE and t-ratio in separate columns,semi_elasticity,100.0,100.0,100*b/0.001 (X per m->km),reported,0.04071125617829592,0.007220868424671146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.638,19147.0,m,implausible_magnitude,2022.0,Taiwan,Asia,High income,2008.0,2016.0,mixed,transaction,journal,False,False,unknown,,2012.0,other,journal,False,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LogBLDAGE,age,A_structural,log,years,-0.163,,-3.454,,**,,,high,"building age, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.163,0.047191661841343366,b (log-log),|b/t|,-0.19310555692836473,0.05590780455366669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.454,308.0,years,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LogBFLOAREA,living_area,A_structural,log,m2,2.381,,10.627,,***,,,high,"building floor area, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,2.381,0.22405194316363974,b (log-log),|b/t|,0.5179694274130044,0.048740889000941416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.627,308.0,m2,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,NBEDROOM,bedrooms,A_structural,level,count,0.025,,0.576,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,semi_elasticity,2.5,4.340277777777779,100*b/1 (per unit count),|b/t|,0.032802983174141986,0.05694962356621873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.576,308.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,NBATROOM,bathrooms,A_structural,level,count,-0.114,,-0.953,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,semi_elasticity,-11.4,11.962224554039876,100*b/1 (per unit count),|b/t|,-0.054222330009594304,0.05689646380859844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.953,308.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,NFLOORS,storeys,A_structural,level,count,0.031,,3.966,,***,,,high,number of floors,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,semi_elasticity,3.1,0.7816439737771054,100*b/1 (per unit count),|b/t|,0.2204254836442602,0.05557879063143222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.966,308.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,GARAGE_YES,garage,A_structural,binary,binary,0.152,,3.468,,**,,,high,,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,pct_premium,16.304259315558145,5.102432408814333,100*(exp(b - se^2/2) - 1),|b/t|,0.19385890812964915,0.05589933913773043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.468,308.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,BLDCOND_Excellent,condition,A_structural,binary,binary,0.028,,0.892,,,,,high,building condition excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,pct_premium,2.788914922661556,3.2281478883183747,100*(exp(b - se^2/2) - 1),|b/t|,0.05076089345661183,0.05690683122938547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.892,308.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LSCAPQUA_Excellent,landscaping,C_exterior,binary,binary,0.028,,0.817,,,,,high,landscape quality excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,pct_premium,2.7791910041724766,3.5244894937331583,100*(exp(b - se^2/2) - 1),|b/t|,0.0465025332590807,0.0569186453599519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.817,308.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LogDISCOAST,water_proximity,E_environment,log,m,0.001,,0.009,,,,,high,"distance to coastline, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,0.001,0.11111111111111112,b (log-log),|b/t|,0.0005128225266355429,0.056980280737282556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.009,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LogDISTWORK,employment_access,D_location,log,m,-0.029,,-0.932,,,,,high,"distance to workplace, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.029,0.03111587982832618,b (log-log),|b/t|,-0.05303090219731193,0.05690010965376817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.932,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LogDISBSTOP,transit_distance,D_location,log,m,-0.437,,-1.22,,,,,high,"distance to bus stop, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.437,0.3581967213114754,b (log-log),|b/t|,-0.06934859118004497,0.05684310752462701,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.22,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1A,2,1A,54,LogDISTSCH,school_distance,D_location,log,m,-0.002,,-0.04,,,,,high,"distance to school, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.002,0.05,b (log-log),|b/t|,-0.0022792056091858783,0.05698014022964695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.04,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LogBLDAGE,age,A_structural,log,years,-0.098,,-1.3,,,,,high,"building age, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,-0.098,0.07538461538461538,b (log-log),|b/t|,-0.12412516364311554,0.09548089511008888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,108.0,years,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LogBFLOAREA,living_area,A_structural,log,m2,1.856,,6.04,,***,,,high,"building floor area, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,1.856,0.3072847682119205,b (log-log),|b/t|,0.5024937535082539,0.08319433005103541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.04,108.0,m2,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,NBEDROOM,bedrooms,A_structural,level,count,0.071,,1.083,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,semi_elasticity,7.1,6.555863342566943,100*b/1 (per unit count),|b/t|,0.10365041734664666,0.095706756552767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.083,108.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,NBATROOM,bathrooms,A_structural,level,count,0.105,,0.624,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,semi_elasticity,10.5,16.826923076923077,100*b/1 (per unit count),|b/t|,0.05993647971147509,0.09605205081967161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.624,108.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,NFLOORS,storeys,A_structural,level,count,0.01,,0.803,,,,,high,number of floors,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,semi_elasticity,1.0,1.2453300124533,100*b/1 (per unit count),|b/t|,0.07703907416269351,0.09593907118641783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.803,108.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,GARAGE_YES,garage,A_structural,binary,binary,0.059,,0.851,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,pct_premium,5.82289000613716,7.354375934626249,100*(exp(b - se^2/2) - 1),|b/t|,0.08161433526052428,0.09590403673387107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.851,108.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,BLDCOND_Excellent,condition,A_structural,binary,binary,-0.007,,-0.162,,,,,high,building condition excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,pct_premium,-0.7902159193335478,4.290846358353485,100*(exp(b - se^2/2) - 1),|b/t|,-0.015586563615672991,0.09621335565230241,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.162,108.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LSCAPQUA_Excellent,landscaping,C_exterior,binary,binary,0.039,,0.743,,,,,high,landscape quality excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,pct_premium,3.833908709060241,5.457745472726265,100*(exp(b - se^2/2) - 1),|b/t|,0.07131317968092271,0.09598005340635629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.743,108.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LogDISCOAST,water_proximity,E_environment,log,m,0.077,,0.359,,,,,high,"distance to coastline, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,0.077,0.21448467966573817,b (log-log),|b/t|,0.03452419765067697,0.09616768147820884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.359,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LogDISTWORK,employment_access,D_location,log,m,-0.048,,-0.852,,,,,high,"distance to workplace, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,-0.048,0.056338028169014086,b (log-log),|b/t|,-0.08170959937118119,0.09590328564692628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.852,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LogDISBSTOP,transit_distance,D_location,log,m,1.074,,0.952,,,,,high,"distance to bus stop, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,1.074,1.128151260504202,b (log-log),|b/t|,0.09122427881009469,0.09582382227951122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.952,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1B,2,1B,54,LogDISTSCH,school_distance,D_location,log,m,-0.073,,-0.653,,,,,high,"distance to school, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,-0.073,0.111791730474732,b (log-log),|b/t|,-0.06271127693434075,0.0960356461475356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.653,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LogBLDAGE,age,A_structural,log,years,-0.235,,-3.775,,***,,,high,"building age, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,elasticity,-0.235,0.06225165562913907,b (log-log),|b/t|,-0.26414066014268034,0.06997103579938552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.775,190.0,years,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LogBFLOAREA,living_area,A_structural,log,m2,2.672,,8.368,,***,,,high,"building floor area, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,elasticity,2.672,0.3193116634799235,b (log-log),|b/t|,0.51893795388882,0.06201457383948614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.368,190.0,m2,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,NBEDROOM,bedrooms,A_structural,level,count,0.029,,0.489,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,semi_elasticity,2.9000000000000004,5.930470347648263,100*b/1 (per unit count),|b/t|,0.03545348597947628,0.0725020163179474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.489,190.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,NBATROOM,bathrooms,A_structural,level,count,-0.252,,-1.471,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,semi_elasticity,-25.2,17.131203263086334,100*b/1 (per unit count),|b/t|,-0.10611501438625313,0.07213801113953305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.471,190.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,NFLOORS,storeys,A_structural,level,count,0.055,,4.682,,***,,,high,number of floors,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,semi_elasticity,5.5,1.1747116616830413,100*b/1 (per unit count),|b/t|,0.3216209163629306,0.0686930620168583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.682,190.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,GARAGE_YES,garage,A_structural,binary,binary,0.183,,3.124,,**,,,high,,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,pct_premium,19.875589557485608,7.034220124161076,100*(exp(b - se^2/2) - 1),|b/t|,0.22103316316951543,0.07075325325528663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.124,190.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,BLDCOND_Excellent,condition,A_structural,binary,binary,0.02,,0.46,,,,,high,building condition excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,pct_premium,1.9237522110731486,4.435658000116329,100*(exp(b - se^2/2) - 1),|b/t|,0.033353340129035396,0.07250726115007694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46,190.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LSCAPQUA_Excellent,landscaping,C_exterior,binary,binary,-0.031,,-0.679,,,,,high,landscape quality excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,pct_premium,-3.15342944846424,4.426177137755052,100*(exp(b - se^2/2) - 1),|b/t|,-0.04920018065425251,0.07245976532290502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.679,190.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LogDISCOAST,water_proximity,E_environment,log,m,0.233,,0.763,,,,,high,"distance to coastline, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,elasticity,0.233,0.3053735255570118,b (log-log),|b/t|,0.05526922887767511,0.07243673509524916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.763,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LogDISTWORK,employment_access,D_location,log,m,-0.065,,-1.51,,,,,high,"distance to workplace, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,elasticity,-0.065,0.04304635761589404,b (log-log),|b/t|,-0.108895460959624,0.07211619931100928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.51,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LogDISBSTOP,transit_distance,D_location,log,m,-0.345,,-0.718,,,,,high,"distance to bus stop, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,elasticity,-0.345,0.48050139275766013,b (log-log),|b/t|,-0.05201867184219125,0.0724494036799321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.718,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T2-1C,2,1C,54,LogDISTSCH,school_distance,D_location,log,m,0.023,,0.301,,,,,high,"distance to school, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.637,t,251-500 m from coastline,elasticity,0.023,0.07641196013289037,b (log-log),|b/t|,0.021831630570889247,0.07253033412255565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.301,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,LogBLDAGE,age,A_structural,log,years,-0.162,,-3.44,,**,,,high,"building age, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.162,0.04709302325581396,b (log-log),|b/t|,-0.19235186405281957,0.05591623955023825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.44,308.0,years,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,LogBFLOAREA,living_area,A_structural,log,m2,2.385,,10.622,,***,,,high,"building floor area, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,2.385,0.2245339860666541,b (log-log),|b/t|,0.5177910732345553,0.04874704135139853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.622,308.0,m2,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,NBEDROOM,bedrooms,A_structural,level,count,0.025,,0.569,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,semi_elasticity,2.5,4.393673110720563,100*b/1 (per unit count),|b/t|,0.032404756988303286,0.05695036377557696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.569,308.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,NBATROOM,bathrooms,A_structural,level,count,-0.117,,-0.986,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,semi_elasticity,-11.700000000000001,11.866125760649087,100*b/1 (per unit count),|b/t|,-0.05609410397212255,0.056890571979840314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.986,308.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,NFLOORS,storeys,A_structural,level,count,0.032,,3.975,,***,,,high,number of floors,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,semi_elasticity,3.2,0.8050314465408804,100*b/1 (per unit count),|b/t|,0.22090131014844505,0.055572656641118254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.975,308.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,GARAGE_YES,garage,A_structural,binary,binary,0.152,,3.484,,**,,,high,,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,pct_premium,16.305283009155836,5.07899988340072,100*(exp(b - se^2/2) - 1),|b/t|,0.19471946111113103,0.05588962718459559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.484,308.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,BLDCOND_Excellent,condition,A_structural,binary,binary,0.028,,0.891,,,,,high,building condition excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,pct_premium,2.7888011865609297,3.231770949921426,100*(exp(b - se^2/2) - 1),|b/t|,0.050704133009561704,0.056906995521393604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.891,308.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,LSCAPQUA_Excellent,landscaping,C_exterior,binary,binary,0.028,,0.841,,,,,high,landscape quality excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,pct_premium,2.782586929248554,3.4239095319619386,100*(exp(b - se^2/2) - 1),|b/t|,0.04786549554683916,0.056914976869012084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.841,308.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,Log(DISCOAST*FLODRATE),other,other,log,index,-0.02,,-0.239,,,,,high,interaction: log of distance to coastline x flood rate (coastal disamenity-adjusted distance),"LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.02,0.08368200836820085,b (log-log),|b/t|,-0.013617026253652595,0.056975005245408356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.239,308.0,index,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,LogDISTWORK,employment_access,D_location,log,m,-0.028,,-0.898,,,,,high,"distance to workplace, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.028,0.031180400890868598,b (log-log),|b/t|,-0.05110144579039009,0.05690584163740545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.898,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,LogDISBSTOP,transit_distance,D_location,log,m,-0.494,,-1.545,,,,,high,"distance to bus stop, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.494,0.3197411003236246,b (log-log),|b/t|,-0.0876953780412515,0.056760762486246924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.545,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2A,3,2A,55,LogDISTSCH,school_distance,D_location,log,m,-0.004,,-0.062,,,,,high,"distance to school, log","LogRENT (annual rent, Naira)",log,OLS,318.0,0.571,t,0-500 m from coastline,elasticity,-0.004,0.06451612903225806,b (log-log),|b/t|,-0.0035327558250036134,0.0569799326613486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.062,308.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,LogBLDAGE,age,A_structural,log,years,-0.101,,-1.318,,,,,high,"building age, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,-0.101,0.07663125948406677,b (log-log),|b/t|,-0.12581679653480743,0.09546039190804811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.318,108.0,years,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,LogBFLOAREA,living_area,A_structural,log,m2,1.863,,6.147,,***,,,high,"building floor area, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,1.863,0.30307467057101023,b (log-log),|b/t|,0.5091032686865636,0.08282141999130689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.147,108.0,m2,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,NBEDROOM,bedrooms,A_structural,level,count,0.073,,1.116,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,semi_elasticity,7.3,6.541218637992831,100*b/1 (per unit count),|b/t|,0.1067732602220089,0.09567496435663879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.116,108.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,NBATROOM,bathrooms,A_structural,level,count,0.103,,0.612,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,semi_elasticity,10.299999999999999,16.83006535947712,100*b/1 (per unit count),|b/t|,0.05878787750522922,0.09605862337455755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.612,108.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,NFLOORS,storeys,A_structural,level,count,0.009,,0.75,,,,,high,number of floors,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,semi_elasticity,0.8999999999999999,1.2,100*b/1 (per unit count),|b/t|,0.07198157507486945,0.09597543343315927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,108.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,GARAGE_YES,garage,A_structural,binary,binary,0.058,,0.833,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,pct_premium,5.7149337467431005,7.378567797262508,100*(exp(b - se^2/2) - 1),|b/t|,0.07989920121147924,0.09591740841714194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.833,108.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,BLDCOND_Excellent,condition,A_structural,binary,binary,-0.006,,-0.14,,,,,high,building condition excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,pct_premium,-0.689449053814073,4.260076988802579,100*(exp(b - se^2/2) - 1),|b/t|,-0.013470284032991461,0.09621631452136757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14,108.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,LSCAPQUA_Excellent,landscaping,C_exterior,binary,binary,0.044,,0.859,,,,,high,landscape quality excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,pct_premium,4.361237828533282,5.3526453568209,100*(exp(b - se^2/2) - 1),|b/t|,0.08237638529738632,0.09589800383863366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.859,108.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,Log(DISCOAST*FLODRATE),other,other,log,index,0.047,,0.387,,,,,high,interaction: log of distance to coastline x flood rate (coastal disamenity-adjusted distance),"LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,0.047,0.12144702842377261,b (log-log),|b/t|,0.03721329853116273,0.09615839413737139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.387,108.0,index,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,LogDISTWORK,employment_access,D_location,log,m,-0.051,,-0.91,,,,,high,"distance to workplace, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,-0.051,0.05604395604395604,b (log-log),|b/t|,-0.08723100354319238,0.09585824565185977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.91,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,LogDISBSTOP,transit_distance,D_location,log,m,1.004,,1.049,,,,,high,"distance to bus stop, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,1.004,0.9571020019065778,b (log-log),|b/t|,0.10042973454491917,0.0957385458006856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.049,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2B,3,2B,55,LogDISTSCH,school_distance,D_location,log,m,-0.079,,-0.751,,,,,high,"distance to school, log","LogRENT (annual rent, Naira)",log,OLS,118.0,0.553,t,0-250 m from coastline,elasticity,-0.079,0.1051930758988016,b (log-log),|b/t|,-0.07207705223642487,0.09597476995529278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.751,108.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,LogBLDAGE,age,A_structural,log,years,-0.237,,-3.78,,***,,,high,"building age, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,elasticity,-0.237,0.0626984126984127,b (log-log),|b/t|,-0.26446606069986733,0.06996456632271623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.78,190.0,years,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,LogBFLOAREA,living_area,A_structural,log,m2,2.712,,8.395,,***,,,high,"building floor area, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,elasticity,2.712,0.3230494341870161,b (log-log),|b/t|,0.5201598448630973,0.0619606724077543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.395,190.0,m2,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,NBEDROOM,bedrooms,A_structural,level,count,0.017,,0.294,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,semi_elasticity,1.7000000000000002,5.782312925170069,100*b/1 (per unit count),|b/t|,0.021324151845840966,0.07253112872735024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.294,190.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,NBATROOM,bathrooms,A_structural,level,count,-0.293,,-1.696,,,,,high,,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,semi_elasticity,-29.299999999999997,17.275943396226413,100*b/1 (per unit count),|b/t|,-0.1221198561898335,0.07200463218740183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.696,190.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,True
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,NFLOORS,storeys,A_structural,level,count,0.053,,4.602,,***,,,high,number of floors,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,semi_elasticity,5.3,1.1516731855714906,100*b/1 (per unit count),|b/t|,0.31668089426888,0.06881375364382443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.602,190.0,count,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,GARAGE_YES,garage,A_structural,binary,binary,0.198,,3.478,,**,,,high,,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,pct_premium,21.69887003542208,6.939463886621198,100*(exp(b - se^2/2) - 1),|b/t|,0.2446528046137066,0.07034295704821926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.478,190.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,BLDCOND_Excellent,condition,A_structural,binary,binary,0.022,,0.517,,,,,high,building condition excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,pct_premium,2.1318677036345823,4.349973550938035,100*(exp(b - se^2/2) - 1),|b/t|,0.03748076772005881,0.07249664936181588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.517,190.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,LSCAPQUA_Excellent,landscaping,C_exterior,binary,binary,-0.028,,-0.597,,,,,high,landscape quality excellent dummy,"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,pct_premium,-2.868053633073875,4.560615455684241,100*(exp(b - se^2/2) - 1),|b/t|,-0.043270367071651133,0.07247967683693658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.597,190.0,binary,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,Log(DISCOAST*FLODRATE),other,other,log,index,-0.085,,-0.319,,,,,high,interaction: log of distance to coastline x flood rate (coastal disamenity-adjusted distance),"LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,elasticity,-0.085,0.2664576802507837,b (log-log),|b/t|,-0.023136497436519673,0.07252820513015572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.319,190.0,index,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,False,False,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,LogDISTWORK,employment_access,D_location,log,m,-0.052,,-1.241,,,,,high,"distance to workplace, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,elasticity,-0.052,0.0419016921837228,b (log-log),|b/t|,-0.0896689218816425,0.07225537621405519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.241,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,LogDISBSTOP,transit_distance,D_location,log,m,-0.687,,-1.525,,,,,high,"distance to bus stop, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,elasticity,-0.687,0.4504918032786886,b (log-log),|b/t|,-0.10996418694095503,0.07210766356783938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.525,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd,c0719dfc2e99247adf55e3df25480bbce55099dd:T3-2C,3,2C,55,LogDISTSCH,school_distance,D_location,log,m,0.015,,0.195,,,,,high,"distance to school, log","LogRENT (annual rent, Naira)",log,OLS,200.0,0.635,t,251-500 m from coastline,elasticity,0.015,0.07692307692307691,b (log-log),|b/t|,0.014145371480193528,0.07254036656509501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.195,190.0,m,,2022.0,Nigeria,Africa,Lower middle income,,,rental,rent,journal,False,False,unknown,,2019.0,ols,journal,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Coal Dummy_0.75,hazard_site,E_environment,dummy,binary,-0.154,,,0.0,***,,,high,1 if located within 0.75 mile of an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-14.27279789885425,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Coal Dummy_0.75,hazard_site,E_environment,dummy,binary,-0.162,,,0.0,***,,,high,1 if located within 0.75 mile of an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-14.955879545976703,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Coal Dummy_0.75~1.00,hazard_site,E_environment,dummy,binary,-0.105,,,0.0,***,,,high,1 if located 0.75-1.00 mile from an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-9.967547741373439,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Coal Dummy_0.75~1.00,hazard_site,E_environment,dummy,binary,-0.11,,,0.02,**,,,high,1 if located 0.75-1.00 mile from an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-10.516676527520364,4.235899367511711,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.08931572570675653,0.038393108229173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Coal Dummy_1.00~1.25,hazard_site,E_environment,dummy,binary,-0.093,,,0.01,**,,,high,1 if located 1.00-1.25 miles from an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-8.940020487372868,3.2898529188555874,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.09880507326491056,0.038358548498838754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Coal Dummy_1.00~1.25,hazard_site,E_environment,dummy,binary,-0.099,,,0.01,**,,,high,1 if located 1.00-1.25 miles from an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-9.49260218226674,3.481151797240162,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.09880507326491056,0.038358548498838754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.575829303548901,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Coal Dummy_1.25~1.50,hazard_site,E_environment,dummy,binary,-0.02,,,0.57,,,,high,1 if located 1.25-1.50 miles from an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-2.0408669797326695,3.451090882333435,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.021891528575072924,0.03853792946429167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5680514983389829,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Coal Dummy_1.25~1.50,hazard_site,E_environment,dummy,binary,-0.021,,,0.57,,,,high,1 if located 1.25-1.50 miles from an active coal mine,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-2.1449940695919434,3.6200235922425805,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.021891528575072924,0.03853792946429167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5680514983389829,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Building size,living_area,A_structural,level,thousand_sqft,0.214,,,0.0,***,,,high,Size of the house in thousands of square feet (Table 2.2); quadratic term also included,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,21.4,,100*b (unit as reported: thousand_sqft; area unit not harmonisable),,,,,,673.0,thousand_sqft,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Building size,living_area,A_structural,level,thousand_sqft,0.225,,,0.0,***,,,high,Size of the house in thousands of square feet (Table 2.2); quadratic term also included,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,22.5,,100*b (unit as reported: thousand_sqft; area unit not harmonisable),,,,,,673.0,thousand_sqft,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Building size squared,living_area,A_structural,squared,thousand_sqft,-0.02,,,0.0,***,,,high,Square of building size (thousands of sq ft),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),other,-0.02,,not harmonised,,,,,,673.0,thousand_sqft,nonstandard_x_transform,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Building size squared,living_area,A_structural,squared,thousand_sqft,-0.021,,,0.0,***,,,high,Square of building size (thousands of sq ft),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,other,-0.021,,not harmonised,,,,,,673.0,thousand_sqft,nonstandard_x_transform,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Lot size,lot_size,A_structural,level,ten_thousand_sqft,0.004,,,0.03,**,,,high,Lot size in tens of thousands of square feet (Table 2.2); quadratic term also included,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,0.4,0.18432412038305232,100*b (unit as reported: ten_thousand_sqft; area unit not harmonisable),|b/z(p)| (two-sided normal),0.08335969194966315,0.038413004735059667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1700903775845606,673.0,ten_thousand_sqft,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Lot size,lot_size,A_structural,level,ten_thousand_sqft,0.004,,,0.03,**,,,high,Lot size in tens of thousands of square feet (Table 2.2); quadratic term also included,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,0.4,0.18432412038305232,100*b (unit as reported: ten_thousand_sqft; area unit not harmonisable),|b/z(p)| (two-sided normal),0.08335969194966315,0.038413004735059667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1700903775845606,673.0,ten_thousand_sqft,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Lot size squared,lot_size,A_structural,squared,ten_thousand_sqft,-0.0,,,0.18,,,,high,Square of lot size (tens of thousands of sq ft); printed as -0.000,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),other,-0.0,,not harmonised,,,,,,673.0,ten_thousand_sqft,nonstandard_x_transform,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Lot size squared,lot_size,A_structural,squared,ten_thousand_sqft,-0.0,,,0.18,,,,high,Square of lot size (tens of thousands of sq ft); printed as -0.000,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,other,-0.0,,not harmonised,,,,,,673.0,ten_thousand_sqft,nonstandard_x_transform,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Age,age,A_structural,level,hundred_years,-0.105,,,0.16,,,,high,Age of house at sale in hundreds of years (Table 2.2); quadratic term also included,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,-0.105,0.07472928992802427,100*b/100 (hundred years->year),|b/z(p)| (two-sided normal),-0.05408226210850041,0.038490752810185984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4050715603096329,673.0,hundred_years,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Age,age,A_structural,level,hundred_years,-0.11,,,0.17,,,,high,Age of house at sale in hundreds of years (Table 2.2); quadratic term also included,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,-0.11,0.08016301899079056,100*b/100 (hundred years->year),|b/z(p)| (two-sided normal),-0.052820729447756835,0.038493355798435824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3722038089987258,673.0,hundred_years,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Age squared,age,A_structural,squared,hundred_years,0.028,,,0.67,,,,high,Square of age (hundreds of years),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),other,0.028,0.0657048712766218,not harmonised,|b/z(p)| (two-sided normal),0.016424582667843673,0.03854196749867508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42614800784127815,673.0,hundred_years,nonstandard_x_transform,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Age squared,age,A_structural,squared,hundred_years,0.075,,,0.36,,,,high,Square of age (hundreds of years),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,other,0.075,0.08193452098631815,not harmonised,|b/z(p)| (two-sided normal),0.03526278664012094,0.03852319376001398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.915365087842814,673.0,hundred_years,nonstandard_x_transform,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Excellent Condition,condition,A_structural,dummy,binary,0.459,,,0.0,***,,,high,Assessor condition = excellent; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,58.24907027825334,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Excellent Condition,condition,A_structural,dummy,binary,0.482,,,0.0,***,,,high,Assessor condition = excellent; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,61.93097853019272,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Good Condition,condition,A_structural,dummy,binary,0.337,,,0.0,***,,,high,Assessor condition = good; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,40.07390637385353,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Good Condition,condition,A_structural,dummy,binary,0.355,,,0.0,***,,,high,Assessor condition = good; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,42.618065428148014,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Average Condition,condition,A_structural,dummy,binary,0.249,,,0.0,***,,,high,Assessor condition = average; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,28.27420330698114,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Average Condition,condition,A_structural,dummy,binary,0.262,,,0.0,***,,,high,Assessor condition = average; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,29.95265424293818,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Fair Condition,condition,A_structural,dummy,binary,-0.026,,,0.75,,,,high,Assessor condition = fair; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-2.8903109145449135,7.950277082733494,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.012281718451325313,0.0385442598758716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31863936396437514,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Fair Condition,condition,A_structural,dummy,binary,-0.027,,,0.75,,,,high,Assessor condition = fair; omitted category = poor condition,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-3.0126894365061885,8.247805040213224,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.012281718451325313,0.0385442598758716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31863936396437514,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Bedrooms,bedrooms,A_structural,level,count,0.01,,,0.3,,,,high,Number of bedrooms,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,1.0,0.964847341022481,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.03991972538786661,0.03851644089483072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0364333894937894,673.0,count,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Bedrooms,bedrooms,A_structural,level,count,0.01,,,0.3,,,,high,Number of bedrooms,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,1.0,0.964847341022481,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.03991972538786661,0.03851644089483072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0364333894937894,673.0,count,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Bathrooms,bathrooms,A_structural,level,count,0.029,,,0.01,**,,,high,Number of bathrooms,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,2.9000000000000004,1.1258510010754463,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.09880507326491056,0.038358548498838754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,673.0,count,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Bathrooms,bathrooms,A_structural,level,count,0.03,,,0.01,**,,,high,Number of bathrooms,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,3.0,1.1646734493883928,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.09880507326491056,0.038358548498838754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,673.0,count,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Heating,heating,A_structural,dummy,binary,0.044,,,0.33,,,,high,1 if presence of a heating system,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,4.391687966720381,4.720107648388637,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.03752288709348223,0.03852002109523141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.974113877059309,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Heating,heating,A_structural,dummy,binary,0.046,,,0.33,,,,high,1 if presence of a heating system,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,4.590759687603696,4.944537187933578,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.03752288709348223,0.03852002109523141,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.974113877059309,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Fireplace,fireplace,B_interior,dummy,binary,0.028,,,0.06,*,,,high,1 if presence of a fireplace,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,2.8281727693970193,1.5310068600299198,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.07230948028286521,0.03844626011566611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.880793608151251,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Fireplace,fireplace,B_interior,dummy,binary,0.028,,,0.06,*,,,high,1 if presence of a fireplace,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,2.8281727693970193,1.5310068600299198,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.07230948028286521,0.03844626011566611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.880793608151251,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Full Basement,basement,A_structural,dummy,binary,-0.01,,,0.62,,,,high,1 if full basement; omitted category = crawl space,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-1.0151483810479922,1.9966706467897628,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.01911013581660229,0.03854012792132097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.49585034734745326,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Full Basement,basement,A_structural,dummy,binary,-0.011,,,0.62,,,,high,1 if full basement; omitted category = crawl space,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-1.118306680330572,2.194142471560161,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.01911013581660229,0.03854012792132097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.49585034734745326,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Part Basement,basement,A_structural,dummy,binary,-0.004,,,0.86,,,,high,1 if part basement; omitted category = crawl space,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,-0.42481209450055957,2.258852345141356,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.006798567301808662,0.03854627637928514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.17637416478086135,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Part Basement,basement,A_structural,dummy,binary,-0.004,,,0.86,,,,high,1 if part basement; omitted category = crawl space,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,-0.42481209450055957,2.258852345141356,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.006798567301808662,0.03854627637928514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.17637416478086135,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,No Basement,basement,A_structural,dummy,binary,0.049,,,0.06,*,,,high,1 if no basement; omitted category = crawl space (sign reversed relative to basement presence),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,4.986399265098074,2.736121441674049,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.07230948028286521,0.03844626011566611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.880793608151251,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,No Basement,basement,A_structural,dummy,binary,0.052,,,0.06,*,,,high,1 if no basement; omitted category = crawl space (sign reversed relative to basement presence),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,5.297321577655056,2.912363077655142,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0723094802828652,0.03844626011566611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8807936081512509,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,True,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Urbanized Area Dummy,other,other,dummy,binary,0.058,,,0.0,***,,,high,1 if located in a Census urbanized area (2000 Census),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,5.97149957102876,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Urbanized Area Dummy,other,other,dummy,binary,0.061,,,0.0,***,,,high,1 if located in a Census urbanized area (2000 Census),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,6.289891418719518,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Lake Dummy,water_proximity,E_environment,dummy,binary,0.102,,,0.0,***,,,high,1 if located within 0.10 mile of Cheat Lake,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),pct_premium,10.73834717279334,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Lake Dummy,water_proximity,E_environment,dummy,binary,0.122,,,0.0,***,,,high,1 if located within 0.10 mile of Cheat Lake,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,pct_premium,12.975410178031876,,100*(exp(b) - 1),,,,,,673.0,binary,,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,Median Income,income,F_neighbourhood,level,ten_thousand_USD,-0.004,,,0.54,,,,high,Census block-group median household income (1999) in tens of thousands of dollars,log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,-0.4,0.6527276768993087,100*b (unit as reported: ten_thousand_usd; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.02361561690280984,0.03853641689878778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6128129910166272,673.0,ten_thousand_usd,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,Median Income,income,F_neighbourhood,level,ten_thousand_USD,-0.005,,,0.54,,,,high,Census block-group median household income (1999) in tens of thousands of dollars,log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,-0.5,0.8159095961241359,100*b (unit as reported: ten_thousand_usd; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.02361561690280984,0.03853641689878778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6128129910166272,673.0,ten_thousand_usd,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,White,demographics,F_neighbourhood,level,share,0.074,,,0.62,,,,high,Census block-group share of white population (0-1),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,7.3999999999999995,14.923857650975195,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.019110135816602293,0.03854012792132097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49585034734745337,673.0,share,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,White,demographics,F_neighbourhood,level,share,0.078,,,0.62,,,,high,Census block-group share of white population (0-1),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,7.8,15.730552659136018,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.01911013581660229,0.03854012792132097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4958503473474533,673.0,share,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,35,College Graduates,education_level,F_neighbourhood,level,share,-0.03,,,0.66,,,,high,Census block-group share of population with a college degree (0-1),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,-3.0,6.819527657029457,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.016954968805304577,0.03854162623061542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43991316567323385,673.0,share,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,35,College Graduates,education_level,F_neighbourhood,level,share,-0.032,,,0.66,,,,high,Census block-group share of population with a college degree (0-1),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,-3.2,7.274162834164756,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.016954968805304574,0.03854162623061542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4399131656732338,673.0,share,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,True,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-direct,2.4,Direct Effects,36,Poverty,other,other,level,share,-0.036,,,0.56,,,,high,Census block-group share of population below the poverty line (0-1); printed on p.36 (table continuation),log sale price of house,log,spatial_lag,683.0,0.5,p,All 683 single-family sales 2004-2005; direct (own-house) effects of the Bayesian heteroskedastic SAR model (2 nearest neighbours),semi_elasticity,-3.5999999999999996,6.17663628119882,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.022461220986032104,0.03853744235065284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5828415072712162,673.0,share,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964,c0c750e0ff9e1fcc59888eee28b0b7208f559964:T2.4-total,2.4,Total Effects,36,Poverty,other,other,level,share,-0.036,,,0.56,,,,high,Census block-group share of population below the poverty line (0-1); printed on p.36 (table continuation),log sale price of house,log,spatial_lag,683.0,0.5,p,Same SAR estimation as T2.4-direct; total effects = direct + indirect (spatial spillover) effects,semi_elasticity,-3.5999999999999996,6.17663628119882,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.022461220986032104,0.03853744235065284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5828415072712162,673.0,share,unit_not_harmonisable,2010.0,United States,North America,High income,2004.0,2005.0,single_family,transaction,thesis,False,False,robust,,2004.5,spatial,other,False,False,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Distance to Metro Station (m),transit_distance,D_location,level,m,0.0,,-2.152,0.047,,,,low,B and SE both printed .000 (rounded to 3 decimals); negative per t=-2.152 and standardized beta -0.379; magnitude unrecoverable,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,semi_elasticity,0.0,0.0,100*b/0.001 (X per m->km),|b/t|,-0.49856636016093997,0.23167581791865235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.152,14.0,m,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,False,False,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Site/ Apartment,dwelling_type,A_structural,dummy,binary,0.14,0.072,1.945,0.07,,,,high,Located in a gated site complex vs apartment building,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,pct_premium,14.729614987077788,8.281971351772036,100*(exp(b - se^2/2) - 1),reported,0.4612291913596902,0.23713583103325978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.945,14.0,binary,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Square Meter (m2),living_area,A_structural,level,m2,0.008,0.004,2.099,0.052,,,,high,Total residential area in m2,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,semi_elasticity,0.8,0.4,100*b/1 (X per m2->m2),reported,0.4892547800992092,0.233089461695669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.099,14.0,m2,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Number of Rooms,bedrooms,A_structural,level,count,0.018,0.099,0.184,0.856,,,,high,Number of rooms incl. living rooms,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,semi_elasticity,1.7999999999999998,9.9,100*b/1 (per unit count),reported,0.04911671524809719,0.2669386698266152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.184,14.0,count,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Floor,floor_level,A_structural,level,count,0.021,0.01,2.015,0.061,,,,high,,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,semi_elasticity,2.1,1.0,100*b/1 (per unit count),reported,0.47414750780755527,0.23530893687719862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.015,14.0,count,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Building Age (years),age,A_structural,level,years,-0.05,0.043,-1.153,0.266,,,,high,,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,semi_elasticity,-5.0,4.3,100*b/1 (per year),reported,-0.29448732117523385,0.25540964542518113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.153,14.0,years,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,True
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T3-Batikent,3,1,11,Distance to Bus Stop (m),transit_distance,D_location,level,m,0.002,0.001,2.854,0.011,,,,high,Walking distance to bus stop in metres; positive sign,"ln(house asking price, TL)",log,OLS,24.0,0.535,se,24 for-sale listings within 1500 m of Batikent metro station (middle-lower income area); distance to school dropped from the printed table,semi_elasticity,200.0,100.0,100*b/0.001 (X per m->km),reported,0.606475174172114,0.21250006102737004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.854,14.0,m,implausible_magnitude,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Distance to Metro Station(M),transit_distance,D_location,level,m,-1.345e-05,,-0.09,0.93,,,,medium,SE printed .000 (rounded); beta printed -1.345E-5,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,-1.345,14.944444444444445,100*b/0.001 (X per m->km),|b/t|,-0.03180370859932566,0.3533745399925073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09,8.0,m,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Site/ Apartment,dwelling_type,A_structural,dummy,binary,-0.168,0.164,-1.027,0.331,,,,high,Located in a gated site complex vs apartment building,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),pct_premium,-16.593838462015242,13.863802888828404,100*(exp(b - se^2/2) - 1),reported,-0.34129719202735964,0.3323244323538069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.027,8.0,binary,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Square Meter (M2),living_area,A_structural,level,m2,0.004,0.001,6.015,0.0,,,,high,Total residential area in m2,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,0.4,0.1,100*b/1 (X per m2->m2),reported,0.9049439273201987,0.1504478682161594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.015,8.0,m2,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Number of Rooms,bedrooms,A_structural,level,count,0.344,0.099,3.484,0.007,,,,high,Number of rooms incl. living rooms,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,34.4,9.9,100*b/1 (per unit count),reported,0.7763672690856761,0.22283790731506203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.484,8.0,count,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Floor,floor_level,A_structural,level,count,0.028,0.009,3.105,0.013,,,,high,,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,2.8000000000000003,0.8999999999999999,100*b/1 (per unit count),reported,0.7392642365409504,0.23808832094716598,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.105,8.0,count,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Building Age (Years),age,A_structural,level,years,0.029,0.055,0.526,0.612,,,,high,,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,2.9000000000000004,5.5,100*b/1 (per year),reported,0.1828343393638654,0.34759380107198745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.526,8.0,years,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,True,True,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Distance to Bus Stop (M),transit_distance,D_location,level,m,0.0,,0.319,0.757,,,,low,B and SE printed .000 (rounded); magnitude unrecoverable,"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,0.0,0.0,100*b/0.001 (X per m->km),|b/t|,0.11207299275643114,0.351325996101665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.319,8.0,m,,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,False,False,False
c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662,c15ba8a0a6017224eaf4694459bf962179074662:T5-Koru,5,1,12,Distance to School(M),school_distance,D_location,level,m,-0.001,,-1.613,0.141,,,,medium,SE printed .000 (rounded),"ln(house asking price, TL)",log,OLS,18.0,0.932,se,18 for-sale listings within 1500 m of Koru metro station (upper-middle income area),semi_elasticity,-100.0,61.99628022318661,100*b/0.001 (X per m->km),|b/t|,-0.4953876766208945,0.3071219321890233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.613,8.0,m,implausible_magnitude,2023.0,Turkey,Asia,Upper middle income,2022.0,2022.0,condo_apartment,listing,journal,False,False,OLS,,2022.0,ols,journal,False,True,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Apartment surface (m2),living_area,A_structural,level,m2,-0.00347,,,,****,,,high,Apartment surface in m2 (price per m2 model). Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,-0.00347,,"b (currency per unit, as reported)",,,,,,212.0,m2,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Energy class A,energy_efficiency,A_structural,binary,binary,0.265,,,,**,,,high,EPC class A (A1-A4) vs classes B-G. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.265,,"b (currency per unit, as reported)",,,,,,212.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Age of the apartment,age,A_structural,level,years,-0.0166,,,,****,,,high,Age in years; quadratic. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,-0.0166,,"b (currency per unit, as reported)",,,,,,212.0,years,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Squared age of the apartment,age,A_structural,squared,years,0.000125,,,,****,,,high,Age squared. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",other,0.000125,,not harmonised,,,,,,212.0,years,nonstandard_x_transform,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Year in which the heating system was installed,heating,A_structural,other,years,0.00691,,,,***,,,high,"Calendar year of installation of the heating system (level in years, not a dummy). Dependent variable is log price per m2; only stars printed (**** p<0.001).",log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",other,0.00691,,not harmonised,,,,,,212.0,years,nonstandard_x_transform,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Bathrooms number,bathrooms,A_structural,level,count,0.173,,,,***,,,high,Number of bathrooms. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.173,,"b (currency per unit, as reported)",,,,,,212.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Elevator,accessibility,B_interior,binary,binary,0.161,,,,****,,,high,Elevator present. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.161,,"b (currency per unit, as reported)",,,,,,212.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Train station distance (Km),transit_distance,D_location,level,km,-0.123,,,,**,,,high,Distance to train station in km. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,-0.123,,"b (currency per unit, as reported)",,,,,,212.0,km,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Number of schools within a radius 200 m,school_distance,D_location,level,count,0.122,,,,**,,,high,Count of schools within 200 m (not a distance). Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.122,,"b (currency per unit, as reported)",,,,,,212.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Number of supermarkets within a radius 200 m,commercial_distance,D_location,level,count,0.0965,,,,***,,,high,Count of supermarkets within 200 m (not a distance). Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.0965,,"b (currency per unit, as reported)",,,,,,212.0,count,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Urban parks area (ha) within a radius of 100 m,green_space,E_environment,level,ha,0.0541,,,,***,,,high,Park area in hectares within 100 m; printed as '0. 0541'. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.0541,,"b (currency per unit, as reported)",,,,,,212.0,ha,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Urban parks area (ha) between 100 and 200 m,green_space,E_environment,level,ha,0.0393,,,,**,,,high,Park area in hectares between 100 and 200 m; printed as '0. 0393'. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.0393,,"b (currency per unit, as reported)",,,,,,212.0,ha,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Waterway distance (Km),water_proximity,E_environment,level,km,-0.366,,,,****,,,high,Distance to waterway in km; printed as '-0. 366'. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,-0.366,,"b (currency per unit, as reported)",,,,,,212.0,km,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7,c457882224ad7235eecc5a3f2b95232d76a155e7:T3-c1,3,1,9,Mediation,other,other,binary,binary,0.1186,,,,**,,,high,Sold through a real estate agency. Dependent variable is log price per m2; only stars printed (**** p<0.001).,log of apartment price per square meter (EUR/m2),per_m2,OLS,222.0,0.551,none,"All 222 apartment sales, Padua 2022-2023; R2 0.551 (adj. 0.521)",monetary,0.1186,,"b (currency per unit, as reported)",,,,,,212.0,binary,,2025.0,Italy,Europe,High income,2022.0,2023.0,condo_apartment,transaction,journal,False,False,unknown,,2022.5,ols,journal,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Bedroom,bedrooms,A_structural,level,count,-3.358,0.868,-3.869,0.0,,-5.077,-1.639,medium,"Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,-3.358,0.868,"b (currency per unit, as reported)",reported,,,,-3.869,,count,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Space,living_area,A_structural,level,sqft,0.0056,0.003,1.804,0.074,,-0.001,0.012,medium,"Total square footage of the house; Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,0.0056,0.003,"b (currency per unit, as reported)",reported,,,,1.804,,sqft,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Room,other,other,level,count,3.0901,0.95,3.253,0.001,,1.209,4.971,medium,"Total number of rooms (distinct from bedrooms); Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,3.0901,0.95,"b (currency per unit, as reported)",reported,,,,3.253,,count,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Lot,lot_size,A_structural,level,sqft (as stated in Table 1; scale uncertain),0.2094,0.089,2.35,0.02,,0.033,0.386,medium,"Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,0.2094,0.089,"b (currency per unit, as reported)",reported,,,,2.35,,sqft,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Tax,public_services,F_neighbourhood,level,USD per year (annual property taxes),0.0062,0.002,2.636,0.01,,0.002,0.011,medium,"Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,0.0062,0.002,"b (currency per unit, as reported)",reported,,,,2.636,,usd per year,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Bathroom,bathrooms,A_structural,level,count,5.6524,1.76,3.212,0.002,,2.167,9.138,medium,"Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,5.6524,1.76,"b (currency per unit, as reported)",reported,,,,3.212,,count,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Garage,garage,A_structural,level,count,4.5383,0.911,4.984,0.0,,2.735,6.342,medium,"Number of garages; Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,4.5383,0.911,"b (currency per unit, as reported)",reported,,,,4.984,,count,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6,c5910ffe5f4e46ed4d95f8ada01d97feb8173ca6:T2-c1,2,1,5,Condition,condition,A_structural,binary,binary,0.5562,1.677,0.332,0.741,,-2.766,3.878,medium,"1 if good condition, 0 otherwise; Price scale (thousands of USD) inferred from text ('each additional room increases the price by approximately $3,090' for coefficient 3.0901); sample period and n not printed",House price (level; text/Figure 1 indicate thousands of USD),level,OLS,,0.732,none,"Full sample, suburban Chicago public dataset; adjusted R2 0.714, F = 39.70; missing values imputed with medians; SE, t, p and 95% CI printed in columns",monetary,0.5562,1.677,"b (currency per unit, as reported)",reported,,,,0.332,,binary,,2024.0,United States,North America,High income,,,single_family,transaction,conference,False,False,OLS,,2021.0,ols,other,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,ln(building age),age,A_structural,log,years,-0.011,,-0.734,,,,,high,"building age in years (age=1 if built 2014), log","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,elasticity,-0.011,0.014986376021798364,b (log-log),|b/t|,-0.03012808436941698,0.04104643647059534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.734,593.0,years,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Inner environment,other,other,level,ordinal,0.013,,1.221,,,,,high,"environment quality inside gated community, 1-5 score","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,1.3,1.0647010647010646,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.050077550768519476,0.041013555092972546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.221,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,External environment,other,other,level,ordinal,0.008,,0.809,,,,,high,"natural environment quality around community, 1-5 score","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,0.8,0.9888751545117428,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.03320333035893061,0.04104243554873994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.809,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Property management,other,other,level,ordinal,0.042,,5.328,,***,,,high,"property management service quality, 1-5 score","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,4.2,0.7882882882882882,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.21373858527847228,0.040116100840554114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.328,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Sports facility,recreation_private,C_exterior,level,ordinal,0.031,,3.925,,***,,,high,"community sports facilities quality, 1-5 score","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,3.1,0.7898089171974522,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.15912669793433834,0.04054183386862123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.925,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Nearby university,university_distance,D_location,binary,binary,0.046,,2.979,,***,,,high,1 = college/university within 1000 m,"ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,pct_premium,4.6949587467338905,1.6168319202423334,100*(exp(b - se^2/2) - 1),|b/t|,0.12142763951526665,0.0407612082964977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.979,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Living facility,commercial_distance,D_location,level,count,0.018,,2.208,,**,,,high,"count 0-5 of supermarket, market, bank, post office, hospital within 1000 m","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,1.7999999999999998,0.8152173913043477,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.09030125293852351,0.04089730658447622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.208,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Education facility,school_distance,D_location,level,count,0.033,,3.39,,***,,,high,"count 0-4 of kindergarten, primary, junior high, high school within 1000 m","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,3.3000000000000003,0.9734513274336283,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.13788098695780956,0.04067285750967834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.39,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Nearby subway,transit_distance,D_location,binary,binary,0.02,,1.162,,,,,high,1 = metro station within 500 m,"ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,pct_premium,2.005023759090818,1.7559403442801307,100*(exp(b - se^2/2) - 1),|b/t|,0.04766338754352698,0.04101840580337951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.162,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,Traffic condition,transit_distance,D_location,level,count,-0.002,,-3.023,,***,,,high,number of bus routes within 1000 m,"ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,semi_elasticity,-0.2,0.06615944426066821,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.12319410834146609,0.04075226872029973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.023,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,ln(distance to Wulin Square),cbd_distance,D_location,log,km,-0.067,,-3.429,,***,,,high,"walking distance to traditional CBD (Wulin Square), km, log","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,elasticity,-0.067,0.019539224263633713,b (log-log),|b/t|,-0.13943655982735914,0.0406639136271097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.429,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,ln(distance to the West Lake),water_proximity,E_environment,log,km,-0.221,,-12.694,,***,,,high,"walking distance to the West Lake, km, log","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,elasticity,-0.221,0.01740979990546715,b (log-log),|b/t|,-0.4622460262236959,0.036414528613809345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.694,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T2-c1,2,1,7,ln(distance to the Qianjiang New Center),cbd_distance,D_location,log,km,-0.059,,-4.018,,***,,,high,"walking distance to new CBD (Qianjiang New Center), km, log","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.567,t,Model 1 traditional hedonic; adj R2,elasticity,-0.059,0.014683922349427576,b (log-log),|b/t|,-0.16279828725190018,0.040517244214011996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.018,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,ln(building age),age,A_structural,log,years,-0.061,,-3.765,,***,,,high,"building age in years (age=1 if built 2014), log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,elasticity,-0.061,0.016201859229747675,b (log-log),|b/t|,-0.15279458362603907,0.04058289073732777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.765,593.0,years,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,Inner environment,other,other,level,ordinal,0.022,,1.649,,*,,,high,"environment quality inside gated community, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,semi_elasticity,2.1999999999999997,1.334141904184354,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.06756158918046037,0.04097124874497293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.649,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,External environment,other,other,level,ordinal,0.022,,1.653,,*,,,high,"natural environment quality around community, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,semi_elasticity,2.1999999999999997,1.3309134906231095,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.06772472339958223,0.04097079455510116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.653,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,Property management,other,other,level,ordinal,0.036,,3.49,,***,,,high,"property management service quality, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,semi_elasticity,3.5999999999999996,1.0315186246418335,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.14186756269884607,0.040649731432334114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.49,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,Sports facility,recreation_private,C_exterior,level,ordinal,0.032,,4.034,,***,,,high,"community sports facilities quality, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,semi_elasticity,3.2,0.7932573128408529,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.1634292816980627,0.04051296026228624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.034,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,Living facility,commercial_distance,D_location,level,count,0.032,,3.043,,***,,,high,"count 0-5 of supermarket, market, bank, post office, hospital within 1000 m; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,semi_elasticity,3.2,1.0515938218862964,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.12399666280493836,0.040748163918809845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.043,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,Traffic condition,transit_distance,D_location,level,count,-0.005,,-3.585,,***,,,high,number of bus routes within 1000 m; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed,"ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,semi_elasticity,-0.5,0.1394700139470014,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.14564842961450367,0.04062717701938735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.585,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,ln(distance to Wulin Square),cbd_distance,D_location,log,km,-0.087,,1.786,,*,,,medium,"walking distance to traditional CBD (Wulin Square), km, log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed; printed t = 1.786 has sign inconsistent with negative coefficient","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,elasticity,-0.087,0.04871220604703247,b (log-log),|b/t|,0.07314576480728978,0.04095507548000547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.786,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,ln(distance to the West Lake),water_proximity,E_environment,log,km,-0.269,,-7.596,,***,,,high,"walking distance to the West Lake, km, log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,elasticity,-0.269,0.03541337546076883,b (log-log),|b/t|,-0.29777951232097755,0.039202147488280346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.596,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c1,3,1,8,ln(distance to the Qianjiang New Center),cbd_distance,D_location,log,km,-0.185,,-4.028,,***,,,high,"walking distance to new CBD (Qianjiang New Center), km, log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.671,t,Model 2 one-degree spatial expansion (OLS with coordinate interactions); base coefficients only; adj R2,elasticity,-0.185,0.04592850049652433,b (log-log),|b/t|,-0.1631926822402144,0.04051456857999366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.028,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,ln(building age),age,A_structural,log,years,-0.079,,-3.247,,***,,,high,"building age in years (age=1 if built 2014), log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,elasticity,-0.079,0.024330150908530954,b (log-log),|b/t|,-0.13216856561431098,0.04070482464253495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.247,593.0,years,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,Inner environment,other,other,level,ordinal,0.029,,1.729,,***,,,medium,"environment quality inside gated community, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed; printed *** inconsistent with t = 1.729","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,semi_elasticity,2.9000000000000004,1.67727009832273,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.07082322689947931,0.04096195887766299,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.729,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,External environment,other,other,level,ordinal,0.05,,2.616,,***,,,high,"natural environment quality around community, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,semi_elasticity,5.0,1.91131498470948,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.10681168763502061,0.04083015582378464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.616,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,Property management,other,other,level,ordinal,0.033,,2.267,,**,,,high,"property management service quality, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,semi_elasticity,3.3000000000000003,1.4556682840758715,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.09269372925614906,0.04088827933663391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.267,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,Sports facility,recreation_private,C_exterior,level,ordinal,0.055,,4.131,,***,,,high,"community sports facilities quality, 1-5 score; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,semi_elasticity,5.5,1.3313967562333575,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.1672503682851573,0.04048665414794415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.131,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,Education facility,school_distance,D_location,level,count,0.05,,2.461,,**,,,high,"count 0-4 of kindergarten, primary, junior high, high school within 1000 m; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,semi_elasticity,5.0,2.0316944331572535,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,0.10054899026936658,0.04085696475797098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.461,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,Nearby subway,transit_distance,D_location,binary,binary,-0.209,,-3.208,,***,,,high,1 = metro station within 500 m; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed,"ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,pct_premium,-19.03249113244415,5.2862096087740955,100*(exp(b - se^2/2) - 1),|b/t|,-0.13060831943954102,0.04071331653352276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.208,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,Traffic condition,transit_distance,D_location,level,count,-0.004,,-2.037,,**,,,high,number of bus routes within 1000 m; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed,"ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,semi_elasticity,-0.4,0.19636720667648502,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/t|,-0.08335843263898311,0.04092215642561763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.037,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,ln(distance to Wulin Square),cbd_distance,D_location,log,km,-0.168,,-2.149,,**,,,high,"walking distance to traditional CBD (Wulin Square), km, log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,elasticity,-0.168,0.07817589576547232,b (log-log),|b/t|,-0.08790721221488794,0.040906101542525795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.149,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,ln(distance to the West Lake),water_proximity,E_environment,log,km,-0.336,,-6.557,,***,,,high,"walking distance to the West Lake, km, log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,elasticity,-0.336,0.05124294646942199,b (log-log),|b/t|,-0.2600031379134061,0.03965275856541195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.557,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,True
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T3-c2,3,2,8,ln(distance to the Qianjiang New Center),cbd_distance,D_location,log,km,-0.237,,-3.061,,***,,,high,"walking distance to new CBD (Qianjiang New Center), km, log; base term of spatial expansion model (interaction terms with coordinates not extracted); only terms significant at 10% are printed","ln(P), average community housing price (RMB, likely per m2)",log,OLS,603.0,0.704,t,Model 3 quadratic spatial expansion; base coefficients only; adj R2,elasticity,-0.237,0.07742567788304476,b (log-log),|b/t|,-0.12471875387342918,0.040744447524805355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.061,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,ols,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,ln(building age),age,A_structural,log,years,-0.061,,,,**,,,medium,"building age in years (age=1 if built 2014), log; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,elasticity,-0.061,,b (log-log),,,,,,593.0,years,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Inner environment,other,other,level,ordinal,0.043,,,,**,,,medium,"environment quality inside gated community, 1-5 score; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,4.3,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,External environment,other,other,level,ordinal,0.015,,,,*,,,medium,"natural environment quality around community, 1-5 score; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,1.5,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Property management,other,other,level,ordinal,0.016,,,,***,,,medium,"property management service quality, 1-5 score; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,1.6,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Sports facility,recreation_private,C_exterior,level,ordinal,0.028,,,,***,,,medium,"community sports facilities quality, 1-5 score; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,2.8000000000000003,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Nearby university,university_distance,D_location,binary,binary,0.015,,,,***,,,medium,1 = college/university within 1000 m; GWR median of local estimates; stars printed on median; no SE,"ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,pct_premium,1.511306461571893,,100*(exp(b) - 1),,,,,,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Living facility,commercial_distance,D_location,level,count,0.012,,,,***,,,medium,"count 0-5 of supermarket, market, bank, post office, hospital within 1000 m; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,1.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Education facility,school_distance,D_location,level,count,0.01,,,,***,,,medium,"count 0-4 of kindergarten, primary, junior high, high school within 1000 m; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,1.0,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Nearby subway,transit_distance,D_location,binary,binary,0.021,,,,**,,,medium,1 = metro station within 500 m; GWR median of local estimates; stars printed on median; no SE,"ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,Traffic condition,transit_distance,D_location,level,count,-0.002,,,,***,,,medium,number of bus routes within 1000 m; GWR median of local estimates; stars printed on median; no SE,"ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,semi_elasticity,-0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,ln(distance to Wulin Square),cbd_distance,D_location,log,km,-0.034,,,,***,,,medium,"walking distance to traditional CBD (Wulin Square), km, log; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,elasticity,-0.034,,b (log-log),,,,,,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,ln(distance to the West Lake),water_proximity,E_environment,log,km,-0.28,,,,***,,,medium,"walking distance to the West Lake, km, log; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,elasticity,-0.28,,b (log-log),,,,,,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T4-c1,4,Median,9,ln(distance to the Qianjiang New Center),cbd_distance,D_location,log,km,-0.131,,,,***,,,medium,"walking distance to new CBD (Qianjiang New Center), km, log; GWR median of local estimates; stars printed on median; no SE","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.757,none,Model 4 GWR; median of local coefficients; adj R2,elasticity,-0.131,,b (log-log),,,,,,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T5-c1,5,1,10,Inner environment,other,other,level,ordinal,0.048,,4.176,,,,,high,"environment quality inside gated community, 1-5 score; MGWR global coefficient; t value printed, no stars (critical values 1.648/1.965)","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,t,"Model 5 MGWR, global (fixed) variables; critical t 1.648 (10%), 1.965 (5%); adj R2 from Table 6",semi_elasticity,4.8,1.1494252873563218,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.16902049233481153,0.04047425582730161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.176,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T5-c1,5,1,10,Nearby university,university_distance,D_location,binary,binary,0.04,,2.635,,,,,high,"1 = college/university within 1000 m; MGWR global coefficient; t value printed, no stars (critical values 1.648/1.965)","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,t,"Model 5 MGWR, global (fixed) variables; critical t 1.648 (10%), 1.965 (5%); adj R2 from Table 6",pct_premium,4.069085864129884,1.5799784048461303,100*(exp(b - se^2/2) - 1),|b/t|,0.10757851446610504,0.04082676070819927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.635,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,True,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T5-c1,5,1,10,Sports facility,recreation_private,C_exterior,level,ordinal,0.021,,3.246,,,,,high,"community sports facilities quality, 1-5 score; MGWR global coefficient; t value printed, no stars (critical values 1.648/1.965)","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,t,"Model 5 MGWR, global (fixed) variables; critical t 1.648 (10%), 1.965 (5%); adj R2 from Table 6",semi_elasticity,2.1,0.6469500924214419,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.13212857152039476,0.040705043598396405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.246,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,True,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T5-c1,5,1,10,Property management,other,other,level,ordinal,0.012,,1.722,,,,,high,"property management service quality, 1-5 score; MGWR global coefficient; t value printed, no stars (critical values 1.648/1.965)","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,t,"Model 5 MGWR, global (fixed) variables; critical t 1.648 (10%), 1.965 (5%); adj R2 from Table 6",semi_elasticity,1.2,0.6968641114982579,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),|b/t|,0.0705379227444298,0.040962789050191524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.722,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,ln(building age),age,A_structural,log,years,-0.079,,,,,,,medium,"building age in years (age=1 if built 2014), log; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",elasticity,-0.079,,b (log-log),,,,,,593.0,years,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,External environment,other,other,level,ordinal,0.015,,,,,,,medium,"natural environment quality around community, 1-5 score; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",semi_elasticity,1.5,,100*b (unit as reported: ordinal; no harmonised unit defined for this characteristic),,,,,,593.0,ordinal,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,Living facility,commercial_distance,D_location,level,count,0.011,,,,,,,medium,"count 0-5 of supermarket, market, bank, post office, hospital within 1000 m; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",semi_elasticity,1.0999999999999999,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,Education facility,school_distance,D_location,level,count,0.025,,,,,,,medium,"count 0-4 of kindergarten, primary, junior high, high school within 1000 m; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",semi_elasticity,2.5,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,Nearby subway,transit_distance,D_location,binary,binary,0.007,,,,,,,medium,1 = metro station within 500 m; MGWR median of local estimates; no SE or stars,"ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",pct_premium,0.7024557266848586,,100*(exp(b) - 1),,,,,,593.0,binary,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,Traffic condition,transit_distance,D_location,level,count,-0.002,,,,,,,medium,number of bus routes within 1000 m; MGWR median of local estimates; no SE or stars,"ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",semi_elasticity,-0.2,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,593.0,count,unit_not_harmonisable,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,ln(distance to Wulin Square),cbd_distance,D_location,log,km,-0.003,,,,,,,medium,"walking distance to traditional CBD (Wulin Square), km, log; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",elasticity,-0.003,,b (log-log),,,,,,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,ln(distance to the West Lake),water_proximity,E_environment,log,km,-0.307,,,,,,,medium,"walking distance to the West Lake, km, log; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",elasticity,-0.307,,b (log-log),,,,,,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7,c63f29dcdec077b7ee126d8f2c6b0bcc6611e6a7:T6-c1,6,Median,10,ln(distance to the Qianjiang New Center),cbd_distance,D_location,log,km,-0.069,,,,,,,medium,"walking distance to new CBD (Qianjiang New Center), km, log; MGWR median of local estimates; no SE or stars","ln(P), average community housing price (RMB, likely per m2)",log,GWR,603.0,0.766,none,"Model 5 MGWR, local variables; median of local coefficients; adj R2",elasticity,-0.069,,b (log-log),,,,,,593.0,km,,2017.0,China,Asia,Upper middle income,2014.0,2014.0,condo_apartment,unknown,journal,False,False,OLS,,2014.0,spatial,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c1,3,Standard,14,ln Size,living_area,A_structural,log,sqft,0.932,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Standard specification (Table 2),elasticity,0.932,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c2,3,Hybrid,14,ln Size,living_area,A_structural,log,sqft,0.924,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Hybrid specification (Table 2),elasticity,0.924,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c3,3,RE,14,ln Size,living_area,A_structural,log,sqft,0.928,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; RE specification (Table 2),elasticity,0.928,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c4,3,Besag (Hybrid),14,ln Size,living_area,A_structural,log,sqft,0.945,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (Hybrid) specification (Table 2),elasticity,0.945,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c5,3,Besag (RE),14,ln Size,living_area,A_structural,log,sqft,0.952,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (RE) specification (Table 2),elasticity,0.952,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c6,3,SRW (Hybrid),14,ln Size,living_area,A_structural,log,sqft,0.956,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (Hybrid) specification (Table 2),elasticity,0.956,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c7,3,SRW (RE),14,ln Size,living_area,A_structural,log,sqft,0.961,,,,***,,,high,building size in square feet (multifamily property),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (RE) specification (Table 2),elasticity,0.961,,b (log-log),,,,,,2253.0,sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c1,3,Standard,14,ln ( NOI / Size ),other,other,log,currency per sqft,0.735,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Standard specification (Table 2),elasticity,0.735,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c2,3,Hybrid,14,ln ( NOI / Size ),other,other,log,currency per sqft,0.708,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Hybrid specification (Table 2),elasticity,0.708,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c3,3,RE,14,ln ( NOI / Size ),other,other,log,currency per sqft,0.7,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; RE specification (Table 2),elasticity,0.7,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c4,3,Besag (Hybrid),14,ln ( NOI / Size ),other,other,log,currency per sqft,0.613,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (Hybrid) specification (Table 2),elasticity,0.613,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c5,3,Besag (RE),14,ln ( NOI / Size ),other,other,log,currency per sqft,0.609,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (RE) specification (Table 2),elasticity,0.609,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c6,3,SRW (Hybrid),14,ln ( NOI / Size ),other,other,log,currency per sqft,0.603,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (Hybrid) specification (Table 2),elasticity,0.603,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c7,3,SRW (RE),14,ln ( NOI / Size ),other,other,log,currency per sqft,0.602,,,,***,,,high,net operating income per square foot (income control),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (RE) specification (Table 2),elasticity,0.602,,b (log-log),,,,,,2253.0,currency per sqft,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c1,3,Standard,14,Age,age,A_structural,level,years,-0.001,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Standard specification (Table 2),semi_elasticity,-0.1,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c2,3,Hybrid,14,Age,age,A_structural,level,years,-0.001,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Hybrid specification (Table 2),semi_elasticity,-0.1,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c3,3,RE,14,Age,age,A_structural,level,years,-0.001,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; RE specification (Table 2),semi_elasticity,-0.1,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c4,3,Besag (Hybrid),14,Age,age,A_structural,level,years,-0.003,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (Hybrid) specification (Table 2),semi_elasticity,-0.3,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c5,3,Besag (RE),14,Age,age,A_structural,level,years,-0.004,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (RE) specification (Table 2),semi_elasticity,-0.4,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c6,3,SRW (Hybrid),14,Age,age,A_structural,level,years,-0.004,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (Hybrid) specification (Table 2),semi_elasticity,-0.4,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c7,3,SRW (RE),14,Age,age,A_structural,level,years,-0.004,,,,***,,,high,,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (RE) specification (Table 2),semi_elasticity,-0.4,,100*b/1 (per year),,,,,,2253.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c1,3,Standard,14,Age2,age,A_structural,squared,years,0.0,,,,***,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Standard specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c2,3,Hybrid,14,Age2,age,A_structural,squared,years,0.0,,,,,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Hybrid specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c3,3,RE,14,Age2,age,A_structural,squared,years,0.0,,,,,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; RE specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c4,3,Besag (Hybrid),14,Age2,age,A_structural,squared,years,0.0,,,,,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (Hybrid) specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c5,3,Besag (RE),14,Age2,age,A_structural,squared,years,0.0,,,,*,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (RE) specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c6,3,SRW (Hybrid),14,Age2,age,A_structural,squared,years,0.0,,,,,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (Hybrid) specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c7,3,SRW (RE),14,Age2,age,A_structural,squared,years,0.0,,,,***,,,medium,printed as 0.000 (rounded),ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (RE) specification (Table 2),other,0.0,,not harmonised,,,,,,2253.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c1,3,Standard,14,Mid/Highrise,storeys,A_structural,binary,binary,0.018,,,,,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Standard specification (Table 2),pct_premium,1.8162976389793695,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c2,3,Hybrid,14,Mid/Highrise,storeys,A_structural,binary,binary,0.014,,,,,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Hybrid specification (Table 2),pct_premium,1.4098458938492264,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c3,3,RE,14,Mid/Highrise,storeys,A_structural,binary,binary,0.019,,,,,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; RE specification (Table 2),pct_premium,1.918164861740812,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c4,3,Besag (Hybrid),14,Mid/Highrise,storeys,A_structural,binary,binary,-0.003,,,,***,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (Hybrid) specification (Table 2),pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c5,3,Besag (RE),14,Mid/Highrise,storeys,A_structural,binary,binary,-0.002,,,,***,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; Besag (RE) specification (Table 2),pct_premium,-0.1998001332666921,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c6,3,SRW (Hybrid),14,Mid/Highrise,storeys,A_structural,binary,binary,-0.004,,,,***,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (Hybrid) specification (Table 2),pct_premium,-0.39920106560085156,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T3-c7,3,SRW (RE),14,Mid/Highrise,storeys,A_structural,binary,binary,-0.005,,,,***,,,high,mid/high-rise property subtype vs garden apartment,ln transaction price (USD),log,other,2263.0,,none,Los Angeles commercial multifamily; SRW (RE) specification (Table 2),pct_premium,-0.498752080731768,,100*(exp(b) - 1),,,,,,2253.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,ln Size,living_area,A_structural,log,m2,0.898,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),elasticity,0.898,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,ln Size,living_area,A_structural,log,m2,0.889,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),elasticity,0.889,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,ln Size,living_area,A_structural,log,m2,0.876,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),elasticity,0.876,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,ln Size,living_area,A_structural,log,m2,0.723,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),elasticity,0.723,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,ln Size,living_area,A_structural,log,m2,0.714,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),elasticity,0.714,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,ln Size,living_area,A_structural,log,m2,0.685,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),elasticity,0.685,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,ln Size,living_area,A_structural,log,m2,0.694,,,,***,,,high,size in square metres,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),elasticity,0.694,,b (log-log),,,,,,2458.0,m2,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Age,age,A_structural,level,years,0.004,,,,***,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),semi_elasticity,0.4,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Age,age,A_structural,level,years,0.002,,,,**,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),semi_elasticity,0.2,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Age,age,A_structural,level,years,0.003,,,,***,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),semi_elasticity,0.3,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Age,age,A_structural,level,years,0.003,,,,***,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),semi_elasticity,0.3,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Age,age,A_structural,level,years,0.003,,,,***,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),semi_elasticity,0.3,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Age,age,A_structural,level,years,0.001,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),semi_elasticity,0.1,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Age,age,A_structural,level,years,0.001,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),semi_elasticity,0.1,,100*b/1 (per year),,,,,,2458.0,years,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Age2,age,A_structural,squared,years,-0.0,,,,***,,,medium,printed as -0.000 (rounded),ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),other,-0.0,,not harmonised,,,,,,2458.0,years,nonstandard_x_transform,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.148,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,15.951289636297394,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.162,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,17.586024132099976,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.137,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,14.682814852039815,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.083,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,8.654180854823812,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.074,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,7.680680549621988,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.088,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,9.19881220281975,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Semidetached (1),dwelling_type,A_structural,binary,binary,0.073,,,,***,,,high,"semidetached connected via garage, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,7.573053691470344,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.199,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,22.018196589987248,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.214,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,23.862265479345226,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.205,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,22.75250649631777,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.167,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,18.17542653083615,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.152,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,16.41602364321124,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.174,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,19.005556582036263,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Semidetached (2),dwelling_type,A_structural,binary,binary,0.163,,,,***,,,high,"semidetached connected wall-to-wall, vs row house",ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,17.7036689688467,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Corner Home,dwelling_type,A_structural,binary,binary,0.094,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,9.855974591717365,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Corner Home,dwelling_type,A_structural,binary,binary,0.117,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,12.41194296905368,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Corner Home,dwelling_type,A_structural,binary,binary,0.104,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,10.960045491558246,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Corner Home,dwelling_type,A_structural,binary,binary,0.1,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,10.517091807564771,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Corner Home,dwelling_type,A_structural,binary,binary,0.087,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,9.089667971827776,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Corner Home,dwelling_type,A_structural,binary,binary,0.109,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,11.516235034144785,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Corner Home,dwelling_type,A_structural,binary,binary,0.094,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,9.855974591717365,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Detached,dwelling_type,A_structural,binary,binary,0.337,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,40.07390637385353,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Detached,dwelling_type,A_structural,binary,binary,0.357,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,42.9035869853877,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Detached,dwelling_type,A_structural,binary,binary,0.347,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,41.48167253704287,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Detached,dwelling_type,A_structural,binary,binary,0.307,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,35.93409680719806,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Detached,dwelling_type,A_structural,binary,binary,0.292,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,33.910301763929375,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Detached,dwelling_type,A_structural,binary,binary,0.301,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,35.12093415380156,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Detached,dwelling_type,A_structural,binary,binary,0.286,,,,***,,,high,vs row house,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,33.109245525229156,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Maintenance [average],condition,A_structural,dummy,binary,0.128,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,13.655300269706029,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Maintenance [average],condition,A_structural,dummy,binary,0.127,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,13.54170177814864,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Maintenance [average],condition,A_structural,dummy,binary,0.133,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,14.224999833089424,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Maintenance [average],condition,A_structural,dummy,binary,0.129,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,13.769012416573156,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Maintenance [average],condition,A_structural,dummy,binary,0.13,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,13.882838332462178,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Maintenance [average],condition,A_structural,dummy,binary,0.123,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,13.088442094748931,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Maintenance [average],condition,A_structural,dummy,binary,0.128,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,13.655300269706029,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Maintenance [good],condition,A_structural,dummy,binary,0.216,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,24.110237900067165,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Maintenance [good],condition,A_structural,dummy,binary,0.209,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,23.24449985302548,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Maintenance [good],condition,A_structural,dummy,binary,0.208,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,23.121316954886773,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Maintenance [good],condition,A_structural,dummy,binary,0.204,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,22.62981534562105,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Maintenance [good],condition,A_structural,dummy,binary,0.208,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,23.121316954886773,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Maintenance [good],condition,A_structural,dummy,binary,0.206,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,22.875320399531198,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Maintenance [good],condition,A_structural,dummy,binary,0.207,,,,***,,,high,vs maintenance [bad],ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,22.998257178075264,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c1,4,Standard,15,Yard,garden,C_exterior,binary,binary,0.025,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Standard specification (Table 2),pct_premium,2.531512052442886,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c2,4,Hybrid,15,Yard,garden,C_exterior,binary,binary,0.031,,,,*,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Hybrid specification (Table 2),pct_premium,3.1485503886522714,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c3,4,RE,15,Yard,garden,C_exterior,binary,binary,0.025,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; RE specification (Table 2),pct_premium,2.531512052442886,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c4,4,Besag (Hybrid),15,Yard,garden,C_exterior,binary,binary,0.013,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (Hybrid) specification (Table 2),pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c5,4,Besag (RE),15,Yard,garden,C_exterior,binary,binary,0.014,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; Besag (RE) specification (Table 2),pct_premium,1.4098458938492264,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c6,4,SRW (Hybrid),15,Yard,garden,C_exterior,binary,binary,0.015,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (Hybrid) specification (Table 2),pct_premium,1.511306461571893,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558,c6b33e6b839caa1157a283c6f9a1e3350edd0558:T4-c7,4,SRW (RE),15,Yard,garden,C_exterior,binary,binary,0.016,,,,,,,high,,ln transaction price (EUR),log,other,2468.0,,none,Heemstede single-family; SRW (RE) specification (Table 2),pct_premium,1.6128685406094911,,100*(exp(b) - 1),,,,,,2458.0,binary,,2021.0,United States,North America,High income,2001.0,2017.0,mixed,transaction,journal,True,True,unknown,,2009.0,other,journal,False,False,False
c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0:T3-c1,3,1,7,Surface,living_area,A_structural,level,m2,1109.951,83.6205,13.27368,0.0,,,,high,"size of the flat in m2 including common areas; Table 3 columns: Coefficients, Standard error, t-Student, Prob.; dependent variable = sales price in euros (level)",Sales price (euros),level,OLS,2888.0,0.7738,se,"Full sample of 2,888 resale flats, Cordoba; parsimonious linear specification with Pav x Carp interaction; R2 0.7738 from text",monetary,1109.951,83.6205,"b (currency per unit, as reported)",reported,0.24018355953369197,0.018094722754631115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.27368,2878.0,m2,,2013.0,Spain,Europe,High income,,,condo_apartment,transaction,journal,False,False,unknown,ANN,2010.0,ols,journal,False,True,False
c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0:T3-c1,3,1,7,Age,age,A_structural,level,years,-1067.449,173.5725,-6.149872,0.0,,,,high,"age of the building in years; Table 3 columns: Coefficients, Standard error, t-Student, Prob.; dependent variable = sales price in euros (level)",Sales price (euros),level,OLS,2888.0,0.7738,se,"Full sample of 2,888 resale flats, Cordoba; parsimonious linear specification with Pav x Carp interaction; R2 0.7738 from text",monetary,-1067.449,173.5725,"b (currency per unit, as reported)",reported,-0.11389001562027995,0.01851908716478651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.149872,2878.0,years,,2013.0,Spain,Europe,High income,,,condo_apartment,transaction,journal,False,False,unknown,ANN,2010.0,ols,journal,False,True,False
c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0:T3-c1,3,1,7,Location,other,other,level,index,64297.29,5494.403,11.70232,0.0,,,,medium,"location index scaled to [0,1]: position in the city, characteristics of the area and income level of the zone (33 income zones); Table 3 columns: Coefficients, Standard error, t-Student, Prob.; dependent variable = sales price in euros (level)",Sales price (euros),level,OLS,2888.0,0.7738,se,"Full sample of 2,888 resale flats, Cordoba; parsimonious linear specification with Pav x Carp interaction; R2 0.7738 from text",monetary,64297.29,5494.403,"b (currency per unit, as reported)",reported,0.21312398285038955,0.018212113739018376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.70232,2878.0,index,,2013.0,Spain,Europe,High income,,,condo_apartment,transaction,journal,False,False,unknown,ANN,2010.0,ols,journal,False,False,False
c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0:T3-c1,3,1,7,Annexes,other,other,level,index,18458.66,4572.246,4.037111,0.0001,,,,medium,"annex index scaled to [0,1] combining garage and store room (closest leaf would be garage, but composite); Table 3 columns: Coefficients, Standard error, t-Student, Prob.; dependent variable = sales price in euros (level)",Sales price (euros),level,OLS,2888.0,0.7738,se,"Full sample of 2,888 resale flats, Cordoba; parsimonious linear specification with Pav x Carp interaction; R2 0.7738 from text",monetary,18458.66,4572.246,"b (currency per unit, as reported)",reported,0.07504107524426565,0.01858781570391937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.037111,2878.0,index,,2013.0,Spain,Europe,High income,,,condo_apartment,transaction,journal,False,False,unknown,ANN,2010.0,ols,journal,False,False,False
c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0:T3-c1,3,1,7,Expenses,other,other,level,currency,1296.708,105.9213,12.24218,0.0,,,,medium,"community (common) expenses in euros (period not stated, presumably monthly); Table 3 columns: Coefficients, Standard error, t-Student, Prob.; dependent variable = sales price in euros (level)",Sales price (euros),level,OLS,2888.0,0.7738,se,"Full sample of 2,888 resale flats, Cordoba; parsimonious linear specification with Pav x Carp interaction; R2 0.7738 from text",monetary,1296.708,105.9213,"b (currency per unit, as reported)",reported,0.22247954035339063,0.018173196306000287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.24218,2878.0,currency,,2013.0,Spain,Europe,High income,,,condo_apartment,transaction,journal,False,False,unknown,ANN,2010.0,ols,journal,False,False,False
c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0,c7b242d28013321bceaeef0f994fd2d0dab032c0:T3-c1,3,1,7,Pav xCarp,other,other,other,index,5117.27,504.0529,10.15225,0.0,,,,medium,"interaction between quality indices of pavement (flooring) condition and external carpentry (windows); Table 3 columns: Coefficients, Standard error, t-Student, Prob.; dependent variable = sales price in euros (level)",Sales price (euros),level,OLS,2888.0,0.7738,se,"Full sample of 2,888 resale flats, Cordoba; parsimonious linear specification with Pav x Carp interaction; R2 0.7738 from text",other,5117.27,504.0529,not harmonised,reported,0.1859415082164541,0.01831530037345949,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.15225,2878.0,index,nonstandard_x_transform,2013.0,Spain,Europe,High income,,,condo_apartment,transaction,journal,False,False,unknown,ANN,2010.0,ols,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Farmers market,other,other,level,km,0.0111,0.0044,,,**,,,medium,Distance to nearest farmers market (km); quadratic with Farmers market2; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,1.11,0.44,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.027795607446484346,0.011018078627435236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5227272727272725,8231.0,km,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Farmers market,other,other,level,km,0.0106,0.0041,,,***,,,medium,Distance to nearest farmers market (km); quadratic with Farmers market2; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,1.06,0.41000000000000003,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.02848521103654151,0.011017864646209454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.585365853658536,8231.0,km,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Farmers market,other,other,level,km,0.0114,0.0041,,,***,,,medium,Distance to nearest farmers market (km); quadratic with Farmers market2; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,1.1400000000000001,0.41000000000000003,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.030633091639147944,0.011017164536886543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7804878048780486,8231.0,km,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Farmers market,other,other,level,km,0.0092,0.004,,,**,,,medium,Distance to nearest farmers market (km); quadratic with Farmers market2; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,0.9199999999999999,0.4,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.025343233276715598,0.01101879707683287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3,8231.0,km,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Farmers market,other,other,level,km,0.0096,0.004,,,**,,,medium,Distance to nearest farmers market (km); quadratic with Farmers market2; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,0.96,0.4,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.026444358477747346,0.011018482699061395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,8231.0,km,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Farmers market,other,other,level,km,0.0105,0.0041,,,**,,,medium,Distance to nearest farmers market (km); quadratic with Farmers market2; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,1.05,0.41000000000000003,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.028216697605285168,0.011017948588730399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5609756097560976,8231.0,km,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Farmers market2,other,other,squared,km,-0.0009,0.0007,,,,,,medium,Square of distance to nearest farmers market; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,other,-0.0009,0.0007,not harmonised,reported,-0.014170153750501114,0.011021230694834202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2857142857142856,8231.0,km,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Farmers market2,other,other,squared,km,-0.0011,0.0006,,,*,,,medium,Square of distance to nearest farmers market; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",other,-0.0011,0.0006,not harmonised,reported,-0.020203493880249657,0.011020087571045264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8333333333333337,8231.0,km,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Farmers market2,other,other,squared,km,-0.0012,0.0006,,,*,,,medium,Square of distance to nearest farmers market; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",other,-0.0012,0.0006,not harmonised,reported,-0.02203932016357621,0.011019660081788105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,8231.0,km,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Farmers market2,other,other,squared,km,-0.001,0.0006,,,,,,medium,Square of distance to nearest farmers market; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",other,-0.001,0.0006,not harmonised,reported,-0.01836746321796112,0.011020477930776668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6666666666666667,8231.0,km,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Farmers market2,other,other,squared,km,-0.001,0.0006,,,*,,,medium,Square of distance to nearest farmers market; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",other,-0.001,0.0006,not harmonised,reported,-0.01836746321796112,0.011020477930776668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6666666666666667,8231.0,km,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Farmers market2,other,other,squared,km,-0.0011,0.0006,,,*,,,medium,Square of distance to nearest farmers market; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",other,-0.0011,0.0006,not harmonised,reported,-0.020203493880249657,0.011020087571045264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8333333333333337,8231.0,km,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Living area,living_area,A_structural,level,sqft,0.0003,4.07e-06,,,***,,,medium,Square feet of living space; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,0.32291731250129163,0.004380911539600857,100*b/0.092903 (X per sqft->m2),reported,0.6305726619654912,0.008554769113998498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),73.7100737100737,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Living area,living_area,A_structural,level,sqft,0.0002,3.95e-06,,,***,,,medium,Square feet of living space; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,0.21527820833419445,0.00425174461460034,100*b/0.092903 (X per sqft->m2),reported,0.4873351962756465,0.009624870126444017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.63291139240506,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Living area,living_area,A_structural,level,sqft,0.0002,3.96e-06,,,***,,,medium,Square feet of living space; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,0.21527820833419445,0.00426250852501705,100*b/0.092903 (X per sqft->m2),reported,0.4863959801751084,0.009630640407467146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.505050505050505,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Living area,living_area,A_structural,level,sqft,0.0002,3.99e-06,,,***,,,medium,Square feet of living space; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,0.21527820833419445,0.004294800256267179,100*b/0.092903 (X per sqft->m2),reported,0.48359661833600825,0.009647752535803364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.12531328320802,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Living area,living_area,A_structural,level,sqft,0.0002,3.97e-06,,,***,,,medium,Square feet of living space; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,0.21527820833419445,0.004273272435433759,100*b/0.092903 (X per sqft->m2),reported,0.4854598192694568,0.009636377412498717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.377833753148614,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Living area,living_area,A_structural,level,sqft,0.0002,3.96e-06,,,***,,,medium,Square feet of living space; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,0.21527820833419445,0.00426250852501705,100*b/0.092903 (X per sqft->m2),reported,0.4863959801751084,0.009630640407467146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.505050505050505,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Lot size,lot_size,A_structural,level,sqft,7.04e-06,3.8e-07,,,***,,,medium,Square feet of lot; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,0.007577792933363644,0.00040902859583496945,100*b/0.092903 (X per sqft->m2),reported,0.20007445400320978,0.010799473369491434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.526315789473685,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Lot size,lot_size,A_structural,level,sqft,6.67e-06,3.58e-07,,,***,,,medium,Square feet of lot; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,0.007179528247945384,0.00038534799291820807,100*b/0.092903 (X per sqft->m2),reported,0.20116231628682757,0.010797017875664807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.631284916201118,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Lot size,lot_size,A_structural,level,sqft,6.81e-06,3.61e-07,,,***,,,medium,Square feet of lot; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,0.00733022299377932,0.000388577166043221,100*b/0.092903 (X per sqft->m2),reported,0.20357418534014743,0.010791524362377857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.86426592797784,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Lot size,lot_size,A_structural,level,sqft,6.29e-06,3.53e-07,,,***,,,medium,Square feet of lot; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,0.006770499652110415,0.00037996603770985317,100*b/0.092903 (X per sqft->m2),reported,0.19272179441381057,0.010815706427356936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.81869688385269,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Lot size,lot_size,A_structural,level,sqft,6.58e-06,3.54e-07,,,***,,,medium,Square feet of lot; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,0.007082653054194996,0.00038104242875152414,100*b/0.092903 (X per sqft->m2),reported,0.20070936724286717,0.010798041945892855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.587570621468924,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Lot size,lot_size,A_structural,level,sqft,6.73e-06,3.56e-07,,,***,,,medium,Square feet of lot; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,0.0072441117104456425,0.0003831952108348661,100*b/0.092903 (X per sqft->m2),reported,0.2039902652270957,0.010790569750497187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.90449438202247,8231.0,sqft,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Bedroom,bedrooms,A_structural,level,count,-0.0188,0.0029,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-1.8800000000000001,0.29,100*b/1 (per unit count),reported,-0.07127342886170468,0.010994305515901255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.482758620689656,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Bedroom,bedrooms,A_structural,level,count,-0.012,0.0027,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-1.2,0.27,100*b/1 (per unit count),reported,-0.04892948972520965,0.01100913518817217,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.444444444444445,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Bedroom,bedrooms,A_structural,level,count,-0.0135,0.0028,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-1.35,0.27999999999999997,100*b/1 (per unit count),reported,-0.0530685264309536,0.011006805481975562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.821428571428571,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Bedroom,bedrooms,A_structural,level,count,-0.0101,0.0027,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-1.01,0.27,100*b/1 (per unit count),reported,-0.041196702974143934,0.011012980002988972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7407407407407405,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Bedroom,bedrooms,A_structural,level,count,-0.0106,0.0027,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-1.06,0.27,100*b/1 (per unit count),reported,-0.04323242165521708,0.011012031931045862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9259259259259256,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Bedroom,bedrooms,A_structural,level,count,-0.0117,0.0027,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-1.17,0.27,100*b/1 (per unit count),reported,-0.04770907237289852,0.011009785932207352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.333333333333333,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Bathroom,bathrooms,A_structural,level,count,0.0319,0.0031,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,3.19,0.31,100*b/1 (per unit count),reported,0.11270078325213381,0.010952113732965984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.29032258064516,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Bathroom,bathrooms,A_structural,level,count,0.0275,0.0029,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,2.75,0.29,100*b/1 (per unit count),reported,0.1039558543314992,0.010962617365867188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.482758620689657,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Bathroom,bathrooms,A_structural,level,count,0.0287,0.0029,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,2.87,0.29,100*b/1 (per unit count),reported,0.10843986963637885,0.010957338743745597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.896551724137932,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Bathroom,bathrooms,A_structural,level,count,0.027,0.0029,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,2.7,0.29,100*b/1 (per unit count),reported,0.10208562603659456,0.010964752426152748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.310344827586208,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Bathroom,bathrooms,A_structural,level,count,0.0263,0.0029,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,2.63,0.29,100*b/1 (per unit count),reported,0.09946549104823459,0.01096767772014754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.06896551724138,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Bathroom,bathrooms,A_structural,level,count,0.0268,0.0029,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,2.68,0.29,100*b/1 (per unit count),reported,0.10133723038048492,0.010965595824753965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.24137931034483,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,House condition,condition,A_structural,other,index,0.018,0.002,,,***,,,medium,Ordinal 1 (poor) to 4 (excellent); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,other,0.018,0.002,not harmonised,reported,0.09871649844116778,0.010968499826796421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,House condition,condition,A_structural,other,index,0.0164,0.0019,,,***,,,medium,Ordinal 1 (poor) to 4 (excellent); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",other,0.0164,0.0019,not harmonised,reported,0.09471248842366531,0.010972788292985616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.631578947368421,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,House condition,condition,A_structural,other,index,0.0168,0.0019,,,***,,,medium,Ordinal 1 (poor) to 4 (excellent); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",other,0.0168,0.0019,not harmonised,reported,0.09700106965886726,0.01097035906856237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.842105263157894,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,House condition,condition,A_structural,other,index,0.0161,0.0018,,,***,,,medium,Ordinal 1 (poor) to 4 (excellent); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",other,0.0161,0.0018,not harmonised,reported,0.09811302119050666,0.010969157648628073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.944444444444445,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,House condition,condition,A_structural,other,index,0.0158,0.0018,,,***,,,medium,Ordinal 1 (poor) to 4 (excellent); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",other,0.0158,0.0018,not harmonised,reported,0.0963019424697351,0.010971107369969821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.777777777777779,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,House condition,condition,A_structural,other,index,0.0159,0.0019,,,***,,,medium,Ordinal 1 (poor) to 4 (excellent); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",other,0.0159,0.0019,not harmonised,reported,0.09184965287879718,0.010975744683629851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.368421052631579,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,12,Basement condition,basement,A_structural,other,index,0.0449,0.002,,,***,,,medium,"Ordinal basement condition 1 (poor) to 4 (excellent), not presence; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,other,0.0449,0.002,not harmonised,reported,0.24020654052799312,0.010699623186102144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.45,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,12,Basement condition,basement,A_structural,other,index,0.0445,0.0019,,,***,,,medium,"Ordinal basement condition 1 (poor) to 4 (excellent), not presence; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",other,0.0445,0.0019,not harmonised,reported,0.2499599263146485,0.010672446292086116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.421052631578945,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,12,Basement condition,basement,A_structural,other,index,0.0446,0.0019,,,***,,,medium,"Ordinal basement condition 1 (poor) to 4 (excellent), not presence; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",other,0.0446,0.0019,not harmonised,reported,0.25048642757184664,0.010670946466065215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.473684210526315,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,12,Basement condition,basement,A_structural,other,index,0.0431,0.0019,,,***,,,medium,"Ordinal basement condition 1 (poor) to 4 (excellent), not presence; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",other,0.0431,0.0019,not harmonised,reported,0.24256577541115917,0.0106931548325105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.684210526315788,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,12,Basement condition,basement,A_structural,other,index,0.044,0.0019,,,***,,,medium,"Ordinal basement condition 1 (poor) to 4 (excellent), not presence; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",other,0.044,0.0019,not harmonised,reported,0.24732409966640007,0.010679904303776369,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.157894736842103,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,12,Basement condition,basement,A_structural,other,index,0.0443,0.0019,,,***,,,medium,"Ordinal basement condition 1 (poor) to 4 (excellent), not presence; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",other,0.0443,0.0019,not harmonised,reported,0.24890625869553804,0.010675437731862806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.31578947368421,8231.0,index,nonstandard_x_transform,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,Garage,garage,A_structural,level,count,0.1169,0.0039,,,***,,,medium,Number of garages; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,11.690000000000001,0.38999999999999996,100*b/1 (per unit count),reported,0.31370923727687633,0.010465919806499722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.974358974358978,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,True
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,Garage,garage,A_structural,level,count,0.1105,0.0037,,,***,,,medium,Number of garages; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,11.05,0.37,100*b/1 (per unit count),reported,0.3126755014958113,0.010469677425651603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.864864864864863,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,Garage,garage,A_structural,level,count,0.1122,0.0037,,,***,,,medium,Number of garages; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,11.219999999999999,0.37,100*b/1 (per unit count),reported,0.3170057838454296,0.01045384492181898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.324324324324323,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,Garage,garage,A_structural,level,count,0.1041,0.0037,,,***,,,medium,Number of garages; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,10.41,0.37,100*b/1 (per unit count),reported,0.29619893619739357,0.010527723957063941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.135135135135133,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,Garage,garage,A_structural,level,count,0.1057,0.0037,,,***,,,medium,Number of garages; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,10.57,0.37,100*b/1 (per unit count),reported,0.3003436303168339,0.010513447797278006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.56756756756757,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,Garage,garage,A_structural,level,count,0.1069,0.0037,,,***,,,medium,Number of garages; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,10.69,0.37,100*b/1 (per unit count),reported,0.3034410394569565,0.010502636538734697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.891891891891888,8231.0,count,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,House age,age,A_structural,level,years,-0.0024,0.0001,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.24,0.01,100*b/1 (per year),reported,-0.2557391654052089,0.010655798558550374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.999999999999996,8231.0,years,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,House age,age,A_structural,level,years,-0.0022,0.0001,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-0.22,0.01,100*b/1 (per year),reported,-0.23566168852837724,0.010711894933108057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,8231.0,years,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,House age,age,A_structural,level,years,-0.0022,0.0001,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-0.22,0.01,100*b/1 (per year),reported,-0.23566168852837724,0.010711894933108057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,8231.0,years,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,House age,age,A_structural,level,years,-0.0023,0.0001,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.22999999999999998,0.01,100*b/1 (per year),reported,-0.24573995982314573,0.010684346079267205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.0,8231.0,years,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,House age,age,A_structural,level,years,-0.0022,0.0001,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.22,0.01,100*b/1 (per year),reported,-0.23566168852837724,0.010711894933108057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,8231.0,years,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,House age,age,A_structural,level,years,-0.0022,0.0001,,,***,,,medium,parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-0.22,0.01,100*b/1 (per year),reported,-0.23566168852837724,0.010711894933108057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,8231.0,years,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,Shopping center,commercial_distance,D_location,level,count,-0.0035,0.0039,,,,,,medium,"Number of shopping centers within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.35000000000000003,0.38999999999999996,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.009891357301321096,0.011021798135757791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8974358974358975,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,Shopping center,commercial_distance,D_location,level,count,-0.0039,0.0037,,,,,,medium,"Number of shopping centers within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-0.38999999999999996,0.37,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.011617355339529721,0.011021593527246147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.054054054054054,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,Shopping center,commercial_distance,D_location,level,count,-0.0042,0.0037,,,,,,medium,"Number of shopping centers within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-0.42,0.37,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.01251086317826978,0.011021474704666235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.135135135135135,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,Shopping center,commercial_distance,D_location,level,count,-0.0046,0.0036,,,,,,medium,"Number of shopping centers within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.45999999999999996,0.36,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.014082701066767508,0.011021244313122396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.277777777777778,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,Shopping center,commercial_distance,D_location,level,count,-0.0042,0.0036,,,,,,medium,"Number of shopping centers within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.42,0.36,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.012858330473330064,0.011021426119997199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1666666666666665,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,Shopping center,commercial_distance,D_location,level,count,-0.0039,0.0036,,,,,,medium,"Number of shopping centers within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-0.38999999999999996,0.36,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.011940014270134245,0.011021551633970072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0833333333333333,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,Grocery store,commercial_distance,D_location,level,count,-0.001,0.0024,,,,,,medium,"Number of grocery stores within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.1,0.24,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.004592592130939459,0.011022221114254702,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4166666666666667,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,Grocery store,commercial_distance,D_location,level,count,-0.0015,0.0022,,,,,,medium,"Number of grocery stores within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-0.15,0.22,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.007515017799231768,0.011022026105539926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6818181818181818,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,Grocery store,commercial_distance,D_location,level,count,-0.0016,0.0022,,,,,,medium,"Number of grocery stores within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-0.16,0.22,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.008015987799435376,0.011021983224223639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7272727272727273,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,Grocery store,commercial_distance,D_location,level,count,-0.0008,0.0022,,,,,,medium,"Number of grocery stores within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.08,0.22,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.004008090479920289,0.011022248819780795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36363636363636365,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,Grocery store,commercial_distance,D_location,level,count,-0.0008,0.0022,,,,,,medium,"Number of grocery stores within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.08,0.22,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.004008090479920289,0.011022248819780795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36363636363636365,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,Grocery store,commercial_distance,D_location,level,count,-0.0009,0.0022,,,,,,medium,"Number of grocery stores within 1,000-m buffer (count, not distance); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-0.09,0.22,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.0045090921692997644,0.011022225302732759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.40909090909090906,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,Bar,neighbourhood_amenities,F_neighbourhood,level,count,-0.0023,0.0014,,,*,,,medium,"Number of bars within 1,000-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.22999999999999998,0.13999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.018105157520687316,0.011020530664766194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6428571428571428,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,Bar,neighbourhood_amenities,F_neighbourhood,level,count,0.0013,0.0013,,,,,,medium,"Number of bars within 1,000-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,0.13,0.13,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.011021667854435138,0.011021667854435136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,Bar,neighbourhood_amenities,F_neighbourhood,level,count,0.0012,0.0013,,,,,,medium,"Number of bars within 1,000-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,0.12,0.13,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.010173938663469011,0.011021766885424762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.923076923076923,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,Bar,neighbourhood_amenities,F_neighbourhood,level,count,0.0002,0.0013,,,,,,medium,"Number of bars within 1,000-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,0.02,0.13,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.001695741770507961,0.011022321508301744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15384615384615385,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,Bar,neighbourhood_amenities,F_neighbourhood,level,count,0.001,0.0013,,,,,,medium,"Number of bars within 1,000-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,0.1,0.13,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.00847841629726936,0.011021941186450168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7692307692307693,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,Bar,neighbourhood_amenities,F_neighbourhood,level,count,0.0013,0.0013,,,,,,medium,"Number of bars within 1,000-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,0.13,0.13,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.011021667854435138,0.011021667854435136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,8231.0,count,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,Downtown,cbd_distance,D_location,level,km,-0.0054,0.0014,,,***,,,medium,Distance to the downtown area (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.54,0.13999999999999999,100*b/1 (X per km->km),reported,-0.04247635907499242,0.01101238938981285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.857142857142857,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,Downtown,cbd_distance,D_location,level,km,-0.0127,0.0013,,,***,,,medium,Distance to the downtown area (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-1.27,0.13,100*b/1 (X per km->km),reported,-0.10706086445931844,0.010958986125757004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.76923076923077,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,Downtown,cbd_distance,D_location,level,km,-0.0143,0.0013,,,***,,,medium,Distance to the downtown area (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-1.43,0.13,100*b/1 (X per km->km),reported,-0.12036423012141234,0.010942202738310213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.0,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,Downtown,cbd_distance,D_location,level,km,-0.0081,0.0013,,,***,,,medium,Distance to the downtown area (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.8099999999999999,0.13,100*b/1 (X per km->km),reported,-0.06851624803644325,0.010996434870046447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.230769230769231,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,Downtown,cbd_distance,D_location,level,km,-0.0096,0.0013,,,***,,,medium,Distance to the downtown area (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.96,0.13,100*b/1 (X per km->km),reported,-0.08112742040027039,0.010986004845869951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.384615384615384,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,Downtown,cbd_distance,D_location,level,km,-0.0108,0.0013,,,***,,,medium,Distance to the downtown area (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-1.08,0.13,100*b/1 (X per km->km),reported,-0.09118867232355501,0.010976414261168658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.307692307692308,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,University of Alberta,university_distance,D_location,level,km,-0.0114,0.0015,,,***,,,medium,Distance to the University of Alberta (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-1.1400000000000001,0.15,100*b/1 (X per km->km),reported,-0.08347737997932358,0.010983865786753104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.6000000000000005,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,University of Alberta,university_distance,D_location,level,km,-0.0028,0.0015,,,***,,,medium,Distance to the University of Alberta (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-0.27999999999999997,0.15,100*b/1 (X per km->km),reported,-0.020570676080559885,0.01102000504315708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8666666666666667,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,University of Alberta,university_distance,D_location,level,km,-0.0012,0.0015,,,***,,,medium,Distance to the University of Alberta (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-0.12,0.15,100*b/1 (X per km->km),reported,-0.008817527088726513,0.011021908860908142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7999999999999999,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,University of Alberta,university_distance,D_location,level,km,-0.007,0.0014,,,***,,,medium,Distance to the University of Alberta (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.7000000000000001,0.13999999999999999,100*b/1 (X per km->km),reported,-0.05502818164535149,0.011005636329070298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,University of Alberta,university_distance,D_location,level,km,-0.0056,0.0014,,,***,,,medium,Distance to the University of Alberta (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.5599999999999999,0.13999999999999999,100*b/1 (X per km->km),reported,-0.04404655979864707,0.011011639949661768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,University of Alberta,university_distance,D_location,level,km,-0.0043,0.0014,,,***,,,medium,Distance to the University of Alberta (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-0.43,0.13999999999999999,100*b/1 (X per km->km),reported,-0.033834938064387116,0.01101602634654464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0714285714285716,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,Hospital,hospital_distance,D_location,level,km,-0.0037,0.0011,,,***,,,medium,Distance to a hospital (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.37,0.11,100*b/1 (X per km->km),reported,-0.0370496798788878,0.011014769693723399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3636363636363638,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,Hospital,hospital_distance,D_location,level,km,-0.005,0.001,,,***,,,medium,Distance to a hospital (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-0.5,0.1,100*b/1 (X per km->km),reported,-0.05502818164535149,0.011005636329070298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,Hospital,hospital_distance,D_location,level,km,-0.0054,0.001,,,***,,,medium,Distance to a hospital (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-0.54,0.1,100*b/1 (X per km->km),reported,-0.05941546904861625,0.011002864638632637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.4,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,Hospital,hospital_distance,D_location,level,km,-0.0035,0.001,,,***,,,medium,Distance to a hospital (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.35000000000000003,0.1,100*b/1 (X per km->km),reported,-0.03854950525862757,0.011014144359607876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,Hospital,hospital_distance,D_location,level,km,-0.0043,0.001,,,***,,,medium,Distance to a hospital (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.43,0.1,100*b/1 (X per km->km),reported,-0.04734290524921149,0.011009977964932905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.3,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,Hospital,hospital_distance,D_location,level,km,-0.0049,0.001,,,***,,,medium,Distance to a hospital (km); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-0.49,0.1,100*b/1 (X per km->km),reported,-0.05393085160884531,0.011006296246703125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.8999999999999995,8231.0,km,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,13,River buffer,water_proximity,E_environment,binary,binary,0.0302,0.0044,,,***,,,medium,1 if within 2 km of the North Saskatchewan River; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,pct_premium,3.0650668724011565,0.4534906840071399,100*(exp(b - se^2/2) - 1),reported,0.07543774240404066,0.010990929356880098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.863636363636363,8231.0,binary,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,13,River buffer,water_proximity,E_environment,binary,binary,0.0106,0.0042,,,***,,,medium,1 if within 2 km of the North Saskatchewan River; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",pct_premium,1.064746507986336,0.42447567919252266,100*(exp(b - se^2/2) - 1),reported,0.027807522555393305,0.01101807497477848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.523809523809524,8231.0,binary,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,13,River buffer,water_proximity,E_environment,binary,binary,0.0088,0.0042,,,***,,,medium,1 if within 2 km of the North Saskatchewan River; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",pct_premium,0.8829935909705711,0.4237123102081717,100*(exp(b - se^2/2) - 1),reported,0.02308826485796865,0.011019399136757766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0952380952380953,8231.0,binary,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,True
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,13,River buffer,water_proximity,E_environment,binary,binary,0.0189,0.0041,,,***,,,medium,1 if within 2 km of the North Saskatchewan River; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",pct_premium,1.9071170219103317,0.41782269157479673,100*(exp(b - se^2/2) - 1),reported,0.050744825474978376,0.01100813674324928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.609756097560975,8231.0,binary,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,13,River buffer,water_proximity,E_environment,binary,binary,0.0164,0.0041,,,***,,,medium,1 if within 2 km of the North Saskatchewan River; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",pct_premium,1.6526674238789063,0.4167794394543707,100*(exp(b - se^2/2) - 1),reported,0.04404655979864707,0.011011639949661768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,8231.0,binary,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,13,River buffer,water_proximity,E_environment,binary,binary,0.0158,0.0041,,,***,,,medium,1 if within 2 km of the North Saskatchewan River; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",pct_premium,1.5916941172457566,0.4165294467959954,100*(exp(b - se^2/2) - 1),reported,0.04243805751662775,0.011012407330264163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8536585365853657,8231.0,binary,,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,True,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Golf,other,other,level,m2,1.29e-07,3.99e-08,,,***,,,medium,Area (m2) of golf course within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,1.29e-05,3.99e-06,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.03561352206348774,0.011015345196381091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2330827067669174,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Golf,other,other,level,m2,8.55e-08,3.76e-08,,,***,,,medium,Area (m2) of golf course within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,8.550000000000001e-06,3.76e-06,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.025056222561179394,0.011018876822226258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.273936170212766,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Golf,other,other,level,m2,1.11e-07,3.8e-08,,,***,,,medium,Area (m2) of golf course within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,1.11e-05,3.8000000000000005e-06,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.03218015231259953,0.011016628719628671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9210526315789473,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Golf,other,other,level,m2,9.61e-08,3.71e-08,,,***,,,medium,Area (m2) of golf course within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,9.61e-06,3.71e-06,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.028539491980106725,0.011017847580249322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.590296495956873,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Golf,other,other,level,m2,1e-07,3.72e-08,,,***,,,medium,Area (m2) of golf course within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,9.999999999999999e-06,3.7200000000000004e-06,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.029616941131656235,0.01101750210097612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6881720430107525,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Golf,other,other,level,m2,1.21e-07,3.74e-08,,,***,,,medium,Area (m2) of golf course within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,1.2100000000000001e-05,3.7399999999999998e-06,100*b (unit as reported: m2; no harmonised unit defined for this characteristic),reported,0.035637850590382956,0.011015335637027456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2352941176470593,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Green space,green_space,E_environment,level,m2,4.12e-07,2.07e-08,,,***,,,medium,Area (m2) of park and natural preserve within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,4.12e-05,2.07e-06,"100*b (unit as reported: m2; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.21428576996682402,0.010766299607556451,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.903381642512077,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Green space,green_space,E_environment,level,m2,2.22e-07,2.05e-08,,,***,,,medium,Area (m2) of park and natural preserve within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,2.22e-05,2.0500000000000003e-06,"100*b (unit as reported: m2; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.11852249651402179,0.010944644948366878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.829268292682926,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Green space,green_space,E_environment,level,m2,2.38e-07,2.06e-08,,,***,,,medium,Area (m2) of park and natural preserve within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,2.38e-05,2.0599999999999998e-06,"100*b (unit as reported: m2; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.12632527054396853,0.010934036021872906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.553398058252426,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Green space,green_space,E_environment,level,m2,2.11e-07,2.02e-08,,,***,,,medium,Area (m2) of park and natural preserve within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,2.11e-05,2.0199999999999997e-06,"100*b (unit as reported: m2; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.11437871473233795,0.010950000178166948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.445544554455447,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Green space,green_space,E_environment,level,m2,2.05e-07,2.03e-08,,,***,,,medium,Area (m2) of park and natural preserve within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,2.05e-05,2.03e-06,"100*b (unit as reported: m2; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.11062611207497712,0.010954683293278223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.098522167487685,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Green space,green_space,E_environment,level,m2,2.11e-07,2.04e-08,,,***,,,medium,Area (m2) of park and natural preserve within 600-m buffer; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,2.11e-05,2.04e-06,"100*b (unit as reported: m2; distance unit not harmonisable (counts, shares, indices excluded))",reported,0.1132718126997727,0.010951398005096508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.34313725490196,8231.0,m2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Population density,density,F_neighbourhood,level,per_km2,-1.21e-05,1.94e-06,,,***,,,medium,"Neighborhood-level population density; unit not stated in paper (mean 3,071), assumed persons per km2; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-0.00121,0.000194,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,-0.0685856833028592,0.010996382281615443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.237113402061855,8231.0,per_km2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Population density,density,F_neighbourhood,level,per_km2,-8.11e-06,1.83e-06,,,***,,,medium,"Neighborhood-level population density; unit not stated in paper (mean 3,071), assumed persons per km2; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-0.000811,0.000183,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,-0.04878945285961719,0.011009210694586862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.4316939890710385,8231.0,per_km2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Population density,density,F_neighbourhood,level,per_km2,-9.33e-06,1.85e-06,,,***,,,medium,"Neighborhood-level population density; unit not stated in paper (mean 3,071), assumed persons per km2; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-0.000933,0.000185,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,-0.05550264123017676,0.011005346867719935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.043243243243244,8231.0,per_km2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Population density,density,F_neighbourhood,level,per_km2,-6.01e-06,1.81e-06,,,***,,,medium,"Neighborhood-level population density; unit not stated in paper (mean 3,071), assumed persons per km2; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-0.000601,0.000181,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,-0.03657454436806657,0.011014962613344509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.320441988950276,8231.0,per_km2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Population density,density,F_neighbourhood,level,per_km2,-6.22e-06,1.81e-06,,,***,,,medium,"Neighborhood-level population density; unit not stated in paper (mean 3,071), assumed persons per km2; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-0.0006219999999999999,0.000181,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,-0.03785072338943165,0.011014438799818536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.43646408839779,8231.0,per_km2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Population density,density,F_neighbourhood,level,per_km2,-7.99e-06,1.82e-06,,,***,,,medium,"Neighborhood-level population density; unit not stated in paper (mean 3,071), assumed persons per km2; parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-0.000799,0.000182,100*b (unit as reported: per_km2; no harmonised unit defined for this characteristic),reported,-0.04833271926919333,0.011009455453057804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.3901098901098905,8231.0,per_km2,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Low education,education_level,F_neighbourhood,level,share,-0.6492,0.0291,,,***,,,medium,Share of residents with high school or lower (inverse of higher-education share); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-64.92,2.91,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.23878697919338138,0.01070348289360351,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.309278350515463,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Low education,education_level,F_neighbourhood,level,share,-0.3169,0.0293,,,***,,,medium,Share of residents with high school or lower (inverse of higher-education share); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-31.69,2.93,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.11837607488899671,0.010944837470014526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.815699658703073,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Low education,education_level,F_neighbourhood,level,share,-0.3494,0.0295,,,***,,,medium,Share of residents with high school or lower (inverse of higher-education share); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-34.94,2.9499999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.1294508481193985,0.010929593644883388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.844067796610169,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Low education,education_level,F_neighbourhood,level,share,-0.3163,0.0285,,,***,,,medium,Share of residents with high school or lower (inverse of higher-education share); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-31.630000000000003,2.85,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.12142347289730007,0.010940780833300828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.098245614035088,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Low education,education_level,F_neighbourhood,level,share,-0.294,0.0288,,,***,,,medium,Share of residents with high school or lower (inverse of higher-education share); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-29.4,2.88,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.11181409879935925,0.010953217841569884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.208333333333334,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Low education,education_level,F_neighbourhood,level,share,-0.2971,0.0292,,,***,,,medium,Share of residents with high school or lower (inverse of higher-education share); parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-29.709999999999997,2.92,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.11144982953706088,0.010953668874056473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.174657534246574,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Low income,income,F_neighbourhood,level,share,-0.1937,0.0383,,,***,,,medium,"Share of households with income below C$30,000 (share, not income level); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-19.37,3.83,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.055658411879728704,0.01100525129062266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.057441253263708,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Low income,income,F_neighbourhood,level,share,-0.1819,0.0361,,,***,,,medium,"Share of households with income below C$30,000 (share, not income level); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-18.19,3.61,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.055453685569467524,0.011005376850235171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.038781163434903,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Low income,income,F_neighbourhood,level,share,-0.1761,0.0364,,,***,,,medium,"Share of households with income below C$30,000 (share, not income level); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-17.61,3.64,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05324944371667856,0.011006699325877908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.837912087912088,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Low income,income,F_neighbourhood,level,share,-0.1678,0.0356,,,***,,,medium,"Share of households with income below C$30,000 (share, not income level); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-16.78,3.56,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05188362700536265,0.011007491784212816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.713483146067416,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Low income,income,F_neighbourhood,level,share,-0.1733,0.0357,,,***,,,medium,"Share of households with income below C$30,000 (share, not income level); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-17.330000000000002,3.5700000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05342976455168876,0.011006593159234213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.8543417366946775,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Low income,income,F_neighbourhood,level,share,-0.171,0.0359,,,***,,,medium,"Share of households with income below C$30,000 (share, not income level); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-17.1,3.5900000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05242973082678645,0.011007177407494935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.763231197771588,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Unemployment,unemployment,F_neighbourhood,level,share,-0.3507,0.0819,,,***,,,medium,Share of residents unemployed; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-35.07,8.19,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0471457304213795,0.011010080757088626,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.282051282051282,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Unemployment,unemployment,F_neighbourhood,level,share,-0.0319,0.0776,,,,,,medium,Share of residents unemployed; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-3.19,7.76,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0045310431993527125,0.011022224209083713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4110824742268041,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Unemployment,unemployment,F_neighbourhood,level,share,-0.0162,0.0784,,,,,,medium,Share of residents unemployed; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-1.6199999999999999,7.84,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.00227756890352298,0.011022308767666768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2066326530612245,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Unemployment,unemployment,F_neighbourhood,level,share,-0.1392,0.0763,,,*,,,medium,Share of residents unemployed; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-13.919999999999998,7.630000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.020104839333903283,0.011020109491212795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.82437745740498,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Unemployment,unemployment,F_neighbourhood,level,share,-0.1048,0.0766,,,,,,medium,Share of residents unemployed; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-10.48,7.66,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.015078454714454223,0.011021084266480855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3681462140992167,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Unemployment,unemployment,F_neighbourhood,level,share,-0.0769,0.0771,,,,,,medium,Share of residents unemployed; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-7.6899999999999995,7.71,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01099308073594481,0.011021671323034396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9974059662775615,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Senior,demographics,F_neighbourhood,level,share,0.1006,0.0316,,,***,,,medium,Share of population aged over 65; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,10.059999999999999,3.16,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.035068515749956224,0.011015557631198974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.183544303797468,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Senior,demographics,F_neighbourhood,level,share,0.1018,0.0298,,,***,,,medium,Share of population aged over 65; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,10.18,2.98,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.037626824085636584,0.011014532001492832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4161073825503356,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Senior,demographics,F_neighbourhood,level,share,0.104,0.03,,,***,,,medium,Share of population aged over 65; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,10.4,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03818290494616973,0.011014299503702805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.466666666666667,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Senior,demographics,F_neighbourhood,level,share,0.0975,0.0293,,,***,,,medium,Share of population aged over 65; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,9.75,2.93,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03665377934938589,0.01101493061473853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3276450511945392,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Senior,demographics,F_neighbourhood,level,share,0.0959,0.0294,,,***,,,medium,Share of population aged over 65; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,9.59,2.94,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.035930598883096306,0.011015220095547774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.261904761904762,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Senior,demographics,F_neighbourhood,level,share,0.0847,0.0296,,,***,,,medium,Share of population aged over 65; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,8.469999999999999,2.96,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.031524593138240894,0.011016858995182177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8614864864864864,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Public school,other,other,level,share,0.053,0.0349,,,,,,medium,Share of residents located in a public school zone; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,5.3,3.49,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01673644861419209,0.01102079352142083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5186246418338107,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Public school,other,other,level,share,0.1528,0.033,,,***,,,medium,Share of residents located in a public school zone; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,15.28,3.3000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.05097042259969933,0.011008010116427214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.63030303030303,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Public school,other,other,level,share,0.1471,0.0334,,,***,,,medium,Share of residents located in a public school zone; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,14.71,3.34,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04848738750813413,0.011009372826456016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.404191616766467,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Public school,other,other,level,share,0.1364,0.0324,,,***,,,medium,Share of residents located in a public school zone; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,13.639999999999999,3.2399999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04635280269213158,0.011010489789040052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.209876543209877,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Public school,other,other,level,share,0.1327,0.0325,,,***,,,medium,Share of residents located in a public school zone; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,13.270000000000001,3.25,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04495954257144572,0.011011191662185274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0830769230769235,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Public school,other,other,level,share,0.1346,0.0328,,,***,,,medium,Share of residents located in a public school zone; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,13.459999999999999,3.2800000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04518570910298935,0.011011079187058327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.103658536585366,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,14,Married,demographics,F_neighbourhood,level,share,-0.151,0.0545,,,***,,,medium,Share of married residents; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-15.1,5.45,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03052472225831265,0.011017201079987016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.770642201834862,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,14,Married,demographics,F_neighbourhood,level,share,-0.3225,0.0518,,,***,,,medium,Share of married residents; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-32.25,5.18,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0684626126328279,0.010996475455443362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.225868725868726,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,14,Married,demographics,F_neighbourhood,level,share,-0.3283,0.0525,,,***,,,medium,Share of married residents; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-32.83,5.25,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.06876320160014253,0.01099624759064113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.253333333333333,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,14,Married,demographics,F_neighbourhood,level,share,-0.2247,0.0506,,,***,,,medium,Share of married residents; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-22.470000000000002,5.06,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.048888491088045265,0.01100915731666707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.440711462450593,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,14,Married,demographics,F_neighbourhood,level,share,-0.248,0.0508,,,***,,,medium,Share of married residents; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-24.8,5.08,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05373210153186088,0.011006414346042472,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.881889763779528,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,14,Married,demographics,F_neighbourhood,level,share,-0.2591,0.0512,,,***,,,medium,Share of married residents; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-25.91,5.12,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05569248405103122,0.011005230348949435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.060546875,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,15,Walk,walkability,D_location,level,share,1.0992,0.0703,,,***,,,medium,"Share of residents whose primary transport mode is walking (proxy, not a walk score); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,109.92,7.03,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.16983971441675133,0.010862201531566248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.635846372688476,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,15,Walk,walkability,D_location,level,share,0.6467,0.0675,,,***,,,medium,"Share of residents whose primary transport mode is walking (proxy, not a walk score); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,64.67,6.75,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1050182082315135,0.010961387127922008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.58074074074074,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,15,Walk,walkability,D_location,level,share,0.6997,0.0679,,,***,,,medium,"Share of residents whose primary transport mode is walking (proxy, not a walk score); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,69.97,6.79,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11285797317495738,0.010951917076718031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.304860088365242,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,15,Walk,walkability,D_location,level,share,0.7167,0.0662,,,***,,,medium,"Share of residents whose primary transport mode is walking (proxy, not a walk score); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,71.67,6.619999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1184902929952632,0.01094468731168749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.826283987915408,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,15,Walk,walkability,D_location,level,share,0.6742,0.0664,,,***,,,medium,"Share of residents whose primary transport mode is walking (proxy, not a walk score); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,67.42,6.64,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11122218492155664,0.010953949983374905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.153614457831326,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,15,Walk,walkability,D_location,level,share,0.7033,0.0667,,,***,,,medium,"Share of residents whose primary transport mode is walking (proxy, not a walk score); parentheses not labelled in table note (only star legend); values consistent with standard errors",Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,70.33,6.67,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11544496222712446,0.010948640666215271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.544227886056973,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,True,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c1,3,OLS,15,Public transportation,other,other,level,share,-0.4968,0.0559,,,***,,,medium,Share of residents whose primary transport mode is public transit; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,OLS,8241.0,0.823,se,OLS,semi_elasticity,-49.68,5.59,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.09749215794706108,0.010969830171579537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.887298747763865,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,ols,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c2,3,800-m weights,15,Public transportation,other,other,level,share,-0.4335,0.0526,,,***,,,medium,Share of residents whose primary transport mode is public transit; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 800-m distance weights",semi_elasticity,-43.35,5.26,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.09046748803055038,0.010977139262761133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.241444866920151,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c3,3,1000-m weights,15,Public transportation,other,other,level,share,-0.4605,0.0531,,,***,,,medium,Share of residents whose primary transport mode is public transit; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 1000-m distance weights",semi_elasticity,-46.050000000000004,5.3100000000000005,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.09515545369583524,0.010972322673721719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.67231638418079,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c4,3,Nearest 10 weights,15,Public transportation,other,other,level,share,-0.3662,0.052,,,***,,,medium,Share of residents whose primary transport mode is public transit; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 10 nearest neighbours weights",semi_elasticity,-36.620000000000005,5.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.07738989331758309,0.010989280318171273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.042307692307693,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c5,3,Nearest 20 weights,15,Public transportation,other,other,level,share,-0.3746,0.0521,,,***,,,medium,Share of residents whose primary transport mode is public transit; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 20 nearest neighbours weights (preferred, lowest AIC)",semi_elasticity,-37.46,5.21,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0790031083907325,0.010987885603729748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.190019193857965,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6,c8acd4a142f8c89ba5c53a61a81bfd91957160a6:T3-c6,3,Nearest 30 weights,15,Public transportation,other,other,level,share,-0.4024,0.0523,,,***,,,medium,Share of residents whose primary transport mode is public transit; parentheses not labelled in table note (only star legend); values consistent with standard errors,Log of transacted price (2016 CAD$),log,spatial_lag,8241.0,,se,"SAR, 30 nearest neighbours weights",semi_elasticity,-40.239999999999995,5.2299999999999995,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.08450332774595676,0.010982912627021715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.694072657743786,8231.0,share,unit_not_harmonisable,2024.0,Canada,North America,High income,2015.0,2017.0,single_family,transaction,journal,True,False,unknown,,2016.0,spatial,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M1,5,Model 1,9,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.00393,0.000164,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.632006,se,"Model 1 (Base): linear ASB, no squared terms",semi_elasticity,-0.393,0.0164,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.19510684891575042,0.008141863415313757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.963414634146343,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.009514,0.000366,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,semi_elasticity,-0.9514,0.0366,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.21093571903791922,0.008114617738898301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.994535519125684,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.008774,0.000376,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,semi_elasticity,-0.8774000000000001,0.037599999999999995,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.19017866803297886,0.00814989505133349,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.335106382978726,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.182533,0.00148,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),semi_elasticity,-18.2533,0.148,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.7153857012496341,0.005800435197194253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-123.3331081081081,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.009762,0.000455,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,semi_elasticity,-0.9762,0.0455,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.17534663560049185,0.008172784183386989,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.454945054945053,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.008565,0.000417,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,semi_elasticity,-0.8564999999999999,0.0417,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.16808138341573398,0.008183296775757276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.53956834532374,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.83e-05,2.81e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,other,4.83e-05,2.81e-06,not harmonised,reported,0.14125875112698322,0.008218159227056373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.18861209964413,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,5.22e-05,2.84e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,other,5.22e-05,2.84e-06,not harmonised,reported,0.15083633652847084,0.008206421374345923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.380281690140848,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.48e-05,2e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),other,4.48e-05,2e-06,not harmonised,reported,0.18281749228401004,0.008161495191250449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.4,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.86e-05,2.84e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,other,4.86e-05,2.84e-06,not harmonised,reported,0.14064706827237586,0.008218882178879576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.112676056338028,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.61e-05,2.85e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,other,4.61e-05,2.85e-06,not harmonised,reported,0.13308433455310556,0.008227556474541233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.17543859649123,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M1,5,Model 1,9,Age,age,A_structural,level,years,0.000901,0.000118,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.632006,se,"Model 1 (Base): linear ASB, no squared terms",semi_elasticity,0.0901,0.0118,100*b/1 (per year),reported,0.06325915794001948,0.008284773181933738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.635593220338984,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Age,age,A_structural,level,years,0.002997,0.000507,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,semi_elasticity,0.2997,0.050699999999999995,100*b/1 (per year),reported,0.04901261181166318,0.008291422818990068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9112426035502965,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Age,age,A_structural,level,years,0.002931,0.000506,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,semi_elasticity,0.2931,0.050600000000000006,100*b/1 (per year),reported,0.0480302794204838,0.008291818965119347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.792490118577074,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Age,age,A_structural,level,years,0.002727,0.000361,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),semi_elasticity,0.2727,0.0361,100*b/1 (per year),reported,0.06258597583337143,0.0082851255136953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.554016620498615,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Age,age,A_structural,level,years,0.002858,0.000507,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,semi_elasticity,0.2858,0.050699999999999995,100*b/1 (per year),reported,0.04674450848376119,0.00829232533284357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6370808678500985,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Age,age,A_structural,level,years,0.002771,0.000509,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,semi_elasticity,0.2771,0.0509,100*b/1 (per year),reported,0.04514680528365604,0.00829293536246154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.444007858546169,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Age × Age,age,A_structural,squared,years,-1.42e-05,3.8e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,other,-1.42e-05,3.8e-06,not harmonised,reported,-0.03100610515635852,0.008297408422124111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7368421052631575,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Age × Age,age,A_structural,squared,years,-1.3e-05,3.8e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,other,-1.3e-05,3.8e-06,not harmonised,reported,-0.0283880798928549,0.008298054122526818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.421052631578947,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Age × Age,age,A_structural,squared,years,-1.47e-05,2.71e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),other,-1.47e-05,2.71e-06,not harmonised,reported,-0.044984149535578846,0.00829299627492644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.424354243542435,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Age × Age,age,A_structural,squared,years,-1.28e-05,3.8e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,other,-1.28e-05,3.8e-06,not harmonised,reported,-0.0279516840891412,0.008298156213963794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3684210526315788,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Age × Age,age,A_structural,squared,years,-1.28e-05,3.81e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,other,-1.28e-05,3.81e-06,not harmonised,reported,-0.02787837718266165,0.008298173208276631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3595800524934383,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M1,5,Model 1,9,Size,living_area,A_structural,level,m2,0.005028,6.64e-05,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.632006,se,"Model 1 (Base): linear ASB, no squared terms",semi_elasticity,0.5028,0.00664,100*b/1 (X per m2->m2),reported,0.5321925218636238,0.007028158999949207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),75.72289156626506,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Size,living_area,A_structural,level,m2,0.008369,0.000173,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,semi_elasticity,0.8369,0.0173,100*b/1 (X per m2->m2),reported,0.37265925591022947,0.007703435448974753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.3757225433526,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Size,living_area,A_structural,level,m2,0.008245,0.000173,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,semi_elasticity,0.8245,0.0173,100*b/1 (X per m2->m2),reported,0.3678898686004863,0.007719217376335249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.65895953757226,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Size,living_area,A_structural,level,m2,0.005017,0.000126,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),semi_elasticity,0.5016999999999999,0.0126,100*b/1 (X per m2->m2),reported,0.31384034410916195,0.00788197794653267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.817460317460316,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Size,living_area,A_structural,level,m2,0.008363,0.000173,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,semi_elasticity,0.8363,0.0173,100*b/1 (X per m2->m2),reported,0.3724291537568201,0.007704202271903608,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.34104046242775,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Size,living_area,A_structural,level,m2,0.008346,0.000173,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,semi_elasticity,0.8345999999999999,0.0173,100*b/1 (X per m2->m2),reported,0.3717768246038096,0.007706373191523972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.24277456647398,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Size × Size,living_area,A_structural,squared,m2,-8.31e-06,3.9e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,other,-8.31e-06,3.9e-07,not harmonised,reported,-0.1741798044308792,0.008174503457044872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.307692307692307,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Size × Size,living_area,A_structural,squared,m2,-8.12e-06,3.9e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,other,-8.12e-06,3.9e-07,not harmonised,reported,-0.17031418406678855,0.008180114751976298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.82051282051282,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Size × Size,living_area,A_structural,squared,m2,-3.92e-06,2.8e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),other,-3.92e-06,2.8e-07,not harmonised,reported,-0.11544257078295109,0.008245897913067936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.999999999999998,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Size × Size,living_area,A_structural,squared,m2,-8.31e-06,3.9e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,other,-8.31e-06,3.9e-07,not harmonised,reported,-0.1741798044308792,0.008174503457044872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.307692307692307,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Size × Size,living_area,A_structural,squared,m2,-8.27e-06,3.9e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,other,-8.27e-06,3.9e-07,not harmonised,reported,-0.17336665218708308,0.008175694601325563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.205128205128204,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M1,5,Model 1,9,Parking onsite,parking,A_structural,binary,binary,0.085923,0.010863,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.632006,se,"Model 1 (Base): linear ASB, no squared terms",pct_premium,8.965812212426139,1.1837654609880022,100*(exp(b - se^2/2) - 1),reported,0.06552043579487225,0.008283561957097601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.909693454846728,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Parking onsite,parking,A_structural,binary,binary,0.063596,0.010765,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,pct_premium,6.560003898339151,1.1471849109306695,100*(exp(b - se^2/2) - 1),reported,0.048983009036047784,0.008291434874411195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.907663725034835,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Parking onsite,parking,A_structural,binary,binary,0.054154,0.010803,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,pct_premium,5.558556154754801,1.1404156261922123,100*(exp(b - se^2/2) - 1),reported,0.04157782651503092,0.008294221291906028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.012866796260298,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Parking onsite,parking,A_structural,binary,binary,0.027686,0.007667,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),pct_premium,2.804260276791304,0.7882234302269092,100*(exp(b - se^2/2) - 1),reported,0.029963396191029126,0.008297672419151208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.611060388678753,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Parking onsite,parking,A_structural,binary,binary,0.064033,0.010774,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,pct_premium,6.606570463458072,1.1486458551134338,100*(exp(b - se^2/2) - 1),reported,0.04927768186025674,0.008291314546599505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.943289400408391,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Parking onsite,parking,A_structural,binary,binary,0.060777,0.010773,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,pct_premium,6.260025098699362,1.1448056801200617,100*(exp(b - se^2/2) - 1),reported,0.04678193379631546,0.008292310788418423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.641604010025063,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M1,5,Model 1,9,Rural,other,other,binary,binary,-0.071657,0.01061,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.632006,se,"Model 1 (Base): linear ASB, no squared terms",pct_premium,-6.92022697493091,0.9876319801345599,100*(exp(b - se^2/2) - 1),reported,-0.055977445196806815,0.008288383459231063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.753722902921772,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Rural,other,other,binary,binary,-0.087541,0.01044,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,pct_premium,-8.386862014180508,0.9564932849748887,100*(exp(b - se^2/2) - 1),reported,-0.06944048112637935,0.008281360996097834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.38515325670498,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Rural,other,other,binary,binary,-0.088568,0.010417,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,pct_premium,-8.480878458548291,0.9534064166130334,100*(exp(b - se^2/2) - 1),reported,-0.07040547648124926,0.008280799481812545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.50225592781031,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Rural,other,other,binary,binary,-0.039933,0.00744,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),pct_premium,-3.9172778789552276,0.7148752377657571,100*(exp(b - se^2/2) - 1),reported,-0.04451226172842228,0.008293171744158008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.367338709677419,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Rural,other,other,binary,binary,-0.08837,0.010449,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,pct_premium,-8.462786438659931,0.956524560518629,100*(exp(b - se^2/2) - 1),reported,-0.07003477780698093,0.008281016106202825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.457268638147191,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Rural,other,other,binary,binary,-0.079871,0.010557,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,pct_premium,-7.681600989974868,0.9746596498658973,100*(exp(b - se^2/2) - 1),reported,-0.06268231987873087,0.008285075320952058,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.565691010703798,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M1,5,Model 1,9,Private,other,other,binary,binary,0.138356,0.024704,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.632006,se,"Model 1 (Base): linear ASB, no squared terms",pct_premium,14.80339334237344,2.8369685804602813,100*(exp(b - se^2/2) - 1),reported,0.04644224243159759,0.008292442373516052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.600550518134715,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M2,5,Model 2,9,Private,other,other,binary,binary,0.124989,0.024144,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.651338,se,Model 2 (Base 2): adds squared terms,pct_premium,13.280576559815206,2.7358435306401887,100*(exp(b - se^2/2) - 1),reported,0.042935174758583265,0.00829374472450563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.176814115308152,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M3,5,Model 3,9,Private,other,other,binary,binary,0.134252,0.024116,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.652927,se,Model 3 (Density): adds ASB x population density interaction,pct_premium,14.334846746501517,2.758101077758756,100*(exp(b - se^2/2) - 1),reported,0.04616401337893347,0.008292549434245743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.566926521811246,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M4,5,Model 4,9,Private,other,other,binary,binary,0.059928,0.017191,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.823421,se,Model 4 (Property price): adds ASB x price interaction (price on RHS),pct_premium,6.160321742109098,1.8252717828665435,100*(exp(b - se^2/2) - 1),reported,0.028926653832886628,0.008297925945153418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4860101215752426,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M5,5,Model 5,9,Private,other,other,binary,binary,0.168379,0.028833,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.65152,se,Model 5 (Private): adds ASB x Private interaction,pct_premium,18.289323204038688,3.412054052942426,100*(exp(b - se^2/2) - 1),reported,0.04842166170578134,0.008291662095408533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.839801616203656,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T5-M6,5,Model 6,9,Private,other,other,binary,binary,0.124831,0.024126,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.651877,se,Model 6 (Belfast): adds ASB x Belfast interaction,pct_premium,13.262728847329042,2.733371978968462,100*(exp(b - se^2/2) - 1),reported,0.04291293367830699,0.008293752656974906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.174127497305811,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.006672,0.000834,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,semi_elasticity,-0.6672,0.0834,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.12536782816144454,0.015670978520180567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,4008.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.01133,0.000732,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,semi_elasticity,-1.133,0.0732,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.2142403877764443,0.013841479598619352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.478142076502731,4980.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.007973,0.000642,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,semi_elasticity,-0.7972999999999999,0.0642,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.203748203335506,0.01640616412158471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.419003115264797,3561.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.013072,0.002463,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,semi_elasticity,-1.3072,0.2463,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.2287803456787895,0.043106333491956736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.307348761672757,510.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.006937,0.001467,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,semi_elasticity,-0.6937,0.1467,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.18715806594724232,0.03957919601334935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.728698023176551,616.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,2.65e-05,7.97e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,other,2.65e-05,7.97e-06,not harmonised,reported,0.05244759222524844,0.015773860756046417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3249686323713927,4008.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,6.88e-05,7.21e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,other,6.88e-05,7.21e-06,not harmonised,reported,0.13399975064390351,0.01404270642649047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.54230235783634,4980.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.01e-05,4.21e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,other,4.01e-05,4.21e-06,not harmonised,reported,0.1576207082121354,0.01654820901678529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.524940617577196,3561.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,0.000104,2.39e-05,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,other,0.000104,2.39e-05,not harmonised,reported,0.18920568699000126,0.04348092229866375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.351464435146443,510.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,1.8e-05,1.28e-05,,,,,,high,squared term of ASB,ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,other,1.8e-05,1.28e-05,not harmonised,reported,0.05656869893203593,0.04022663035166999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.40625,616.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Age,age,A_structural,level,years,0.006723,0.000856,,,***,,,high,,ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,semi_elasticity,0.6723,0.0856,100*b/1 (per year),reported,0.1231144290853871,0.01567543526656126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.853971962616822,4008.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Age,age,A_structural,level,years,0.000851,0.000923,,,,,,high,,ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,semi_elasticity,0.0851,0.0923,100*b/1 (per year),reported,0.013063998576847273,0.01416929575373682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9219934994582881,4980.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Age,age,A_structural,level,years,-0.002881,0.001315,,,**,,,high,,ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,semi_elasticity,-0.28809999999999997,0.1315,100*b/1 (per year),reported,-0.03668926565468227,0.016746402060363482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.190874524714829,3561.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,Age,age,A_structural,level,years,-0.005064,0.002595,,,**,,,high,,ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,semi_elasticity,-0.5064,0.2595,100*b/1 (per year),reported,-0.0860906219946471,0.0441163436169252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9514450867052022,510.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Age,age,A_structural,level,years,0.003915,0.002399,,,*,,,high,,ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,semi_elasticity,0.3915,0.2399,100*b/1 (per year),reported,0.06561065572911921,0.04020433284652796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6319299708211754,616.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Age × Age,age,A_structural,squared,years,-3.41e-05,6.4e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,other,-3.41e-05,6.4e-06,not harmonised,reported,-0.08386444988679355,0.01573995540397298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.328125000000001,4008.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Age × Age,age,A_structural,squared,years,9.6e-06,7.21e-06,,,,,,high,squared term of age,ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,other,9.6e-06,7.21e-06,not harmonised,reported,0.01886444391409205,0.014167983397979549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.331484049930652,4980.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Age × Age,age,A_structural,squared,years,1.9e-05,9.28e-06,,,**,,,high,squared term of age,ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,other,1.9e-05,9.28e-06,not harmonised,reported,0.03428973817200239,0.01674783001243064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0474137931034484,3561.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,Age × Age,age,A_structural,squared,years,6.06e-05,1.95e-05,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,other,6.06e-05,1.95e-05,not harmonised,reported,0.13632619330545234,0.04386733942997228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1076923076923078,510.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Age × Age,age,A_structural,squared,years,-2.86e-05,1.86e-05,,,,,,high,squared term of age,ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,other,-2.86e-05,1.86e-05,not harmonised,reported,-0.06183450347165122,0.04021404771233261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5376344086021505,616.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Size,living_area,A_structural,level,m2,0.007221,0.000264,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,semi_elasticity,0.7221,0.026400000000000003,100*b/1 (X per m2->m2),reported,0.39661194654237797,0.014500145947540202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.352272727272727,4008.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Size,living_area,A_structural,level,m2,0.010776,0.00046,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,semi_elasticity,1.0776000000000001,0.046,100*b/1 (X per m2->m2),reported,0.31505402741777866,0.01344885417707667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.42608695652174,4980.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Size,living_area,A_structural,level,m2,0.008178,0.000864,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,semi_elasticity,0.8178,0.08639999999999999,100*b/1 (X per m2->m2),reported,0.15665769982744557,0.016550776797617142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.465277777777777,3561.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,Size,living_area,A_structural,level,m2,0.007011,0.001226,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,semi_elasticity,0.7011,0.12260000000000001,100*b/1 (X per m2->m2),reported,0.2454757778668499,0.04292587414987278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.718597063621533,510.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Size,living_area,A_structural,level,m2,0.01103,0.000841,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,semi_elasticity,1.103,0.0841,100*b/1 (X per m2->m2),reported,0.4672110112518819,0.03562325117523415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.115338882282996,616.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Size × Size,living_area,A_structural,squared,m2,-6.12e-06,5.23e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,other,-6.12e-06,5.23e-07,not harmonised,reported,-0.18175698870481802,0.015532500832127424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.70172084130019,4008.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Size × Size,living_area,A_structural,squared,m2,-1.4e-05,1.28e-06,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,other,-1.4e-05,1.28e-06,not harmonised,reported,-0.15316120656315702,0.014003310314345785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.9375,4980.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Size × Size,living_area,A_structural,squared,m2,-5.6e-06,3.44e-06,,,*,,,high,squared term of size,ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,other,-5.6e-06,3.44e-06,not harmonised,reported,-0.027269806584022737,0.016751452615899685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6279069767441858,3561.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,Size × Size,living_area,A_structural,squared,m2,-1.96e-05,3.97e-06,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,other,-1.96e-05,3.97e-06,not harmonised,reported,-0.21357125826855558,0.04325907629215132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.937027707808563,510.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Size × Size,living_area,A_structural,squared,m2,-1.5e-05,2.39e-06,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,other,-1.5e-05,2.39e-06,not harmonised,reported,-0.2451565102928604,0.03906160397332909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.276150627615063,616.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Parking onsite,parking,A_structural,binary,binary,-0.011208,0.043728,,,,,,high,,ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,pct_premium,-1.2090385769979228,4.324063288320339,100*(exp(b - se^2/2) - 1),reported,-0.004048564775705302,0.015795471137762446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2563117453347969,4008.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Parking onsite,parking,A_structural,binary,binary,0.063166,0.030378,,,**,,,high,,ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,pct_premium,6.471226458372237,3.2358756429921685,100*(exp(b - se^2/2) - 1),reported,0.029452426556998614,0.014164357628289015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.079333728356047,4980.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Parking onsite,parking,A_structural,binary,binary,0.027108,0.014338,,,*,,,high,,ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,pct_premium,2.73731562657189,1.4731990525085745,100*(exp(b - se^2/2) - 1),reported,0.03166686362010658,0.016749280307845955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8906402566606222,3561.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Parking onsite,parking,A_structural,binary,binary,0.091669,0.02917,,,***,,,high,,ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,pct_premium,9.553579659137522,3.1970377913461543,100*(exp(b - se^2/2) - 1),reported,0.12561513982083203,0.03997200393343082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.142577991086733,616.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Rural,other,other,binary,binary,-0.079585,0.015479,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,pct_premium,-7.661110918428326,1.4294849057282584,100*(exp(b - se^2/2) - 1),reported,-0.08094629356746318,0.01574376676673698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.141482007881646,4008.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Rural,other,other,binary,binary,-0.107333,0.021478,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,pct_premium,-10.198064031405064,1.929210906240792,100*(exp(b - se^2/2) - 1),reported,-0.07063802374562696,0.014135107320288968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.9973461216128126,4980.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Rural,other,other,binary,binary,-0.050162,0.027596,,,*,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,pct_premium,-4.928673383094095,2.624587502235111,100*(exp(b - se^2/2) - 1),reported,-0.03044677729230291,0.016749915596634725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8177272068415713,3561.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,Rural,other,other,binary,binary,0.044664,0.083693,,,,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,pct_premium,4.202062924538996,8.751579943008657,100*(exp(b - se^2/2) - 1),reported,0.023624474892086147,0.04426838566056255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5336647031412424,510.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Rural,other,other,binary,binary,-0.019899,0.038885,,,,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,pct_premium,-2.044316777596533,3.8118875223511983,100*(exp(b - se^2/2) - 1),reported,-0.02061420057262646,0.04028258652528167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5117397454031117,616.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M7,6,Model 7,10,Private,other,other,binary,binary,-0.008144,0.089321,,,,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,4018.0,0.523332,se,Model 7: detached houses only,pct_premium,-1.2059811004319965,8.859652384711426,100*(exp(b - se^2/2) - 1),reported,-0.0014401903001273319,0.01579558420894811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09117676694170464,4008.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M8,6,Model 8,10,Private,other,other,binary,binary,0.231046,0.046031,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,4990.0,0.471448,se,Model 8: semi-detached houses only,pct_premium,25.858311147443082,5.799524837115462,100*(exp(b - se^2/2) - 1),reported,0.07094758047629576,0.014134796001248107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.019356520605679,4980.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M9,6,Model 9,10,Private,other,other,binary,binary,0.070744,0.036865,,,**,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,3571.0,0.379724,se,Model 9: terraced houses only,pct_premium,7.257734512615843,3.9567441329999955,100*(exp(b - se^2/2) - 1),reported,0.03214141132128141,0.016749026466683244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9190017631900176,3561.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M10,6,Model 10,10,Private,other,other,binary,binary,0.275627,0.07613,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,520.0,0.492908,se,Model 10: apartments only,pct_premium,31.35443726269742,10.029034261308887,100*(exp(b - se^2/2) - 1),reported,0.1582961326751885,0.043722438587519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.620478129515303,510.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T6-M11,6,Model 11,10,Private,other,other,binary,binary,0.277295,0.067088,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,626.0,0.790389,se,Model 11: townhouses only,pct_premium,31.658939361365878,8.852634492012092,100*(exp(b - se^2/2) - 1),reported,0.1642731026253307,0.039743788777035945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.133302528022896,616.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.013723,0.000376,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,semi_elasticity,-1.3723,0.037599999999999995,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.2899624373791761,0.007944755261573287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-36.49734042553192,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.013102,0.000393,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),semi_elasticity,-1.3102,0.0393,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.2667291416418096,0.008000652775548098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.338422391857506,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.01284,0.000368,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,semi_elasticity,-1.284,0.0368,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.27821137922650274,0.007973659466927805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.891304347826086,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.012497,0.000364,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,semi_elasticity,-1.2497,0.0364,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.2740923157800141,0.007983484271739229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.33241758241758,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.005718,0.000502,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,semi_elasticity,-0.5718,0.050199999999999995,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.09413668244284333,0.008264535604460886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.390438247011954,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.010956,0.000384,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,semi_elasticity,-1.0956000000000001,0.038400000000000004,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.23047302443531192,0.008077915423800637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.53125,14511.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,5.2e-05,2.71e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,other,5.2e-05,2.71e-06,not harmonised,reported,0.15730569869391867,0.008198046989625379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.18819188191882,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,5.2e-05,2.71e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),other,5.2e-05,2.71e-06,not harmonised,reported,0.15730569869391867,0.008198046989625379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.18819188191882,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.49e-05,2.72e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,other,4.49e-05,2.72e-06,not harmonised,reported,0.13576533993595868,0.008224537296788587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.50735294117647,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.14e-05,2.72e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,other,4.14e-05,2.72e-06,not harmonised,reported,0.12535550745200963,0.008235917397813193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.220588235294118,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,4.82e-05,2.85e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,other,4.82e-05,2.85e-06,not harmonised,reported,0.13903206426325837,0.008220775584030835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.912280701754387,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,5.57e-05,2.86e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,other,5.57e-05,2.86e-06,not harmonised,reported,0.15960169367959037,0.008194988221250064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.475524475524473,14511.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Age,age,A_structural,level,years,0.001584,0.000491,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,semi_elasticity,0.15839999999999999,0.0491,100*b/1 (per year),reported,0.026771291838238592,0.008298424427130777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.226069246435845,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Age,age,A_structural,level,years,0.002053,0.0005,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),semi_elasticity,0.2053,0.05,100*b/1 (per year),reported,0.03406576400245073,0.008296581588516982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.106,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Age,age,A_structural,level,years,0.002412,0.00049,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,semi_elasticity,0.2412,0.049,100*b/1 (per year),reported,0.04082914277035956,0.008294477594310191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.922448979591837,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Age,age,A_structural,level,years,0.001954,0.00049,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,semi_elasticity,0.1954,0.049,100*b/1 (per year),reported,0.03308582536707044,0.008296854877105688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9877551020408166,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Age,age,A_structural,level,years,0.000785,0.000122,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,semi_elasticity,0.0785,0.012199999999999999,100*b/1 (per year),reported,0.05333870739016917,0.008289582549809731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.434426229508197,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Age,age,A_structural,level,years,0.002597,0.000505,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,semi_elasticity,0.2597,0.0505,100*b/1 (per year),reported,0.04265171643602745,0.008293845514129329,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.142574257425742,14511.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Age × Age,age,A_structural,squared,years,-3.94e-06,3.68e-06,,,,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,other,-3.94e-06,3.68e-06,not harmonised,reported,-0.008887560686802244,0.0083010719105158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0706521739130437,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Age × Age,age,A_structural,squared,years,-3.94e-06,3.68e-06,,,,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),other,-3.94e-06,3.68e-06,not harmonised,reported,-0.008887560686802244,0.0083010719105158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0706521739130437,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Age × Age,age,A_structural,squared,years,-1.05e-05,3.68e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,other,-1.05e-05,3.68e-06,not harmonised,reported,-0.023679417648796316,0.008299072090244804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8532608695652173,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Age × Age,age,A_structural,squared,years,-8.68e-06,3.67e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,other,-8.68e-06,3.67e-06,not harmonised,reported,-0.01963004515075342,0.008299800196228694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3651226158038146,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Age × Age,age,A_structural,squared,years,-1.42e-05,3.81e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,other,-1.42e-05,3.81e-06,not harmonised,reported,-0.03092480223327839,0.008297429331604976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.727034120734908,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Age × Age,age,A_structural,squared,years,-1.04e-05,3.8e-06,,,***,,,high,squared term of age,ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,other,-1.04e-05,3.8e-06,not harmonised,reported,-0.022713758987420413,0.00829925809155746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.736842105263158,14511.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Size,living_area,A_structural,level,m2,0.007935,0.000167,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,semi_elasticity,0.7935,0.0167,100*b/1 (X per m2->m2),reported,0.36692822505647293,0.007722370962121108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.514970059880234,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Size,living_area,A_structural,level,m2,0.008168,0.00017,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),semi_elasticity,0.8168,0.017,100*b/1 (X per m2->m2),reported,0.3704759417421579,0.007710689286993985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.047058823529404,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Size,living_area,A_structural,level,m2,0.008002,0.000168,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,semi_elasticity,0.8002,0.0168,100*b/1 (X per m2->m2),reported,0.3677029132144088,0.007719831219697658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.63095238095239,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Size,living_area,A_structural,level,m2,0.007704,0.000168,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,semi_elasticity,0.7704,0.0168,100*b/1 (X per m2->m2),reported,0.3557717743505355,0.007758262992067751,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.85714285714286,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Size,living_area,A_structural,level,m2,0.005029,6.64e-05,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,semi_elasticity,0.5029,0.00664,100*b/1 (X per m2->m2),reported,0.5322683826430585,0.007027763095545652,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),75.73795180722891,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Size,living_area,A_structural,level,m2,0.008321,0.00017,,,***,,,high,property size in square metres,ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,semi_elasticity,0.8321000000000001,0.017,100*b/1 (X per m2->m2),reported,0.3764399490305086,0.007690757280998252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),48.94705882352941,14511.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Size × Size,living_area,A_structural,squared,m2,-7.59e-06,3.77e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,other,-7.59e-06,3.77e-07,not harmonised,reported,-0.16484263252286432,0.008187835633876133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.132625994694962,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Size × Size,living_area,A_structural,squared,m2,-8e-06,3.84e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),other,-8e-06,3.84e-07,not harmonised,reported,-0.17041601255351374,0.00817996860256866,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.833333333333332,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Size × Size,living_area,A_structural,squared,m2,-7.71e-06,3.77e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,other,-7.71e-06,3.77e-07,not harmonised,reported,-0.16737638014209635,0.008184292517972803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.450928381962868,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Size × Size,living_area,A_structural,squared,m2,-7.26e-06,3.77e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,other,-7.26e-06,3.77e-07,not harmonised,reported,-0.15785811246178702,0.008197315206348997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.25729442970822,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Size × Size,living_area,A_structural,squared,m2,-8.3e-06,3.9e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,other,-8.3e-06,3.9e-07,not harmonised,reported,-0.17397654970366477,0.008174801733063767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.28205128205128,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Size × Size,living_area,A_structural,squared,m2,-8.15e-06,3.76e-07,,,***,,,high,squared term of size,ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,other,-8.15e-06,3.76e-07,not harmonised,reported,-0.17709318348047914,0.008170188587565663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.675531914893618,14511.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Parking onsite,parking,A_structural,binary,binary,0.037509,0.010415,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,pct_premium,3.8165033469359955,1.0813075266846683,100*(exp(b - se^2/2) - 1),reported,0.02988364265065943,0.008297692239372363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.60144023043687,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Parking onsite,parking,A_structural,binary,binary,0.037509,0.010415,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),pct_premium,3.8165033469359955,1.0813075266846683,100*(exp(b - se^2/2) - 1),reported,0.02988364265065943,0.008297692239372363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.60144023043687,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Parking onsite,parking,A_structural,binary,binary,0.055876,0.010401,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,pct_premium,5.740935251203605,1.0998709583994504,100*(exp(b - se^2/2) - 1),reported,0.044552296566252245,0.008293156929371995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.372175752331507,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Parking onsite,parking,A_structural,binary,binary,0.036991,0.010409,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,pct_premium,3.762746806293471,1.0801249442221945,100*(exp(b - se^2/2) - 1),reported,0.029488283145845808,0.008297789712770917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.553751561149006,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Parking onsite,parking,A_structural,binary,binary,0.086069,0.010858,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,pct_premium,8.981728300403446,1.183393362598725,100*(exp(b - se^2/2) - 1),reported,0.06566138096704442,0.008283485047347688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.926782096150305,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Parking onsite,parking,A_structural,binary,binary,0.054918,0.010735,,,***,,,high,,ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,pct_premium,5.6393110653516265,1.134103349566519,100*(exp(b - se^2/2) - 1),reported,0.04242996855770863,0.008293923895025349,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.11578947368421,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Rural,other,other,binary,binary,-0.057876,0.010111,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,pct_premium,-5.628127322546761,0.9542427806222289,100*(exp(b - se^2/2) - 1),reported,-0.04746417918418849,0.008292043605835405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.724062901790129,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Rural,other,other,binary,binary,-0.057876,0.010111,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),pct_premium,-5.628127322546761,0.9542427806222289,100*(exp(b - se^2/2) - 1),reported,-0.04746417918418849,0.008292043605835405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.724062901790129,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Rural,other,other,binary,binary,-0.067325,0.010103,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,pct_premium,-6.51563989643188,0.9445206927974826,100*(exp(b - se^2/2) - 1),reported,-0.05523493297324959,0.008288726740865067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.663862219142829,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Rural,other,other,binary,binary,-0.061424,0.010095,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,pct_premium,-5.9623502367690095,0.9493584472242644,100*(exp(b - se^2/2) - 1),reported,-0.05044635505678782,0.008290830201521767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.084596334819217,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Rural,other,other,binary,binary,-0.064312,0.010782,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,pct_premium,-6.234211604231088,1.0110414963326468,100*(exp(b - se^2/2) - 1),reported,-0.04945523426842542,0.008291241694896177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.9647560749397135,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Rural,other,other,binary,binary,-0.082464,0.010363,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,pct_premium,-7.920485657187348,0.9542712461975095,100*(exp(b - se^2/2) - 1),reported,-0.0659150690855559,0.008283346198748738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.95754125253305,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M12,7,Model 12,11,Private,other,other,binary,binary,0.030205,0.023468,,,,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 12 (MDM): adds ASB x multiple deprivation rank interaction,pct_premium,3.038201980057398,2.4187664965930566,100*(exp(b - se^2/2) - 1),reported,0.010683887386250297,0.008300925978497666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2870717572865178,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M13,7,Model 13,11,Private,other,other,binary,binary,0.030205,0.023468,,,,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.67557,se,Model 13 (Income): adds ASB x income rank interaction (column printed identical to Model 12),pct_premium,3.038201980057398,2.4187664965930566,100*(exp(b - se^2/2) - 1),reported,0.010683887386250297,0.008300925978497666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2870717572865178,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M14,7,Model 14,11,Private,other,other,binary,binary,0.03564,0.023485,,,,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.674713,se,Model 14 (Employment): adds ASB x employment rank interaction,pct_premium,3.599697856242212,2.4337099621742198,100*(exp(b - se^2/2) - 1),reported,0.012596909221727466,0.008300741107527205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5175644028103044,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M15,7,Model 15,11,Private,other,other,binary,binary,0.042281,0.023409,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.675966,se,Model 15 (Education): adds ASB x education rank interaction,pct_premium,4.290178879870465,2.4419977902844674,100*(exp(b - se^2/2) - 1),reported,0.014992184044150701,0.008300466788617199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8061856550899227,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M16,7,Model 16,11,Private,other,other,binary,binary,0.133855,0.024722,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.651339,se,Model 16 (Access): adds ASB x access-to-services rank interaction,pct_premium,14.287773588093234,2.8262858875590333,100*(exp(b - se^2/2) - 1),reported,0.04490183364565276,0.008293027017203896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.414408219399725,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T7-M17,7,Model 17,11,Private,other,other,binary,binary,0.107201,0.024071,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,OLS,14521.0,0.654687,se,Model 17 (Crime): adds ASB x crime rank interaction,pct_premium,11.283553433269056,2.6794825658296553,100*(exp(b - se^2/2) - 1),reported,0.03694532009226653,0.008295732315379032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.453533297328736,14511.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,ols,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M18,8,Model 18,12,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.002611,0.000755,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,spatial_lag,13421.0,0.693112,se,"Model 18 (SLM 1): spatial lag model, reformulation of Model 1 (SL term 1.418***)",semi_elasticity,-0.2611,0.0755,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.029849411780613424,0.008631292950732722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.458278145695364,13411.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,ASB,crime,F_neighbourhood,level,incidents per 1000 population,-0.004122,0.000978,,,***,,,high,"ASB incidents per 1,000 population per year (SOA level)",ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",semi_elasticity,-0.41219999999999996,0.0978,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.0363706542638228,0.008629427430863342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.214723926380368,13411.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,ASB-hat (instrumented),crime,F_neighbourhood,level,incidents per 1000 population,-0.004934,0.000723,,,**,,,high,"ASB incidents per 1,000 population per year (SOA level); predicted ASB from first stage (instrument: 2010 ASB)",ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",semi_elasticity,-0.4934,0.0723,100*b (unit as reported: incidents per 1000 population; no harmonised unit defined for this characteristic),reported,-0.05882710783883767,0.008620186252022627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.824343015214384,13411.0,incidents per 1000 population,unit_not_harmonisable,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,ASB × ASB,crime,F_neighbourhood,squared,incidents per 1000 population,3.42e-05,1.61e-06,,,***,,,high,squared term of ASB,ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",other,3.42e-05,1.61e-06,not harmonised,reported,0.18041956827837935,0.008493435816613765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.24223602484472,13411.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,ASB-hat × ASB-hat (instrumented),crime,F_neighbourhood,squared,incidents per 1000 population,3.72e-05,1.43e-06,,,***,,,high,squared term of ASB; predicted ASB from first stage (instrument: 2010 ASB),ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",other,3.72e-05,1.43e-06,not harmonised,reported,0.21917268683829502,0.008425186617708653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.013986013986013,13411.0,incidents per 1000 population,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M18,8,Model 18,12,Age,age,A_structural,level,years,0.001332,5.1e-05,,,***,,,high,,ln property price (GBP),log,spatial_lag,13421.0,0.693112,se,"Model 18 (SLM 1): spatial lag model, reformulation of Model 1 (SL term 1.418***)",semi_elasticity,0.1332,0.0051,100*b/1 (per year),reported,0.22000385804726671,0.008423571141449399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.117647058823533,13411.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Age,age,A_structural,level,years,0.002344,7e-06,,,***,,,high,,ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",semi_elasticity,0.23440000000000003,0.0007,100*b/1 (per year),reported,0.945078700961426,0.002822334004577637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),334.8571428571429,13411.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Age,age,A_structural,level,years,0.002423,0.000126,,,***,,,high,,ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",semi_elasticity,0.2423,0.0126,100*b/1 (per year),reported,0.1638119838038374,0.008518493586167361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.230158730158728,13411.0,years,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Age × Age,age,A_structural,squared,years,-1.11e-05,3.01e-06,,,***,,,high,squared term of age,ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",other,-1.11e-05,3.01e-06,not harmonised,reported,-0.03182774128374928,0.008630765879647328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6877076411960132,13411.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Age × Age,age,A_structural,squared,years,-1.24e-05,1.72e-06,,,***,,,high,squared term of age,ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",other,-1.24e-05,1.72e-06,not harmonised,reported,-0.0621330587554808,0.008618456537050564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.209302325581395,13411.0,years,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M18,8,Model 18,12,Size,living_area,A_structural,level,m2,0.004137,6.23e-05,,,***,,,high,property size in square metres,ln property price (GBP),log,spatial_lag,13421.0,0.693112,se,"Model 18 (SLM 1): spatial lag model, reformulation of Model 1 (SL term 1.418***)",semi_elasticity,0.4137,0.006229999999999999,100*b/1 (X per m2->m2),reported,0.4974355718564769,0.007490992537263357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),66.40449438202248,13411.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Size,living_area,A_structural,level,m2,0.006478,0.000154,,,***,,,high,property size in square metres,ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",semi_elasticity,0.6478,0.0154,100*b/1 (X per m2->m2),reported,0.3414112083304778,0.008116289917087616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.064935064935064,13411.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Size,living_area,A_structural,level,m2,0.005343,0.000243,,,***,,,high,property size in square metres,ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",semi_elasticity,0.5343,0.0243,100*b/1 (X per m2->m2),reported,0.1865340466829433,0.008483581011408427,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.98765432098765,13411.0,m2,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Size × Size,living_area,A_structural,squared,m2,-5.21e-06,2.82e-07,,,***,,,high,squared term of size,ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",other,-5.21e-06,2.82e-07,not harmonised,reported,-0.15754348131703141,0.008527305514664655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.47517730496454,13411.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Size × Size,living_area,A_structural,squared,m2,-2.66e-06,1.23e-07,,,***,,,high,squared term of size,ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",other,-2.66e-06,1.23e-07,not harmonised,reported,-0.18357028084955332,0.008488400204697391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.6260162601626,13411.0,m2,nonstandard_x_transform,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,False,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M18,8,Model 18,12,Parking onsite,parking,A_structural,binary,binary,0.077516,0.019778,,,**,,,high,,ln property price (GBP),log,spatial_lag,13421.0,0.693112,se,"Model 18 (SLM 1): spatial lag model, reformulation of Model 1 (SL term 1.418***)",pct_premium,8.038819390888552,2.1372097344387297,100*(exp(b - se^2/2) - 1),reported,0.03382437825551439,0.00863019961217766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.919304277480028,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Parking onsite,parking,A_structural,binary,binary,0.075456,0.006855,,,***,,,high,,ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",pct_premium,7.835044144600567,0.739226595914922,100*(exp(b - se^2/2) - 1),reported,0.09462430398099836,0.008596395300436597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.007439824945294,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Parking onsite,parking,A_structural,binary,binary,0.043993,0.009233,,,***,,,high,,ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",pct_premium,4.493049981970709,0.9648254544666806,100*(exp(b - se^2/2) - 1),reported,0.041109564249281305,0.00862784094545983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.764756850427813,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,True,True,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M18,8,Model 18,12,Rural,other,other,binary,binary,-0.043112,0.009224,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,spatial_lag,13421.0,0.693112,se,"Model 18 (SLM 1): spatial lag model, reformulation of Model 1 (SL term 1.418***)",pct_premium,-4.223663516311326,0.8834785110632059,100*(exp(b - se^2/2) - 1),reported,-0.040326902934561246,0.008628116363620172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.673894189071986,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Rural,other,other,binary,binary,-0.064521,0.008455,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",pct_premium,-6.251708332084593,0.7926701383608725,100*(exp(b - se^2/2) - 1),reported,-0.06575306965888592,0.008616453619222897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.631105854523949,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Rural,other,other,binary,binary,-0.068233,0.008433,,,***,,,high,rural location dummy (vs urban),ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",pct_premium,-6.59903956204051,0.7876783069393528,100*(exp(b - se^2/2) - 1),reported,-0.06969864487306413,0.008614140844086434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.091189375074114,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M18,8,Model 18,12,Private,other,other,binary,binary,0.163219,0.022415,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,spatial_lag,13421.0,0.693112,se,"Model 18 (SLM 1): spatial lag model, reformulation of Model 1 (SL term 1.418***)",pct_premium,17.699877075285908,2.6389055969847823,100*(exp(b - se^2/2) - 1),reported,0.0627544529130451,0.00861812081954862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.281686370733883,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M19,8,Model 19,12,Private,other,other,binary,binary,0.138541,0.012894,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,spatial_lag,13421.0,0.71551,se,"Model 19 (SLM 2): spatial lag model, reformulation of Model 2 (SL term 1.219***)",pct_premium,14.850129692711157,1.4810006791996713,100*(exp(b - se^2/2) - 1),reported,0.09238443143678958,0.008598211785290744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.744609896075694,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,spatial,journal,False,False,False
cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1,cc4e9f217dbac07b4b73c8b633af8dfec0f75eb1:T8-M20,8,Model 20,12,Private,other,other,binary,binary,0.112243,0.015488,,,***,,,high,privately built dwelling (vs public housing),ln property price (GBP),log,IV,13421.0,0.703323,se,"Model 20 (2SLS): ASB instrumented by 2010 ASB, with spatial lag term (SL term 1.218***)",pct_premium,11.865051434537843,1.7327737314286487,100*(exp(b - se^2/2) - 1),reported,0.0624575019051965,0.008618281670194875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.247094524793388,13411.0,binary,,2025.0,United Kingdom,Europe,High income,2018.0,2022.0,mixed,transaction,journal,True,True,unknown,,2020.0,iv,journal,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,AGE,age,A_structural,level,years,-290.0,,-7.2,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,-290.0,40.27777777777778,"b (currency per unit, as reported)",|b/t|,-0.14977783828474936,0.020802477539548524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.2,2259.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,BEDROOM,bedrooms,A_structural,level,count,1781.0,,2.1,,**,,,high,,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,1781.0,848.0952380952381,"b (currency per unit, as reported)",|b/t|,0.04414054383268654,0.02101930658699359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,BATHROOM,bathrooms,A_structural,level,count,7836.0,,7.8,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,7836.0,1004.6153846153846,"b (currency per unit, as reported)",|b/t|,0.16194427009625278,0.020762085909775997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,GARAGE,garage,A_structural,level,count,3312.0,,1.9,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,3312.0,1743.157894736842,"b (currency per unit, as reported)",|b/t|,0.03994374217700528,0.021023022198423835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,FIREPLACE,fireplace,B_interior,level,count,551.0,,0.6,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,551.0,918.3333333333334,"b (currency per unit, as reported)",|b/t|,0.01262288229467072,0.021038137157784534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,DCBD,cbd_distance,D_location,level,feet,-0.23,,-4.5,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,-0.23,0.051111111111111114,"b (currency per unit, as reported)",|b/t|,-0.09425763354100423,0.02094614078688983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,DSCH,school_distance,D_location,level,feet,1.39,,3.7,,***,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,1.39,0.3756756756756756,"b (currency per unit, as reported)",|b/t|,0.07761249118962832,0.020976348970169815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,DUGB,other,other,level,feet,-0.68,,-4.8,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,-0.68,0.1416666666666667,"b (currency per unit, as reported)",|b/t|,-0.10047999646310418,0.020933332596480038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.8,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,DREC,green_space,E_environment,level,feet,0.57,,1.6,,*,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,0.57,0.35624999999999996,"b (currency per unit, as reported)",|b/t|,0.03364464308809188,0.02102790193005743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,DHWYS,highway_distance,D_location,level,feet,-0.28,,-2.9,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,-0.28,0.09655172413793105,"b (currency per unit, as reported)",|b/t|,-0.06090219830452242,0.021000758036042212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,DEMP,employment_access,D_location,level,feet,-0.11,,-1.2,,*,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,-0.11,0.09166666666666667,"b (currency per unit, as reported)",|b/t|,-0.02523973286912888,0.02103311072427407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,CRIME,crime,F_neighbourhood,level,count,-8370.0,,-4.9,,***,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,-8370.0,1708.1632653061224,"b (currency per unit, as reported)",|b/t|,-0.10255153684832367,0.020928885071086464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.9,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,EDUCATION,education_level,F_neighbourhood,level,years,1.5,,1.4,,*,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),monetary,1.5,1.0714285714285714,"b (currency per unit, as reported)",|b/t|,0.029442968623884062,0.0210306918742029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4,2259.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.0015,,3.5,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),other,0.0015,0.0004285714285714286,not harmonised,|b/t|,0.07344049141145632,0.02098299754613038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,2259.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T1-c1,I,1,90,TIME*SQFT,living_area,A_structural,other,sqft x month,0.207,,22.7,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,2269.0,0.53,t,Full sample 1978-1990 with UGB dummy (not extracted),other,0.207,0.009118942731277533,not harmonised,|b/t|,0.4309728670645175,0.01898558885746773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.7,2259.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,AGE,age,A_structural,level,years,-287.0,,-7.0,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,-287.0,41.0,"b (currency per unit, as reported)",|b/t|,-0.14570690649263465,0.020815272356090663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,2259.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,BEDROOM,bedrooms,A_structural,level,count,1754.0,,2.0,,**,,,high,,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,1754.0,877.0,"b (currency per unit, as reported)",|b/t|,0.042042421183442494,0.02102121059172125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,BATHROOM,bathrooms,A_structural,level,count,7695.0,,7.6,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,7695.0,1012.5,"b (currency per unit, as reported)",|b/t|,0.15789670570166336,0.020775882329166235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,GARAGE,garage,A_structural,level,count,3001.0,,1.7,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,3001.0,1765.2941176470588,"b (currency per unit, as reported)",|b/t|,0.03574482548777338,0.021026367933984342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,FIREPLACE,fireplace,B_interior,level,count,502.0,,0.5,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,502.0,1004.0,"b (currency per unit, as reported)",|b/t|,0.010519324655789383,0.021038649311578766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,DCBD,cbd_distance,D_location,level,feet,-0.23,,-4.4,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,-0.23,0.05227272727272727,"b (currency per unit, as reported)",|b/t|,-0.09218101861049258,0.02095023150238468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.4,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,DSCH,school_distance,D_location,level,feet,1.3,,3.5,,***,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,1.3,0.37142857142857144,"b (currency per unit, as reported)",|b/t|,0.07344049141145632,0.02098299754613038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,DUGB,other,other,level,feet,-0.67,,-4.7,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,-0.67,0.1425531914893617,"b (currency per unit, as reported)",|b/t|,-0.0984071484978859,0.020937691169762955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.7,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,DREC,green_space,E_environment,level,feet,0.61,,1.7,,*,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,0.61,0.3588235294117647,"b (currency per unit, as reported)",|b/t|,0.03574482548777338,0.021026367933984342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,DHWYS,highway_distance,D_location,level,feet,-0.26,,-2.7,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,-0.26,0.0962962962962963,"b (currency per unit, as reported)",|b/t|,-0.056716056035933576,0.021005946679975396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,DEMP,employment_access,D_location,level,feet,-0.09,,-1.0,,*,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,-0.09,0.09,"b (currency per unit, as reported)",|b/t|,-0.021035158095583564,0.021035158095583564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,CRIME,crime,F_neighbourhood,level,count,-6560.0,,-3.8,,***,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,-6560.0,1726.3157894736844,"b (currency per unit, as reported)",|b/t|,-0.07969697695451596,0.020972888672241043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,EDUCATION,education_level,F_neighbourhood,level,years,1.5,,1.4,,*,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",monetary,1.5,1.0714285714285714,"b (currency per unit, as reported)",|b/t|,0.029442968623884062,0.0210306918742029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4,2259.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.0015,,3.6,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",other,0.0015,0.00041666666666666664,not harmonised,|b/t|,0.07552698709986687,0.02097971863885191,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6,2259.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c1,II,Before-After UGB,91,TIME*SQFT,living_area,A_structural,other,sqft x month,0.207,,22.4,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, no UGB dummy",other,0.207,0.009241071428571428,not harmonised,|b/t|,0.42631802115172707,0.019032054515702102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.4,2259.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,AGE,age,A_structural,level,years,-140.0,,-1.9,,*,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,-140.0,73.6842105263158,"b (currency per unit, as reported)",|b/t|,-0.10481253217040705,0.05516449061600371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9,325.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,BEDROOM,bedrooms,A_structural,level,count,149.0,,0.1,,,,,high,,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,149.0,1490.0,"b (currency per unit, as reported)",|b/t|,0.005546916625729864,0.055469166257298634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,BATHROOM,bathrooms,A_structural,level,count,11589.0,,6.9,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,11589.0,1679.5652173913043,"b (currency per unit, as reported)",|b/t|,0.3574553797532395,0.051805127500469485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,GARAGE,garage,A_structural,level,count,1741.0,,0.6,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,1741.0,2901.666666666667,"b (currency per unit, as reported)",|b/t|,0.03326359395884284,0.055439323264738063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,FIREPLACE,fireplace,B_interior,level,count,-1628.0,,-0.9,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,-1628.0,1808.888888888889,"b (currency per unit, as reported)",|b/t|,-0.049860921946942555,0.05540102438549172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,DCBD,cbd_distance,D_location,level,feet,-0.11,,-1.4,,*,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,-0.11,0.07857142857142858,"b (currency per unit, as reported)",|b/t|,-0.07742491251878633,0.055303508941990244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,DSCH,school_distance,D_location,level,feet,0.6,,0.3,,,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,0.6,2.0,"b (currency per unit, as reported)",|b/t|,0.016638702225921842,0.055462340753072804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,DUGB,other,other,level,feet,-0.5,,-2.1,,**,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,-0.5,0.23809523809523808,"b (currency per unit, as reported)",|b/t|,-0.1157046745777119,0.05509746408462471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,DREC,green_space,E_environment,level,feet,0.39,,0.6,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,0.39,0.65,"b (currency per unit, as reported)",|b/t|,0.03326359395884284,0.055439323264738063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,DHWYS,highway_distance,D_location,level,feet,-0.43,,-2.9,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,-0.43,0.1482758620689655,"b (currency per unit, as reported)",|b/t|,-0.15882127827958095,0.05476595802744171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,DEMP,employment_access,D_location,level,feet,0.25,,1.6,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,0.25,0.15625,"b (currency per unit, as reported)",|b/t|,0.08840453648467905,0.05525283530292441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,CRIME,crime,F_neighbourhood,level,count,-1240.0,,-0.5,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,-1240.0,2480.0,"b (currency per unit, as reported)",|b/t|,-0.027724348650071385,0.05544869730014276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,EDUCATION,education_level,F_neighbourhood,level,years,1.8,,1.1,,*,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),monetary,1.8,1.6363636363636362,"b (currency per unit, as reported)",|b/t|,0.0609037522372106,0.05536704748837326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,325.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.013,,6.0,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),other,0.013,0.0021666666666666666,not harmonised,|b/t|,0.3157894736842105,0.05263157894736842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,325.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c2,II,Before UGB,91,TIME*SQFT,living_area,A_structural,other,sqft x month,0.547,,6.6,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,335.0,0.62,t,Sales before UGB adoption (Jan 1978-Oct 1980),other,0.547,0.08287878787878789,not harmonised,|b/t|,0.3437873053340943,0.05208898565668096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.6,325.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,AGE,age,A_structural,level,years,-341.0,,-7.6,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,-341.0,44.86842105263158,"b (currency per unit, as reported)",|b/t|,-0.1707214391493974,0.02246334725649966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.6,1924.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,BEDROOM,bedrooms,A_structural,level,count,1222.0,,1.2,,*,,,high,,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,1222.0,1018.3333333333334,"b (currency per unit, as reported)",|b/t|,0.02734741311024688,0.022789510925205734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,BATHROOM,bathrooms,A_structural,level,count,6277.0,,5.5,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,6277.0,1141.2727272727273,"b (currency per unit, as reported)",|b/t|,0.12441496713654908,0.0226209031157362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,GARAGE,garage,A_structural,level,count,3820.0,,1.9,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,3820.0,2010.5263157894738,"b (currency per unit, as reported)",|b/t|,0.0432756914455437,0.022776679708180893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,FIREPLACE,fireplace,B_interior,level,count,726.0,,0.7,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,726.0,1037.142857142857,"b (currency per unit, as reported)",|b/t|,0.01595659457507815,0.022795135107254505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,DCBD,cbd_distance,D_location,level,feet,-0.23,,-4.0,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,-0.23,0.0575,"b (currency per unit, as reported)",|b/t|,-0.09081532183729997,0.02270383045932499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,DSCH,school_distance,D_location,level,feet,1.3,,3.3,,***,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,1.3,0.393939393939394,"b (currency per unit, as reported)",|b/t|,0.07502150976639273,0.02273379083830083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,DUGB,other,other,level,feet,-0.65,,-4.1,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,-0.65,0.1585365853658537,"b (currency per unit, as reported)",|b/t|,-0.0930662781259473,0.022699092225840805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.1,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,DREC,green_space,E_environment,level,feet,0.64,,1.6,,*,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,0.64,0.39999999999999997,"b (currency per unit, as reported)",|b/t|,0.03645261704725585,0.022782885654534905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,DHWYS,highway_distance,D_location,level,feet,-0.27,,-2.5,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,-0.27,0.10800000000000001,"b (currency per unit, as reported)",|b/t|,-0.056902746409049805,0.022761098563619922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,DEMP,employment_access,D_location,level,feet,-0.15,,-1.5,,*,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,-0.15,0.09999999999999999,"b (currency per unit, as reported)",|b/t|,-0.034177078284690625,0.022784718856460415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,CRIME,crime,F_neighbourhood,level,count,-10326.0,,-3.4,,***,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,-10326.0,3037.0588235294117,"b (currency per unit, as reported)",|b/t|,-0.0772815097615063,0.022729855812207735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,EDUCATION,education_level,F_neighbourhood,level,years,0.87,,0.6,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),monetary,0.87,1.45,"b (currency per unit, as reported)",|b/t|,0.013677543033659605,0.022795905056099346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,1924.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.0013,,2.9,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),other,0.0013,0.0004482758620689655,not harmonised,|b/t|,0.06597028492176932,0.02274837411095494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9,1924.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T2-c3,II,After UGB,91,TIME*SQFT,living_area,A_structural,other,sqft x month,0.217,,21.9,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,Sales after UGB adoption (Oct 1980-Dec 1990),other,0.217,0.009908675799086759,not harmonised,|b/t|,0.44669605163969933,0.020397079983547915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.9,1924.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,AGE,age,A_structural,level,years,-442.0,,-2.1,,**,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-442.0,210.47619047619045,"b (currency per unit, as reported)",|b/t|,-0.1599329944957576,0.07615856880750362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1,168.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,BEDROOM,bedrooms,A_structural,level,count,-3308.0,,-0.8,,,,,high,,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-3308.0,4135.0,"b (currency per unit, as reported)",|b/t|,-0.06160411036336975,0.07700513795421218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,BATHROOM,bathrooms,A_structural,level,count,15800.0,,3.1,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,15800.0,5096.774193548387,"b (currency per unit, as reported)",|b/t|,0.23260978865339565,0.07503541569464377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,GARAGE,garage,A_structural,level,count,-10106.0,,-1.2,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-10106.0,8421.666666666668,"b (currency per unit, as reported)",|b/t|,-0.09218776257945031,0.07682313548287525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,FIREPLACE,fireplace,B_interior,level,count,3107.0,,0.7,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,3107.0,4438.571428571428,"b (currency per unit, as reported)",|b/t|,0.05392758535282174,0.07703940764688821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,DCBD,cbd_distance,D_location,level,feet,0.09,,0.3,,,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,0.09,0.3,"b (currency per unit, as reported)",|b/t|,0.023139305295980295,0.07713101765326766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,DSCH,school_distance,D_location,level,feet,2.4,,1.4,,*,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,2.4,1.7142857142857144,"b (currency per unit, as reported)",|b/t|,0.10738773303283204,0.07670552359488003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,DUGB,other,other,level,feet,0.1,,0.2,,,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,0.1,0.5,"b (currency per unit, as reported)",|b/t|,0.015428498379527545,0.07714249189763772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,DREC,green_space,E_environment,level,feet,-0.9,,-0.6,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-0.9,1.5,"b (currency per unit, as reported)",|b/t|,-0.04624148705148565,0.07706914508580942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,DHWYS,highway_distance,D_location,level,feet,-0.09,,-0.2,,,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-0.09,0.44999999999999996,"b (currency per unit, as reported)",|b/t|,-0.015428498379527545,0.07714249189763772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,DEMP,employment_access,D_location,level,feet,0.003,,0.0,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,0.003,,"b (currency per unit, as reported)",,0.0,0.07715167498104596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,CRIME,crime,F_neighbourhood,level,count,-25861.0,,-0.8,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-25861.0,32326.25,"b (currency per unit, as reported)",|b/t|,-0.06160411036336975,0.07700513795421218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,EDUCATION,education_level,F_neighbourhood,level,years,-0.1,,-0.03,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,monetary,-0.1,3.3333333333333335,"b (currency per unit, as reported)",|b/t|,-0.0023145440497681197,0.077151468325604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.03,168.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.009,,0.8,,,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,other,0.009,0.011249999999999998,not harmonised,|b/t|,0.06160411036336975,0.07700513795421218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,168.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c1,IV,1980-1982,97,TIME*SQFT,living_area,A_structural,other,sqft x month,0.465,,4.7,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,178.0,0.5,t,Post-UGB sub-period 1980-1982,other,0.465,0.09893617021276596,not harmonised,|b/t|,0.340893109268833,0.07253044878060276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.7,168.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,AGE,age,A_structural,level,years,-160.0,,-1.6,,*,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,-160.0,100.0,"b (currency per unit, as reported)",|b/t|,-0.08075454233417081,0.050471588958856756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,390.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,BEDROOM,bedrooms,A_structural,level,count,7194.0,,3.1,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,7194.0,2320.6451612903224,"b (currency per unit, as reported)",|b/t|,0.15507561780001122,0.050024392838713294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,BATHROOM,bathrooms,A_structural,level,count,4280.0,,1.7,,*,,,high,,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,4280.0,2517.6470588235293,"b (currency per unit, as reported)",|b/t|,0.08576565995964393,0.050450388211555244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,GARAGE,garage,A_structural,level,count,3077.0,,0.7,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,3077.0,4395.714285714286,"b (currency per unit, as reported)",|b/t|,0.035423631526376995,0.05060518789482429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,FIREPLACE,fireplace,B_interior,level,count,2058.0,,0.8,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,2058.0,2572.5,"b (currency per unit, as reported)",|b/t|,0.04047637691117903,0.05059547113897378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,DCBD,cbd_distance,D_location,level,feet,-0.17,,-1.4,,*,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,-0.17,0.12142857142857144,"b (currency per unit, as reported)",|b/t|,-0.07071428608243589,0.05051020434459707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,DSCH,school_distance,D_location,level,feet,0.6,,0.7,,,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,0.6,0.8571428571428572,"b (currency per unit, as reported)",|b/t|,0.035423631526376995,0.05060518789482429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,DUGB,other,other,level,feet,-0.35,,-0.9,,,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,-0.35,0.38888888888888884,"b (currency per unit, as reported)",|b/t|,-0.04552601902123673,0.050584465579151916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,DREC,green_space,E_environment,level,feet,0.01,,0.01,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,0.01,1.0,"b (currency per unit, as reported)",|b/t|,0.0005063696186226556,0.05063696186226556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,DHWYS,highway_distance,D_location,level,feet,-0.07,,-0.2,,,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,-0.07,0.35000000000000003,"b (currency per unit, as reported)",|b/t|,-0.010126874357261942,0.05063437178630971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,DEMP,employment_access,D_location,level,feet,0.004,,0.01,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,0.004,0.4,"b (currency per unit, as reported)",|b/t|,0.0005063696186226556,0.05063696186226556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,CRIME,crime,F_neighbourhood,level,count,-103321.0,,-2.0,,**,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,-103321.0,51660.5,"b (currency per unit, as reported)",|b/t|,-0.10075854437197568,0.05037927218598783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,EDUCATION,education_level,F_neighbourhood,level,years,-0.9,,-0.3,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,monetary,-0.9,3.0,"b (currency per unit, as reported)",|b/t|,-0.015189337991433701,0.05063112663811234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,390.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,-0.002,,-2.4,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,other,-0.002,0.0008333333333333334,not harmonised,|b/t|,-0.12064110128134099,0.05026712553389208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,390.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c2,IV,1983-1985,97,TIME*SQFT,living_area,A_structural,other,sqft x month,0.224,,7.1,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,Post-UGB sub-period 1983-1985,other,0.224,0.03154929577464789,not harmonised,|b/t|,0.33832162876255006,0.04765093362852818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.1,390.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,AGE,age,A_structural,level,years,-298.0,,-2.9,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-298.0,102.75862068965517,"b (currency per unit, as reported)",|b/t|,-0.135595466756554,0.04675705750226001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9,449.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,BEDROOM,bedrooms,A_structural,level,count,6890.0,,3.2,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,6890.0,2153.125,"b (currency per unit, as reported)",|b/t|,0.14932417506477433,0.04666380470774197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,BATHROOM,bathrooms,A_structural,level,count,6465.0,,2.7,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,6465.0,2394.4444444444443,"b (currency per unit, as reported)",|b/t|,0.12639889831963233,0.04681440678504901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,GARAGE,garage,A_structural,level,count,3535.0,,0.7,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,3535.0,5050.0,"b (currency per unit, as reported)",|b/t|,0.03301703141057877,0.04716718772939824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,FIREPLACE,fireplace,B_interior,level,count,-2194.0,,-1.0,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-2194.0,2194.0,"b (currency per unit, as reported)",|b/t|,-0.04714045207910317,0.04714045207910317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,DCBD,cbd_distance,D_location,level,feet,-0.3,,-2.4,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-0.3,0.125,"b (currency per unit, as reported)",|b/t|,-0.11254342112174733,0.04689309213406139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,DSCH,school_distance,D_location,level,feet,0.6,,0.7,,,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,0.6,0.8571428571428572,"b (currency per unit, as reported)",|b/t|,0.03301703141057877,0.04716718772939824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,DUGB,other,other,level,feet,-0.82,,-2.8,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-0.82,0.29285714285714287,"b (currency per unit, as reported)",|b/t|,-0.13100140994368073,0.04678621783702883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,DREC,green_space,E_environment,level,feet,0.54,,0.6,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,0.54,0.9000000000000001,"b (currency per unit, as reported)",|b/t|,0.02830440598661799,0.04717400997769665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,DHWYS,highway_distance,D_location,level,feet,-0.28,,-1.3,,*,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-0.28,0.2153846153846154,"b (currency per unit, as reported)",|b/t|,-0.06123565834746323,0.04710435257497172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,DEMP,employment_access,D_location,level,feet,-0.1,,-0.6,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-0.1,0.16666666666666669,"b (currency per unit, as reported)",|b/t|,-0.02830440598661799,0.04717400997769665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,CRIME,crime,F_neighbourhood,level,count,-1947.0,,-0.04,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,-1947.0,48675.0,"b (currency per unit, as reported)",|b/t|,-0.0018877133493259454,0.04719283373314864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.04,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,EDUCATION,education_level,F_neighbourhood,level,years,5.3,,1.8,,*,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,monetary,5.3,2.944444444444444,"b (currency per unit, as reported)",|b/t|,0.08464240970046244,0.04702356094470135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,449.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.003,,2.4,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,other,0.003,0.00125,not harmonised,|b/t|,0.11254342112174733,0.04689309213406139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,449.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c3,IV,1986-1987,97,TIME*SQFT,living_area,A_structural,other,sqft x month,0.162,,7.6,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,Post-UGB sub-period 1986-1987,other,0.162,0.02131578947368421,not harmonised,|b/t|,0.33760776630241873,0.04442207451347615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6,449.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,AGE,age,A_structural,level,years,-488.0,,-8.5,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,-488.0,57.411764705882355,"b (currency per unit, as reported)",|b/t|,-0.27444354581300584,0.03228747597800068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.5,887.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,BEDROOM,bedrooms,A_structural,level,count,-5261.0,,-3.8,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,-5261.0,1384.4736842105265,"b (currency per unit, as reported)",|b/t|,-0.12656545477142664,0.03330669862405965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,BATHROOM,bathrooms,A_structural,level,count,4855.0,,3.1,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,4855.0,1566.1290322580644,"b (currency per unit, as reported)",|b/t|,0.10352849595222725,0.0333962890168475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,GARAGE,garage,A_structural,level,count,5586.0,,2.1,,**,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,5586.0,2660.0,"b (currency per unit, as reported)",|b/t|,0.07033646601633083,0.03349355524587182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,FIREPLACE,fireplace,B_interior,level,count,988.0,,0.7,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,988.0,1411.4285714285716,"b (currency per unit, as reported)",|b/t|,0.023497210411330398,0.03356744344475771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,DCBD,cbd_distance,D_location,level,feet,-0.26,,-3.2,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,-0.26,0.08125,"b (currency per unit, as reported)",|b/t|,-0.10683059936392997,0.03338456230122811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,DSCH,school_distance,D_location,level,feet,1.2,,2.1,,**,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,1.2,0.5714285714285714,"b (currency per unit, as reported)",|b/t|,0.07033646601633083,0.03349355524587182,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,DUGB,other,other,level,feet,-0.68,,-2.0,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,-0.68,0.34,"b (currency per unit, as reported)",|b/t|,-0.0670025210172808,0.033501260508640406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,DREC,green_space,E_environment,level,feet,1.3,,2.4,,***,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,1.3,0.5416666666666667,"b (currency per unit, as reported)",|b/t|,0.080323732277167,0.033468221782152925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,DHWYS,highway_distance,D_location,level,feet,-0.37,,-2.5,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,-0.37,0.148,"b (currency per unit, as reported)",|b/t|,-0.08364760220474927,0.03345904088189971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,DEMP,employment_access,D_location,level,feet,0.2,,1.4,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,0.2,0.14285714285714288,"b (currency per unit, as reported)",|b/t|,0.046955549343084796,0.03353967810220342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,CRIME,crime,F_neighbourhood,level,count,-1920.0,,-0.2,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,-1920.0,9600.0,"b (currency per unit, as reported)",|b/t|,-0.00671519136687817,0.033575956834390845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,EDUCATION,education_level,F_neighbourhood,level,years,0.5,,0.2,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,monetary,0.5,2.5,"b (currency per unit, as reported)",|b/t|,0.00671519136687817,0.033575956834390845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,887.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,TIME*LOTSIZE,lot_size,A_structural,other,sqft x month,0.002,,4.2,,***,,,high,Interaction of lot size (sqft) with monthly time index; LOTSIZE main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,other,0.002,0.0004761904761904762,not harmonised,|b/t|,0.13964050057985736,0.033247738233299365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2,887.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T4-c4,IV,1988-1990,97,TIME*SQFT,living_area,A_structural,other,sqft x month,0.252,,21.5,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,Post-UGB sub-period 1988-1990,other,0.252,0.01172093023255814,not harmonised,|b/t|,0.5853184940567818,0.02722411600264102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.5,887.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,AGE,age,A_structural,level,years,-323.0,,-7.7,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,-323.0,41.94805194805195,"b (currency per unit, as reported)",|b/t|,-0.15992149616840812,0.020769025476416635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.7,2259.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,BEDROOM,bedrooms,A_structural,level,count,1849.0,,2.1,,**,,,high,,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,1849.0,880.4761904761905,"b (currency per unit, as reported)",|b/t|,0.04414054383268654,0.02101930658699359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,BATHROOM,bathrooms,A_structural,level,count,7587.0,,7.5,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,7587.0,1011.6,"b (currency per unit, as reported)",|b/t|,0.15586992159713742,0.020782656212951653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,GARAGE,garage,A_structural,level,count,2630.0,,1.4,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,2630.0,1878.5714285714287,"b (currency per unit, as reported)",|b/t|,0.029442968623884062,0.0210306918742029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,FIREPLACE,fireplace,B_interior,level,count,513.0,,0.5,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,513.0,1026.0,"b (currency per unit, as reported)",|b/t|,0.010519324655789383,0.021038649311578766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,DCBD,cbd_distance,D_location,level,feet,-0.22,,-4.2,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,-0.22,0.05238095238095238,"b (currency per unit, as reported)",|b/t|,-0.08802420459487496,0.020958143951160704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.2,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,DSCH,school_distance,D_location,level,feet,1.2,,3.3,,***,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,1.2,0.36363636363636365,"b (currency per unit, as reported)",|b/t|,0.06926463246488226,0.020989282565115838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,DUGB,other,other,level,feet,-0.67,,-4.7,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,-0.67,0.1425531914893617,"b (currency per unit, as reported)",|b/t|,-0.0984071484978859,0.020937691169762955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.7,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,DREC,green_space,E_environment,level,feet,0.48,,1.3,,*,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,0.48,0.3692307692307692,"b (currency per unit, as reported)",|b/t|,0.02734153202484498,0.02103194771141921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,DHWYS,highway_distance,D_location,level,feet,-0.26,,-2.7,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,-0.26,0.0962962962962963,"b (currency per unit, as reported)",|b/t|,-0.056716056035933576,0.021005946679975396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,DEMP,employment_access,D_location,level,feet,-0.1,,-1.1,,*,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,-0.1,0.09090909090909091,"b (currency per unit, as reported)",|b/t|,-0.023137598953170056,0.021034180866518228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1,2259.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,CRIME,crime,F_neighbourhood,level,count,-6464.0,,-3.7,,***,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,-6464.0,1747.0270270270269,"b (currency per unit, as reported)",|b/t|,-0.07761249118962832,0.020976348970169815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7,2259.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,EDUCATION,education_level,F_neighbourhood,level,years,1.7,,1.5,,*,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",monetary,1.7,1.1333333333333333,"b (currency per unit, as reported)",|b/t|,0.03154401489382559,0.021029343262550394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,2259.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,TIME*LOTAMT,lot_size,A_structural,other,binary x month,75.0,,5.3,,***,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",other,75.0,14.150943396226415,not harmonised,|b/t|,0.11082410740273667,0.020910208943912583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3,2259.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c1,VI,Before-After UGB,104,TIME*SQFT,living_area,A_structural,other,sqft x month,0.204,,22.1,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,2269.0,0.52,t,"Full sample 1978-1990, TIME*LOTAMT specification",other,0.204,0.00923076923076923,not harmonised,|b/t|,0.42162906017526197,0.01907823801698018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.1,2259.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,AGE,age,A_structural,level,years,-173.0,,-2.2,,**,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,-173.0,78.63636363636363,"b (currency per unit, as reported)",|b/t|,-0.12113538387796403,0.05506153812634728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2,325.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,BEDROOM,bedrooms,A_structural,level,count,620.0,,0.4,,,,,high,,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,620.0,1550.0,"b (currency per unit, as reported)",|b/t|,0.02218254820132587,0.05545637050331468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,BATHROOM,bathrooms,A_structural,level,count,10512.0,,6.1,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,10512.0,1723.2786885245903,"b (currency per unit, as reported)",|b/t|,0.32051592820053915,0.05254359478697363,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,GARAGE,garage,A_structural,level,count,2280.0,,0.8,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,2280.0,2850.0,"b (currency per unit, as reported)",|b/t|,0.04433238681619272,0.0554154835202409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,FIREPLACE,fireplace,B_interior,level,count,-296.0,,-0.1,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,-296.0,2960.0,"b (currency per unit, as reported)",|b/t|,-0.005546916625729864,0.055469166257298634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,DCBD,cbd_distance,D_location,level,feet,-0.13,,-1.5,,*,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,-0.13,0.08666666666666667,"b (currency per unit, as reported)",|b/t|,-0.08291849892341732,0.05527899928227821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,DSCH,school_distance,D_location,level,feet,1.3,,1.9,,*,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,1.3,0.6842105263157895,"b (currency per unit, as reported)",|b/t|,0.10481253217040705,0.05516449061600371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,DUGB,other,other,level,feet,-0.62,,-2.5,,**,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,-0.62,0.248,"b (currency per unit, as reported)",|b/t|,-0.137360563948689,0.054944225579475606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,DREC,green_space,E_environment,level,feet,0.33,,0.5,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,0.33,0.66,"b (currency per unit, as reported)",|b/t|,0.027724348650071385,0.05544869730014276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,DHWYS,highway_distance,D_location,level,feet,-0.43,,-2.8,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,-0.43,0.15357142857142858,"b (currency per unit, as reported)",|b/t|,-0.1534759303427347,0.0548128322652624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,DEMP,employment_access,D_location,level,feet,0.17,,1.2,,*,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,0.17,0.1416666666666667,"b (currency per unit, as reported)",|b/t|,0.06641704686819362,0.05534753905682801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,325.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,CRIME,crime,F_neighbourhood,level,count,-2841.0,,-1.2,,*,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,-2841.0,2367.5,"b (currency per unit, as reported)",|b/t|,-0.06641704686819362,0.05534753905682801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,325.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,EDUCATION,education_level,F_neighbourhood,level,years,1.5,,0.9,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",monetary,1.5,1.6666666666666665,"b (currency per unit, as reported)",|b/t|,0.049860921946942555,0.05540102438549172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9,325.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,TIME*LOTAMT,lot_size,A_structural,other,binary x month,506.0,,4.3,,***,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",other,506.0,117.67441860465117,not harmonised,|b/t|,0.23201251219872435,0.053956398185749856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.3,325.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c2,VI,Before UGB,104,TIME*SQFT,living_area,A_structural,other,sqft x month,0.587,,6.9,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,335.0,0.6,t,"Sales before UGB adoption, TIME*LOTAMT specification",other,0.587,0.08507246376811593,not harmonised,|b/t|,0.3574553797532395,0.051805127500469485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9,325.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,AGE,age,A_structural,level,years,-390.0,,-8.5,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,-390.0,45.88235294117647,"b (currency per unit, as reported)",|b/t|,-0.19024421572160644,0.022381672437836047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.5,1924.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,BEDROOM,bedrooms,A_structural,level,count,1335.0,,1.3,,*,,,high,,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,1335.0,1026.923076923077,"b (currency per unit, as reported)",|b/t|,0.029624441039783313,0.022788031569064088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,BATHROOM,bathrooms,A_structural,level,count,6229.0,,5.5,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,6229.0,1132.5454545454545,"b (currency per unit, as reported)",|b/t|,0.12441496713654908,0.0226209031157362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,GARAGE,garage,A_structural,level,count,3320.0,,1.6,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,3320.0,2075.0,"b (currency per unit, as reported)",|b/t|,0.03645261704725585,0.022782885654534905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,FIREPLACE,fireplace,B_interior,level,count,686.0,,0.7,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,686.0,980.0000000000001,"b (currency per unit, as reported)",|b/t|,0.01595659457507815,0.022795135107254505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,DCBD,cbd_distance,D_location,level,feet,-0.22,,-3.8,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,-0.22,0.05789473684210526,"b (currency per unit, as reported)",|b/t|,-0.0863092643873498,0.022712964312460476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,DSCH,school_distance,D_location,level,feet,1.2,,3.0,,***,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,1.2,0.39999999999999997,"b (currency per unit, as reported)",|b/t|,0.06823470647795327,0.022744902159317754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,DUGB,other,other,level,feet,-0.63,,-4.0,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,-0.63,0.1575,"b (currency per unit, as reported)",|b/t|,-0.09081532183729997,0.02270383045932499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,DREC,green_space,E_environment,level,feet,0.48,,1.2,,*,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,0.48,0.4,"b (currency per unit, as reported)",|b/t|,0.02734741311024688,0.022789510925205734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,DHWYS,highway_distance,D_location,level,feet,-0.27,,-2.5,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,-0.27,0.10800000000000001,"b (currency per unit, as reported)",|b/t|,-0.056902746409049805,0.022761098563619922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,DEMP,employment_access,D_location,level,feet,-0.16,,-1.5,,*,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,-0.16,0.10666666666666667,"b (currency per unit, as reported)",|b/t|,-0.034177078284690625,0.022784718856460415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,1924.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,CRIME,crime,F_neighbourhood,level,count,-9765.0,,-3.2,,***,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,-9765.0,3051.5625,"b (currency per unit, as reported)",|b/t|,-0.07276035313504536,0.022737610354701673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2,1924.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,EDUCATION,education_level,F_neighbourhood,level,years,1.1,,0.8,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",monetary,1.1,1.375,"b (currency per unit, as reported)",|b/t|,0.018235397441342237,0.022794246801677798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,1924.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,TIME*LOTAMT,lot_size,A_structural,other,binary x month,77.0,,5.3,,***,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",other,77.0,14.528301886792454,not harmonised,|b/t|,0.11995709813379818,0.022633414742226073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3,1924.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T6-c3,VI,After UGB,104,TIME*SQFT,living_area,A_structural,other,sqft x month,0.211,,21.5,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,1934.0,0.53,t,"Sales after UGB adoption, TIME*LOTAMT specification",other,0.211,0.009813953488372093,not harmonised,|b/t|,0.4401295109173024,0.020471140042665228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.5,1924.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,AGE,age,A_structural,level,years,-390.0,,-1.8,,*,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-390.0,216.66666666666666,"b (currency per unit, as reported)",|b/t|,-0.13755294578519067,0.07641830321399481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8,168.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,BEDROOM,bedrooms,A_structural,level,count,-3127.0,,-0.8,,,,,high,,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-3127.0,3908.75,"b (currency per unit, as reported)",|b/t|,-0.06160411036336975,0.07700513795421218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,BATHROOM,bathrooms,A_structural,level,count,15922.0,,3.1,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,15922.0,5136.129032258064,"b (currency per unit, as reported)",|b/t|,0.23260978865339565,0.07503541569464377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,GARAGE,garage,A_structural,level,count,-9105.0,,-1.1,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-9105.0,8277.272727272726,"b (currency per unit, as reported)",|b/t|,-0.08456286187036482,0.07687532897305893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,FIREPLACE,fireplace,B_interior,level,count,3358.0,,0.8,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,3358.0,4197.5,"b (currency per unit, as reported)",|b/t|,0.06160411036336975,0.07700513795421218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,DCBD,cbd_distance,D_location,level,feet,0.08,,0.3,,,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,0.08,0.26666666666666666,"b (currency per unit, as reported)",|b/t|,0.023139305295980295,0.07713101765326766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,DSCH,school_distance,D_location,level,feet,2.7,,1.6,,*,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,2.7,1.6875,"b (currency per unit, as reported)",|b/t|,0.12251277836633778,0.07657048647896111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,DUGB,other,other,level,feet,0.1,,0.1,,,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,0.1,1.0,"b (currency per unit, as reported)",|b/t|,0.007714937890274594,0.07714937890274594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,DREC,green_space,E_environment,level,feet,-0.9,,-0.6,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-0.9,1.5,"b (currency per unit, as reported)",|b/t|,-0.04624148705148565,0.07706914508580942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,DHWYS,highway_distance,D_location,level,feet,-0.1,,-0.2,,,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-0.1,0.5,"b (currency per unit, as reported)",|b/t|,-0.015428498379527545,0.07714249189763772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,DEMP,employment_access,D_location,level,feet,-0.001,,-0.0,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-0.001,,"b (currency per unit, as reported)",,-0.0,0.07715167498104596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,168.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,CRIME,crime,F_neighbourhood,level,count,-23783.0,,-0.7,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-23783.0,33975.71428571429,"b (currency per unit, as reported)",|b/t|,-0.05392758535282174,0.07703940764688821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7,168.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,EDUCATION,education_level,F_neighbourhood,level,years,-0.4,,-0.09,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",monetary,-0.4,4.444444444444445,"b (currency per unit, as reported)",|b/t|,-0.006943483362766352,0.07714981514184836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09,168.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,TIME*LOTAMT,lot_size,A_structural,other,binary x month,20.0,,0.1,,,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",other,20.0,200.0,not harmonised,|b/t|,0.007714937890274594,0.07714937890274594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1,168.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c1,VII,1980-1982,105,TIME*SQFT,living_area,A_structural,other,sqft x month,0.481,,4.8,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,178.0,0.49,t,"Post-UGB sub-period 1980-1982, TIME*LOTAMT specification",other,0.481,0.10020833333333333,not harmonised,|b/t|,0.3472794481039396,0.07234988502165408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,168.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,AGE,age,A_structural,level,years,-216.0,,-2.1,,*,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,-216.0,102.85714285714285,"b (currency per unit, as reported)",|b/t|,-0.10574146812718103,0.05035308006056239,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1,390.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,BEDROOM,bedrooms,A_structural,level,count,8005.0,,3.5,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,8005.0,2287.1428571428573,"b (currency per unit, as reported)",|b/t|,0.17450987923047986,0.04985996549442282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,BATHROOM,bathrooms,A_structural,level,count,4514.0,,1.8,,*,,,high,,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,4514.0,2507.777777777778,"b (currency per unit, as reported)",|b/t|,0.09077027712997951,0.0504279317388775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,GARAGE,garage,A_structural,level,count,2150.0,,0.5,,,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,2150.0,4300.0,"b (currency per unit, as reported)",|b/t|,0.025310373177627687,0.050620746355255375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,FIREPLACE,fireplace,B_interior,level,count,1226.0,,0.5,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,1226.0,2452.0,"b (currency per unit, as reported)",|b/t|,0.025310373177627687,0.050620746355255375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,DCBD,cbd_distance,D_location,level,feet,-0.21,,-1.6,,*,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,-0.21,0.13124999999999998,"b (currency per unit, as reported)",|b/t|,-0.08075454233417081,0.050471588958856756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,DSCH,school_distance,D_location,level,feet,0.03,,0.03,,,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,0.03,1.0,"b (currency per unit, as reported)",|b/t|,0.001519107297810398,0.05063690992701327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,DUGB,other,other,level,feet,-0.35,,-0.9,,,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,-0.35,0.38888888888888884,"b (currency per unit, as reported)",|b/t|,-0.04552601902123673,0.050584465579151916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,DREC,green_space,E_environment,level,feet,0.14,,0.16,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,0.14,0.8750000000000001,"b (currency per unit, as reported)",|b/t|,0.008101649040756472,0.05063530650472795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,DHWYS,highway_distance,D_location,level,feet,-0.07,,-0.3,,,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,-0.07,0.23333333333333336,"b (currency per unit, as reported)",|b/t|,-0.015189337991433701,0.05063112663811234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,DEMP,employment_access,D_location,level,feet,0.05,,0.2,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,0.05,0.25,"b (currency per unit, as reported)",|b/t|,0.010126874357261942,0.05063437178630971,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,390.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,CRIME,crime,F_neighbourhood,level,count,-104495.0,,-2.0,,**,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,-104495.0,52247.5,"b (currency per unit, as reported)",|b/t|,-0.10075854437197568,0.05037927218598783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,390.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,EDUCATION,education_level,F_neighbourhood,level,years,-0.9,,-0.3,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",monetary,-0.9,3.0,"b (currency per unit, as reported)",|b/t|,-0.015189337991433701,0.05063112663811234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3,390.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,TIME*LOTAMT,lot_size,A_structural,other,binary x month,58.0,,1.3,,*,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",other,58.0,44.61538461538461,not harmonised,|b/t|,0.06568589327125231,0.05052761020865563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3,390.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c2,VII,1983-1985,105,TIME*SQFT,living_area,A_structural,other,sqft x month,0.179,,6.0,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,400.0,0.41,t,"Post-UGB sub-period 1983-1985, TIME*LOTAMT specification",other,0.179,0.029833333333333333,not harmonised,|b/t|,0.29070094986690553,0.048450158311150925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,390.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,AGE,age,A_structural,level,years,-293.0,,-2.8,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-293.0,104.64285714285715,"b (currency per unit, as reported)",|b/t|,-0.13100140994368073,0.04678621783702883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8,449.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,BEDROOM,bedrooms,A_structural,level,count,7115.0,,3.3,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,7115.0,2156.060606060606,"b (currency per unit, as reported)",|b/t|,0.1538816933578363,0.0466308161690413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,BATHROOM,bathrooms,A_structural,level,count,6743.0,,2.8,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,6743.0,2408.214285714286,"b (currency per unit, as reported)",|b/t|,0.13100140994368073,0.04678621783702883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,GARAGE,garage,A_structural,level,count,5137.0,,1.1,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,5137.0,4670.0,"b (currency per unit, as reported)",|b/t|,0.05184240213745095,0.047129456488591764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,FIREPLACE,fireplace,B_interior,level,count,-2054.0,,-0.9,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-2054.0,2282.222222222222,"b (currency per unit, as reported)",|b/t|,-0.042435366394370067,0.047150407104855636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,DCBD,cbd_distance,D_location,level,feet,-0.3,,-2.3,,**,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-0.3,0.13043478260869565,"b (currency per unit, as reported)",|b/t|,-0.10790988941639885,0.04691734322452124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,DSCH,school_distance,D_location,level,feet,0.6,,0.7,,,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,0.6,0.8571428571428572,"b (currency per unit, as reported)",|b/t|,0.03301703141057877,0.04716718772939824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,DUGB,other,other,level,feet,-0.8,,-2.7,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-0.8,0.2962962962962963,"b (currency per unit, as reported)",|b/t|,-0.12639889831963233,0.04681440678504901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,DREC,green_space,E_environment,level,feet,0.59,,0.7,,,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,0.59,0.8428571428571429,"b (currency per unit, as reported)",|b/t|,0.03301703141057877,0.04716718772939824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,DHWYS,highway_distance,D_location,level,feet,-0.27,,-1.2,,*,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-0.27,0.22500000000000003,"b (currency per unit, as reported)",|b/t|,-0.05654090702739641,0.04711742252283035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,DEMP,employment_access,D_location,level,feet,-0.1,,-0.6,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-0.1,0.16666666666666669,"b (currency per unit, as reported)",|b/t|,-0.02830440598661799,0.04717400997769665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,449.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,CRIME,crime,F_neighbourhood,level,count,-1269.0,,-0.02,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,-1269.0,63450.0,"b (currency per unit, as reported)",|b/t|,-0.0009438579359394135,0.047192896796970675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02,449.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,EDUCATION,education_level,F_neighbourhood,level,years,5.1,,1.7,,*,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",monetary,5.1,3.0,"b (currency per unit, as reported)",|b/t|,0.07997100538988697,0.0470417678764041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7,449.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,TIME*LOTAMT,lot_size,A_structural,other,binary x month,66.0,,2.2,,**,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",other,66.0,29.999999999999996,not harmonised,|b/t|,0.10326931482172029,0.04694059764623649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2,449.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c3,VII,1986-1987,105,TIME*SQFT,living_area,A_structural,other,sqft x month,0.159,,7.4,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,459.0,0.47,t,"Post-UGB sub-period 1986-1987, TIME*LOTAMT specification",other,0.159,0.021486486486486486,not harmonised,|b/t|,0.3297007078746822,0.044554149712794894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.4,449.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,AGE,age,A_structural,level,years,-529.0,,-8.9,,***,,,high,Age of house in years at sale,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,-529.0,59.43820224719101,"b (currency per unit, as reported)",|b/t|,-0.2863216859479045,0.032170975949202756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.9,887.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,BEDROOM,bedrooms,A_structural,level,count,-4814.0,,-3.5,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,-4814.0,1375.4285714285713,"b (currency per unit, as reported)",|b/t|,-0.11671530818083618,0.03334723090881034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,BATHROOM,bathrooms,A_structural,level,count,4276.0,,2.8,,***,,,high,,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,4276.0,1527.1428571428573,"b (currency per unit, as reported)",|b/t|,0.09360204490227658,0.03342930175081306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,GARAGE,garage,A_structural,level,count,4801.0,,1.8,,*,,,high,Number of garage spaces,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,4801.0,2667.222222222222,"b (currency per unit, as reported)",|b/t|,0.06032800351931835,0.033515557510732416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,FIREPLACE,fireplace,B_interior,level,count,835.0,,0.6,,,,,high,Number of fireplaces,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,835.0,1391.6666666666667,"b (currency per unit, as reported)",|b/t|,0.02014194132193747,0.03356990220322912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,DCBD,cbd_distance,D_location,level,feet,-0.24,,-3.1,,***,,,high,Straight-line distance to Portland CBD in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,-0.24,0.07741935483870967,"b (currency per unit, as reported)",|b/t|,-0.10352849595222725,0.0333962890168475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,DSCH,school_distance,D_location,level,feet,1.1,,2.0,,**,,,high,Distance to nearest elementary school in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,1.1,0.55,"b (currency per unit, as reported)",|b/t|,0.0670025210172808,0.033501260508640406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,DUGB,other,other,level,feet,-0.72,,-3.2,,***,,,high,Distance to the urban growth boundary in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,-0.72,0.22499999999999998,"b (currency per unit, as reported)",|b/t|,-0.10683059936392997,0.03338456230122811,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,False,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,DREC,green_space,E_environment,level,feet,1.1,,2.0,,**,,,high,Distance to nearest public park in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,1.1,0.55,"b (currency per unit, as reported)",|b/t|,0.0670025210172808,0.033501260508640406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,DHWYS,highway_distance,D_location,level,feet,-0.39,,-2.7,,***,,,high,"Distance to nearest major highway (I-5, SR-26, I-217) in feet","Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,-0.39,0.14444444444444443,"b (currency per unit, as reported)",|b/t|,-0.09028686572673016,0.03343957989878895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,DEMP,employment_access,D_location,level,feet,0.2,,1.5,,,,,high,Distance to nearest of five employment centres in feet,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,0.2,0.13333333333333333,"b (currency per unit, as reported)",|b/t|,0.05030131307702596,0.03353420871801731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,887.0,ft,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,CRIME,crime,F_neighbourhood,level,count,-130.0,,-0.01,,,,,high,"Annual county-wide count of burglaries, motor-vehicle thefts and vandalism in year of sale (time series, not neighbourhood-level)","Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,-130.0,13000.0,"b (currency per unit, as reported)",|b/t|,-0.0003357671200094172,0.03357671200094172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.01,887.0,count,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,EDUCATION,education_level,F_neighbourhood,level,years,0.5,,0.3,,,,,high,Years of education of census-tract residents,"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",monetary,0.5,1.6666666666666667,"b (currency per unit, as reported)",|b/t|,0.010072503174696117,0.033575010582320396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,887.0,years,,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,TIME*LOTAMT,lot_size,A_structural,other,binary x month,75.0,,4.5,,***,,,high,"Interaction of large-lot dummy (>= 12,800 sqft) with monthly time index; LOTAMT main effect omitted","Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",other,75.0,16.666666666666668,not harmonised,|b/t|,0.1493994591749367,0.033199879816652594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,887.0,binary x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d,d37971d97af2ef251a0da6b9c73ae5f307074e7d:T7-c4,VII,1988-1990,105,TIME*SQFT,living_area,A_structural,other,sqft x month,0.25,,21.4,,***,,,high,Interaction of interior square footage with monthly time index; SQFT main effect omitted (collinearity),"Sale price of house (USD, nominal)",level,OLS,897.0,0.67,t,"Post-UGB sub-period 1988-1990, TIME*LOTAMT specification",other,0.25,0.011682242990654207,not harmonised,|b/t|,0.5835244919782888,0.027267499625153686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.4,887.0,sqft x month,nonstandard_x_transform,1996.0,United States,North America,High income,1978.0,1990.0,single_family,transaction,thesis,False,False,unknown,,1984.0,ols,other,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Living area,living_area,A_structural,level,1000 sqft,0.32,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.3444451333347111,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3940635690280088,0.003694345959637582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),106.66666666666667,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Living area,living_area,A_structural,level,1000 sqft,0.286,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.30784783791789805,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.1881779320619847,0.003947788784517162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.666666666666664,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Living area,living_area,A_structural,level,1000 sqft,0.307,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.3304520497929885,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3804131357949758,0.0037173922064655624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),102.33333333333333,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Living area,living_area,A_structural,level,1000 sqft,0.321,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.34552152437638206,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3951030334703139,0.003692551714675831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),107.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Living area,living_area,A_structural,level,1000 sqft,0.341,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.36704934520980154,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.3241666022833246,0.003802540789247209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),85.25,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Living area,living_area,A_structural,level,1000 sqft,0.348,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.37458408250149827,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.2694192561544632,0.0038709663240583804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),69.6,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Lot area,lot_size,A_structural,level,1000 sqft,0.021,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.022604211875090416,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08411244939884005,0.004005354733278097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Lot area,lot_size,A_structural,level,1000 sqft,0.008,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.008611128333367779,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Lot area,lot_size,A_structural,level,1000 sqft,0.013,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.013993083541722639,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.05218359172933882,0.004014122440718371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Lot area,lot_size,A_structural,level,1000 sqft,0.02,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.021527820833419444,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08013345319170002,0.004006672659585001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Lot area,lot_size,A_structural,level,1000 sqft,0.026,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.027986167083445278,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.10394347012419862,0.003997825774007639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Lot area,lot_size,A_structural,level,1000 sqft,0.038,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.04090285958349694,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.0761506225926853,0.004007927504878174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Bedrooms,bedrooms,A_structural,level,count,-0.019,0.002,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.9,0.2,100*b/1 (per unit count),reported,-0.03815838070142761,0.004016671652781854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Bedrooms,bedrooms,A_structural,level,count,-0.009,0.003,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.8999999999999999,0.3,100*b/1 (per unit count),reported,-0.012057920667875591,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9999999999999996,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Bedrooms,bedrooms,A_structural,level,count,-0.012,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-1.2,0.2,100*b/1 (per unit count),reported,-0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Bedrooms,bedrooms,A_structural,level,count,-0.012,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-1.2,0.1,100*b/1 (per unit count),reported,-0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Bedrooms,bedrooms,A_structural,level,count,-0.014,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-1.4000000000000001,0.1,100*b/1 (per unit count),reported,-0.056185493585738,0.004013249541838429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Bedrooms,bedrooms,A_structural,level,count,-0.018,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-1.7999999999999998,0.3,100*b/1 (per unit count),reported,-0.02411058362758312,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Bathrooms (Full),bathrooms,A_structural,level,count,0.003,0.002,,,,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.3,0.2,100*b/1 (per unit count),reported,0.006029289075079203,0.004019526050052801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Bathrooms (Full),bathrooms,A_structural,level,count,-0.01,0.004,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.0,0.4,100*b/1 (per unit count),reported,-0.010048490431704502,0.0040193961726818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Bathrooms (Full),bathrooms,A_structural,level,count,0.002,0.003,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.2,0.3,100*b/1 (per unit count),reported,0.0026797231195861745,0.004019584679379263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.5,0.2,100*b/1 (per unit count),reported,0.010048490431704502,0.0040193961726818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Bathrooms (Full),bathrooms,A_structural,level,count,0.004,0.002,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.4,0.2,100*b/1 (per unit count),reported,0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Bathrooms (Full),bathrooms,A_structural,level,count,0.01,0.003,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.0,0.3,100*b/1 (per unit count),reported,0.013397461175148244,0.004019238352544473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3333333333333335,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Age,age,A_structural,level,years,-0.0,0.0,,,,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,One story,storeys,A_structural,binary,binary,-0.093,0.007,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-8.882882367114696,0.6378354502072984,100*(exp(b - se^2/2) - 1),reported,-0.05332725730017737,0.004013879581733781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.285714285714285,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,One story,storeys,A_structural,binary,binary,-0.062,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-6.012886167156795,0.46994144339554444,100*(exp(b - se^2/2) - 1),reported,-0.04978123090037987,0.004014615395191924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.4,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,One story,storeys,A_structural,binary,binary,-0.076,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-7.318564707117725,0.18536324131187645,100*(exp(b - se^2/2) - 1),reported,-0.15099350992814012,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,One story,storeys,A_structural,binary,binary,-0.081,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-7.781045841766621,0.2766581074333824,100*(exp(b - se^2/2) - 1),reported,-0.10789560755340154,0.003996133613088947,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-27.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,One story,storeys,A_structural,binary,binary,-0.087,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-8.332473771920556,0.18333541912663046,100*(exp(b - se^2/2) - 1),reported,-0.17223941045694194,0.003959526677171079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-43.49999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,One story,storeys,A_structural,binary,binary,-0.099,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-9.426136780946702,0.27172281240706453,100*(exp(b - se^2/2) - 1),reported,-0.13149497712968528,0.003984696276657129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Renovated,renovation,A_structural,binary,binary,-0.046,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-4.501581610018701,1.0505461584097862,100*(exp(b - se^2/2) - 1),reported,-0.016806858424771982,0.004019031362445475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.181818181818182,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Renovated,renovation,A_structural,binary,binary,-0.275,0.017,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-24.05376270171916,1.2912726094824465,100*(exp(b - se^2/2) - 1),reported,-0.06488590330233307,0.00401112856778059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.176470588235293,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,True
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Renovated,renovation,A_structural,binary,binary,-0.107,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-10.153901852922708,1.0782308075883666,100*(exp(b - se^2/2) - 1),reported,-0.03581842648102667,0.004017019792264673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.916666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Renovated,renovation,A_structural,binary,binary,-0.006,0.006,,,,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-0.5999928108387786,0.5964107784323611,100*(exp(b - se^2/2) - 1),reported,-0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Renovated,renovation,A_structural,binary,binary,0.025,0.005,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,2.5302304165524703,0.5126575602622144,100*(exp(b - se^2/2) - 1),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Renovated,renovation,A_structural,binary,binary,0.047,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,4.805859961626191,1.1529342099876212,100*(exp(b - se^2/2) - 1),reported,0.01717211831823326,0.004019006414905655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.272727272727273,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Fireplace,fireplace,B_interior,binary,binary,0.04,0.005,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,4.079776413902381,0.5204053870961941,100*(exp(b - se^2/2) - 1),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Fireplace,fireplace,B_interior,binary,binary,0.062,0.008,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.392829847774539,0.851169875782427,100*(exp(b - se^2/2) - 1),reported,0.031136788579101626,0.00401765013923892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.75,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Fireplace,fireplace,B_interior,binary,binary,0.041,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,4.185002184335218,0.2083704211090959,100*(exp(b - se^2/2) - 1),reported,0.08212344218496902,0.004006021569998489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Fireplace,fireplace,B_interior,binary,binary,0.029,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.942407976296235,0.10294245944751308,100*(exp(b - se^2/2) - 1),reported,0.11578437869023656,0.00399256478242195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Fireplace,fireplace,B_interior,binary,binary,0.02,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,2.019929962611622,0.20404026800535116,100*(exp(b - se^2/2) - 1),reported,0.04016355772598999,0.004016355772598999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Fireplace,fireplace,B_interior,binary,binary,0.014,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,1.4093895505694842,0.3042295376815477,100*(exp(b - se^2/2) - 1),reported,0.018754829870992466,0.004018892115212671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.666666666666667,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Lake/Mountain view,view,E_environment,binary,binary,0.105,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,11.069061774465293,0.6664263662134231,100*(exp(b - se^2/2) - 1),reported,0.07016959434880864,0.004009691105646207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Lake/Mountain view,view,E_environment,binary,binary,0.065,0.007,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.713287930837364,0.7470113170689349,100*(exp(b - se^2/2) - 1),reported,0.03729887657665404,0.004016802092870434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.285714285714286,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Lake/Mountain view,view,E_environment,binary,binary,0.069,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,7.142763769295879,0.42857448365933853,100*(exp(b - se^2/2) - 1),reported,0.06917200251825996,0.004009971160478838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Lake/Mountain view,view,E_environment,binary,binary,0.081,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,8.436222163428763,0.4337483586267042,100*(exp(b - se^2/2) - 1),reported,0.08112856896415577,0.004006349084649667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Lake/Mountain view,view,E_environment,binary,binary,0.104,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,10.958048228714823,0.6657602729493495,100*(exp(b - se^2/2) - 1),reported,0.06950455633494922,0.0040098782500932245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Lake/Mountain view,view,E_environment,binary,binary,0.176,0.008,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,19.23999012433144,0.9539504469205357,100*(exp(b - se^2/2) - 1),reported,0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.012,0.005,,,**,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,1.2,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.009646588996759433,0.004019412081983097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.027,0.008,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-2.7,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.01356489881477472,0.004019229278451769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.375,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.0,0.006,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.0,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.014,0.004,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,1.4000000000000001,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.022,0.004,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,2.1999999999999997,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.022102394450125195,0.004018617172750035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.027,0.007,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,2.7,0.7000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01550230489784681,0.004019116084626951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.857142857142857,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,% Male population,demographics,F_neighbourhood,level,share,0.726,0.153,,,***,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,72.6,15.299999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.019069923413839184,0.004018868157461977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.745098039215686,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,% Male population,demographics,F_neighbourhood,level,share,1.706,0.206,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,170.6,20.599999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03327009605829465,0.004017373849946482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.281553398058252,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,% Male population,demographics,F_neighbourhood,level,share,1.043,0.1,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,104.3,10.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04188762286120781,0.004016071223509859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.429999999999998,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,% Male population,demographics,F_neighbourhood,level,share,0.762,0.093,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,76.2,9.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.032916932162630655,0.004017420854494293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.193548387096774,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,% Male population,demographics,F_neighbourhood,level,share,0.521,0.101,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,52.1,10.100000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.020730307917933552,0.004018735316144508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.158415841584159,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,% Male population,demographics,F_neighbourhood,level,share,1.083,0.237,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,108.3,23.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.018364943743460975,0.004018921207017774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.569620253164557,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,% White population,demographics,F_neighbourhood,level,share,0.793,0.034,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,79.3,3.4000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09334193065077274,0.004002049990070962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.323529411764707,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,% White population,demographics,F_neighbourhood,level,share,1.093,0.163,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,109.3,16.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.026943722736079895,0.0040181398041912375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.705521472392638,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,% White population,demographics,F_neighbourhood,level,share,0.792,0.022,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,79.2,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1432139080541784,0.003978164112616067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.00000000000001,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,% White population,demographics,F_neighbourhood,level,share,0.78,0.024,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,78.0,2.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12953631268868462,0.003985732698113373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.5,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,% White population,demographics,F_neighbourhood,level,share,0.773,0.019,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,77.3,1.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.16139038817533638,0.003966904754633105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.684210526315795,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,% White population,demographics,F_neighbourhood,level,share,0.749,0.026,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,74.9,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11502677012229208,0.003992918589024825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.80769230769231,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,% Asian population,demographics,F_neighbourhood,level,share,0.043,0.032,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,4.3,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.005401257517481302,0.004019540478125621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3437499999999998,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,% Asian population,demographics,F_neighbourhood,level,share,0.08,0.149,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,8.0,14.899999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0021581689935924386,0.004019589750565917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5369127516778524,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,% Asian population,demographics,F_neighbourhood,level,share,0.011,0.023,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,1.0999999999999999,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.001922413414104874,0.004019591684037465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47826086956521735,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,% Asian population,demographics,F_neighbourhood,level,share,0.088,0.035,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,8.799999999999999,3.5000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.010105904529427473,0.004019393846931382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.514285714285714,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,% Asian population,demographics,F_neighbourhood,level,share,0.16,0.022,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,16.0,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02922096471322672,0.004017882648068673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.272727272727273,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,% Asian population,demographics,F_neighbourhood,level,share,0.23,0.053,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,23.0,5.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01744089008370537,0.004018987714940802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.339622641509434,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,% Old population,demographics,F_neighbourhood,level,share,0.283,0.042,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,28.299999999999997,4.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02707451296913433,0.004018125599659512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.738095238095237,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,% Old population,demographics,F_neighbourhood,level,share,0.427,0.107,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,42.699999999999996,10.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.016038766778961256,0.004019082073416521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9906542056074765,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,% Old population,demographics,F_neighbourhood,level,share,0.124,0.04,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,12.4,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01245978996479856,0.00401928708541889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,% Old population,demographics,F_neighbourhood,level,share,0.08,0.029,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,8.0,2.9000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.011087867634840126,0.004019352017629547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.758620689655172,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,% Old population,demographics,F_neighbourhood,level,share,0.078,0.031,,,**,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,7.8,3.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01011331279283307,0.004019393545869553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5161290322580645,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,% Old population,demographics,F_neighbourhood,level,share,0.372,0.057,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,37.2,5.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0262241512413549,0.0040182167224656696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.526315789473684,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.828,0.347,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,other,-0.828,0.347,not harmonised,reported,-0.00959099418948269,0.0040194142315827215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3861671469740635,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.117,0.79,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-0.117,0.79,not harmonised,reported,-0.0005953076110432282,0.004019598399351711,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14810126582278482,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.948,0.17,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-0.948,0.17,not harmonised,reported,-0.022409547201496134,0.004018589688032008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.576470588235294,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.798,0.176,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-0.798,0.176,not harmonised,reported,-0.01822220170789861,0.004018931705000195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.534090909090909,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.757,0.136,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.757,0.136,not harmonised,reported,-0.022368200083434642,0.004018593409969765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.566176470588235,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.104,0.183,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-1.104,0.183,not harmonised,reported,-0.024242258232121405,0.004018417804780993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.032786885245902,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c1,6,(1) 2SLS,30,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.072,0.003,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-7.199999999999999,0.3,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.09602458544121233,0.004001024393383848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.999999999999996,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c2,6,(2) Q: 0.05,30,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.072,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-7.199999999999999,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.14321390805417838,0.003978164112616067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-36.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c3,6,(3) Q: 0.25,30,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.068,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-6.800000000000001,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.26366097296639146,0.0038773672495057573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-68.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c4,6,(4) Q: 0.50,30,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.07,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-7.000000000000001,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.2708543506959655,0.003869347867085221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-70.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c5,6,(5) Q: 0.75,30,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.073,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-7.3,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.2815596234261276,0.003856981142823666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-73.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T6-c6,6,(6) Q: 0.95,30,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.084,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Without spatial autocorrelation adjustment; 911 calls as IV for number of crimes; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-8.4,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.31990310072026185,0.003808370246669784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-84.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Living area,living_area,A_structural,level,1000 sqft,0.261,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.28093806187612375,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.25369799088088457,0.00388809181426643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),65.25,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Living area,living_area,A_structural,level,1000 sqft,0.241,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.25941024104270427,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.15938982626211692,0.0039682114422103796,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.166666666666664,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Living area,living_area,A_structural,level,1000 sqft,0.252,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.271250542501085,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.31990310072026185,0.003808370246669784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),84.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Living area,living_area,A_structural,level,1000 sqft,0.267,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.2873964081261496,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.33683860199528165,0.0037847033932054117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),89.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Living area,living_area,A_structural,level,1000 sqft,0.286,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.30784783791789805,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4983425486371538,0.0034849129275325453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),142.99999999999997,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Living area,living_area,A_structural,level,1000 sqft,0.299,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3218409214596207,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.233714961155575,0.00390827694240092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.8,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Lot area,lot_size,A_structural,level,1000 sqft,0.008,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.008611128333367779,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Lot area,lot_size,A_structural,level,1000 sqft,0.014,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.015069474583393612,,100*b/92.903 (X per 1000sqft->m2),,,,,,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Lot area,lot_size,A_structural,level,1000 sqft,0.034,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.03659729541681306,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Bedrooms,bedrooms,A_structural,level,count,-0.007,0.001,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per unit count),reported,-0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Bedrooms,bedrooms,A_structural,level,count,-0.0,0.004,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.0,0.4,100*b/1 (per unit count),reported,-0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8,0.4,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.8,0.1,100*b/1 (per unit count),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Bathrooms (Full),bathrooms,A_structural,level,count,-0.007,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.7000000000000001,0.2,100*b/1 (per unit count),reported,-0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Bathrooms (Full),bathrooms,A_structural,level,count,0.006,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.6,0.2,100*b/1 (per unit count),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Bathrooms (Full),bathrooms,A_structural,level,count,0.015,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.5,0.2,100*b/1 (per unit count),reported,0.030133303253020185,0.004017773767069358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Age,age,A_structural,level,years,-0.002,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.2,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,One story,storeys,A_structural,binary,binary,-0.076,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-7.319537857079251,0.4634081032796911,100*(exp(b - se^2/2) - 1),reported,-0.06098418693732637,0.004012117561666209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.2,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,One story,storeys,A_structural,binary,binary,-0.053,0.007,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-5.164322254337739,0.6638660087376087,100*(exp(b - se^2/2) - 1),reported,-0.030420022776587435,0.004017738857285133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.571428571428571,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,One story,storeys,A_structural,binary,binary,-0.064,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-6.19968764773936,0.1876009999061459,100*(exp(b - se^2/2) - 1),reported,-0.12757613078535945,0.003986754087042483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,One story,storeys,A_structural,binary,binary,-0.067,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.480666905175569,0.18703904026735532,100*(exp(b - se^2/2) - 1),reported,-0.133452102765036,0.003983644858657791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,One story,storeys,A_structural,binary,binary,-0.071,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.8539970794329275,0.18629237842551843,100*(exp(b - se^2/2) - 1),reported,-0.1412647934828903,0.003979289957264516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-35.49999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,One story,storeys,A_structural,binary,binary,-0.084,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-8.057609932550235,0.3677725024380499,100*(exp(b - se^2/2) - 1),reported,-0.08411244939884005,0.004005354733278097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Renovated,renovation,A_structural,binary,binary,-0.028,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-2.767046091538816,1.0696272034813716,100*(exp(b - se^2/2) - 1),reported,-0.010231171304026659,0.004019388726581901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.545454545454546,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Renovated,renovation,A_structural,binary,binary,-0.264,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-23.20817524476095,0.9215682475880473,100*(exp(b - se^2/2) - 1),reported,-0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Renovated,renovation,A_structural,binary,binary,-0.102,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.703546411824693,1.083635462002652,100*(exp(b - se^2/2) - 1),reported,-0.03414666760248229,0.004017255012056741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Renovated,renovation,A_structural,binary,binary,0.021,0.006,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.120366980603605,0.6127332309825171,100*(exp(b - se^2/2) - 1),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Renovated,renovation,A_structural,binary,binary,0.048,0.002,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,4.916855698525824,0.20983413106489415,100*(exp(b - se^2/2) - 1),reported,0.09602458544121234,0.004001024393383848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Renovated,renovation,A_structural,binary,binary,0.069,0.01,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,7.138263867716166,1.0714362091483463,100*(exp(b - se^2/2) - 1),reported,0.027724572450614767,0.004018053978349966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Fireplace,fireplace,B_interior,binary,binary,0.025,0.004,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,2.5306918036274606,0.41012604820977155,100*(exp(b - se^2/2) - 1),reported,0.025114570296134366,0.004018331247381499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Fireplace,fireplace,B_interior,binary,binary,0.044,0.007,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,4.495675313437175,0.7314876484219107,100*(exp(b - se^2/2) - 1),reported,0.025257990830651594,0.004018316723058209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.285714285714286,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Fireplace,fireplace,B_interior,binary,binary,0.025,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,2.5313069896238494,0.20506302410488578,100*(exp(b - se^2/2) - 1),reported,0.05018168551256579,0.004014534841005264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Fireplace,fireplace,B_interior,binary,binary,0.013,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.308284119210068,0.20261697347196184,100*(exp(b - se^2/2) - 1),reported,0.026118480976874945,0.004018227842596146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Fireplace,fireplace,B_interior,binary,binary,0.01,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,1.004814698585399,0.20201003341683357,100*(exp(b - se^2/2) - 1),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Fireplace,fireplace,B_interior,binary,binary,0.007,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,0.7022543219748156,0.20140491145336972,100*(exp(b - se^2/2) - 1),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Lake/Mountain view,view,E_environment,binary,binary,0.063,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,6.501352647901992,0.5325134196156527,100*(exp(b - se^2/2) - 1),reported,0.05058211592327567,0.004014453644704418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.6,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Lake/Mountain view,view,E_environment,binary,binary,0.037,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,3.7674342531887284,0.6226158125028943,100*(exp(b - se^2/2) - 1),reported,0.024779916366743225,0.0040183648162286315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.166666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Lake/Mountain view,view,E_environment,binary,binary,0.036,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.665118155011071,0.3109967539472771,100*(exp(b - se^2/2) - 1),reported,0.04817917426179948,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.999999999999998,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Lake/Mountain view,view,E_environment,binary,binary,0.047,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,4.811729254122699,0.31443660272389673,100*(exp(b - se^2/2) - 1),reported,0.0628492224763869,0.004011652498492782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.666666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Lake/Mountain view,view,E_environment,binary,binary,0.059,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,6.077046726231594,0.3182325722220477,100*(exp(b - se^2/2) - 1),reported,0.07880625986810323,0.00400709795939508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Lake/Mountain view,view,E_environment,binary,binary,0.107,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,11.292535104090362,0.4451737017917303,100*(exp(b - se^2/2) - 1),reported,0.10690804517368448,0.00399656243639942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.75,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.001,0.002,,,,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0020097954967298742,0.0040195909934597485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.017,0.013,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.7000000000000002,1.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.005256326223322949,0.004019543582541078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.307692307692308,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.005,0.003,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.5,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.006699181521223725,0.004019508912734235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6666666666666667,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.001,0.004,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.1,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0010048992705163852,0.004019597082065541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.003,0.004,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.3,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.003014685634383684,0.0040195808458449115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.013,0.005,,,**,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.3,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.010450386996976943,0.004019379614221902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5999999999999996,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,% Male population,demographics,F_neighbourhood,level,share,-0.188,0.099,,,*,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-18.8,9.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.007632955745528722,0.004019482014932679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.898989898989899,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,% Male population,demographics,F_neighbourhood,level,share,-0.241,0.234,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-24.099999999999998,23.400000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.004139808054982127,0.00401956466749302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0299145299145298,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,% Male population,demographics,F_neighbourhood,level,share,0.07,0.06,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,7.000000000000001,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.004689480732300701,0.004019554913400601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1666666666666667,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,% Male population,demographics,F_neighbourhood,level,share,0.052,0.079,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,5.2,7.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.002645802812743817,0.004019585042437723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6582278481012658,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,% Male population,demographics,F_neighbourhood,level,share,0.009,0.074,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8999999999999999,7.3999999999999995,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0004888701038039513,0.004019598631276934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12162162162162161,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,% Male population,demographics,F_neighbourhood,level,share,0.377,0.142,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,37.7,14.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.010671144939633587,0.004019370242514506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.654929577464789,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,% White population,demographics,F_neighbourhood,level,share,0.605,0.029,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,60.5,2.9000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.08356385714244027,0.004005540259720278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.86206896551724,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,% White population,demographics,F_neighbourhood,level,share,0.883,0.067,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,88.3,6.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0529005407151986,0.004013970813044514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.17910447761194,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,% White population,demographics,F_neighbourhood,level,share,0.626,0.018,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,62.6,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.13844650853748117,0.003980890021844506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.77777777777778,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,% White population,demographics,F_neighbourhood,level,share,0.557,0.015,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,55.7,1.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1476257045896009,0.003975557574226235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.13333333333334,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,% White population,demographics,F_neighbourhood,level,share,0.52,0.018,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,52.0,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11534667385737521,0.003992769479678372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.888888888888893,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,% White population,demographics,F_neighbourhood,level,share,0.458,0.02,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,45.800000000000004,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09166131626397761,0.004002677566112559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.900000000000002,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,% Asian population,demographics,F_neighbourhood,level,share,0.147,0.016,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,14.7,1.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03690490940429867,0.004016860887542713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.1875,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,% Asian population,demographics,F_neighbourhood,level,share,0.234,0.104,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,23.400000000000002,10.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.009043728139602738,0.004019434728712327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2500000000000004,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,% Asian population,demographics,F_neighbourhood,level,share,0.102,0.031,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,10.2,3.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.013224621139361575,0.0040192476011785185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290322580645161,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,% Asian population,demographics,F_neighbourhood,level,share,0.129,0.027,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,12.9,2.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.019201210718433337,0.004018858057346512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.777777777777778,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,% Asian population,demographics,F_neighbourhood,level,share,0.152,0.019,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,15.2,1.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,% Asian population,demographics,F_neighbourhood,level,share,0.151,0.032,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,15.1,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.018964072305910063,0.004018876250259086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.71875,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,% Old population,demographics,F_neighbourhood,level,share,-0.167,0.028,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-16.7,2.8000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.023967150934251804,0.004018444468018267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.964285714285714,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,% Old population,demographics,F_neighbourhood,level,share,-0.246,0.053,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-24.6,5.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.018653760942644627,0.0040188997152852245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.6415094339622645,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,% Old population,demographics,F_neighbourhood,level,share,-0.184,0.03,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-18.4,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02464605245605721,0.004018378117835415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.133333333333334,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,% Old population,demographics,F_neighbourhood,level,share,-0.206,0.025,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-20.599999999999998,2.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.033103343917503976,0.004017396106493202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.239999999999998,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,% Old population,demographics,F_neighbourhood,level,share,-0.227,0.015,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-22.7,1.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.06071770067914491,0.0040121828642606765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.133333333333335,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,% Old population,demographics,F_neighbourhood,level,share,-0.222,0.032,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-22.2,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.027875132711472534,0.004018037147599645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.9375,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.73,0.148,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,other,0.73,0.148,not harmonised,reported,0.019822505429810754,0.0040188093200164265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9324324324324325,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),1.294,0.31,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,1.294,0.31,not harmonised,reported,0.01677622341615733,0.0040190334304550015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.174193548387097,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.599,0.094,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,0.599,0.094,not harmonised,reported,0.025605855417755743,0.004018281150699566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.372340425531915,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.373,0.1,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.373,0.1,not harmonised,reported,0.014991419796451204,0.004019147398512387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.73,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.214,0.118,,,*,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.214,0.118,not harmonised,reported,0.007289587754591148,0.004019492313279231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8135593220338984,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.111,0.126,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-0.111,0.126,not harmonised,reported,-0.0035410532068999943,0.004019573910535129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8809523809523809,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c1,7 Panel A,(1) 2SLS,34,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.038,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-3.8,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.0761506225926853,0.004007927504878174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c2,7 Panel A,(2) Q: 0.05,34,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.036,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-3.5999999999999996,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c3,7 Panel A,(3) Q: 0.25,34,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.038,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-3.8,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.15099350992814012,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c4,7 Panel A,(4) Q: 0.50,34,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.04,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-4.0,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1587451580364743,0.003968628950911857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c5,7 Panel A,(5) Q: 0.75,34,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.042,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-4.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1664675615205028,0.0039635133695357806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7A-c6,7 Panel A,(6) Q: 0.95,34,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.046,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial autocorrelation adjustment (log WY spatial lag of neighbour prices as regressor); 911 calls as IV; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-4.6,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.18181960208964543,0.003952600045427074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-46.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Living area,living_area,A_structural,level,1000 sqft,0.26,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,0.2798616708344528,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.2527881938022088,0.0038890491354185972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),65.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Living area,living_area,A_structural,level,1000 sqft,0.237,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,0.2551046768760204,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.18716216573631722,0.00394856889739066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.4,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Living area,living_area,A_structural,level,1000 sqft,0.252,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,0.271250542501085,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.31990310072026185,0.003808370246669784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),84.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Living area,living_area,A_structural,level,1000 sqft,0.266,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,0.28632001708447863,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4714622721708457,0.0035448291140665095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),133.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Living area,living_area,A_structural,level,1000 sqft,0.285,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,0.30677144687622704,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4970311166303093,0.0034879376605635747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),142.49999999999997,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Living area,living_area,A_structural,level,1000 sqft,0.299,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,0.3218409214596207,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.233714961155575,0.00390827694240092,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.8,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Lot area,lot_size,A_structural,level,1000 sqft,0.005,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,0.005381955208354861,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.010048490431704502,0.0040193961726818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Lot area,lot_size,A_structural,level,1000 sqft,0.013,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,0.013993083541722639,,100*b/92.903 (X per 1000sqft->m2),,,,,,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Lot area,lot_size,A_structural,level,1000 sqft,0.017,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,0.01829864770840653,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.0681742031339563,0.0040102472431739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Lot area,lot_size,A_structural,level,1000 sqft,0.034,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,0.03659729541681306,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.001,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,-0.8,0.1,100*b/1 (per unit count),reported,-0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.004,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,-0.3,0.4,100*b/1 (per unit count),reported,-0.003014685634383684,0.0040195808458449115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.75,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,-0.3,0.1,100*b/1 (per unit count),reported,-0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.001,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,-0.3,0.1,100*b/1 (per unit count),reported,-0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.001,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,-0.3,0.1,100*b/1 (per unit count),reported,-0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,-0.8,0.4,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,0.8,0.1,100*b/1 (per unit count),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Bathrooms (Full),bathrooms,A_structural,level,count,-0.006,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,-0.6,0.2,100*b/1 (per unit count),reported,-0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.003,,,*,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,0.5,0.3,100*b/1 (per unit count),reported,0.006699181521223725,0.004019508912734235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,0.8999999999999999,0.1,100*b/1 (per unit count),reported,0.03615274262598033,0.004016971402886703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Bathrooms (Full),bathrooms,A_structural,level,count,0.014,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,1.4000000000000001,0.2,100*b/1 (per unit count),reported,0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Age,age,A_structural,level,years,-0.002,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,-0.2,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,One story,storeys,A_structural,binary,binary,-0.08,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",pct_premium,-7.689519249557586,0.4615581731933179,100*(exp(b - se^2/2) - 1),reported,-0.06418098885674876,0.004011311803546798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,One story,storeys,A_structural,binary,binary,-0.061,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",pct_premium,-5.9188520440986,0.47041161988800484,100*(exp(b - se^2/2) - 1),reported,-0.04898024983644974,0.0040147745767581755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.2,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,One story,storeys,A_structural,binary,binary,-0.068,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",pct_premium,-6.5741394941865146,0.18685209471544273,100*(exp(b - se^2/2) - 1),reported,-0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,One story,storeys,A_structural,binary,binary,-0.07,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",pct_premium,-6.761037585680086,0.27971814597178446,100*(exp(b - se^2/2) - 1),reported,-0.09338082437105044,0.004002035330187876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.333333333333336,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,One story,storeys,A_structural,binary,binary,-0.072,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",pct_premium,-6.947329156840376,0.2791592687433617,100*(exp(b - se^2/2) - 1),reported,-0.09602458544121233,0.004001024393383848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.999999999999996,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,One story,storeys,A_structural,binary,binary,-0.085,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",pct_premium,-8.149184889452254,0.2755536853204372,100*(exp(b - se^2/2) - 1),reported,-0.11315714651535581,0.003993781641718441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.333333333333336,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Renovated,renovation,A_structural,binary,binary,-0.028,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",pct_premium,-2.767046091538816,1.0696272034813716,100*(exp(b - se^2/2) - 1),reported,-0.010231171304026659,0.004019388726581901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.545454545454546,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Renovated,renovation,A_structural,binary,binary,-0.262,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",pct_premium,-23.05443790916064,0.923413228449091,100*(exp(b - se^2/2) - 1),reported,-0.08742521695147598,0.004004208409991267,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.833333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Renovated,renovation,A_structural,binary,binary,-0.104,0.017,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",pct_premium,-9.890492022582098,1.5320830056160482,100*(exp(b - se^2/2) - 1),reported,-0.024583057215379938,0.004018384352514029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.117647058823529,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Renovated,renovation,A_structural,binary,binary,0.021,0.006,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",pct_premium,2.120366980603605,0.6127332309825171,100*(exp(b - se^2/2) - 1),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Renovated,renovation,A_structural,binary,binary,0.048,0.003,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",pct_premium,4.916593406714442,0.3147511965973412,100*(exp(b - se^2/2) - 1),reported,0.06418098885674876,0.004011311803546798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Renovated,renovation,A_structural,binary,binary,0.07,0.009,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",pct_premium,7.246474555245452,0.9652573631287948,100*(exp(b - se^2/2) - 1),reported,0.0312482811925183,0.0040176361533237805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.7777777777777795,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Fireplace,fireplace,B_interior,binary,binary,0.025,0.004,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",pct_premium,2.5306918036274606,0.41012604820977155,100*(exp(b - se^2/2) - 1),reported,0.025114570296134366,0.004018331247381499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Fireplace,fireplace,B_interior,binary,binary,0.047,0.005,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",pct_premium,4.810890763642628,0.5240610045398278,100*(exp(b - se^2/2) - 1),reported,0.03775728919950944,0.004016732893564834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.4,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Fireplace,fireplace,B_interior,binary,binary,0.026,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",pct_premium,2.633889579359794,0.2052681896946884,100*(exp(b - se^2/2) - 1),reported,0.05218359172933882,0.004014122440718371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Fireplace,fireplace,B_interior,binary,binary,0.014,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",pct_premium,1.4096430743602673,0.20281969178769846,100*(exp(b - se^2/2) - 1),reported,0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Fireplace,fireplace,B_interior,binary,binary,0.01,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",pct_premium,1.004814698585399,0.20201003341683357,100*(exp(b - se^2/2) - 1),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Fireplace,fireplace,B_interior,binary,binary,0.006,0.003,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",pct_premium,0.6013508983088611,0.3018054108162195,100*(exp(b - se^2/2) - 1),reported,0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Lake/Mountain view,view,E_environment,binary,binary,0.063,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",pct_premium,6.501352647901992,0.5325134196156527,100*(exp(b - se^2/2) - 1),reported,0.05058211592327567,0.004014453644704418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.6,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Lake/Mountain view,view,E_environment,binary,binary,0.036,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",pct_premium,3.6642888373831006,0.5183279232454618,100*(exp(b - se^2/2) - 1),reported,0.028929000846614854,0.004017916784252063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.199999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Lake/Mountain view,view,E_environment,binary,binary,0.037,0.002,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",pct_premium,3.7690945454190716,0.20753860416763142,100*(exp(b - se^2/2) - 1),reported,0.07415782749058782,0.004008531215707449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Lake/Mountain view,view,E_environment,binary,binary,0.046,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",pct_premium,4.706603439515589,0.4188297643827749,100*(exp(b - se^2/2) - 1),reported,0.04617608189162238,0.004015311468836729,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Lake/Mountain view,view,E_environment,binary,binary,0.06,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",pct_premium,6.183176829165116,0.3185509639636079,100*(exp(b - se^2/2) - 1),reported,0.08013345319170002,0.004006672659585001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Lake/Mountain view,view,E_environment,binary,binary,0.107,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",pct_premium,11.292535104090362,0.4451737017917303,100*(exp(b - se^2/2) - 1),reported,0.10690804517368448,0.00399656243639942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.75,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.012,0.002,,,***,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,1.2,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.011,0.009,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,1.0999999999999999,0.8999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0049127840714369205,0.004019550603902935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2222222222222223,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.012,0.002,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,1.2,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.015,0.003,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,1.5,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.009,0.003,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,0.8999999999999999,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.012057920667875591,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.015,0.005,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,1.5,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,% Male population,demographics,F_neighbourhood,level,share,0.307,0.07,,,***,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,30.7,7.000000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.017626074587216576,0.004018974661580328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.385714285714285,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,% Male population,demographics,F_neighbourhood,level,share,0.787,0.128,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,78.7,12.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02470670970528098,0.004018372099461201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1484375,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,% Male population,demographics,F_neighbourhood,level,share,0.525,0.041,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,52.5,4.1000000000000005,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0514024335452346,0.00401428528638975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.804878048780488,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,% Male population,demographics,F_neighbourhood,level,share,0.44,0.047,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,44.0,4.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.037603674844730026,0.004016756176596162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.361702127659575,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,% Male population,demographics,F_neighbourhood,level,share,0.274,0.062,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,27.400000000000002,6.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.017761232629039406,0.004018965047446873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.419354838709678,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,% Male population,demographics,F_neighbourhood,level,share,0.465,0.106,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,46.5,10.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.017630406366830827,0.004018974354589392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.386792452830189,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,% White population,demographics,F_neighbourhood,level,share,0.455,0.023,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,45.5,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07926794022243124,0.004006950824430589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.782608695652176,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,% White population,demographics,F_neighbourhood,level,share,0.602,0.037,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,60.199999999999996,3.6999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.06526054809174489,0.004011030364442792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.27027027027027,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,% White population,demographics,F_neighbourhood,level,share,0.476,0.017,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,47.599999999999994,1.7000000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11184263627430628,0.003994379866939511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.999999999999996,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,% White population,demographics,F_neighbourhood,level,share,0.454,0.012,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,45.4,1.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1503462594637161,0.003973909941772232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.833333333333336,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,% White population,demographics,F_neighbourhood,level,share,0.449,0.014,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,44.9,1.4000000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12785624886611657,0.003986609096048178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.07142857142857,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,% White population,demographics,F_neighbourhood,level,share,0.41,0.021,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,41.0,2.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07823733400981406,0.004007278083429501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.523809523809522,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,% Asian population,demographics,F_neighbourhood,level,share,0.006,0.028,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,0.6,2.8000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0008613423472532151,0.004019597620515004,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21428571428571427,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,% Asian population,demographics,F_neighbourhood,level,share,0.021,0.053,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,2.1,5.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0015926693261255843,0.004019594013555046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39622641509433965,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,% Asian population,demographics,F_neighbourhood,level,share,-0.026,0.029,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,-2.6,2.9000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.003603755112551015,0.004019573010153056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8965517241379309,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,% Asian population,demographics,F_neighbourhood,level,share,0.041,0.022,,,*,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,4.1000000000000005,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007490860895458579,0.004019486334148506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8636363636363638,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,% Asian population,demographics,F_neighbourhood,level,share,0.078,0.018,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,7.8,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.017415621103478125,0.0040189894854180275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.333333333333334,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,% Asian population,demographics,F_neighbourhood,level,share,0.097,0.039,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,9.700000000000001,3.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.00999696487475808,0.004019398248614073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4871794871794872,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,% Old population,demographics,F_neighbourhood,level,share,-0.183,0.022,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,-18.3,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03341708215418041,0.004017354138753929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.318181818181818,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,% Old population,demographics,F_neighbourhood,level,share,-0.172,0.044,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,-17.2,4.3999999999999995,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01571103895547209,0.004019102988609139,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.909090909090909,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,% Old population,demographics,F_neighbourhood,level,share,-0.212,0.027,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,-21.2,2.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.031545589114482166,0.004017598613636879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.851851851851852,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,% Old population,demographics,F_neighbourhood,level,share,-0.223,0.022,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,-22.3,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.040710341004063705,0.0040162668255130105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.136363636363637,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,% Old population,demographics,F_neighbourhood,level,share,-0.222,0.015,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,-22.2,1.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05938507575016631,0.00401250511825448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.8,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,% Old population,demographics,F_neighbourhood,level,share,-0.214,0.026,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,-21.4,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.033066300830385344,0.004017401035467378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.230769230769232,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Number of crimes—violent,crime,F_neighbourhood,level,violent crimes per capita (tract),-2.139,0.455,,,***,,,high,Violent crimes per tract population (not instrumented),Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,-213.89999999999998,45.5,100*b (unit as reported: violent crimes per capita; no harmonised unit defined for this characteristic),reported,-0.018893160092541248,0.0040188816466134964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.7010989010989,61892.0,violent crimes per capita,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Number of crimes—violent,crime,F_neighbourhood,level,violent crimes per capita (tract),-3.152,0.578,,,***,,,high,Violent crimes per tract population (not instrumented),Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,-315.2,57.8,100*b (unit as reported: violent crimes per capita; no harmonised unit defined for this characteristic),reported,-0.02191476411891809,0.004018633775613786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.453287197231835,61892.0,violent crimes per capita,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Number of crimes—violent,crime,F_neighbourhood,level,violent crimes per capita (tract),-2.447,0.322,,,***,,,high,Violent crimes per tract population (not instrumented),Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,-244.70000000000002,32.2,100*b (unit as reported: violent crimes per capita; no harmonised unit defined for this characteristic),reported,-0.030532215332604182,0.004017725107110153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.599378881987578,61892.0,violent crimes per capita,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Number of crimes—violent,crime,F_neighbourhood,level,violent crimes per capita (tract),-2.13,0.211,,,***,,,high,Violent crimes per tract population (not instrumented),Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,-213.0,21.099999999999998,100*b (unit as reported: violent crimes per capita; no harmonised unit defined for this characteristic),reported,-0.040543632103976934,0.0040162940722718934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.09478672985782,61892.0,violent crimes per capita,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Number of crimes—violent,crime,F_neighbourhood,level,violent crimes per capita (tract),-1.472,0.151,,,***,,,high,Violent crimes per tract population (not instrumented),Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,-147.2,15.1,100*b (unit as reported: violent crimes per capita; no harmonised unit defined for this characteristic),reported,-0.03915438868549068,0.004016516774123026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.748344370860927,61892.0,violent crimes per capita,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Number of crimes—violent,crime,F_neighbourhood,level,violent crimes per capita (tract),-1.577,0.331,,,***,,,high,Violent crimes per tract population (not instrumented),Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,-157.7,33.1,100*b (unit as reported: violent crimes per capita; no harmonised unit defined for this characteristic),reported,-0.019147268018242604,0.004018862215623527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.764350453172205,61892.0,violent crimes per capita,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c1,7 Panel B,(1) 2SLS,35,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.04,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; 2SLS; SE clustered by year and month",semi_elasticity,-4.0,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.08013345319170002,0.004006672659585001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c2,7 Panel B,(2) Q: 0.05,35,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.04,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.05; SE bootstrap",semi_elasticity,-4.0,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.08013345319170002,0.004006672659585001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c3,7 Panel B,(3) Q: 0.25,35,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.039,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.25; SE bootstrap",semi_elasticity,-3.9,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1548729094804115,0.003971100243087474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c4,7 Panel B,(4) Q: 0.50,35,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.041,0.0,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.50; SE bootstrap",semi_elasticity,-4.1000000000000005,,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),,,,,,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c5,7 Panel B,(5) Q: 0.75,35,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.043,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.75; SE bootstrap",semi_elasticity,-4.3,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.17031740022105954,0.003960869772582781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.99999999999999,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7B-c6,7 Panel B,(6) Q: 0.95,35,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.046,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,"With spatial lag of neighbour prices; violent crimes (exogenous, OLS/quantile); year + month dummies; quantile regression at tau=0.95; SE bootstrap",semi_elasticity,-4.6,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.18181960208964543,0.003952600045427074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-46.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,35,Living area,living_area,A_structural,level,1000 sqft,0.268,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.28847279916782054,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.33795631871264253,0.00378309311991764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),89.33333333333334,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,35,Living area,living_area,A_structural,level,1000 sqft,0.252,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.271250542501085,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.1664675615205028,0.0039635133695357806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,35,Living area,living_area,A_structural,level,1000 sqft,0.259,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.27878527979278184,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4617283980831373,0.003565470255468242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),129.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,35,Living area,living_area,A_structural,level,1000 sqft,0.276,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.29708392750118834,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3468464527220603,0.003770070138283264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),92.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,35,Living area,living_area,A_structural,level,1000 sqft,0.288,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.31000062000123996,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.5009551799969574,0.003478855416645538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),144.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,35,Living area,living_area,A_structural,level,1000 sqft,0.302,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3250700945846336,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.23593001210574505,0.003906126028240812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.4,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,35,Lot area,lot_size,A_structural,level,1000 sqft,0.013,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.013993083541722639,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.05218359172933882,0.004014122440718371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,35,Lot area,lot_size,A_structural,level,1000 sqft,-0.0,0.001,,,,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.0,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,-0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,35,Lot area,lot_size,A_structural,level,1000 sqft,0.009,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.009687519375038749,,100*b/92.903 (X per 1000sqft->m2),,,,,,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,35,Lot area,lot_size,A_structural,level,1000 sqft,0.012,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.012916692500051665,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,35,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,35,Lot area,lot_size,A_structural,level,1000 sqft,0.03,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.032291731250129165,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.11972065843265513,0.003990688614421837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,35,Bedrooms,bedrooms,A_structural,level,count,-0.011,0.002,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.0999999999999999,0.2,100*b/1 (per unit count),reported,-0.022102394450125195,0.004018617172750035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,35,Bedrooms,bedrooms,A_structural,level,count,-0.009,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.8999999999999999,0.4,100*b/1 (per unit count),reported,-0.009043728139602736,0.004019434728712327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,35,Bedrooms,bedrooms,A_structural,level,count,-0.007,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per unit count),reported,-0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,35,Bedrooms,bedrooms,A_structural,level,count,-0.006,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.6,0.1,100*b/1 (per unit count),reported,-0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,35,Bedrooms,bedrooms,A_structural,level,count,-0.004,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.4,0.1,100*b/1 (per unit count),reported,-0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,35,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8,0.4,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.5,0.1,100*b/1 (per unit count),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Bathrooms (Full),bathrooms,A_structural,level,count,-0.012,0.005,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.2,0.5,100*b/1 (per unit count),reported,-0.009646588996759433,0.004019412081983097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.0,0.001,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.0,0.1,100*b/1 (per unit count),reported,0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.002,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.5,0.2,100*b/1 (per unit count),reported,0.010048490431704502,0.0040193961726818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8,0.1,100*b/1 (per unit count),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.016,0.003,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.6,0.3,100*b/1 (per unit count),reported,0.02143293739900748,0.004018675762313902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333333,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Age,age,A_structural,level,years,-0.0,0.0,,,,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,One story,storeys,A_structural,binary,binary,-0.08,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-7.689519249557586,0.4615581731933179,100*(exp(b - se^2/2) - 1),reported,-0.06418098885674876,0.004011311803546798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,One story,storeys,A_structural,binary,binary,-0.066,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-6.388083727104988,0.4680654321458094,100*(exp(b - se^2/2) - 1),reported,-0.05298417945655243,0.0040139529891327595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.200000000000001,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,One story,storeys,A_structural,binary,binary,-0.068,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-6.5741394941865146,0.18685209471544273,100*(exp(b - se^2/2) - 1),reported,-0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,One story,storeys,A_structural,binary,binary,-0.067,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.480666905175569,0.18703904026735532,100*(exp(b - se^2/2) - 1),reported,-0.133452102765036,0.003983644858657791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,One story,storeys,A_structural,binary,binary,-0.071,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.854229944149148,0.2794385676382776,100*(exp(b - se^2/2) - 1),reported,-0.09470295537376093,0.00400153332565187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,One story,storeys,A_structural,binary,binary,-0.085,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-8.149506366742553,0.36740491376058293,100*(exp(b - se^2/2) - 1),reported,-0.0851065776146112,0.004005015417158174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Renovated,renovation,A_structural,binary,binary,-0.031,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-3.058307842191943,1.0664231303836285,100*(exp(b - se^2/2) - 1),reported,-0.01132723438535498,0.004019341233513057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8181818181818183,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Renovated,renovation,A_structural,binary,binary,-0.259,0.01,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-22.8215567142686,0.7718230230437034,100*(exp(b - se^2/2) - 1),reported,-0.1035479822755175,0.003997991593649324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.9,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Renovated,renovation,A_structural,binary,binary,-0.103,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.793797732231724,1.0825523681778197,100*(exp(b - se^2/2) - 1),reported,-0.03448104275966151,0.004017208865203283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.583333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Renovated,renovation,A_structural,binary,binary,0.015,0.008,,,*,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.5080581517383562,0.8120904516925751,100*(exp(b - se^2/2) - 1),reported,0.007536534290023725,0.00401948495467932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.875,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Renovated,renovation,A_structural,binary,binary,0.049,0.003,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,5.0215624759083255,0.3150661052220084,100*(exp(b - se^2/2) - 1),reported,0.06551241241849964,0.0040109640256224275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Renovated,renovation,A_structural,binary,binary,0.065,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,6.7094463216212485,1.1738749268226119,100*(exp(b - se^2/2) - 1),reported,0.023745479318286147,0.004018465730786886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.90909090909091,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Fireplace,fireplace,B_interior,binary,binary,0.031,0.005,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,3.147261039830851,0.5157427519432614,100*(exp(b - se^2/2) - 1),reported,0.024913778944489432,0.004018351442659586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.2,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Fireplace,fireplace,B_interior,binary,binary,0.061,0.006,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.287978217892842,0.6377393485123172,100*(exp(b - se^2/2) - 1),reported,0.04083184344981298,0.004016246896702915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.166666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Fireplace,fireplace,B_interior,binary,binary,0.031,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.1480862212198923,0.3094456511659568,100*(exp(b - se^2/2) - 1),reported,0.041500074379557404,0.004016136230279748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.333333333333334,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Fireplace,fireplace,B_interior,binary,binary,0.016,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.6126653150756232,0.203225737081219,100*(exp(b - se^2/2) - 1),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Fireplace,fireplace,B_interior,binary,binary,0.014,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,1.4096430743602673,0.20281969178769846,100*(exp(b - se^2/2) - 1),reported,0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Fireplace,fireplace,B_interior,binary,binary,0.01,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,1.0045621868642884,0.3030150501252504,100*(exp(b - se^2/2) - 1),reported,0.013397461175148244,0.004019238352544473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3333333333333335,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Lake/Mountain view,view,E_environment,binary,binary,0.06,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,6.182327367148344,0.5309182732726798,100*(exp(b - se^2/2) - 1),reported,0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Lake/Mountain view,view,E_environment,binary,binary,0.039,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,3.9762165519561243,0.41590819346006314,100*(exp(b - se^2/2) - 1),reported,0.0391610283504275,0.004016515728248974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.75,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Lake/Mountain view,view,E_environment,binary,binary,0.04,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,4.080609055444251,0.3122432322577165,100*(exp(b - se^2/2) - 1),reported,0.05351784795060012,0.004013838596295009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.333333333333334,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Lake/Mountain view,view,E_environment,binary,binary,0.045,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,4.602315279393832,0.3138083579726151,100*(exp(b - se^2/2) - 1),reported,0.06018468926667303,0.004012312617778202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Lake/Mountain view,view,E_environment,binary,binary,0.053,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,5.4424900196629045,0.3163288935358068,100*(exp(b - se^2/2) - 1),reported,0.07083453881226437,0.0040095021969206255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Lake/Mountain view,view,E_environment,binary,binary,0.093,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,9.744801708212991,0.548730867634041,100*(exp(b - se^2/2) - 1),reported,0.07455645836043782,0.004008411739808485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.599999999999998,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Δ Population,density,F_neighbourhood,other,annual change (log difference),-6.758,1.261,,,***,,,high,Annual change in tract population,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,-6.758,1.261,not harmonised,reported,-0.02153699449415874,0.004018666773769483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.3592386994448855,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Δ Population,density,F_neighbourhood,other,annual change (log difference),-7.272,1.1,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-7.272,1.1,not harmonised,reported,-0.026563827137303438,0.004018180672584404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.61090909090909,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Δ Population,density,F_neighbourhood,other,annual change (log difference),-8.306,0.488,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-8.306,0.488,not harmonised,reported,-0.06825599770715521,0.004010224762953497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.020491803278688,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Δ Population,density,F_neighbourhood,other,annual change (log difference),-6.953,0.637,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-6.953,0.637,not harmonised,reported,-0.04383267058772407,0.004015735821139109,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.915227629513344,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Δ Population,density,F_neighbourhood,other,annual change (log difference),-6.174,0.737,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-6.174,0.737,not harmonised,reported,-0.03365393110446863,0.004017322193714509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.377204884667572,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Δ Population,density,F_neighbourhood,other,annual change (log difference),-7.3,0.742,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-7.3,0.742,not harmonised,reported,-0.03951503486575654,0.0040164597082727885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.838274932614555,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Δ Male population,demographics,F_neighbourhood,other,annual change in share,0.541,1.205,,,,,,high,Annual change in male share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,0.541,1.205,not harmonised,reported,0.0018046469529514066,0.004019592566185665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.44896265560165977,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Δ Male population,demographics,F_neighbourhood,other,annual change in share,-0.057,1.394,,,,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-0.057,1.394,not harmonised,reported,-0.00016435950234354788,0.004019599057314136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.04088952654232425,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Δ Male population,demographics,F_neighbourhood,other,annual change in share,1.574,0.632,,,**,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,1.574,0.632,not harmonised,reported,0.010010335438341863,0.00401939771094794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.490506329113924,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Δ Male population,demographics,F_neighbourhood,other,annual change in share,0.607,0.503,,,,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.607,0.503,not harmonised,reported,0.004850632120996594,0.004019551823494708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2067594433399602,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Δ Male population,demographics,F_neighbourhood,other,annual change in share,0.403,0.497,,,,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.403,0.497,not harmonised,reported,0.0032593356894289527,0.004019577760908658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8108651911468814,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Δ Male population,demographics,F_neighbourhood,other,annual change in share,2.223,0.691,,,***,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,2.223,0.691,not harmonised,reported,0.01293027759148307,0.004019263074995412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2170767004341534,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.432,0.952,,,***,,,high,Annual change in white share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,3.432,0.952,not harmonised,reported,0.01448930250727385,0.004019177152367338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6050420168067228,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.445,0.238,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,3.445,0.238,not harmonised,reported,0.058084620435238335,0.004012812674480906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.474789915966387,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.56,0.218,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,3.56,0.218,not harmonised,reported,0.065500199102664,0.004010967248421559,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.3302752293578,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.744,0.307,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,3.744,0.307,not harmonised,reported,0.048961985316702525,0.004014778176342861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.19543973941368,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.715,0.263,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,3.715,0.263,not harmonised,reported,0.05668744619577831,0.00401313549111432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.1254752851711,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.541,0.282,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,3.541,0.282,not harmonised,reported,0.050408883054344725,0.004014488850981421,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.556737588652483,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,3.347,0.259,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,3.347,0.259,not harmonised,reported,0.05187445748882846,0.004014187179446242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.922779922779922,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,4.176,0.234,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,4.176,0.234,not harmonised,reported,0.07155052723196051,0.004009296784549511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.846153846153847,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,3.646,0.085,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,3.646,0.085,not harmonised,reported,0.16991013597183524,0.003961152374549093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.89411764705882,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,3.242,0.062,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,3.242,0.062,not harmonised,reported,0.20569169137375468,0.003933647398264278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),52.29032258064516,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,2.895,0.076,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,2.895,0.076,not harmonised,reported,0.15135111773418833,0.003973293591640177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.0921052631579,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,2.525,0.141,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,2.525,0.141,not harmonised,reported,0.07179641876739927,0.004009225760872592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.907801418439718,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.652,0.301,,,**,,,high,Annual change in old-population share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,-0.652,0.301,not harmonised,reported,-0.008706575699437155,0.004019446756948748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.166112956810631,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.968,0.251,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-0.968,0.251,not harmonised,reported,-0.015500017959646175,0.004019116227139659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8565737051792826,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.867,0.095,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-0.867,0.095,not harmonised,reported,-0.03665947234016051,0.004016897199902247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.126315789473685,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.741,0.091,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-0.741,0.091,not harmonised,reported,-0.03271350271722219,0.004017447702115007,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.142857142857142,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.581,0.058,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.581,0.058,not harmonised,reported,-0.040232693246771686,0.004016344592620927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.017241379310343,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.404,0.098,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-0.404,0.098,not harmonised,reported,-0.01656831771029363,0.004019047365368257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.122448979591836,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Δ Crimes—violent,crime,F_neighbourhood,other,annual log change in violent crime count,-0.002,0.009,,,,,,high,Change in violent crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,-0.002,0.009,not harmonised,reported,-0.0008932438906707605,0.004019597508018422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22222222222222224,61892.0,annual log change in violent crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Δ Crimes—violent,crime,F_neighbourhood,other,annual log change in violent crime count,0.002,0.011,,,,,,high,Change in violent crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,0.002,0.011,not harmonised,reported,0.0007308360069327493,0.004019598038130121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18181818181818182,61892.0,annual log change in violent crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Δ Crimes—violent,crime,F_neighbourhood,other,annual log change in violent crime count,0.002,0.002,,,,,,high,Change in violent crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,0.002,0.002,not harmonised,reported,0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,61892.0,annual log change in violent crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Δ Crimes—violent,crime,F_neighbourhood,other,annual log change in violent crime count,0.0,0.004,,,,,,high,Change in violent crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.0,0.004,not harmonised,reported,0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,61892.0,annual log change in violent crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Δ Crimes—violent,crime,F_neighbourhood,other,annual log change in violent crime count,-0.0,0.002,,,,,,high,Change in violent crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.0,0.002,not harmonised,reported,-0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,61892.0,annual log change in violent crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Δ Crimes—violent,crime,F_neighbourhood,other,annual log change in violent crime count,0.005,0.003,,,*,,,high,Change in violent crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,0.005,0.003,not harmonised,reported,0.006699181521223725,0.004019508912734235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6666666666666667,61892.0,annual log change in violent crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c1,7 Panel C,(1) 2SLS,36,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.031,0.004,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-3.1,0.4,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.031136788579101626,0.00401765013923892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c2,7 Panel C,(2) Q: 0.05,36,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.028,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-2.8000000000000003,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.056185493585738,0.004013249541838429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c3,7 Panel C,(3) Q: 0.25,36,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.032,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-3.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.12757613078535945,0.003986754087042483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c4,7 Panel C,(4) Q: 0.50,36,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.032,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-3.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.12757613078535945,0.003986754087042483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c5,7 Panel C,(5) Q: 0.75,36,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.034,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-3.4000000000000004,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7C-c6,7 Panel C,(6) Q: 0.95,36,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.037,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in violent crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-3.6999999999999997,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1471071215427055,0.003975868149802851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,36,Living area,living_area,A_structural,level,1000 sqft,0.268,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.28847279916782054,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.33795631871264253,0.00378309311991764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),89.33333333333334,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,36,Living area,living_area,A_structural,level,1000 sqft,0.252,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.271250542501085,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.1985542516600187,0.003939568485317832,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),50.4,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,36,Living area,living_area,A_structural,level,1000 sqft,0.259,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.27878527979278184,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4617283980831373,0.003565470255468242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),129.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,36,Living area,living_area,A_structural,level,1000 sqft,0.276,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.29708392750118834,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.48507424669809696,0.003515030773174615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),138.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,36,Living area,living_area,A_structural,level,1000 sqft,0.288,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.31000062000123996,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.5009551799969574,0.003478855416645538,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),144.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,36,Living area,living_area,A_structural,level,1000 sqft,0.302,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3250700945846336,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.23593001210574505,0.003906126028240812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.4,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,36,Lot area,lot_size,A_structural,level,1000 sqft,0.013,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.013993083541722639,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.05218359172933882,0.004014122440718371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,36,Lot area,lot_size,A_structural,level,1000 sqft,-0.001,0.001,,,,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.0010763910416709723,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,-0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,36,Lot area,lot_size,A_structural,level,1000 sqft,0.009,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.009687519375038749,,100*b/92.903 (X per 1000sqft->m2),,,,,,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,36,Lot area,lot_size,A_structural,level,1000 sqft,0.012,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.012916692500051665,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,36,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,36,Lot area,lot_size,A_structural,level,1000 sqft,0.03,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.032291731250129165,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.11972065843265513,0.003990688614421837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,36,Bedrooms,bedrooms,A_structural,level,count,-0.011,0.002,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.0999999999999999,0.2,100*b/1 (per unit count),reported,-0.022102394450125195,0.004018617172750035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,36,Bedrooms,bedrooms,A_structural,level,count,-0.009,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.8999999999999999,0.4,100*b/1 (per unit count),reported,-0.009043728139602736,0.004019434728712327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,36,Bedrooms,bedrooms,A_structural,level,count,-0.007,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per unit count),reported,-0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,36,Bedrooms,bedrooms,A_structural,level,count,-0.006,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.6,0.1,100*b/1 (per unit count),reported,-0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,36,Bedrooms,bedrooms,A_structural,level,count,-0.004,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.4,0.1,100*b/1 (per unit count),reported,-0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,36,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8,0.4,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.5,0.1,100*b/1 (per unit count),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,36,Bathrooms (Full),bathrooms,A_structural,level,count,-0.012,0.005,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.2,0.5,100*b/1 (per unit count),reported,-0.009646588996759433,0.004019412081983097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.0,0.001,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.0,0.1,100*b/1 (per unit count),reported,0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.002,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.5,0.2,100*b/1 (per unit count),reported,0.010048490431704502,0.0040193961726818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8,0.1,100*b/1 (per unit count),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,36,Bathrooms (Full),bathrooms,A_structural,level,count,0.016,0.003,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.6,0.3,100*b/1 (per unit count),reported,0.02143293739900748,0.004018675762313902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333333,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,36,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,36,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,36,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,36,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,36,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,36,Age,age,A_structural,level,years,-0.0,0.0,,,,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,36,One story,storeys,A_structural,binary,binary,-0.08,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-7.689519249557586,0.4615581731933179,100*(exp(b - se^2/2) - 1),reported,-0.06418098885674876,0.004011311803546798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,36,One story,storeys,A_structural,binary,binary,-0.066,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-6.388083727104988,0.4680654321458094,100*(exp(b - se^2/2) - 1),reported,-0.05298417945655243,0.0040139529891327595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.200000000000001,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,36,One story,storeys,A_structural,binary,binary,-0.068,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-6.5741394941865146,0.18685209471544273,100*(exp(b - se^2/2) - 1),reported,-0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,36,One story,storeys,A_structural,binary,binary,-0.067,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.480900703216063,0.280558560401033,100*(exp(b - se^2/2) - 1),reported,-0.08941149327137186,0.004003499698718143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.333333333333336,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,36,One story,storeys,A_structural,binary,binary,-0.071,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.854229944149148,0.2794385676382776,100*(exp(b - se^2/2) - 1),reported,-0.09470295537376093,0.00400153332565187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,36,One story,storeys,A_structural,binary,binary,-0.085,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-8.149506366742553,0.36740491376058293,100*(exp(b - se^2/2) - 1),reported,-0.0851065776146112,0.004005015417158174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Renovated,renovation,A_structural,binary,binary,-0.031,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-3.058307842191943,1.0664231303836285,100*(exp(b - se^2/2) - 1),reported,-0.01132723438535498,0.004019341233513057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8181818181818183,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Renovated,renovation,A_structural,binary,binary,-0.26,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-22.899506140628255,0.8481567443839229,100*(exp(b - se^2/2) - 1),reported,-0.09458278224477856,0.004001579248817554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.63636363636364,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Renovated,renovation,A_structural,binary,binary,-0.103,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.793797732231724,1.0825523681778197,100*(exp(b - se^2/2) - 1),reported,-0.03448104275966151,0.004017208865203283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.583333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Renovated,renovation,A_structural,binary,binary,0.014,0.007,,,*,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.4073613830602127,0.7098689212569446,100*(exp(b - se^2/2) - 1),reported,0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Renovated,renovation,A_structural,binary,binary,0.049,0.003,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,5.0215624759083255,0.3150661052220084,100*(exp(b - se^2/2) - 1),reported,0.06551241241849964,0.0040109640256224275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Renovated,renovation,A_structural,binary,binary,0.066,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,6.814980761113465,1.281872060599192,100*(exp(b - se^2/2) - 1),reported,0.022102394450125195,0.004018617172750035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Fireplace,fireplace,B_interior,binary,binary,0.031,0.005,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,3.147261039830851,0.5157427519432614,100*(exp(b - se^2/2) - 1),reported,0.024913778944489432,0.004018351442659586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.2,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Fireplace,fireplace,B_interior,binary,binary,0.061,0.006,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.287978217892842,0.6377393485123172,100*(exp(b - se^2/2) - 1),reported,0.04083184344981298,0.004016246896702915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.166666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Fireplace,fireplace,B_interior,binary,binary,0.031,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.1480862212198923,0.3094456511659568,100*(exp(b - se^2/2) - 1),reported,0.041500074379557404,0.004016136230279748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.333333333333334,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Fireplace,fireplace,B_interior,binary,binary,0.016,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.6126653150756232,0.203225737081219,100*(exp(b - se^2/2) - 1),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Fireplace,fireplace,B_interior,binary,binary,0.014,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,1.4096430743602673,0.20281969178769846,100*(exp(b - se^2/2) - 1),reported,0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Fireplace,fireplace,B_interior,binary,binary,0.01,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,1.0045621868642884,0.3030150501252504,100*(exp(b - se^2/2) - 1),reported,0.013397461175148244,0.004019238352544473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3333333333333335,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Lake/Mountain view,view,E_environment,binary,binary,0.06,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,6.182327367148344,0.5309182732726798,100*(exp(b - se^2/2) - 1),reported,0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Lake/Mountain view,view,E_environment,binary,binary,0.04,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,4.079776413902381,0.5204053870961941,100*(exp(b - se^2/2) - 1),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Lake/Mountain view,view,E_environment,binary,binary,0.04,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,4.080609055444251,0.3122432322577165,100*(exp(b - se^2/2) - 1),reported,0.05351784795060012,0.004013838596295009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.333333333333334,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Lake/Mountain view,view,E_environment,binary,binary,0.045,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,4.602315279393832,0.3138083579726151,100*(exp(b - se^2/2) - 1),reported,0.06018468926667303,0.004012312617778202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Lake/Mountain view,view,E_environment,binary,binary,0.054,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,5.547985248505705,0.31664538064652403,100*(exp(b - se^2/2) - 1),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Lake/Mountain view,view,E_environment,binary,binary,0.093,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,9.744801708212991,0.548730867634041,100*(exp(b - se^2/2) - 1),reported,0.07455645836043782,0.004008411739808485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.599999999999998,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Δ Population,density,F_neighbourhood,other,annual change (log difference),-6.743,1.257,,,***,,,high,Annual change in tract population,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,-6.743,1.257,not harmonised,reported,-0.02155756407293183,0.0040186649917952405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.364359586316628,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Δ Population,density,F_neighbourhood,other,annual change (log difference),-7.292,1.092,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-7.292,1.092,not harmonised,reported,-0.02683183485668655,0.004018151901193324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.677655677655677,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Δ Population,density,F_neighbourhood,other,annual change (log difference),-8.304,0.527,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-8.304,0.527,not harmonised,reported,-0.06321062711157929,0.004011560752384668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.757115749525617,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Δ Population,density,F_neighbourhood,other,annual change (log difference),-6.972,0.616,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-6.972,0.616,not harmonised,reported,-0.04544754526419796,0.0040154457663146795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.318181818181818,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Δ Population,density,F_neighbourhood,other,annual change (log difference),-6.166,0.733,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-6.166,0.733,not harmonised,reported,-0.03379357688778245,0.0040173032531210726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.412005457025922,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Δ Population,density,F_neighbourhood,other,annual change (log difference),-7.275,0.749,,,***,,,high,Annual change in tract population,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-7.275,0.749,not harmonised,reported,-0.039012445770840544,0.0040165390903587036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.712950600801069,61892.0,annual change,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Δ Male population,demographics,F_neighbourhood,other,annual change in share,0.513,1.207,,,,,,high,Annual change in male share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,0.513,1.207,not harmonised,reported,0.0017084103852760338,0.004019593245668953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4250207125103562,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Δ Male population,demographics,F_neighbourhood,other,annual change in share,-0.079,1.382,,,,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-0.079,1.382,not harmonised,reported,-0.00022977447281784334,0.004019599005496955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.057163531114327065,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Δ Male population,demographics,F_neighbourhood,other,annual change in share,1.57,0.626,,,**,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,1.57,0.626,not harmonised,reported,0.010080590977860578,0.004019394873974982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5079872204472844,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Δ Male population,demographics,F_neighbourhood,other,annual change in share,0.618,0.532,,,,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.618,0.532,not harmonised,reported,0.00466933302760248,0.004019555292369773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1616541353383458,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Δ Male population,demographics,F_neighbourhood,other,annual change in share,0.37,0.539,,,,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.37,0.539,not harmonised,reported,0.002759269034582024,0.004019583809837057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.686456400742115,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Δ Male population,demographics,F_neighbourhood,other,annual change in share,2.208,0.674,,,***,,,high,Annual change in male share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,2.208,0.674,not harmonised,reported,0.013166922049677919,0.004019250661903496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2759643916913945,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.44,0.95,,,***,,,high,Annual change in white share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,3.44,0.95,not harmonised,reported,0.014553638404724194,0.0040191733966534835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6210526315789475,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.465,0.215,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,3.465,0.215,not harmonised,reported,0.06464547822917559,0.004011191289833406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.11627906976744,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.56,0.215,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,3.56,0.215,not harmonised,reported,0.06641015215470761,0.004010725481253408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.558139534883722,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.745,0.31,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,3.745,0.31,not harmonised,reported,0.04850219987965943,0.004014868347848978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.080645161290324,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.724,0.259,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,3.724,0.259,not harmonised,reported,0.05769903129624036,0.00401290255255807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.378378378378379,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Δ White population,demographics,F_neighbourhood,other,annual change in share,3.545,0.29,,,***,,,high,Annual change in white share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,3.545,0.29,not harmonised,reported,0.04907692501010931,0.004014755501532214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.224137931034484,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,3.347,0.26,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,3.347,0.26,not harmonised,reported,0.05167547415194881,0.004014228646401759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.873076923076923,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,4.18,0.219,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,4.18,0.219,not harmonised,reported,0.07649631218139254,0.004007821140604059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.08675799086758,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,3.645,0.093,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,3.645,0.093,not harmonised,reported,0.15562294004120714,0.003970626453726272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.193548387096776,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,3.239,0.057,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,3.239,0.057,not harmonised,reported,0.22267705001555993,0.003918676088572682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.82456140350877,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,2.901,0.075,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,2.901,0.075,not harmonised,reported,0.15363227192083356,0.003971878798366948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.68,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Δ Asian population,demographics,F_neighbourhood,other,annual change in share,2.519,0.139,,,***,,,high,Annual change in Asian share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,2.519,0.139,not harmonised,reported,0.07265188814329253,0.004008976757410743,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.12230215827338,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.652,0.299,,,**,,,high,Annual change in old-population share,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,-0.652,0.299,not harmonised,reported,-0.008764809204894426,0.0040194447120604805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1806020066889635,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.982,0.243,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-0.982,0.243,not harmonised,reported,-0.01624166941400149,0.004019068907945379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.041152263374486,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.866,0.088,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-0.866,0.088,not harmonised,reported,-0.03952559832978755,0.004016458028892961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.840909090909092,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.732,0.092,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-0.732,0.092,not harmonised,reported,-0.031965683840951026,0.004017544963616795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.956521739130435,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.574,0.062,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.574,0.062,not harmonised,reported,-0.03718796674826704,0.00401681870800097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.258064516129032,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Δ Old population,demographics,F_neighbourhood,other,annual change in share,-0.406,0.096,,,***,,,high,Annual change in old-population share,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-0.406,0.096,not harmonised,reported,-0.016997098801505697,0.004019018435824006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.229166666666667,61892.0,annual change in share,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Δ Crimes—social,crime,F_neighbourhood,other,annual log change in social crime count,0.005,0.006,,,,,,high,Change in social crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,other,0.005,0.006,not harmonised,reported,0.0033496471344329174,0.0040195765613195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8333333333333334,61892.0,annual log change in social crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Δ Crimes—social,crime,F_neighbourhood,other,annual log change in social crime count,0.007,0.003,,,**,,,high,Change in social crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,0.007,0.003,not harmonised,reported,0.009378652097567278,0.004019422327528833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3333333333333335,61892.0,annual log change in social crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Δ Crimes—social,crime,F_neighbourhood,other,annual log change in social crime count,0.002,0.002,,,,,,high,Change in social crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,0.002,0.002,not harmonised,reported,0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,61892.0,annual log change in social crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Δ Crimes—social,crime,F_neighbourhood,other,annual log change in social crime count,0.006,0.002,,,***,,,high,Change in social crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.006,0.002,not harmonised,reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,annual log change in social crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Δ Crimes—social,crime,F_neighbourhood,other,annual log change in social crime count,0.006,0.002,,,***,,,high,Change in social crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.006,0.002,not harmonised,reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,annual log change in social crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Δ Crimes—social,crime,F_neighbourhood,other,annual log change in social crime count,0.004,0.004,,,,,,high,Change in social crimes: log(1+n_t)-log(1+n_t-1),Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,0.004,0.004,not harmonised,reported,0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,61892.0,annual log change in social crime count,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c1,7 Panel D,(1) 2SLS,37,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.031,0.004,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-3.1,0.4,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.031136788579101626,0.00401765013923892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c2,7 Panel D,(2) Q: 0.05,37,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.029,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-2.9000000000000004,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.05818544158358281,0.004012789074729849,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.5,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c3,7 Panel D,(3) Q: 0.25,37,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.032,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-3.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.12757613078535945,0.003986754087042483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c4,7 Panel D,(4) Q: 0.50,37,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.032,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-3.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.12757613078535945,0.003986754087042483,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c5,7 Panel D,(5) Q: 0.75,37,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.034,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-3.4000000000000004,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7D-c6,7 Panel D,(6) Q: 0.95,37,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.037,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,With spatial lag of neighbour prices; change in social crimes and changes in demographics; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-3.6999999999999997,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1471071215427055,0.003975868149802851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Living area,living_area,A_structural,level,1000 sqft,0.262,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.28201445291779476,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.26182986586282503,0.00399740253225687,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),65.5,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Living area,living_area,A_structural,level,1000 sqft,0.242,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.2604866320843753,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.16477308260079432,0.004085283039689116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.33333333333333,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Living area,living_area,A_structural,level,1000 sqft,0.253,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.27232693354275594,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.46410457213618095,0.003668810846926332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),126.5,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Living area,living_area,A_structural,level,1000 sqft,0.267,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.2873964081261496,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3458769761338051,0.0038862581588067985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),89.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Living area,living_area,A_structural,level,1000 sqft,0.287,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.308924228959569,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.2848679086457831,0.003970284441056212,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.74999999999999,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Living area,living_area,A_structural,level,1000 sqft,0.3,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.32291731250129163,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.38266457888843425,0.003826645788884342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),100.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07434779973495277,0.004130433318608488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Lot area,lot_size,A_structural,level,1000 sqft,0.009,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.009687519375038749,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.03725119613901589,0.004139021793223988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Lot area,lot_size,A_structural,level,1000 sqft,0.014,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.015069474583393612,,100*b/92.903 (X per 1000sqft->m2),,,,,,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07434779973495277,0.004130433318608488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.09074576411495175,0.0041248074597705344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Lot area,lot_size,A_structural,level,1000 sqft,0.034,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.03659729541681306,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.13944853165344473,0.004101427401571905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.0,58291.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Bedrooms,bedrooms,A_structural,level,count,-0.007,0.001,,,***,,,high,,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per unit count),reported,-0.02898109747697928,0.004140156782425611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Bedrooms,bedrooms,A_structural,level,count,0.001,0.003,,,,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.1,0.3,100*b/1 (per unit count),reported,0.0013806308660163915,0.004141892598049175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3333333333333333,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Bedrooms,bedrooms,A_structural,level,count,-0.001,0.002,,,,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,0.2,100*b/1 (per unit count),reported,-0.002070943831830954,0.004141887663661908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Bedrooms,bedrooms,A_structural,level,count,-0.001,0.002,,,,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,0.2,100*b/1 (per unit count),reported,-0.002070943831830954,0.004141887663661908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.003,,,,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.2,0.3,100*b/1 (per unit count),reported,-0.0027612538370329082,0.004141880755549362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.004,,,**,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8,0.4,100*b/1 (per unit count),reported,-0.008283508883743173,0.004141754441871586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.8999999999999999,0.1,100*b/1 (per unit count),reported,0.03725119613901589,0.004139021793223988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Bathrooms (Full),bathrooms,A_structural,level,count,-0.005,0.006,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.5,0.6,100*b/1 (per unit count),reported,-0.0034515598947843043,0.0041418718737411655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8333333333333334,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Bathrooms (Full),bathrooms,A_structural,level,count,0.007,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.7000000000000001,0.1,100*b/1 (per unit count),reported,0.02898109747697928,0.004140156782425611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.018635297831810595,0.00414117729595791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8999999999999999,0.1,100*b/1 (per unit count),reported,0.03725119613901589,0.004139021793223988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Bathrooms (Full),bathrooms,A_structural,level,count,0.015,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.5,0.1,100*b/1 (per unit count),reported,0.062008888026408374,0.004133925868427225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,58291.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,58291.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Age,age,A_structural,level,years,-0.002,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.2,,100*b/1 (per year),,,,,,58291.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,58291.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,58291.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,58291.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,58291.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,One story,storeys,A_structural,binary,binary,-0.075,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-7.226811039254654,0.46387174316427643,100*(exp(b - se^2/2) - 1),reported,-0.062008888026408374,0.004133925868427225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.0,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,One story,storeys,A_structural,binary,binary,-0.05,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-4.8782465792777785,0.47561471225035706,100*(exp(b - se^2/2) - 1),reported,-0.04138348334878986,0.004138348334878986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,One story,storeys,A_structural,binary,binary,-0.064,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-6.200250447925071,0.3752019998122918,100*(exp(b - se^2/2) - 1),reported,-0.06612530062581795,0.004132831289113623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.0,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,One story,storeys,A_structural,binary,binary,-0.066,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.3873348287792115,0.28083925928748565,100*(exp(b - se^2/2) - 1),reported,-0.09074576411495175,0.0041248074597705344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,One story,storeys,A_structural,binary,binary,-0.071,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.854229944149148,0.2794385676382776,100*(exp(b - se^2/2) - 1),reported,-0.0975572973870148,0.004122139326211893,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.666666666666664,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,One story,storeys,A_structural,binary,binary,-0.083,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-7.9656215559601895,0.36814045888804986,100*(exp(b - se^2/2) - 1),reported,-0.0856286899127531,0.004126683851217017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.75,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Renovated,renovation,A_structural,binary,binary,-0.027,0.011,,,**,,,high,Previously renovated =1,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-2.669764504943861,1.0706973656767704,100*(exp(b - se^2/2) - 1),reported,-0.01016594799084581,0.004141682514789034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4545454545454546,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Renovated,renovation,A_structural,binary,binary,-0.265,0.015,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-23.288035583872347,1.1508089249637834,100*(exp(b - se^2/2) - 1),reported,-0.0729783901127667,0.004130852270533963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.666666666666668,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Renovated,renovation,A_structural,binary,binary,-0.102,0.015,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.707203344141636,1.354544327503315,100*(exp(b - se^2/2) - 1),reported,-0.028153732089123,0.004140254718988677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.8,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Renovated,renovation,A_structural,binary,binary,0.022,0.006,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.222538424792031,0.6133462706822629,100*(exp(b - se^2/2) - 1),reported,0.015185202916673055,0.00414141897727447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6666666666666665,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Renovated,renovation,A_structural,binary,binary,0.049,0.004,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,5.021194901082926,0.4200881402960112,100*(exp(b - se^2/2) - 1),reported,0.05067304906348116,0.004136575433753564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.25,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Renovated,renovation,A_structural,binary,binary,0.072,0.014,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,7.4550033000421845,1.504517481689339,100*(exp(b - se^2/2) - 1),reported,0.021296351275210873,0.004140957192402115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.142857142857142,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Fireplace,fireplace,B_interior,binary,binary,0.025,0.004,,,***,,,high,,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,2.5306918036274606,0.41012604820977155,100*(exp(b - se^2/2) - 1),reported,0.025878183998881536,0.004140509439821046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Fireplace,fireplace,B_interior,binary,binary,0.043,0.006,,,***,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,4.391910413762212,0.6263627369103676,100*(exp(b - se^2/2) - 1),reported,0.029670523207201698,0.004140073005656051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Fireplace,fireplace,B_interior,binary,binary,0.024,0.002,,,***,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,2.4288269312034316,0.2048580635781243,100*(exp(b - se^2/2) - 1),reported,0.04964148010072049,0.004136790008393374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Fireplace,fireplace,B_interior,binary,binary,0.014,0.003,,,***,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.4093895505694842,0.3042295376815477,100*(exp(b - se^2/2) - 1),reported,0.01932524088499034,0.004141123046783644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.666666666666667,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Fireplace,fireplace,B_interior,binary,binary,0.011,0.002,,,***,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,1.1058700325296877,0.2022121444889439,100*(exp(b - se^2/2) - 1),reported,0.02277452237027888,0.004140822249141614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Fireplace,fireplace,B_interior,binary,binary,0.006,0.003,,,*,,,high,,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,0.6013508983088611,0.3018054108162195,100*(exp(b - se^2/2) - 1),reported,0.008283508883743173,0.004141754441871586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,37,Lake/Mountain view,view,E_environment,binary,binary,0.062,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,6.394904528184631,0.5319811723640169,100*(exp(b - se^2/2) - 1),reported,0.05129191281352017,0.004136444581735498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.4,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,37,Lake/Mountain view,view,E_environment,binary,binary,0.038,0.008,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,3.8697994010870262,0.8309849863027982,100*(exp(b - se^2/2) - 1),reported,0.019670202120503025,0.004141095183263795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.75,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,37,Lake/Mountain view,view,E_environment,binary,binary,0.035,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.5606763634170813,0.5178098543998116,100*(exp(b - se^2/2) - 1),reported,0.028981097476979283,0.004140156782425611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.000000000000001,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,37,Lake/Mountain view,view,E_environment,binary,binary,0.047,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,4.811362413712272,0.4192488036318623,100*(exp(b - se^2/2) - 1),reported,0.04860975224640102,0.004137000191183065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.75,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,37,Lake/Mountain view,view,E_environment,binary,binary,0.059,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,6.07619811325224,0.5303876203700795,100*(exp(b - se^2/2) - 1),reported,0.04881611037163148,0.004136958506070465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.799999999999999,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,37,Lake/Mountain view,view,E_environment,binary,binary,0.103,0.012,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,10.84116005676945,1.3301896908912085,100*(exp(b - se^2/2) - 1),reported,0.03552883344334841,0.00413928156621535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.583333333333332,58291.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.006,0.003,,,**,,,high,Census tract population level,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.6,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.008283508883743173,0.004141754441871586,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,58291.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.026,0.011,,,**,,,high,Census tract population level,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-2.6,1.0999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.009789468175399826,0.004141698074207618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3636363636363638,58291.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.009,0.004,,,**,,,high,Census tract population level,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.8999999999999999,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.009318862570578118,0.004141716698034719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,58291.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.002,0.003,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.2,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0027612538370329082,0.004141880755549362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,58291.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.0,0.003,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.0,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0,0.004141896545571686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,58291.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.012,0.004,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.2,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.012424730498438733,0.004141576832812912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,58291.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,% Male population,demographics,F_neighbourhood,level,share,-0.339,0.142,,,**,,,high,Share of males in census tract,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-33.900000000000006,14.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009887565436244751,0.0041416940765390976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3873239436619724,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,% Male population,demographics,F_neighbourhood,level,share,-0.674,0.316,,,**,,,high,Share of males in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-67.4,31.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.008833953612748205,0.0041417349282320964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1329113924050636,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,% Male population,demographics,F_neighbourhood,level,share,-0.018,0.131,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-1.7999999999999998,13.100000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0005691154637139667,0.004141895874807202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13740458015267173,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,% Male population,demographics,F_neighbourhood,level,share,-0.057,0.114,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-5.7,11.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.002070943831830954,0.004141887663661908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,% Male population,demographics,F_neighbourhood,level,share,-0.07,0.126,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-7.000000000000001,12.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0023010475445884703,0.004141885580259246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5555555555555556,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,% Male population,demographics,F_neighbourhood,level,share,0.316,0.148,,,**,,,high,Share of males in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,31.6,14.799999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.00884316304581171,0.00414173459107637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.135135135135135,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,% White population,demographics,F_neighbourhood,level,share,0.636,0.038,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,63.6,3.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.06915629969605615,0.004131980170519078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.736842105263158,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,% White population,demographics,F_neighbourhood,level,share,0.922,0.063,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,92.2,6.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.060505270702052416,0.004134308084847399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.634920634920636,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,% White population,demographics,F_neighbourhood,level,share,0.639,0.016,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,63.9,1.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.16319926870546,0.004086366665551424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),39.9375,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,% White population,demographics,F_neighbourhood,level,share,0.578,0.016,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,57.8,1.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.14797871205763763,0.004096296527547063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.125,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,% White population,demographics,F_neighbourhood,level,share,0.54,0.009,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,54.0,0.8999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.2411778950812592,0.0040196315846876525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.00000000000001,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,% White population,demographics,F_neighbourhood,level,share,0.467,0.016,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,46.7,1.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12001776990427415,0.00411195785539269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.1875,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,% Asian population,demographics,F_neighbourhood,level,share,0.174,0.023,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,17.4,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03131897641862029,0.004139864699013027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.565217391304348,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,% Asian population,demographics,F_neighbourhood,level,share,0.22,0.104,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,22.0,10.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.008761367943484795,0.004141737573283721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1153846153846154,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,% Asian population,demographics,F_neighbourhood,level,share,0.105,0.026,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,10.5,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01672455038788563,0.004141317238905013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.038461538461538,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,% Asian population,demographics,F_neighbourhood,level,share,0.147,0.026,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,14.7,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.023411227537297775,0.0041407613331274976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.653846153846154,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,% Asian population,demographics,F_neighbourhood,level,share,0.169,0.018,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,16.900000000000002,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0388584354977919,0.004138768277871326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.388888888888891,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,% Asian population,demographics,F_neighbourhood,level,share,0.167,0.028,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,16.7,2.8000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02469592007187226,0.004140633305463612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.964285714285714,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,% Old population,demographics,F_neighbourhood,level,share,-0.155,0.031,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-15.5,3.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.020705043186665526,0.004141008637333105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,% Old population,demographics,F_neighbourhood,level,share,-0.338,0.091,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-33.800000000000004,9.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01538236697983478,0.004141406494570903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7142857142857144,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,% Old population,demographics,F_neighbourhood,level,share,-0.176,0.032,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-17.599999999999998,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02277452237027888,0.004140822249141614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,% Old population,demographics,F_neighbourhood,level,share,-0.208,0.03,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-20.8,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.028705315548598877,0.004140189742586376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.933333333333334,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,% Old population,demographics,F_neighbourhood,level,share,-0.223,0.031,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-22.3,3.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.029781716965807833,0.004140059309148174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.193548387096774,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,% Old population,demographics,F_neighbourhood,level,share,-0.236,0.051,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-23.599999999999998,5.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01916290379027367,0.004141135988576089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.627450980392157,58291.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,Number of crimes (IV— Lag1),crime,F_neighbourhood,other,"reported crimes per capita (tract), lagged 1 year",1.106,0.223,,,***,,,high,Lagged crimes per capita instrumented by lagged 911 calls,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,other,1.106,0.223,not harmonised,reported,0.020537988059443336,0.004141022908911269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.959641255605382,58291.0,reported crimes per capita lagged 1 year,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,Number of crimes (IV— Lag1),crime,F_neighbourhood,other,"reported crimes per capita (tract), lagged 1 year",1.773,0.403,,,***,,,high,Lagged crimes per capita instrumented by lagged 911 calls,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,1.773,0.403,not harmonised,reported,0.018219264649743314,0.004141209054622987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.399503722084367,58291.0,reported crimes per capita lagged 1 year,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,Number of crimes (IV— Lag1),crime,F_neighbourhood,other,"reported crimes per capita (tract), lagged 1 year",0.784,0.13,,,***,,,high,Lagged crimes per capita instrumented by lagged 911 calls,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,0.784,0.13,not harmonised,reported,0.024971033226124924,0.0041406049992298985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.030769230769231,58291.0,reported crimes per capita lagged 1 year,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,Number of crimes (IV— Lag1),crime,F_neighbourhood,other,"reported crimes per capita (tract), lagged 1 year",0.575,0.095,,,***,,,high,Lagged crimes per capita instrumented by lagged 911 calls,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.575,0.095,not harmonised,reported,0.0250614998211112,0.004140595622618373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.052631578947368,58291.0,reported crimes per capita lagged 1 year,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,Number of crimes (IV— Lag1),crime,F_neighbourhood,other,"reported crimes per capita (tract), lagged 1 year",0.429,0.139,,,***,,,high,Lagged crimes per capita instrumented by lagged 911 calls,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.429,0.139,not harmonised,reported,0.012782219099003228,0.004141558169607107,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.086330935251798,58291.0,reported crimes per capita lagged 1 year,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,Number of crimes (IV— Lag1),crime,F_neighbourhood,other,"reported crimes per capita (tract), lagged 1 year",0.019,0.197,,,,,,high,Lagged crimes per capita instrumented by lagged 911 calls,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,0.019,0.197,not harmonised,reported,0.0003994722237907696,0.00414189621509377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09644670050761421,58291.0,reported crimes per capita lagged 1 year,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c1,7 Panel E,(1) 2SLS,38,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.038,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-3.8,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07845347556269293,0.0041291302927733125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.0,58291.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c2,7 Panel E,(2) Q: 0.05,38,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.035,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-3.5000000000000004,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.0722935299946852,0.004131058856839154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.5,58291.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c3,7 Panel E,(3) Q: 0.25,38,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.038,0.0,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-3.8,,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),,,,,,58291.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c4,7 Panel E,(4) Q: 0.50,38,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.04,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-4.0,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.16344784880410515,0.004086196220102629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,58291.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c5,7 Panel E,(5) Q: 0.75,38,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.042,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-4.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1713857481867787,0.00408061305206616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.0,58291.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T7E-c6,7 Panel E,(6) Q: 0.95,38,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.046,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,58301.0,,se,With spatial lag of neighbour prices; lagged crimes instrumented by lagged 911 calls; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-4.6,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.0948342744601167,0.0041232293243529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.0,58291.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Living area,living_area,A_structural,level,1000 sqft,0.318,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.34229235125136914,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.3043938972733358,0.0038288540537526517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),79.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Living area,living_area,A_structural,level,1000 sqft,0.284,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.3056950558345561,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.1869081301840186,0.003948763313746872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.33333333333333,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Living area,living_area,A_structural,level,1000 sqft,0.306,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.32937565875131747,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3793525756781874,0.0037191428988057585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),102.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Living area,living_area,A_structural,level,1000 sqft,0.32,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.3444451333347111,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3940635690280088,0.003694345959637582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),106.66666666666667,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Living area,living_area,A_structural,level,1000 sqft,0.34,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.3659729541681305,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.3233154705538053,0.0038037114182800616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),85.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Living area,living_area,A_structural,level,1000 sqft,0.347,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3735076914598273,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.22642910546765005,0.003915200670910376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),57.83333333333333,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Lot area,lot_size,A_structural,level,1000 sqft,0.021,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.022604211875090416,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08411244939884005,0.004005354733278097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Lot area,lot_size,A_structural,level,1000 sqft,0.007,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.007534737291696806,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Lot area,lot_size,A_structural,level,1000 sqft,0.013,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.013993083541722639,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.05218359172933882,0.004014122440718371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Lot area,lot_size,A_structural,level,1000 sqft,0.019,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.02045142979174847,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.0761506225926853,0.004007927504878174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Lot area,lot_size,A_structural,level,1000 sqft,0.026,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.027986167083445278,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.10394347012419862,0.003997825774007639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Lot area,lot_size,A_structural,level,1000 sqft,0.038,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.04090285958349694,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.0761506225926853,0.004007927504878174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Bedrooms,bedrooms,A_structural,level,count,-0.019,0.002,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.9,0.2,100*b/1 (per unit count),reported,-0.03815838070142761,0.004016671652781854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.8,0.4,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Bedrooms,bedrooms,A_structural,level,count,-0.012,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-1.2,0.2,100*b/1 (per unit count),reported,-0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Bedrooms,bedrooms,A_structural,level,count,-0.012,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-1.2,0.1,100*b/1 (per unit count),reported,-0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Bedrooms,bedrooms,A_structural,level,count,-0.014,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-1.4000000000000001,0.1,100*b/1 (per unit count),reported,-0.056185493585738,0.004013249541838429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Bedrooms,bedrooms,A_structural,level,count,-0.019,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-1.9,0.3,100*b/1 (per unit count),reported,-0.02544921578242598,0.004018297228804102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.333333333333333,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Bathrooms (Full),bathrooms,A_structural,level,count,0.003,0.002,,,,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.3,0.2,100*b/1 (per unit count),reported,0.006029289075079203,0.004019526050052801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Bathrooms (Full),bathrooms,A_structural,level,count,-0.01,0.004,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.0,0.4,100*b/1 (per unit count),reported,-0.010048490431704502,0.0040193961726818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Bathrooms (Full),bathrooms,A_structural,level,count,0.002,0.003,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.2,0.3,100*b/1 (per unit count),reported,0.0026797231195861745,0.004019584679379263,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6666666666666666,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Bathrooms (Full),bathrooms,A_structural,level,count,0.006,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.6,0.2,100*b/1 (per unit count),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Bathrooms (Full),bathrooms,A_structural,level,count,0.004,0.002,,,*,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.4,0.2,100*b/1 (per unit count),reported,0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.003,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.8999999999999999,0.3,100*b/1 (per unit count),reported,0.012057920667875591,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Age,age,A_structural,level,years,-0.0,0.0,,,**,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Age,age,A_structural,level,years,-0.0,0.0,,,,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,One story,storeys,A_structural,binary,binary,-0.095,0.008,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-9.065616500007756,0.7274983475745852,100*(exp(b - se^2/2) - 1),reported,-0.047678454714258044,0.004015027765411204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.875,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,One story,storeys,A_structural,binary,binary,-0.064,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-6.200672545848329,0.46900249976536473,100*(exp(b - se^2/2) - 1),reported,-0.051382903375746324,0.004014289326230181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.8,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,One story,storeys,A_structural,binary,binary,-0.077,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-7.411199817136005,0.18517797072129907,100*(exp(b - se^2/2) - 1),reported,-0.152934093480691,0.003972314116381585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,One story,storeys,A_structural,binary,binary,-0.082,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-7.873218701813767,0.27638158760890463,100*(exp(b - se^2/2) - 1),reported,-0.10921186022302366,0.003995555861817939,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-27.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,One story,storeys,A_structural,binary,binary,-0.088,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-8.424324419134754,0.2747282630169977,100*(exp(b - se^2/2) - 1),reported,-0.11709708757489604,0.003991946167326002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-29.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,One story,storeys,A_structural,binary,binary,-0.1,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-9.516665372326017,0.27145122541078787,100*(exp(b - se^2/2) - 1),reported,-0.13279989984554053,0.003983996995366215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.333333333333336,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Renovated,renovation,A_structural,binary,binary,-0.046,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-4.501581610018701,1.0505461584097862,100*(exp(b - se^2/2) - 1),reported,-0.016806858424771982,0.004019031362445475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.181818181818182,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Renovated,renovation,A_structural,binary,binary,-0.269,0.017,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-23.59671550748754,1.2990435346019382,100*(exp(b - se^2/2) - 1),reported,-0.06347597832690245,0.004011493054116511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.823529411764707,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Renovated,renovation,A_structural,binary,binary,-0.107,0.012,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-10.153901852922708,1.0782308075883666,100*(exp(b - se^2/2) - 1),reported,-0.03581842648102667,0.004017019792264673,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.916666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Renovated,renovation,A_structural,binary,binary,-0.007,0.006,,,,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-0.6993431345868717,0.595814665759941,100*(exp(b - se^2/2) - 1),reported,-0.004689480732300701,0.004019554913400601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1666666666666667,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Renovated,renovation,A_structural,binary,binary,0.026,0.004,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,2.6332737778697046,0.4105363793893768,100*(exp(b - se^2/2) - 1),reported,0.026118480976874945,0.004018227842596146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Renovated,renovation,A_structural,binary,binary,0.049,0.01,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,5.016784103524463,1.050220350740028,100*(exp(b - se^2/2) - 1),reported,0.0196922163788799,0.004018819669159164,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Fireplace,fireplace,B_interior,binary,binary,0.04,0.005,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,4.079776413902381,0.5204053870961941,100*(exp(b - se^2/2) - 1),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Fireplace,fireplace,B_interior,binary,binary,0.059,0.008,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.0741296475566475,0.8486201925921272,100*(exp(b - se^2/2) - 1),reported,0.029631526230191076,0.004017834065110655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.374999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Fireplace,fireplace,B_interior,binary,binary,0.042,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,4.289239296389158,0.20857889575015265,100*(exp(b - se^2/2) - 1),reported,0.08411244939884005,0.004005354733278097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Fireplace,fireplace,B_interior,binary,binary,0.029,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.942407976296235,0.10294245944751308,100*(exp(b - se^2/2) - 1),reported,0.11578437869023656,0.00399256478242195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Fireplace,fireplace,B_interior,binary,binary,0.021,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,2.122154102663054,0.10212220516375285,100*(exp(b - se^2/2) - 1),reported,0.08411244939884005,0.004005354733278097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Fireplace,fireplace,B_interior,binary,binary,0.013,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,1.308030848816344,0.30392546020794275,100*(exp(b - se^2/2) - 1),reported,0.01741562110347812,0.0040189894854180275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.333333333333333,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Lake/Mountain view,view,E_environment,binary,binary,0.104,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,10.958658499658336,0.5548002274577912,100*(exp(b - se^2/2) - 1),reported,0.0833169658071174,0.004005623356111414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.799999999999997,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Lake/Mountain view,view,E_environment,binary,binary,0.066,0.007,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.820054593202163,0.7477587020161953,100*(exp(b - se^2/2) - 1),reported,0.03787188864209804,0.0040167154620407005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.428571428571429,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Lake/Mountain view,view,E_environment,binary,binary,0.069,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,7.142763769295879,0.42857448365933853,100*(exp(b - se^2/2) - 1),reported,0.06917200251825996,0.004009971160478838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Lake/Mountain view,view,E_environment,binary,binary,0.08,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,8.328219289412232,0.3249861203024876,100*(exp(b - se^2/2) - 1),reported,0.1065787872397259,0.003996704521489721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.666666666666668,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Lake/Mountain view,view,E_environment,binary,binary,0.103,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,10.84714564102185,0.6650948454456043,100*(exp(b - se^2/2) - 1),reported,0.06883942564001688,0.004010063629515547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.166666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Lake/Mountain view,view,E_environment,binary,binary,0.173,0.007,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,18.88369782212631,0.8322062735588225,100*(exp(b - se^2/2) - 1),reported,0.09885493175931549,0.003999910533613923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.71428571428571,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.017,0.007,,,**,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,1.7000000000000002,0.7000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.009761418463725455,0.0040194076027104805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.428571428571429,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.019,0.009,,,**,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.9,0.8999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.008485514834883228,0.004019454395471003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.111111111111111,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.004,0.005,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.4,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.003215662663398661,0.004019578329248326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.017,0.004,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,1.7000000000000002,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017080803983688202,0.004019012702044283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.025,0.004,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,2.5,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.025114570296134366,0.004018331247381499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.03,0.007,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,3.0,0.7000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017224297745200363,0.0040190028072134175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.285714285714286,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,% Male population,demographics,F_neighbourhood,level,share,0.907,0.229,,,***,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,90.7,22.900000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01591840372556931,0.004019089805022461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.960698689956332,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,% Male population,demographics,F_neighbourhood,level,share,1.745,0.183,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,174.5,18.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0383008404674894,0.00401664974530118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.535519125683061,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,% Male population,demographics,F_neighbourhood,level,share,1.12,0.102,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,112.00000000000001,10.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0440938468385369,0.004015689622795324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.980392156862747,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,% Male population,demographics,F_neighbourhood,level,share,0.87,0.088,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,87.0,8.799999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.039707877409446125,0.004016428979346275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.886363636363637,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,% Male population,demographics,F_neighbourhood,level,share,0.647,0.091,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,64.7,9.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.028567244145283003,0.004017958604668861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.10989010989011,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,% Male population,demographics,F_neighbourhood,level,share,1.337,0.209,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,133.7,20.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025705397938686824,0.00401827088196376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.3971291866028706,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,% White population,demographics,F_neighbourhood,level,share,0.747,0.042,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,74.7,4.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07130944138631186,0.004009366182362917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.785714285714285,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,% White population,demographics,F_neighbourhood,level,share,0.978,0.162,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,97.8,16.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.024259327070108395,0.004018416140447403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.037037037037036,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,% White population,demographics,F_neighbourhood,level,share,0.762,0.021,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,76.2,2.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.14432694159622422,0.003977514138478621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.285714285714285,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,% White population,demographics,F_neighbourhood,level,share,0.756,0.024,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,75.6,2.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12561445284989362,0.00398776040793313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.5,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,% White population,demographics,F_neighbourhood,level,share,0.751,0.018,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,75.1,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1653968014949453,0.0039642375857643335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),41.72222222222223,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,% White population,demographics,F_neighbourhood,level,share,0.728,0.02,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,72.8,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.14477200191895626,0.003977252799971326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.4,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,% Asian population,demographics,F_neighbourhood,level,share,0.008,0.053,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.8,5.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0006067318296984772,0.004019598371752412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1509433962264151,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,% Asian population,demographics,F_neighbourhood,level,share,0.007,0.13,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.7000000000000001,13.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.00021643994709375115,0.004019599017455379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05384615384615384,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,% Asian population,demographics,F_neighbourhood,level,share,-0.002,0.022,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.2,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0003654180766579695,0.004019598843237665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.09090909090909091,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,% Asian population,demographics,F_neighbourhood,level,share,0.077,0.032,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,7.7,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.009671707975425955,0.004019411106670527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.40625,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,% Asian population,demographics,F_neighbourhood,level,share,0.146,0.023,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,14.6,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025507414127266082,0.004018291266624108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.3478260869565215,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,% Asian population,demographics,F_neighbourhood,level,share,0.232,0.04,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,23.200000000000003,4.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02330734161797258,0.0040185071755125135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,% Old population,demographics,F_neighbourhood,level,share,0.268,0.038,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,26.8,3.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.028337367229099925,0.004017984905618646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.052631578947369,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,% Old population,demographics,F_neighbourhood,level,share,0.381,0.083,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,38.1,8.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01844827265671318,0.004018915040701296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.590361445783133,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,% Old population,demographics,F_neighbourhood,level,share,0.091,0.032,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,9.1,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.011429988267162334,0.004019336533507634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.84375,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,% Old population,demographics,F_neighbourhood,level,share,0.059,0.027,,,**,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,5.8999999999999995,2.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.008783229617801203,0.004019444062383602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.185185185185185,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,% Old population,demographics,F_neighbourhood,level,share,0.065,0.035,,,*,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,6.5,3.5000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007464761791782616,0.004019487118652179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.857142857142857,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,% Old population,demographics,F_neighbourhood,level,share,0.416,0.054,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,41.6,5.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.030950964975929834,0.004017673338221662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.703703703703703,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.255,0.534,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,other,-1.255,0.534,not harmonised,reported,-0.009446389147173027,0.0040194197646138624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3501872659176026,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.9,0.776,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,-0.9,0.776,not harmonised,reported,-0.004661855527503455,0.004019555432602979,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1597938144329898,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.298,0.185,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-1.298,0.185,not harmonised,reported,-0.02819116743659798,0.004018001522165351,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0162162162162165,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.062,0.163,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-1.062,0.163,not harmonised,reported,-0.02618006804750154,0.004018221366989408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.515337423312883,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.994,0.109,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.994,0.109,not harmonised,reported,-0.03663119229476909,0.00401690136833987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.119266055045872,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.371,0.163,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-1.371,0.163,not harmonised,reported,-0.033789714599267974,0.004017303778031131,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.411042944785276,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c1,8 Panel A,(1) 2SLS,41,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.073,0.003,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-7.3,0.3,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.09734570784093759,0.0040005085414083945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.333333333333332,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c2,8 Panel A,(2) Q: 0.05,41,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.073,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-7.3,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1451613576428164,0.003977023497063463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-36.5,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c3,8 Panel A,(3) Q: 0.25,41,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.068,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-6.800000000000001,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.26366097296639146,0.0038773672495057573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-68.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c4,8 Panel A,(4) Q: 0.50,41,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.07,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-7.000000000000001,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.2708543506959655,0.003869347867085221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-70.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c5,8 Panel A,(5) Q: 0.75,41,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.074,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-7.3999999999999995,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.2851050385919529,0.003852770791783147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-74.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8A-c6,8 Panel A,(6) Q: 0.95,41,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.085,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-8.5,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.3233154705538053,0.0038037114182800616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-85.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,41,Living area,living_area,A_structural,level,1000 sqft,0.261,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.28093806187612375,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.25369799088088457,0.00388809181426643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),65.25,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,41,Living area,living_area,A_structural,level,1000 sqft,0.24,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.2583338500010333,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.1587451580364743,0.003968628950911857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,41,Living area,living_area,A_structural,level,1000 sqft,0.252,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.271250542501085,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.31990310072026185,0.003808370246669784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),84.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,41,Living area,living_area,A_structural,level,1000 sqft,0.266,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.28632001708447863,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3357194594671822,0.003786309693238897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),88.66666666666667,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,41,Living area,living_area,A_structural,level,1000 sqft,0.285,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.30677144687622704,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4970311166303093,0.0034879376605635747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),142.49999999999997,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,41,Living area,living_area,A_structural,level,1000 sqft,0.3,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.32291731250129163,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.23445371777087137,0.0039075619628478565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Lot area,lot_size,A_structural,level,1000 sqft,0.007,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.007534737291696806,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Lot area,lot_size,A_structural,level,1000 sqft,0.013,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.013993083541722639,,100*b/92.903 (X per 1000sqft->m2),,,,,,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Lot area,lot_size,A_structural,level,1000 sqft,0.033,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.03552090437514208,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.13149497712968528,0.003984696276657129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Bedrooms,bedrooms,A_structural,level,count,-0.007,0.001,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per unit count),reported,-0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Bedrooms,bedrooms,A_structural,level,count,-0.001,0.004,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,0.4,100*b/1 (per unit count),reported,-0.0010048992705163852,0.004019597082065541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Bedrooms,bedrooms,A_structural,level,count,-0.009,0.004,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8999999999999999,0.4,100*b/1 (per unit count),reported,-0.009043728139602736,0.004019434728712327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.8,0.1,100*b/1 (per unit count),reported,0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Bathrooms (Full),bathrooms,A_structural,level,count,-0.007,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.7000000000000001,0.2,100*b/1 (per unit count),reported,-0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Bathrooms (Full),bathrooms,A_structural,level,count,0.006,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.6,0.2,100*b/1 (per unit count),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Bathrooms (Full),bathrooms,A_structural,level,count,0.014,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.4000000000000001,0.2,100*b/1 (per unit count),reported,0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Age,age,A_structural,level,years,-0.002,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.2,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,One story,storeys,A_structural,binary,binary,-0.077,0.006,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-7.412681226087625,0.5555339121638972,100*(exp(b - se^2/2) - 1),reported,-0.05151635835780875,0.004014261690218864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.833333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,One story,storeys,A_structural,binary,binary,-0.055,0.007,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-5.353804064857948,0.6625396035674387,100*(exp(b - se^2/2) - 1),reported,-0.03156682507514917,0.004017595918655349,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.857142857142857,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,One story,storeys,A_structural,binary,binary,-0.065,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-6.293441075564921,0.1874134926754807,100*(exp(b - se^2/2) - 1),reported,-0.12953631268868462,0.003985732698113373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,One story,storeys,A_structural,binary,binary,-0.067,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.480666905175569,0.18703904026735532,100*(exp(b - se^2/2) - 1),reported,-0.133452102765036,0.003983644858657791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,One story,storeys,A_structural,binary,binary,-0.072,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.947096524872487,0.18610617916224115,100*(exp(b - se^2/2) - 1),reported,-0.14321390805417838,0.003978164112616067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-36.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,One story,storeys,A_structural,binary,binary,-0.085,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-8.149184889452254,0.2755536853204372,100*(exp(b - se^2/2) - 1),reported,-0.11315714651535581,0.003993781641718441,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.333333333333336,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Renovated,renovation,A_structural,binary,binary,-0.028,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-2.767046091538816,1.0696272034813716,100*(exp(b - se^2/2) - 1),reported,-0.010231171304026659,0.004019388726581901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.545454545454546,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Renovated,renovation,A_structural,binary,binary,-0.264,0.013,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-23.209135136571046,0.9983656015537179,100*(exp(b - se^2/2) - 1),reported,-0.0813581767257305,0.004006273853918546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.30769230769231,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Renovated,renovation,A_structural,binary,binary,-0.102,0.013,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.704675110440164,1.1739384171695397,100*(exp(b - se^2/2) - 1),reported,-0.03152271957060379,0.004017601513900484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.846153846153846,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Renovated,renovation,A_structural,binary,binary,0.021,0.007,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.1197032003755334,0.71485543614627,100*(exp(b - se^2/2) - 1),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Renovated,renovation,A_structural,binary,binary,0.048,0.002,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,4.916855698525824,0.20983413106489415,100*(exp(b - se^2/2) - 1),reported,0.09602458544121234,0.004001024393383848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Renovated,renovation,A_structural,binary,binary,0.069,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,7.137138921851527,1.178579830063181,100*(exp(b - se^2/2) - 1),reported,0.025205838088994806,0.004018322014187577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.272727272727273,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Fireplace,fireplace,B_interior,binary,binary,0.025,0.004,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,2.5306918036274606,0.41012604820977155,100*(exp(b - se^2/2) - 1),reported,0.025114570296134366,0.004018331247381499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Fireplace,fireplace,B_interior,binary,binary,0.045,0.007,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,4.600223254008573,0.7322195019361019,100*(exp(b - se^2/2) - 1),reported,0.02583165728432461,0.0040182577997838285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.428571428571428,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Fireplace,fireplace,B_interior,binary,binary,0.025,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,2.5313069896238494,0.20506302410488578,100*(exp(b - se^2/2) - 1),reported,0.05018168551256579,0.004014534841005264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Fireplace,fireplace,B_interior,binary,binary,0.013,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.308284119210068,0.20261697347196184,100*(exp(b - se^2/2) - 1),reported,0.026118480976874945,0.004018227842596146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Fireplace,fireplace,B_interior,binary,binary,0.01,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,1.004814698585399,0.20201003341683357,100*(exp(b - se^2/2) - 1),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Fireplace,fireplace,B_interior,binary,binary,0.006,0.002,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,0.6016024020004895,0.20120360721081298,100*(exp(b - se^2/2) - 1),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Lake/Mountain view,view,E_environment,binary,binary,0.062,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,6.394904528184631,0.5319811723640169,100*(exp(b - se^2/2) - 1),reported,0.04978123090037987,0.004014615395191924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.4,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Lake/Mountain view,view,E_environment,binary,binary,0.035,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,3.560106781263417,0.621371825279774,100*(exp(b - se^2/2) - 1),reported,0.023441218462925982,0.004018494593644454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.833333333333334,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Lake/Mountain view,view,E_environment,binary,binary,0.037,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.768835123006986,0.31130790625144716,100*(exp(b - se^2/2) - 1),reported,0.04951424780807698,0.0040146687411954304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Lake/Mountain view,view,E_environment,binary,binary,0.046,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,4.706969913268955,0.3141223232870812,100*(exp(b - se^2/2) - 1),reported,0.06151712027689369,0.004011986105014805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Lake/Mountain view,view,E_environment,binary,binary,0.059,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,6.077046726231594,0.3182325722220477,100*(exp(b - se^2/2) - 1),reported,0.07880625986810323,0.00400709795939508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Lake/Mountain view,view,E_environment,binary,binary,0.106,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,11.1812981967097,0.44472875060261235,100*(exp(b - se^2/2) - 1),reported,0.1059201662805985,0.003996987406815038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.003,0.003,,,,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.3,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.011,0.014,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.0999999999999999,1.4000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0031582406938051267,0.0040195790648428886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7857142857142857,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.001,0.003,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0013398651678451523,0.004019595503535456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3333333333333333,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.005,0.004,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.5,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005024435467492358,0.004019548373993887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.25,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.006,0.003,,,*,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.6,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.014,0.005,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.4000000000000001,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.011254164739785875,0.004019344549923528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,% Male population,demographics,F_neighbourhood,level,share,-0.048,0.098,,,,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-4.8,9.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0019687794227415136,0.00401959132143059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4897959183673469,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,% Male population,demographics,F_neighbourhood,level,share,0.097,0.226,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,9.700000000000001,22.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0017252235998906787,0.0040195931296422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42920353982300885,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,% Male population,demographics,F_neighbourhood,level,share,0.171,0.065,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,17.1,6.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.010574046468773066,0.004019374388714908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6307692307692307,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,% Male population,demographics,F_neighbourhood,level,share,0.144,0.068,,,**,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,14.399999999999999,6.800000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.008511783878239433,0.004019453498057511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.117647058823529,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,% Male population,demographics,F_neighbourhood,level,share,0.115,0.088,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,11.5,8.799999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0052528127332547355,0.00401954365675145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3068181818181819,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,% Male population,demographics,F_neighbourhood,level,share,0.496,0.164,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,49.6,16.400000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.012155938115766735,0.004019302118922871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.024390243902439,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,% White population,demographics,F_neighbourhood,level,share,0.575,0.02,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,57.49999999999999,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11479944834383936,0.0039930242902205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.749999999999996,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,% White population,demographics,F_neighbourhood,level,share,0.844,0.072,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,84.39999999999999,7.199999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.047066415378064996,0.004015144439834928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.722222222222223,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,% White population,demographics,F_neighbourhood,level,share,0.606,0.018,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,60.6,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.13410413234368251,0.003983291059713341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.66666666666667,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,% White population,demographics,F_neighbourhood,level,share,0.543,0.014,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,54.300000000000004,1.4000000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1540422060719754,0.003971622255999366,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.785714285714285,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,% White population,demographics,F_neighbourhood,level,share,0.502,0.018,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,50.2,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11140433534188292,0.003994577761262733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),27.888888888888893,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,% White population,demographics,F_neighbourhood,level,share,0.443,0.018,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,44.3,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09844625052432357,0.004000073384735495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.611111111111114,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,% Asian population,demographics,F_neighbourhood,level,share,0.122,0.018,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,12.2,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.027233844482817193,0.004018108202382864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.777777777777779,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,% Asian population,demographics,F_neighbourhood,level,share,0.217,0.092,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,21.7,9.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.009480584854763245,0.0040194184637705925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.358695652173913,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,% Asian population,demographics,F_neighbourhood,level,share,0.088,0.03,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,8.799999999999999,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.011790004545158463,0.004019319731304022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.933333333333333,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,% Asian population,demographics,F_neighbourhood,level,share,0.12,0.025,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,12.0,2.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.019290485518634454,0.004018851149715512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,% Asian population,demographics,F_neighbourhood,level,share,0.135,0.019,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,13.5,1.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.028548668400706148,0.004017960737877161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.105263157894737,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,% Asian population,demographics,F_neighbourhood,level,share,0.138,0.029,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,13.8,2.9000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.019124249315079445,0.004018863986502202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.758620689655173,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,% Old population,demographics,F_neighbourhood,level,share,-0.173,0.025,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-17.299999999999997,2.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0278048714918681,0.004018045013275738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.919999999999999,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,% Old population,demographics,F_neighbourhood,level,share,-0.187,0.068,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-18.7,6.800000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01105322228838462,0.004019353559412589,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.75,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,% Old population,demographics,F_neighbourhood,level,share,-0.188,0.025,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-18.8,2.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03021358547245787,0.004017764025592802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.52,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,% Old population,demographics,F_neighbourhood,level,share,-0.213,0.026,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-21.3,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03291195316899731,0.004017421513586527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.192307692307692,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,% Old population,demographics,F_neighbourhood,level,share,-0.228,0.017,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-22.8,1.7000000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05383174920373512,0.004013770773962706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.411764705882353,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,% Old population,demographics,F_neighbourhood,level,share,-0.203,0.034,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-20.3,3.4000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.023992462693817742,0.004018442027535977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.970588235294118,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.409,0.191,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,other,0.409,0.191,not harmonised,reported,0.008607094970955211,0.004019450218710136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1413612565445024,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.943,0.371,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,0.943,0.371,not harmonised,reported,0.01021639930365788,0.004019389333676643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5417789757412397,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.386,0.109,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,0.386,0.109,not harmonised,reported,0.01423310174454783,0.004019191943408584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.541284403669725,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.188,0.088,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.188,0.088,not harmonised,reported,0.0085870087683586,0.004019450912848706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1363636363636367,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.016,0.131,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.016,0.131,not harmonised,reported,0.0004909433437794503,0.004019598627194249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12213740458015267,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.285,0.115,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-0.285,0.115,not harmonised,reported,-0.009961120962066297,0.004019399686447805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.478260869565217,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c1,8 Panel B,(1) 2SLS,42,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.039,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-3.9,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07814250558625158,0.0040073079787821325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.5,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c2,8 Panel B,(2) Q: 0.05,42,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.037,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-3.6999999999999997,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07415782749058782,0.004008531215707449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.5,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c3,8 Panel B,(3) Q: 0.25,42,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.038,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-3.8,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.15099350992814012,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c4,8 Panel B,(4) Q: 0.50,42,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.04,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-4.0,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1587451580364743,0.003968628950911857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c5,8 Panel B,(5) Q: 0.75,42,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.042,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-4.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1664675615205028,0.0039635133695357806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T8B-c6,8 Panel B,(6) Q: 0.95,42,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.047,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Robustness: high-priority 911 calls as IV; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-4.7,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.18563739077641886,0.00394973171864721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-47.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Living area,living_area,A_structural,level,1000 sqft,0.318,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.34229235125136914,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.391990818954096,0.0036980265939065655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),106.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Living area,living_area,A_structural,level,1000 sqft,0.285,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.30677144687622704,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.22333715494345455,0.003918195700762361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.99999999999999,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Living area,living_area,A_structural,level,1000 sqft,0.306,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.32937565875131747,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.5238712347629869,0.003423994998450895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),153.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Living area,living_area,A_structural,level,1000 sqft,0.321,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.34552152437638206,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.39511380832191995,0.0036926524142235516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),107.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Living area,living_area,A_structural,level,1000 sqft,0.339,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.3648965631264596,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.4135644276900801,0.003659862191947612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),113.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Living area,living_area,A_structural,level,1000 sqft,0.348,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.37458408250149827,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.2694273306786193,0.0038710823373364843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),69.6,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.0880902506486793,0.004004102302212695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Lot area,lot_size,A_structural,level,1000 sqft,0.009,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.009687519375038749,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.018085821904466885,0.004019071534325974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Lot area,lot_size,A_structural,level,1000 sqft,0.014,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.015069474583393612,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.05618730353962703,0.004013378824259074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Lot area,lot_size,A_structural,level,1000 sqft,0.02,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.021527820833419444,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.080136026148505,0.004006801307425249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Lot area,lot_size,A_structural,level,1000 sqft,0.026,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.027986167083445278,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.10394679285959862,0.003997953571523024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Lot area,lot_size,A_structural,level,1000 sqft,0.038,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.04090285958349694,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.0761530691990343,0.0040080562736333845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Bedrooms,bedrooms,A_structural,level,count,-0.019,0.002,,,***,,,high,,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.9,0.2,100*b/1 (per unit count),reported,-0.038159612029031974,0.0040168012662138925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.005,,,*,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.8,0.5,100*b/1 (per unit count),reported,-0.006431433396947985,0.00401964587309249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Bedrooms,bedrooms,A_structural,level,count,-0.011,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-1.0999999999999999,0.2,100*b/1 (per unit count),reported,-0.022103108360402076,0.00401874697461856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Bedrooms,bedrooms,A_structural,level,count,-0.013,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-1.3,0.2,100*b/1 (per unit count),reported,-0.026119324443815584,0.004018357606740859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Bedrooms,bedrooms,A_structural,level,count,-0.014,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-1.4000000000000001,0.2,100*b/1 (per unit count),reported,-0.028126970459270723,0.0040181386370386745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Bedrooms,bedrooms,A_structural,level,count,-0.018,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-1.7999999999999998,0.3,100*b/1 (per unit count),reported,-0.024111362330324623,0.004018560388387438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Bathrooms (Full),bathrooms,A_structural,level,count,0.004,0.002,,,*,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.4,0.2,100*b/1 (per unit count),reported,0.008039198223213907,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Bathrooms (Full),bathrooms,A_structural,level,count,-0.009,0.004,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.8999999999999999,0.4,100*b/1 (per unit count),reported,-0.009044020372097315,0.0040195646098210285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.25,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Bathrooms (Full),bathrooms,A_structural,level,count,0.003,0.002,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.3,0.2,100*b/1 (per unit count),reported,0.006029483910021998,0.0040196559400146655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Bathrooms (Full),bathrooms,A_structural,level,count,0.006,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.6,0.2,100*b/1 (per unit count),reported,0.012058310274023482,0.004019436758007827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Bathrooms (Full),bathrooms,A_structural,level,count,0.005,0.002,,,**,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.5,0.2,100*b/1 (per unit count),reported,0.010048815125132005,0.004019526050052802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Bathrooms (Full),bathrooms,A_structural,level,count,0.011,0.004,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.0999999999999999,0.4,100*b/1 (per unit count),reported,0.011053579439794807,0.004019483432652657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Age,age,A_structural,level,years,-0.0,0.0,,,**,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Age,age,A_structural,level,years,-0.0,0.0,,,,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,One story,storeys,A_structural,binary,binary,-0.092,0.007,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-8.791719675733011,0.6384736046815632,100*(exp(b - se^2/2) - 1),reported,-0.05275715055885089,0.004014131020782133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.142857142857142,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,One story,storeys,A_structural,binary,binary,-0.063,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-6.106826303093326,0.4694717368445666,100*(exp(b - se^2/2) - 1),reported,-0.05058374634848155,0.004014583043530282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.6,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,One story,storeys,A_structural,binary,binary,-0.073,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-7.040335316853019,0.27888024900773783,100*(exp(b - se^2/2) - 1),reported,-0.09734882384531506,0.0040006365963828104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.333333333333332,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,One story,storeys,A_structural,binary,binary,-0.079,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-7.596423372521732,0.27721197733352604,100*(exp(b - se^2/2) - 1),reported,-0.10526476981695282,0.0039973963221627655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-26.333333333333332,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,One story,storeys,A_structural,binary,binary,-0.087,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-8.332702940449666,0.2750031286899457,100*(exp(b - se^2/2) - 1),reported,-0.11578807020557912,0.003992692076054453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-28.999999999999996,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,One story,storeys,A_structural,binary,binary,-0.099,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-9.426861368954098,0.45287135401177425,100*(exp(b - se^2/2) - 1),reported,-0.07933973559927165,0.004007057353498569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.8,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Renovated,renovation,A_structural,binary,binary,-0.046,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-4.501581610018701,1.0505461584097862,100*(exp(b - se^2/2) - 1),reported,-0.01680740140044642,0.0040191612044545785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.181818181818182,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Renovated,renovation,A_structural,binary,binary,-0.278,0.023,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-24.290345700695294,1.7417826894829715,100*(exp(b - se^2/2) - 1),reported,-0.04852904402159897,0.004014992850707827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.086956521739133,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,True
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Renovated,renovation,A_structural,binary,binary,-0.11,0.014,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-10.425365214707583,1.2541677894151395,100*(exp(b - se^2/2) - 1),reported,-0.031567844169603475,0.004017725621585897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.857142857142857,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Renovated,renovation,A_structural,binary,binary,-0.003,0.008,,,,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-0.30274081300222244,0.7976035964026984,100*(exp(b - se^2/2) - 1),reported,-0.0015073966656545604,0.004019724441745494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.375,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Renovated,renovation,A_structural,binary,binary,0.023,0.007,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,2.324146982410391,0.7162865776830524,100*(exp(b - se^2/2) - 1),reported,0.013206529186442002,0.004019378448047566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2857142857142856,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Renovated,renovation,A_structural,binary,binary,0.046,0.013,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,4.698593690729247,1.3611967342440183,100*(exp(b - se^2/2) - 1),reported,0.014222217899364332,0.004019322449820354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5384615384615388,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Fireplace,fireplace,B_interior,binary,binary,0.039,0.005,,,***,,,high,,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,3.975748660034384,0.5198852418250789,100*(exp(b - se^2/2) - 1),reported,0.03133848614839422,0.0040177546344095155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Fireplace,fireplace,B_interior,binary,binary,0.062,0.007,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.393627796990686,0.7447736413096236,100*(exp(b - se^2/2) - 1),reported,0.035580770197988686,0.004017183732030981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.857142857142858,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Fireplace,fireplace,B_interior,binary,binary,0.038,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.8729155418109507,0.20774624657569954,100*(exp(b - se^2/2) - 1),reported,0.0761530691990343,0.0040080562736333845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Fireplace,fireplace,B_interior,binary,binary,0.027,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.736574802993852,0.2054735605526979,100*(exp(b - se^2/2) - 1),reported,0.05418661492651993,0.004013823327890365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.5,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Fireplace,fireplace,B_interior,binary,binary,0.02,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,2.0200829926213304,0.10202013400267558,100*(exp(b - se^2/2) - 1),reported,0.080136026148505,0.004006801307425249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Fireplace,fireplace,B_interior,binary,binary,0.013,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,1.308284119210068,0.20261697347196184,100*(exp(b - se^2/2) - 1),reported,0.026119324443815584,0.004018357606740859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Lake/Mountain view,view,E_environment,binary,binary,0.109,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,11.51422775997968,0.6690974102048687,100*(exp(b - se^2/2) - 1),reported,0.07283114326220178,0.004009053757552391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.166666666666668,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Lake/Mountain view,view,E_environment,binary,binary,0.066,0.007,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.820054593202163,0.7477587020161953,100*(exp(b - se^2/2) - 1),reported,0.03787311075158865,0.004016845079713948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.428571428571429,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Lake/Mountain view,view,E_environment,binary,binary,0.074,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,7.679819107623365,0.43072272219848795,100*(exp(b - se^2/2) - 1),reported,0.07416021078925239,0.004008660042662291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.5,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Lake/Mountain view,view,E_environment,binary,binary,0.085,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,8.870835699670398,0.4354868266793595,100*(exp(b - se^2/2) - 1),reported,0.0851093079902839,0.004005143905425125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.25,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Lake/Mountain view,view,E_environment,binary,binary,0.106,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,11.180186389286773,0.6670931259039186,100*(exp(b - se^2/2) - 1),reported,0.07083681640948297,0.004009631117517905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.666666666666664,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Lake/Mountain view,view,E_environment,binary,binary,0.18,0.009,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,19.716887680045847,1.077495626809629,100*(exp(b - se^2/2) - 1),reported,0.080136026148505,0.004006801307425249,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.014,0.005,,,***,,,high,Census tract population level,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,1.4000000000000001,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.011254528382412633,0.004019474422290226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.019,0.013,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.9,1.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.005874887164675728,0.004019659638988656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4615384615384617,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.001,0.006,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.1,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0006699546844243058,0.004019728106545835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16666666666666666,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.016,0.002,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,1.6,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.03214121732666125,0.004017652165832657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.022,0.003,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,2.1999999999999997,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.029465213563664758,0.004017983667772468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.333333333333333,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.026,0.005,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,2.6,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.020898025978520657,0.004018851149715512,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.199999999999999,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,% Male population,demographics,F_neighbourhood,level,share,0.614,0.133,,,***,,,high,Share of males in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,61.4,13.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01855405073109149,0.00401903704761428,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.616541353383458,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,% Male population,demographics,F_neighbourhood,level,share,1.484,0.272,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,148.4,27.200000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.021925896279156865,0.004018762660330639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.455882352941176,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,% Male population,demographics,F_neighbourhood,level,share,0.858,0.115,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,85.8,11.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.029977195499032192,0.004017922473646506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.460869565217391,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,% Male population,demographics,F_neighbourhood,level,share,0.629,0.117,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,62.9,11.700000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.021605293684096843,0.004018790717073659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.376068376068376,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,% Male population,demographics,F_neighbourhood,level,share,0.452,0.079,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,45.2,7.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02299287558748679,0.004018666308432426,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7215189873417724,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,% Male population,demographics,F_neighbourhood,level,share,1.008,0.241,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,100.8,24.099999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.016810432728646713,0.004019160999607001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.182572614107884,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,% White population,demographics,F_neighbourhood,level,share,0.776,0.037,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,77.60000000000001,3.6999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0840076565186941,0.00400551970514392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.972972972972975,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,% White population,demographics,F_neighbourhood,level,share,1.106,0.154,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,110.60000000000001,15.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.028856940452094807,0.00401805499965877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.1818181818181825,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,% White population,demographics,F_neighbourhood,level,share,0.801,0.023,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,80.10000000000001,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.13863952238252328,0.0039809101308340014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.82608695652174,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,% White population,demographics,F_neighbourhood,level,share,0.754,0.017,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,75.4,1.7000000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.17551908500753224,0.0039573268503024514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),44.35294117647059,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,% White population,demographics,F_neighbourhood,level,share,0.758,0.021,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,75.8,2.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.14358952424711058,0.003978073890751084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.095238095238095,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,% White population,demographics,F_neighbourhood,level,share,0.738,0.023,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,73.8,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.1279212061718968,0.003986704257389739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.08695652173913,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,% Asian population,demographics,F_neighbourhood,level,share,0.02,0.032,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,2.0,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0025123227017743136,0.004019716322838902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.625,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,% Asian population,demographics,F_neighbourhood,level,share,0.111,0.107,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,11.1,10.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0041699629966020085,0.004019694059787522,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0373831775700935,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,% Asian population,demographics,F_neighbourhood,level,share,-0.0,0.024,,,,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.0,2.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0,0.004019729008652072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,% Asian population,demographics,F_neighbourhood,level,share,0.067,0.016,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,6.7,1.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01683023107955063,0.00401915966078821,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.1875,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,% Asian population,demographics,F_neighbourhood,level,share,0.136,0.018,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,13.600000000000001,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.030357288061355216,0.00401787636106172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.555555555555556,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,% Asian population,demographics,F_neighbourhood,level,share,0.221,0.046,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,22.1,4.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01930857599509783,0.00401897961888914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.804347826086957,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,% Old population,demographics,F_neighbourhood,level,share,0.329,0.041,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,32.9,4.1000000000000005,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03223910714906034,0.004017639492740043,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.024390243902438,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,% Old population,demographics,F_neighbourhood,level,share,0.427,0.101,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,42.699999999999996,10.100000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01699184636986682,0.004019148672966157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.227722772277227,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,% Old population,demographics,F_neighbourhood,level,share,0.196,0.037,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,19.6,3.6999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02128887374231943,0.004018818002376627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.297297297297297,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,% Old population,demographics,F_neighbourhood,level,share,0.119,0.021,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,11.899999999999999,2.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.022772557281730964,0.0040186865791289935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.666666666666666,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,% Old population,demographics,F_neighbourhood,level,share,0.127,0.027,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,12.7,2.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.018904235415832752,0.004019010678956569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.703703703703704,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,% Old population,demographics,F_neighbourhood,level,share,0.376,0.045,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,37.6,4.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03356814042483931,0.004017463614674917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.355555555555556,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.694,0.305,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,other,-0.694,0.305,not harmonised,reported,-0.009146148353665852,0.004019560875890613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2754098360655735,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.093,0.744,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,0.093,0.744,not harmonised,reported,0.0005024660626521549,0.004019728501217239,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.125,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.588,0.164,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,-0.588,0.164,not harmonised,reported,-0.014410702572084412,0.00401931160173783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.585365853658536,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.713,0.097,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,-0.713,0.097,not harmonised,reported,-0.029534190934801287,0.004017975484818689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.350515463917525,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.659,0.12,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.659,0.12,not harmonised,reported,-0.02206963512636904,0.00401874994713852,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.491666666666667,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.007,0.202,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-1.007,0.202,not harmonised,reported,-0.020034923895719342,0.004018922171733175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.985148514851485,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.072,0.003,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-7.199999999999999,0.3,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.09602765994967197,0.004001152497902999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.999999999999996,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.071,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-7.1,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1412692674803968,0.003979415985363291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-35.49999999999999,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.067,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-6.7,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.26005546974001464,0.0038814249214927563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-67.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.069,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-6.9,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.26727126667759804,0.003873496618515914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-69.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.073,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-7.3,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.2815680009328516,0.0038570959031897487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-73.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.084,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-8.4,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.16647279199075748,0.003963637904541845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c1,9 Panel A,(1) 2SLS,43,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.01,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.0,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.04016485355453171,0.004016485355453171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c2,9 Panel A,(2) Q: 0.05,43,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.011,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.0999999999999999,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.044173857038938676,0.004015805185358062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c3,9 Panel A,(3) Q: 0.25,43,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.011,0.0,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-1.0999999999999999,,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),,,,,,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c4,9 Panel A,(4) Q: 0.50,43,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.009,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.8999999999999999,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.03615390940805667,0.004017101045339631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c5,9 Panel A,(5) Q: 0.75,43,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.008,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.8,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.03214121732666125,0.004017652165832657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9A-c6,9 Panel A,(6) Q: 0.95,43,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.006,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; no spatial adjustment; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.6,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.024111362330324626,0.004018560388387438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Living area,living_area,A_structural,level,1000 sqft,0.26,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.2798616708344528,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.25279584084198436,0.0038891667821843745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),65.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Living area,living_area,A_structural,level,1000 sqft,0.239,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.25725745895936236,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.1886914695379195,0.003947520283220073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.8,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Living area,living_area,A_structural,level,1000 sqft,0.25,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.269097760417743,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.44897560062781094,0.003591804805022488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),125.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Living area,living_area,A_structural,level,1000 sqft,0.267,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.2873964081261496,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4728508565532898,0.0035419539816725827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),133.5,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Living area,living_area,A_structural,level,1000 sqft,0.285,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.30677144687622704,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.35674728441121906,0.0037552345727496746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),94.99999999999999,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Living area,living_area,A_structural,level,1000 sqft,0.299,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3218409214596207,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.23372210130125307,0.003908396342830319,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),59.8,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Lot area,lot_size,A_structural,level,1000 sqft,0.019,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.02045142979174847,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.0761530691990343,0.0040080562736333845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Lot area,lot_size,A_structural,level,1000 sqft,0.008,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.008611128333367779,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.03214121732666125,0.004017652165832657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Lot area,lot_size,A_structural,level,1000 sqft,0.014,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.015069474583393612,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.05618730353962703,0.004013378824259074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.019375038750077497,,100*b/92.903 (X per 1000sqft->m2),,,,,,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.0880902506486793,0.004004102302212695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Lot area,lot_size,A_structural,level,1000 sqft,0.034,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.03659729541681306,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.0681763960061346,0.004010376235654978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,61888.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Bedrooms,bedrooms,A_structural,level,count,-0.007,0.001,,,***,,,high,,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.7000000000000001,0.1,100*b/1 (per unit count),reported,-0.028126970459270723,0.0040181386370386745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Bedrooms,bedrooms,A_structural,level,count,0.0,0.005,,,,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.0,0.5,100*b/1 (per unit count),reported,0.0,0.004019729008652072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Bedrooms,bedrooms,A_structural,level,count,-0.001,0.002,,,,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,0.2,100*b/1 (per unit count),reported,-0.0020098604448589032,0.0040197208897178064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008039198223213907,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.001,,,*,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.3,0.1,100*b/1 (per unit count),reported,-0.012058310274023482,0.004019436758007827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8,0.3,100*b/1 (per unit count),reported,-0.010718661571406801,0.004019498089277551,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6666666666666665,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.8999999999999999,0.1,100*b/1 (per unit count),reported,0.03615390940805667,0.004017101045339631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Bathrooms (Full),bathrooms,A_structural,level,count,-0.005,0.004,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.5,0.4,100*b/1 (per unit count),reported,-0.005024597832650012,0.00401967826612001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Bathrooms (Full),bathrooms,A_structural,level,count,0.007,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.7000000000000001,0.2,100*b/1 (per unit count),reported,0.014067659335510333,0.004019331238717238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Bathrooms (Full),bathrooms,A_structural,level,count,0.01,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,1.0,0.1,100*b/1 (per unit count),reported,0.04016485355453171,0.004016485355453171,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.018085821904466885,0.004019071534325974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Bathrooms (Full),bathrooms,A_structural,level,count,0.016,0.003,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.6,0.3,100*b/1 (per unit count),reported,0.021433629705920238,0.004018805569860045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.333333333333333,61888.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Age,age,A_structural,level,years,-0.002,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.2,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61888.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,One story,storeys,A_structural,binary,binary,-0.076,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-7.319537857079251,0.4634081032796911,100*(exp(b - se^2/2) - 1),reported,-0.060986150367709005,0.004012246734717698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.2,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,One story,storeys,A_structural,binary,binary,-0.054,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-5.257547292473596,0.37897284260071934,100*(exp(b - se^2/2) - 1),reported,-0.05418661492651993,0.004013823327890365,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.5,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,One story,storeys,A_structural,binary,binary,-0.063,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-6.105840419593623,0.18778869473782664,100*(exp(b - se^2/2) - 1),reported,-0.1256184481411094,0.003987887242574902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-31.5,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,One story,storeys,A_structural,binary,binary,-0.066,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.3873348287792115,0.28083925928748565,100*(exp(b - se^2/2) - 1),reported,-0.0880902506486793,0.004004102302212695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,One story,storeys,A_structural,binary,binary,-0.072,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.947329156840376,0.2791592687433617,100*(exp(b - se^2/2) - 1),reported,-0.09602765994967197,0.004001152497902999,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.999999999999996,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,One story,storeys,A_structural,binary,binary,-0.084,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-8.057609932550235,0.3677725024380499,100*(exp(b - se^2/2) - 1),reported,-0.08411514833833693,0.004005483254206521,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Renovated,renovation,A_structural,binary,binary,-0.029,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,-2.864230445171767,1.0685581109132651,100*(exp(b - se^2/2) - 1),reported,-0.010596872355309931,0.004019503307186524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6363636363636367,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Renovated,renovation,A_structural,binary,binary,-0.265,0.016,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-23.28922461010582,1.2275295199613692,100*(exp(b - se^2/2) - 1),reported,-0.06642970081765222,0.004010849860688436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.5625,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Renovated,renovation,A_structural,binary,binary,-0.104,0.015,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.887608472190268,1.351837946131807,100*(exp(b - se^2/2) - 1),reported,-0.027859303458335576,0.004018168768029169,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.933333333333334,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Renovated,renovation,A_structural,binary,binary,0.02,0.007,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.0176345400110485,0.714140938018729,100*(exp(b - se^2/2) - 1),reported,0.011484182645757235,0.004019463926015032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.857142857142857,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Renovated,renovation,A_structural,binary,binary,0.048,0.007,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,4.914495095829485,0.7344194587271295,100*(exp(b - se^2/2) - 1),reported,0.02755339097966279,0.004018202851200824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.857142857142857,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Renovated,renovation,A_structural,binary,binary,0.07,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,7.244329647203496,1.1797589993796382,100*(exp(b - se^2/2) - 1),reported,0.025571728741831924,0.004018414516573587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.363636363636364,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Fireplace,fireplace,B_interior,binary,binary,0.025,0.004,,,***,,,high,,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,2.5306918036274606,0.41012604820977155,100*(exp(b - se^2/2) - 1),reported,0.02511538138465511,0.004018461021544817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.25,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Fireplace,fireplace,B_interior,binary,binary,0.043,0.006,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,4.391910413762212,0.6263627369103676,100*(exp(b - se^2/2) - 1),reported,0.028796111366565316,0.004018062051148649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666666,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Fireplace,fireplace,B_interior,binary,binary,0.024,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,2.4285708594562028,0.30728709536718646,100*(exp(b - se^2/2) - 1),reported,0.03214121732666125,0.004017652165832657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Fireplace,fireplace,B_interior,binary,binary,0.012,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.2070264723524238,0.20241445777321557,100*(exp(b - se^2/2) - 1),reported,0.024111362330324626,0.004018560388387438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Fireplace,fireplace,B_interior,binary,binary,0.01,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,1.004814698585399,0.20201003341683357,100*(exp(b - se^2/2) - 1),reported,0.020094586793302126,0.0040189173586604255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Fireplace,fireplace,B_interior,binary,binary,0.007,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,0.7022543219748156,0.20140491145336972,100*(exp(b - se^2/2) - 1),reported,0.014067659335510333,0.004019331238717238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Lake/Mountain view,view,E_environment,binary,binary,0.065,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,pct_premium,6.714568497975937,0.5335795121920963,100*(exp(b - se^2/2) - 1),reported,0.05218527349774073,0.004014251807518518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Lake/Mountain view,view,E_environment,binary,binary,0.04,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,4.079776413902381,0.5204053870961941,100*(exp(b - se^2/2) - 1),reported,0.03214121732666125,0.004017652165832657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Lake/Mountain view,view,E_environment,binary,binary,0.039,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,3.976580469350899,0.3119311450950473,100*(exp(b - se^2/2) - 1),reported,0.05218527349774073,0.004014251807518518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Lake/Mountain view,view,E_environment,binary,binary,0.048,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,4.916226199280138,0.4196682621297883,100*(exp(b - se^2/2) - 1),reported,0.04818072760193375,0.004015060633494479,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Lake/Mountain view,view,E_environment,binary,binary,0.063,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,6.501831905067235,0.4260107356925221,100*(exp(b - se^2/2) - 1),reported,0.06318422946770427,0.0040116971090605885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.75,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Lake/Mountain view,view,E_environment,binary,binary,0.11,0.008,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,11.624235013214479,0.893022456367097,100*(exp(b - se^2/2) - 1),reported,0.05518704232369077,0.0040136030780866016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.75,61888.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.001,0.002,,,,,,high,Census tract population level,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.1,0.2,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0020098604448589032,0.0040197208897178064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.015,0.014,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-1.5,1.4000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.004306812565968379,0.0040196917282371535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0714285714285714,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",-0.003,0.005,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.3,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.002411830390441389,0.004019717317402315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.004,0.003,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.4,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005359561700103093,0.00401967127507732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333333,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.004,0.004,,,,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.4,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0040196965332101806,0.00401969653321018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.014,0.005,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.4000000000000001,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.011254528382412633,0.004019474422290226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8,61888.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,% Male population,demographics,F_neighbourhood,level,share,-0.217,0.094,,,**,,,high,Share of males in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-21.7,9.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.009279187669298057,0.0040195559489125225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3085106382978724,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,% Male population,demographics,F_neighbourhood,level,share,-0.322,0.249,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-32.2,24.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.005198133548509759,0.004019674700555683,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2931726907630523,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,% Male population,demographics,F_neighbourhood,level,share,0.01,0.126,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,1.0,12.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0003190260955629191,0.004019728804092781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.07936507936507936,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,% Male population,demographics,F_neighbourhood,level,share,0.036,0.077,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,3.5999999999999996,7.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0018793505033243329,0.0040197219098881565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4675324675324675,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,% Male population,demographics,F_neighbourhood,level,share,-0.003,0.1,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.3,10.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0001205918693827146,0.00401972897942382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.03,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,% Male population,demographics,F_neighbourhood,level,share,0.332,0.095,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,33.2,9.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01404650913756371,0.004019332433941423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4947368421052634,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,% White population,demographics,F_neighbourhood,level,share,0.592,0.03,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,59.199999999999996,3.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.07907427158548978,0.004007142141156577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.733333333333334,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,% White population,demographics,F_neighbourhood,level,share,0.873,0.067,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,87.3,6.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.05230477445909272,0.004014226676700129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.029850746268655,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,% White population,demographics,F_neighbourhood,level,share,0.618,0.02,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,61.8,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.12326241755861964,0.003989075001897075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.9,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,% White population,demographics,F_neighbourhood,level,share,0.541,0.012,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,54.1,1.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.17831829942347144,0.003955304238598257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),45.083333333333336,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,% White population,demographics,F_neighbourhood,level,share,0.507,0.017,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,50.7,1.7000000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.11903021747563004,0.0039911512763031775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.823529411764703,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,% White population,demographics,F_neighbourhood,level,share,0.453,0.019,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,45.300000000000004,1.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.09540166785238582,0.004001394457384836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.842105263157897,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,% Asian population,demographics,F_neighbourhood,level,share,0.129,0.018,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,12.9,1.7999999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02879611136656532,0.004018062051148649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.166666666666667,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,% Asian population,demographics,F_neighbourhood,level,share,0.21,0.092,,,**,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,21.0,9.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.009175082175936426,0.004019559810410245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.282608695652174,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,% Asian population,demographics,F_neighbourhood,level,share,0.09,0.027,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,9.0,2.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01339789404872272,0.004019368214616816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.333333333333333,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,% Asian population,demographics,F_neighbourhood,level,share,0.107,0.017,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,10.7,1.7000000000000002,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025292553415347488,0.004018443065989788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.294117647058823,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,% Asian population,demographics,F_neighbourhood,level,share,0.133,0.021,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,13.3,2.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025450037664585517,0.004018426999671397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.333333333333333,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,% Asian population,demographics,F_neighbourhood,level,share,0.14,0.022,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,14.000000000000002,2.1999999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025571728741831924,0.004018414516573587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.363636363636364,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,% Old population,demographics,F_neighbourhood,level,share,-0.138,0.032,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-13.8,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.017332477296991562,0.004019125170316884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.3125,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,% Old population,demographics,F_neighbourhood,level,share,-0.224,0.076,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-22.400000000000002,7.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.011846790926218388,0.0040194469213955236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.947368421052632,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,% Old population,demographics,F_neighbourhood,level,share,-0.157,0.041,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-15.7,4.1000000000000005,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.015390797653787276,0.00401925289047948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8292682926829267,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,% Old population,demographics,F_neighbourhood,level,share,-0.175,0.019,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-17.5,1.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03699847042907376,0.004016976789442295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.210526315789473,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,% Old population,demographics,F_neighbourhood,level,share,-0.205,0.02,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-20.5,2.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.04116729388118056,0.0040163213542615175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.25,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,% Old population,demographics,F_neighbourhood,level,share,-0.211,0.041,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-21.099999999999998,4.1000000000000005,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.020682473033369866,0.004018869167621633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.146341463414633,61888.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.734,0.148,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,other,0.734,0.148,not harmonised,reported,0.019931722703059884,0.004018930463287279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.95945945945946,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),1.338,0.234,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,other,1.338,0.234,not harmonised,reported,0.02297853544431348,0.00401866763375886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.717948717948718,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.612,0.167,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,other,0.612,0.167,not harmonised,reported,0.01472938488860395,0.004019292935288988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6646706586826343,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.354,0.111,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,other,0.354,0.111,not harmonised,reported,0.012818623008603812,0.004019398739985941,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.189189189189189,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),0.195,0.096,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,other,0.195,0.096,not harmonised,reported,0.008164802386031872,0.00401959502081569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.03125,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.072,0.126,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,other,-0.072,0.126,not harmonised,reported,-0.002296981945336915,0.004019718404339601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5714285714285714,61888.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.039,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-3.9,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07814501541271027,0.004007436687831296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.5,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.036,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-3.5999999999999996,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07216646351934773,0.004009247973297095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.038,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-3.8,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1509982781693403,0.003973638899193166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-38.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.04,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-4.0,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.15875015875023812,0.003968753968755953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-40.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.042,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-4.2,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.16647279199075748,0.003963637904541845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-42.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.046,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-4.6,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.18182528350835128,0.0039527235545293755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-46.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c1,9 Panel B,(1) 2SLS,44,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.006,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,IV,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.6,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.024111362330324626,0.004018560388387438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c2,9 Panel B,(2) Q: 0.05,44,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.007,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.7000000000000001,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.028126970459270723,0.0040181386370386745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c3,9 Panel B,(3) Q: 0.25,44,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.006,0.0,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.6,,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),,,,,,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c4,9 Panel B,(4) Q: 0.50,44,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.005,0.0,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.5,,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),,,,,,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c5,9 Panel B,(5) Q: 0.75,44,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.005,0.0,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.5,,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),,,,,,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T9B-c6,9 Panel B,(6) Q: 0.95,44,Distance to crime coldspot,crime,F_neighbourhood,level,miles (straight-line to nearest coldspot),-0.004,0.001,,,***,,,high,Straight-line distance in miles to the nearest crime coldspot tract,Log (Sale Price),log,quantile,61898.0,,se,Robustness: adds distance to crime coldspot; with spatial lag of neighbour prices; year + month dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.4,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.016076837984062016,0.004019209496015504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,61888.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Living area,living_area,A_structural,level,1000 sqft,0.288,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.31000062000123996,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.27800263889251203,0.003861147762396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),72.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Living area,living_area,A_structural,level,1000 sqft,0.255,0.006,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.2744797156260979,0.006458346250025833,100*b/92.903 (X per 1000sqft->m2),reported,0.16839344410959117,0.003962198684931557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),42.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Living area,living_area,A_structural,level,1000 sqft,0.28,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.30138949166787227,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3512568944782872,0.0037634667265530755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),93.33333333333334,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Living area,living_area,A_structural,level,1000 sqft,0.294,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.3164589662512658,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.5087114308842847,0.0034606219788046576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),147.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Living area,living_area,A_structural,level,1000 sqft,0.306,0.003,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.32937565875131747,0.0032291731250129164,100*b/92.903 (X per 1000sqft->m2),reported,0.3793525756781874,0.0037191428988057585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),102.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Living area,living_area,A_structural,level,1000 sqft,0.312,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3358340050013433,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.2991691448646159,0.0038355018572386653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),78.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Lot area,lot_size,A_structural,level,1000 sqft,0.026,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.027986167083445278,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.10394347012419862,0.003997825774007639,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Lot area,lot_size,A_structural,level,1000 sqft,0.016,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.017222256666735557,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.06418098885674876,0.004011311803546798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Lot area,lot_size,A_structural,level,1000 sqft,0.021,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.022604211875090416,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08411244939884005,0.004005354733278097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Lot area,lot_size,A_structural,level,1000 sqft,0.024,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.02583338500010333,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.09602458544121234,0.004001024393383848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Lot area,lot_size,A_structural,level,1000 sqft,0.03,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.032291731250129165,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.11972065843265513,0.003990688614421837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Lot area,lot_size,A_structural,level,1000 sqft,0.041,0.002,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.044132032708509863,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.08212344218496902,0.004006021569998489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Bedrooms,bedrooms,A_structural,level,count,-0.016,0.002,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-1.6,0.2,100*b/1 (per unit count),reported,-0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.003,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.3,0.3,100*b/1 (per unit count),reported,-0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Bedrooms,bedrooms,A_structural,level,count,-0.009,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.8999999999999999,0.2,100*b/1 (per unit count),reported,-0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Bedrooms,bedrooms,A_structural,level,count,-0.01,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-1.0,0.2,100*b/1 (per unit count),reported,-0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Bedrooms,bedrooms,A_structural,level,count,-0.011,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-1.0999999999999999,0.1,100*b/1 (per unit count),reported,-0.04417243235155397,0.0040156756683230885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Bedrooms,bedrooms,A_structural,level,count,-0.017,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-1.7000000000000002,0.3,100*b/1 (per unit count),reported,-0.02277182177096999,0.004018556783112351,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.666666666666667,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Bathrooms (Full),bathrooms,A_structural,level,count,0.007,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.7000000000000001,0.2,100*b/1 (per unit count),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Bathrooms (Full),bathrooms,A_structural,level,count,0.001,0.004,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.1,0.4,100*b/1 (per unit count),reported,0.0010048992705163852,0.004019597082065541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Bathrooms (Full),bathrooms,A_structural,level,count,0.006,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.6,0.2,100*b/1 (per unit count),reported,0.012057920667875593,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.8,0.2,100*b/1 (per unit count),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.003,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8,0.3,100*b/1 (per unit count),reported,0.010718315238978177,0.004019368214616816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6666666666666665,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Bathrooms (Full),bathrooms,A_structural,level,count,0.013,0.004,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.3,0.4,100*b/1 (per unit count),reported,0.0130625825289168,0.004019256162743631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,One story,storeys,A_structural,binary,binary,-0.076,0.006,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,-7.320047598219248,0.5560897239356293,100*(exp(b - se^2/2) - 1),reported,-0.050849055990756155,0.00401439915716496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.666666666666666,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,One story,storeys,A_structural,binary,binary,-0.053,0.006,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-5.163705820428987,0.5690280074893789,100*(exp(b - se^2/2) - 1),reported,-0.035484098310227835,0.0040170677332333395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.833333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,One story,storeys,A_structural,binary,binary,-0.058,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-5.635429897841049,0.28309498423103957,100*(exp(b - se^2/2) - 1),reported,-0.07747864737192982,0.004007516243375681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,One story,storeys,A_structural,binary,binary,-0.065,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-6.2936753416694025,0.28112023901322103,100*(exp(b - se^2/2) - 1),reported,-0.08676289187188262,0.004004441163317659,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.666666666666668,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,One story,storeys,A_structural,binary,binary,-0.075,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-7.226393558965006,0.3710973945314211,100*(exp(b - se^2/2) - 1),reported,-0.07515433759809606,0.004008231338565123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.75,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,One story,storeys,A_structural,binary,binary,-0.086,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-8.241310950433578,0.3670376924880604,100*(exp(b - se^2/2) - 1),reported,-0.086100451724248,0.004004672173220838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.499999999999996,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Renovated,renovation,A_structural,binary,binary,-0.033,0.011,,,***,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,-3.2519974723142275,1.0642924155479352,100*(exp(b - se^2/2) - 1),reported,-0.012057920667875595,0.004019306889291864,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0000000000000004,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Renovated,renovation,A_structural,binary,binary,-0.265,0.015,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-23.288035583872347,1.1508089249637834,100*(exp(b - se^2/2) - 1),reported,-0.07083453881226438,0.0040095021969206255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.666666666666668,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Renovated,renovation,A_structural,binary,binary,-0.106,0.01,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-10.06203220322176,0.899424648075924,100*(exp(b - se^2/2) - 1),reported,-0.04256912767268908,0.004015955440819725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.6,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Renovated,renovation,A_structural,binary,binary,0.018,0.009,,,**,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.8121741624259613,0.9163466787508142,100*(exp(b - se^2/2) - 1),reported,0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Renovated,renovation,A_structural,binary,binary,0.041,0.004,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,4.184377076197432,0.4167408422181918,100*(exp(b - se^2/2) - 1),reported,0.041165965819615624,0.004016191787279573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Renovated,renovation,A_structural,binary,binary,0.052,0.009,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,5.333308165968109,0.9480381682620282,100*(exp(b - se^2/2) - 1),reported,0.02321808969094556,0.004018515523432886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.777777777777778,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Fireplace,fireplace,B_interior,binary,binary,0.02,0.005,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,2.018858758970832,0.5101006700133779,100*(exp(b - se^2/2) - 1),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Fireplace,fireplace,B_interior,binary,binary,0.036,0.005,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,3.6642888373831006,0.5183279232454618,100*(exp(b - se^2/2) - 1),reported,0.028929000846614854,0.004017916784252063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.199999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Fireplace,fireplace,B_interior,binary,binary,0.021,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,2.1220009195467737,0.2042444103275057,100*(exp(b - se^2/2) - 1),reported,0.04216824962463218,0.004016023773774493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Fireplace,fireplace,B_interior,binary,binary,0.014,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,1.4096430743602673,0.20281969178769846,100*(exp(b - se^2/2) - 1),reported,0.028126062258739027,0.004018008894105575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Fireplace,fireplace,B_interior,binary,binary,0.006,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,0.6017533045172607,0.10060180360540649,100*(exp(b - se^2/2) - 1),reported,0.024110583627583122,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Fireplace,fireplace,B_interior,binary,binary,-0.006,0.003,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-0.5986509016838615,0.29820538921618056,100*(exp(b - se^2/2) - 1),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,True
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Lake/Mountain view,view,E_environment,binary,binary,0.14,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,15.025309411519117,0.6901642793143364,100*(exp(b - se^2/2) - 1),reported,0.09338082437105044,0.004002035330187876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.333333333333336,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Lake/Mountain view,view,E_environment,binary,binary,0.094,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,9.855095747436016,0.4394238983668695,100*(exp(b - se^2/2) - 1),reported,0.09404195206802067,0.004001785194383859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Lake/Mountain view,view,E_environment,binary,binary,0.107,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,11.292924628644885,0.33388027634379774,100*(exp(b - se^2/2) - 1),reported,0.1419146820560218,0.003978916319327714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Lake/Mountain view,view,E_environment,binary,binary,0.114,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,12.07470815109386,0.3362256374652459,100*(exp(b - se^2/2) - 1),reported,0.15099350992814012,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Lake/Mountain view,view,E_environment,binary,binary,0.138,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,14.795488690024472,0.6887853301645072,100*(exp(b - se^2/2) - 1),reported,0.09205819917863656,0.004002530399071154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Lake/Mountain view,view,E_environment,binary,binary,0.211,0.01,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,23.485061098725613,1.2349123550613943,100*(exp(b - se^2/2) - 1),reported,0.08451013099345056,0.0040052194783625865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.099999999999998,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.073,0.008,,,***,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,7.3,0.8,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.036654194050700424,0.004016897978158951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.125,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.08,0.007,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,8.0,0.7000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.045889879796468726,0.004015364482191013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.428571428571429,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.075,0.005,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,7.5,0.5,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06018468926667303,0.004012312617778202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.071,0.003,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,7.1,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.09470295537376093,0.00400153332565187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.666666666666664,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.073,0.006,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,7.3,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.048846743762577846,0.004014800857198179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.166666666666666,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.087,0.014,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,8.7,1.4000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.024971148210843577,0.004018345689101265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.2142857142857135,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,% Male population,demographics,F_neighbourhood,level,share,0.355,0.188,,,*,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,35.5,18.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0075899818194942155,0.004019483329760318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8882978723404253,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,% Male population,demographics,F_neighbourhood,level,share,0.304,0.162,,,*,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,30.4,16.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.007542736846148156,0.004019484766697373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.876543209876543,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,% Male population,demographics,F_neighbourhood,level,share,0.233,0.145,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,23.3,14.499999999999998,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.006458945220486746,0.004019515265968146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.606896551724138,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,% Male population,demographics,F_neighbourhood,level,share,0.296,0.127,,,**,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,29.599999999999998,12.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.009368103359039839,0.0040194227249934445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3307086614173227,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,% Male population,demographics,F_neighbourhood,level,share,0.627,0.114,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,62.7,11.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.022102394450125195,0.004018617172750035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,% Male population,demographics,F_neighbourhood,level,share,1.935,0.283,,,***,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,193.5,28.299999999999997,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.02747345715132391,0.004018081846937812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.837455830388693,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,% White population,demographics,F_neighbourhood,level,share,0.261,0.043,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,26.1,4.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.024390773422822157,0.004018403284219742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.069767441860466,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,% White population,demographics,F_neighbourhood,level,share,0.262,0.083,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,26.200000000000003,8.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.012687351841480411,0.0040192755833697484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1566265060240966,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,% White population,demographics,F_neighbourhood,level,share,0.285,0.032,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,28.499999999999996,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.035776636115050095,0.004017025809409134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.906249999999998,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,% White population,demographics,F_neighbourhood,level,share,0.25,0.032,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,25.0,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03138764531850876,0.004017618600769122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8125,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,% White population,demographics,F_neighbourhood,level,share,0.242,0.036,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,24.2,3.5999999999999996,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.027010779785196632,0.00401813253002925,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.722222222222222,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,% White population,demographics,F_neighbourhood,level,share,0.104,0.065,,,,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,10.4,6.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.006431225574570386,0.004019515984106491,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5999999999999999,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,% Asian population,demographics,F_neighbourhood,level,share,-0.493,0.06,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-49.3,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03300970696198885,0.004017408555211624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.216666666666667,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,% Asian population,demographics,F_neighbourhood,level,share,-0.437,0.078,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-43.7,7.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.022514353300133925,0.0040185802229071995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.602564102564102,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,% Asian population,demographics,F_neighbourhood,level,share,-0.399,0.052,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-39.900000000000006,5.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.030828033750351026,0.004017688609068304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.673076923076924,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,% Asian population,demographics,F_neighbourhood,level,share,-0.418,0.033,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-41.8,3.3000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.050849055990756155,0.00401439915716496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.666666666666666,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,% Asian population,demographics,F_neighbourhood,level,share,-0.405,0.028,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-40.5,2.8000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.05804261109870276,0.004012822495712783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.464285714285715,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,% Asian population,demographics,F_neighbourhood,level,share,-0.576,0.061,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-57.599999999999994,6.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03792824847583532,0.004016706869836726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.442622950819672,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,% Old population,demographics,F_neighbourhood,level,share,0.664,0.06,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,66.4,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.044439616953729354,0.004015628037987592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.066666666666668,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,% Old population,demographics,F_neighbourhood,level,share,0.246,0.054,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,24.6,5.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01830843780829626,0.004018925372552838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.555555555555555,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,% Old population,demographics,F_neighbourhood,level,share,0.283,0.029,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,28.299999999999997,2.9000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03919560031504997,0.004016510279634096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.75862068965517,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,% Old population,demographics,F_neighbourhood,level,share,0.493,0.048,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,49.3,4.8,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04124949425862405,0.004016177939987738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.270833333333332,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,% Old population,demographics,F_neighbourhood,level,share,0.678,0.031,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,67.80000000000001,3.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.08757475821494055,0.004004155611597577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.870967741935484,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,% Old population,demographics,F_neighbourhood,level,share,1.148,0.183,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,114.8,18.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.025207833006398377,0.00401832181199556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.273224043715847,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.849,0.376,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,other,-1.849,0.376,not harmonised,reported,-0.019762731987536766,0.004018814076427163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.917553191489362,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-3.07,0.515,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,other,-3.07,0.515,not harmonised,reported,-0.023954617910023485,0.0040184456754599664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.9611650485436884,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-2.646,0.185,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,other,-2.646,0.185,not harmonised,reported,-0.05739635524474155,0.004012972683400297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.302702702702703,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-2.263,0.18,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,other,-2.263,0.18,not harmonised,reported,-0.050470887692259304,0.004014476263635296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.572222222222223,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.898,0.209,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,other,-1.898,0.209,not harmonised,reported,-0.03647904907247209,0.004016923738749561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.08133971291866,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.87,0.336,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,other,-1.87,0.336,not harmonised,reported,-0.022365387350142808,0.00401859366291336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.565476190476191,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c1,11 Panel A,(1) 2SLS,47,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.071,0.004,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-7.1,0.4,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.07116697569109424,0.004009407081188409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.749999999999996,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c2,11 Panel A,(2) Q: 0.05,47,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.082,0.005,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-8.200000000000001,0.5,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.06577865537730128,0.004010893620567151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.4,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c3,11 Panel A,(3) Q: 0.25,47,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.074,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-7.3999999999999995,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.2851050385919529,0.003852770791783147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-74.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c4,11 Panel A,(4) Q: 0.50,47,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.071,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-7.1,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.27443418170891076,0.0038652701649142373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-70.99999999999999,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c5,11 Panel A,(5) Q: 0.75,47,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.068,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-6.800000000000001,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.13540766833933668,0.003982578480568726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-34.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11A-c6,11 Panel A,(6) Q: 0.95,47,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.065,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: baseline with zip code fixed effects; no spatial adjustment; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-6.5,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.12953631268868462,0.003985732698113373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-32.5,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Living area,living_area,A_structural,level,1000 sqft,0.256,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.2755561066677689,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.2491423124624276,0.0038928486322254313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),64.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Living area,living_area,A_structural,level,1000 sqft,0.231,0.005,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.2486463306259946,0.005381955208354861,100*b/92.903 (X per 1000sqft->m2),reported,0.18258382379806556,0.003952030818139948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),46.2,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Living area,living_area,A_structural,level,1000 sqft,0.249,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.26802136937607207,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4475284146859931,0.0035946057404497435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),124.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Living area,living_area,A_structural,level,1000 sqft,0.265,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.2852436260428076,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.4700820959868574,0.0035477894036743955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),132.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Living area,living_area,A_structural,level,1000 sqft,0.281,0.002,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.3024658827095432,0.0021527820833419447,100*b/92.903 (X per 1000sqft->m2),reported,0.49175121401230815,0.0035000086406569977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),140.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Living area,living_area,A_structural,level,1000 sqft,0.29,0.004,,,***,,,high,"Total living area; Table 10 says ft2 but summary stats (mean 1.891, max 4.59) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.3121534020845819,0.004305564166683889,100*b/92.903 (X per 1000sqft->m2),reported,0.27978257133473344,0.0038590699494445986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),72.5,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Lot area,lot_size,A_structural,level,1000 sqft,0.023,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.02475699395843236,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.09205819917863656,0.004002530399071154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Lot area,lot_size,A_structural,level,1000 sqft,0.012,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.012916692500051665,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.04817917426179949,0.004014931188483291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Lot area,lot_size,A_structural,level,1000 sqft,0.018,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.019375038750077497,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.07216414361374496,0.0040091190896524975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Lot area,lot_size,A_structural,level,1000 sqft,0.022,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,0.023680602916761384,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Lot area,lot_size,A_structural,level,1000 sqft,0.027,0.0,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.02906255812511625,,100*b/92.903 (X per 1000sqft->m2),,,,,,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Lot area,lot_size,A_structural,level,1000 sqft,0.038,0.001,,,***,,,high,"Total lot area; Table 10 says ft2 but summary stats (mean 4.66, max 14.47) imply thousands of sq ft",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,0.04090285958349694,0.0010763910416709723,100*b/92.903 (X per 1000sqft->m2),reported,0.15099350992814012,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.0,61892.0,1000sqft,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.001,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.8,0.1,100*b/1 (per unit count),reported,-0.03214017976012635,0.004017522470015793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Bedrooms,bedrooms,A_structural,level,count,-0.001,0.004,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.1,0.4,100*b/1 (per unit count),reported,-0.0010048992705163852,0.004019597082065541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Bedrooms,bedrooms,A_structural,level,count,-0.002,0.001,,,**,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.2,0.1,100*b/1 (per unit count),reported,-0.008038938454307684,0.004019469227153842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Bedrooms,bedrooms,A_structural,level,count,-0.003,0.002,,,*,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.3,0.2,100*b/1 (per unit count),reported,-0.006029289075079203,0.004019526050052801,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Bedrooms,bedrooms,A_structural,level,count,-0.004,0.001,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.4,0.1,100*b/1 (per unit count),reported,-0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Bedrooms,bedrooms,A_structural,level,count,-0.008,0.003,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.8,0.3,100*b/1 (per unit count),reported,-0.010718315238978177,0.004019368214616816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6666666666666665,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.001,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,0.8999999999999999,0.1,100*b/1 (per unit count),reported,0.03615274262598033,0.004016971402886703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Bathrooms (Full),bathrooms,A_structural,level,count,0.0,0.004,,,,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.0,0.4,100*b/1 (per unit count),reported,0.0,0.004019599111606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Bathrooms (Full),bathrooms,A_structural,level,count,0.008,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,0.8,0.2,100*b/1 (per unit count),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Bathrooms (Full),bathrooms,A_structural,level,count,0.01,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,1.0,0.2,100*b/1 (per unit count),reported,0.020093937701409933,0.004018787540281986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Bathrooms (Full),bathrooms,A_structural,level,count,0.009,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,0.8999999999999999,0.2,100*b/1 (per unit count),reported,0.0180852376545314,0.004018941701006978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Bathrooms (Full),bathrooms,A_structural,level,count,0.015,0.002,,,***,,,high,Number of full bathrooms,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,1.5,0.2,100*b/1 (per unit count),reported,0.030133303253020185,0.004017773767069358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,61892.0,count,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Age,age,A_structural,level,years,-0.002,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-0.2,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Age,age,A_structural,level,years,-0.001,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-0.1,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Age,age,A_structural,level,years,-0.0,0.0,,,***,,,high,Year of sale minus year built; printed to 3 decimals,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-0.0,,100*b/1 (per year),,,,,,61892.0,years,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,False,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,One story,storeys,A_structural,binary,binary,-0.07,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,-6.761783494395768,0.46619690995297414,100*(exp(b - se^2/2) - 1),reported,-0.05618549358573801,0.004013249541838429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.000000000000002,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,One story,storeys,A_structural,binary,binary,-0.054,0.005,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-5.257973632551516,0.47371605325089916,100*(exp(b - se^2/2) - 1),reported,-0.043370821899063806,0.004015816842505908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.799999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,One story,storeys,A_structural,binary,binary,-0.058,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-5.635429897841049,0.28309498423103957,100*(exp(b - se^2/2) - 1),reported,-0.07747864737192982,0.004007516243375681,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,One story,storeys,A_structural,binary,binary,-0.06,0.003,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,-5.823970434661707,0.2825293600752746,100*(exp(b - se^2/2) - 1),reported,-0.08013345319170002,0.004006672659585001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,One story,storeys,A_structural,binary,binary,-0.067,0.002,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,-6.480666905175569,0.18703904026735532,100*(exp(b - se^2/2) - 1),reported,-0.133452102765036,0.003983644858657791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,One story,storeys,A_structural,binary,binary,-0.08,0.004,,,***,,,high,One-story property =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-7.689103851459567,0.36924653855465434,100*(exp(b - se^2/2) - 1),reported,-0.08013345319170002,0.004006672659585001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Renovated,renovation,A_structural,binary,binary,-0.025,0.011,,,**,,,high,Previously renovated =1,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,-2.4749092436441966,1.0728409032311659,100*(exp(b - se^2/2) - 1),reported,-0.009135071343824115,0.00401943139128261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.272727272727273,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Renovated,renovation,A_structural,binary,binary,-0.258,0.013,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,-22.74700494320011,1.0043738017390065,100*(exp(b - se^2/2) - 1),reported,-0.07952095495207298,0.004006869823166468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.846153846153847,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Renovated,renovation,A_structural,binary,binary,-0.102,0.014,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,-9.705894089098043,1.2642413723364274,100*(exp(b - se^2/2) - 1),reported,-0.029273100333129874,0.004017876516311944,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.285714285714285,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Renovated,renovation,A_structural,binary,binary,0.028,0.008,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,2.8362776286056546,0.8227165475371401,100*(exp(b - se^2/2) - 1),reported,0.01406720483080051,0.004019201380228717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Renovated,renovation,A_structural,binary,binary,0.053,0.003,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,5.4424900196629045,0.3163288935358068,100*(exp(b - se^2/2) - 1),reported,0.07083453881226437,0.0040095021969206255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.666666666666664,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Renovated,renovation,A_structural,binary,binary,0.067,0.007,,,***,,,high,Previously renovated =1,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,6.926928075630467,0.7485068347222201,100*(exp(b - se^2/2) - 1),reported,0.03844486335150914,0.0040166275143367755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.571428571428571,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Fireplace,fireplace,B_interior,binary,binary,0.016,0.004,,,***,,,high,,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,1.6120556409127573,0.406451474162438,100*(exp(b - se^2/2) - 1),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Fireplace,fireplace,B_interior,binary,binary,0.032,0.005,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,3.250459891696722,0.5162587526525592,100*(exp(b - se^2/2) - 1),reported,0.025716926017020324,0.004018269690159425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.4,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Fireplace,fireplace,B_interior,binary,binary,0.015,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,1.511103439161987,0.20302261292314377,100*(exp(b - se^2/2) - 1),reported,0.030133303253020185,0.004017773767069358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Fireplace,fireplace,B_interior,binary,binary,0.008,0.002,,,***,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,0.8030069442118482,0.2016064171008547,100*(exp(b - se^2/2) - 1),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Fireplace,fireplace,B_interior,binary,binary,0.002,0.002,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,0.1999997332001513,0.20040040026680006,100*(exp(b - se^2/2) - 1),reported,0.004019566639313255,0.004019566639313255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Fireplace,fireplace,B_interior,binary,binary,-0.001,0.004,,,,,,high,,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,-0.10074921386556968,0.39960019993335,100*(exp(b - se^2/2) - 1),reported,-0.0010048992705163852,0.004019597082065541,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,True
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Lake/Mountain view,view,E_environment,binary,binary,0.095,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,pct_premium,9.964510947632444,0.5498294275630514,100*(exp(b - se^2/2) - 1),reported,0.0761506225926853,0.004007927504878174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Lake/Mountain view,view,E_environment,binary,binary,0.059,0.005,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,pct_premium,6.07619811325224,0.5303876203700795,100*(exp(b - se^2/2) - 1),reported,0.04737800570765221,0.004015085229462052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.799999999999999,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Lake/Mountain view,view,E_environment,binary,binary,0.066,0.003,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,pct_premium,6.822191015658174,0.320468015149798,100*(exp(b - se^2/2) - 1),reported,0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Lake/Mountain view,view,E_environment,binary,binary,0.076,0.002,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,pct_premium,7.89604162342934,0.21579251483145678,100*(exp(b - se^2/2) - 1),reported,0.15099350992814012,0.003973513419161582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.0,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Lake/Mountain view,view,E_environment,binary,binary,0.094,0.004,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,pct_premium,9.855095747436016,0.4394238983668695,100*(exp(b - se^2/2) - 1),reported,0.09404195206802067,0.004001785194383859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.5,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Lake/Mountain view,view,E_environment,binary,binary,0.158,0.006,,,***,,,high,"Natural view (Mt Rainier, Cascades, Puget Sound, Lake Washington, Olympics) =1",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,pct_premium,17.11451139058926,0.7026997168246017,100*(exp(b - se^2/2) - 1),reported,0.10526140588935778,0.003997268578076878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.333333333333332,61892.0,binary,,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,True,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.031,0.004,,,***,,,high,Census tract population level,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,3.1,0.4,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.031136788579101626,0.00401765013923892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.75,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.056,0.017,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,5.6000000000000005,1.7000000000000002,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.013239871778663826,0.004019246789951519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2941176470588234,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.045,0.003,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,4.5,0.3,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06018468926667303,0.004012312617778202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.036,0.006,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,3.5999999999999996,0.6,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.02411058362758312,0.0040184306045971865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.999999999999999,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.028,0.007,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,2.8000000000000003,0.7000000000000001,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01607631859730484,0.00401907964932621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Population,density,F_neighbourhood,level,"count (census tract population; scale not stated, mean 5,138)",0.029,0.01,,,***,,,high,Census tract population level,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,2.9000000000000004,1.0,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01165604552799447,0.004019326044136024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9,61892.0,count,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,% Male population,demographics,F_neighbourhood,level,share,-0.315,0.104,,,***,,,high,Share of males in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-31.5,10.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.012173845111742626,0.004019301243242009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.028846153846154,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,% Male population,demographics,F_neighbourhood,level,share,0.089,0.1,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,8.9,10.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0035774203173121,0.004019573390238315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8899999999999999,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,% Male population,demographics,F_neighbourhood,level,share,-0.09,0.111,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-9.0,11.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0032591171057613165,0.004019577763772291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8108108108108107,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,% Male population,demographics,F_neighbourhood,level,share,-0.137,0.089,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-13.700000000000001,8.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.006187354350038285,0.004019522169002973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5393258426966294,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,% Male population,demographics,F_neighbourhood,level,share,-0.061,0.117,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-6.1,11.700000000000001,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.0020956838236873133,0.004019590284777306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5213675213675213,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,% Male population,demographics,F_neighbourhood,level,share,0.21,0.273,,,,,,high,Share of males in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,21.0,27.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0030919845362591698,0.00401957989713692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7692307692307692,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,% White population,demographics,F_neighbourhood,level,share,0.359,0.023,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,35.9,2.3,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.06261757645487791,0.004011711026357081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.608695652173912,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,% White population,demographics,F_neighbourhood,level,share,0.27,0.076,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,27.0,7.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.014278698936545728,0.004019189330286946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5526315789473686,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,% White population,demographics,F_neighbourhood,level,share,0.309,0.026,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,30.9,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.04771697288349162,0.00401502037207373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.884615384615385,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,% White population,demographics,F_neighbourhood,level,share,0.309,0.036,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,30.9,3.5999999999999996,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.03448104275966152,0.004017208865203283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.583333333333334,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,% White population,demographics,F_neighbourhood,level,share,0.334,0.021,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,33.4,2.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0638005188522057,0.004011409867953054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.904761904761905,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,% White population,demographics,F_neighbourhood,level,share,0.299,0.045,,,***,,,high,Share of white population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,29.9,4.5,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.026698482437149614,0.004018166253082717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.644444444444445,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,% Asian population,demographics,F_neighbourhood,level,share,-0.149,0.034,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-14.899999999999999,3.4000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.017612569620873098,0.004018975618185807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.38235294117647,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,% Asian population,demographics,F_neighbourhood,level,share,-0.2,0.06,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-20.0,6.0,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.013397461175148244,0.004019238352544473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,% Asian population,demographics,F_neighbourhood,level,share,-0.159,0.033,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-15.9,3.3000000000000003,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.019363528193883706,0.004018845474202278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.818181818181818,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,% Asian population,demographics,F_neighbourhood,level,share,-0.151,0.027,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-15.1,2.7,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.022474302245194512,0.0040185838451672305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.592592592592593,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,% Asian population,demographics,F_neighbourhood,level,share,-0.122,0.032,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-12.2,3.2,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.015322922446734819,0.004019127199143558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8125,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,% Asian population,demographics,F_neighbourhood,level,share,-0.166,0.046,,,***,,,high,Share of Asian population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-16.6,4.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.014503984027492582,0.004019176296775053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.608695652173913,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,% Old population,demographics,F_neighbourhood,level,share,0.098,0.039,,,**,,,high,Share of old population in census tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,9.8,3.9,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.010100015908610264,0.004019394086079595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5128205128205128,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,% Old population,demographics,F_neighbourhood,level,share,0.009,0.054,,,,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,0.8999999999999999,5.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0006699330349313613,0.004019598209588168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.16666666666666666,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,% Old population,demographics,F_neighbourhood,level,share,0.023,0.031,,,,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,2.3,3.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0029822699496928223,0.0040195812365425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7419354838709677,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,% Old population,demographics,F_neighbourhood,level,share,0.088,0.026,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,8.799999999999999,2.6,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.01360353810853696,0.004019227168431375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3846153846153846,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,% Old population,demographics,F_neighbourhood,level,share,0.143,0.034,,,***,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,14.299999999999999,3.4000000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.016903545527994532,0.004019024810851848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.205882352941176,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,% Old population,demographics,F_neighbourhood,level,share,0.198,0.094,,,**,,,high,Share of old population in census tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,19.8,9.4,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.008466511686181961,0.00401945504293487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1063829787234045,61892.0,share,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.055,0.192,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,other,-0.055,0.192,not harmonised,reported,-0.0011514468988669177,0.004019596446953604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2864583333333333,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-1.43,0.521,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,other,-1.43,0.521,not harmonised,reported,-0.011032009475025592,0.004019354501040793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7447216890595008,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.956,0.196,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,other,-0.956,0.196,not harmonised,reported,-0.01960203272337707,0.004018826792658897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.877551020408163,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.632,0.195,,,***,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,other,-0.632,0.195,not harmonised,reported,-0.013026518410542033,0.004019258053885596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.241025641025641,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.249,0.099,,,**,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,other,-0.249,0.099,not harmonised,reported,-0.010109384168507119,0.004019393705551023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.515151515151515,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Number of crimes (IV),crime,F_neighbourhood,other,reported crimes per capita (tract),-0.124,0.274,,,,,,high,"Reported crimes per tract population, instrumented by number of 911 calls (2SLS / IV quantile)",Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,other,-0.124,0.274,not harmonised,reported,-0.001819085639297743,0.004019592461028884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4525547445255474,61892.0,reported crimes per capita,nonstandard_x_transform,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c1,11 Panel B,(1) 2SLS,48,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.045,0.004,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,IV,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; 2SLS; SE clustered by year and month,semi_elasticity,-4.5,0.4,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.04517432526965365,0.0040154955795247685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.25,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,iv,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c2,11 Panel B,(2) Q: 0.05,48,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.056,0.004,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.05; SE bootstrap,semi_elasticity,-5.6000000000000005,0.4,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.056185493585738,0.004013249541838429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c3,11 Panel B,(3) Q: 0.25,48,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.051,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.25; SE bootstrap,semi_elasticity,-5.1,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.10196554146949112,0.003998648685078083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.499999999999996,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c4,11 Panel B,(4) Q: 0.50,48,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.048,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.50; SE bootstrap,semi_elasticity,-4.8,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.1894467909724459,0.00394680814525929,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-48.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c5,11 Panel B,(5) Q: 0.75,48,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.046,0.001,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.75; SE bootstrap,semi_elasticity,-4.6,0.1,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.18181960208964543,0.003952600045427074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-46.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a,d3812508998e06d0728e9af8b9c2c72e4dad465a:T11B-c6,11 Panel B,(6) Q: 0.95,48,Distance to crime hotspot,crime,F_neighbourhood,level,miles (straight-line to nearest hotspot),-0.044,0.002,,,***,,,high,Straight-line distance in miles to the nearest Getis-Ord crime hotspot tract,Log (Sale Price),log,quantile,61902.0,,se,Appendix 2: zip code fixed effects plus spatial lag of neighbour prices; year + month + zip dummies; quantile regression at tau=0.95; SE bootstrap,semi_elasticity,-4.3999999999999995,0.2,100*b (unit as reported: miles; no harmonised unit defined for this characteristic),reported,-0.08808742611158363,0.004003973914162892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.0,61892.0,miles,unit_not_harmonisable,2025.0,United States,North America,High income,2008.0,2020.0,single_family,transaction,journal,True,False,clustered,,2014.0,quantile,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Year of construction (log),age,A_structural,other,years,-0.4313,0.2871,,,,,,high,"Variable is log of the calendar year of construction, not age; sign reversed relative to age.","log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.4313,0.2871,not harmonised,reported,-0.0008582062279511618,0.0005712752331202841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5022640195053987,3064142.0,years,year_built_not_age;nonstandard_x_transform,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Year of construction (log),age,A_structural,other,years,-1.0019,0.1922,,,***,,,high,"Variable is log of the calendar year of construction, not age; sign reversed relative to age.","log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-1.0019,0.1922,not harmonised,reported,-0.0036697996487347283,0.0007039978964834962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.212799167533818,2017676.0,years,year_built_not_age;nonstandard_x_transform,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Year of construction (log),age,A_structural,other,years,0.5455,0.7097,,,,,,high,"Variable is log of the calendar year of construction, not age; sign reversed relative to age.","log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.5455,0.7097,not harmonised,reported,0.0005867085106922961,0.000763312612352562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7686346343525433,1716307.0,years,year_built_not_age;nonstandard_x_transform,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Year of construction (log),age,A_structural,other,years,-0.5379,0.3098,,,*,,,high,"Variable is log of the calendar year of construction, not age; sign reversed relative to age.","log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.5379,0.3098,not harmonised,reported,-0.001578219234797136,0.0009089650844769524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.736281471917366,1210332.0,years,year_built_not_age;nonstandard_x_transform,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Living space (log),living_area,A_structural,log,m2,-0.0013,0.0311,,,,,,high,"Dependent variable is log price per m2, so coefficient is elasticity of per-m2 price w.r.t. living space.","log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.0013,0.0311,not harmonised,reported,-2.3879680911098643e-05,0.0005712754433347446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.04180064308681672,3064142.0,m2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Living space (log),living_area,A_structural,log,m2,0.0209,0.0362,,,,,,high,"Dependent variable is log price per m2, so coefficient is elasticity of per-m2 price w.r.t. living space.","log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.0209,0.0362,not harmonised,reported,0.00040645452759690637,0.000704002578899905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5773480662983425,2017676.0,m2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Living space (log),living_area,A_structural,log,m2,0.2474,0.0269,,,***,,,high,"Dependent variable is log price per m2, so coefficient is elasticity of per-m2 price w.r.t. living space.","log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.2474,0.0269,not harmonised,reported,0.007020034184107572,0.0007632939351353828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.197026022304833,1716307.0,m2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Living space (log),living_area,A_structural,log,m2,0.2512,0.0289,,,***,,,high,"Dependent variable is log price per m2, so coefficient is elasticity of per-m2 price w.r.t. living space.","log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.2512,0.0289,not harmonised,reported,0.00790052551605837,0.0009089378479860147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.69204152249135,1210332.0,m2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Number of rooms (log),bedrooms,A_structural,log,count,-0.065,0.0138,,,***,,,high,"Total number of rooms (German convention), not bedrooms; mapped to bedrooms as closest leaf.","log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.065,0.0138,not harmonised,reported,-0.0026907803913360177,0.0005712733753913391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.710144927536232,3064142.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Number of rooms (log),bedrooms,A_structural,log,count,-0.0703,0.0171,,,***,,,high,"Total number of rooms (German convention), not bedrooms; mapped to bedrooms as closest leaf.","log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.0703,0.0171,not harmonised,reported,-0.0028942209416190664,0.0007039996885019352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.111111111111111,2017676.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Number of rooms (log),bedrooms,A_structural,log,count,-0.1438,0.0144,,,***,,,high,"Total number of rooms (German convention), not bedrooms; mapped to bedrooms as closest leaf.","log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.1438,0.0144,not harmonised,reported,-0.0076223044356221745,0.0007632905693529853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.986111111111112,1716307.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Number of rooms (log),bedrooms,A_structural,log,count,-0.1403,0.0151,,,***,,,high,"Total number of rooms (German convention), not bedrooms; mapped to bedrooms as closest leaf.","log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.1403,0.0151,not harmonised,reported,-0.008445259092234767,0.0009089338010887026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.291390728476822,1210332.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Basement (dummy),basement,A_structural,dummy,binary,-0.0018,0.0004,,,***,,,high,,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,-0.0018,0.0004,"b (currency per unit, as reported)",reported,-0.0025707310011563707,0.0005712735558125268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,3064142.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Basement (dummy),basement,A_structural,dummy,binary,-0.0017,0.0005,,,***,,,high,,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,-0.0017,0.0005,"b (currency per unit, as reported)",reported,-0.0023936021090791935,0.0007040006203174099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4,2017676.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Basement (dummy),basement,A_structural,dummy,binary,0.0009,0.0014,,,,,,high,,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.0009,0.0014,"b (currency per unit, as reported)",reported,0.0004907009904627081,0.0007633126518308794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6428571428571428,1716307.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Basement (dummy),basement,A_structural,dummy,binary,0.0016,0.0014,,,,,,high,,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.0016,0.0014,"b (currency per unit, as reported)",reported,0.0010388179726182369,0.0009089657260409571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.142857142857143,1210332.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Balcony (dummy),terrace_balcony,C_exterior,dummy,binary,0.02,0.0035,,,***,,,high,Balcony or terrace dummy.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.02,0.0035,"b (currency per unit, as reported)",reported,0.0032644137121176525,0.0005712723996205892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.714285714285714,3064142.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Balcony (dummy),terrace_balcony,C_exterior,dummy,binary,0.0169,0.0052,,,***,,,high,Balcony or terrace dummy.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.0169,0.0052,"b (currency per unit, as reported)",reported,0.0022880025816005294,0.0007040007943386245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,2017676.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Balcony (dummy),terrace_balcony,C_exterior,dummy,binary,0.0329,0.0039,,,***,,,high,Balcony or terrace dummy.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.0329,0.0039,"b (currency per unit, as reported)",reported,0.006439094524789808,0.0007632969193519833,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.435897435897436,1716307.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Balcony (dummy),terrace_balcony,C_exterior,dummy,binary,0.0262,0.0037,,,***,,,high,Balcony or terrace dummy.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.0262,0.0037,"b (currency per unit, as reported)",reported,0.006436330157990235,0.0009089473887238118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.081081081081081,1210332.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Urban area (dummy) (Regional centers),other,other,dummy,binary,0.0442,0.0246,,,*,,,high,Region type: located in urban area / regional centre.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.0442,0.0246,"b (currency per unit, as reported)",reported,0.0010264374512611585,0.0005712751425571153,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.796747967479675,3064142.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Urban area (dummy) (Regional centers),other,other,dummy,binary,0.1112,0.0229,,,***,,,high,Region type: located in urban area / regional centre.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.1112,0.0229,"b (currency per unit, as reported)",reported,0.0034185430481222874,0.000703998523399284,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.85589519650655,2017676.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Urban area (dummy) (Regional centers),other,other,dummy,binary,0.0454,0.0465,,,,,,high,Region type: located in urban area / regional centre.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.0454,0.0465,"b (currency per unit, as reported)",reported,0.0007452556761647816,0.000763312531754677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9763440860215055,1716307.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Urban area (dummy) (Regional centers),other,other,dummy,binary,0.1922,0.0908,,,**,,,high,Region type: located in urban area / regional centre.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.1922,0.0908,"b (currency per unit, as reported)",reported,0.0019240416678352882,0.0009089645340241633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.116740088105727,1210332.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,City/metropolitan area (dummy),other,other,dummy,binary,-0.009,0.0325,,,,,,high,Region type: located in city or metropolitan area.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,-0.009,0.0325,"b (currency per unit, as reported)",reported,-0.00015819935160432766,0.0005712754363489611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2769230769230769,3064142.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,City/metropolitan area (dummy),other,other,dummy,binary,-0.1104,0.0089,,,***,,,high,Region type: located in city or metropolitan area.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,-0.1104,0.0089,"b (currency per unit, as reported)",reported,-0.008732463786155557,0.0007039757943549317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.404494382022472,2017676.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,City/metropolitan area (dummy),other,other,dummy,binary,-0.0113,0.0442,,,,,,high,Region type: located in city or metropolitan area.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,-0.0113,0.0442,"b (currency per unit, as reported)",reported,-0.00019514556198871482,0.0007633127291947961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25565610859728505,1716307.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,City/metropolitan area (dummy),other,other,dummy,binary,-0.1282,0.0344,,,***,,,high,Region type: located in city or metropolitan area.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,-0.1282,0.0344,"b (currency per unit, as reported)",reported,-0.003387465127016019,0.000908961001320991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.726744186046512,1210332.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,"West/East Germany (dummy) (1 = West, 0 = East)",other,other,dummy,binary,0.0991,0.0645,,,,,,high,Macro-region dummy (West Germany = 1).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.0991,0.0645,"b (currency per unit, as reported)",reported,0.0008777267386513267,0.0005712752234410755,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5364341085271316,3064142.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,"West/East Germany (dummy) (1 = West, 0 = East)",other,other,dummy,binary,0.2437,0.0508,,,***,,,high,Macro-region dummy (West Germany = 1).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.2437,0.0508,"b (currency per unit, as reported)",reported,0.0033772532327349144,0.0007039986221704295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.797244094488189,2017676.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,"West/East Germany (dummy) (1 = West, 0 = East)",other,other,dummy,binary,0.0982,0.0557,,,*,,,high,Macro-region dummy (West Germany = 1).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,monetary,0.0982,0.0557,"b (currency per unit, as reported)",reported,0.001345731482236285,0.0007633120525515386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7630161579892278,1716307.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,"West/East Germany (dummy) (1 = West, 0 = East)",other,other,dummy,binary,0.1968,0.0381,,,***,,,high,Macro-region dummy (West Germany = 1).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,monetary,0.1968,0.0381,"b (currency per unit, as reported)",reported,0.004695080833301781,0.0009089561979105582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.165354330708661,1210332.0,binary,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Population density (log),density,F_neighbourhood,log,per_km2,0.0441,0.0068,,,***,,,high,District-level population density (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.0441,0.0068,not harmonised,reported,0.0037048638464991725,0.0005712715228161989,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.485294117647059,3064142.0,per_km2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Population density (log),density,F_neighbourhood,log,per_km2,0.0213,0.0079,,,***,,,high,District-level population density (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.0213,0.0079,not harmonised,reported,0.0018981302729111692,0.0007040013688262084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6962025316455693,2017676.0,per_km2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Population density (log),density,F_neighbourhood,log,per_km2,0.0495,0.0114,,,***,,,high,District-level population density (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.0495,0.0114,not harmonised,reported,0.003314366077638041,0.0007633085512136095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.342105263157895,1716307.0,per_km2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Population density (log),density,F_neighbourhood,log,per_km2,0.004,0.0137,,,,,,high,District-level population density (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.004,0.0137,not harmonised,reported,0.0002653915867103019,0.0009089661844827841,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.291970802919708,1210332.0,per_km2,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Population growth (log),other,other,log,share,0.0014,0.0022,,,,,,high,District population growth (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.0014,0.0022,not harmonised,reported,0.00036353889456665014,0.0005712754057475931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6363636363636364,3064142.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Population growth (log),other,other,log,share,0.0037,0.0049,,,,,,high,District population growth (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.0037,0.0049,not harmonised,reported,0.0005315937528662663,0.00070400253757965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7551020408163266,2017676.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Population growth (log),other,other,log,share,-0.0069,0.0025,,,***,,,high,District population growth (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.0069,0.0025,not harmonised,reported,-0.0021067384974580035,0.0007633110498036246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.76,1716307.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Population growth (log),other,other,log,share,-0.0036,0.0054,,,,,,high,District population growth (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.0036,0.0054,not harmonised,reported,-0.0006059773664020984,0.0009089660496031475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666666,1210332.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Primary income per capita (log),income,F_neighbourhood,log,currency,0.2254,0.0845,,,***,,,high,District primary income per capita in EUR (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.2254,0.0845,not harmonised,reported,0.0015238501237855113,0.0005712747802124035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.667455621301775,3064142.0,currency,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Primary income per capita (log),income,F_neighbourhood,log,currency,0.1638,0.0882,,,*,,,high,District primary income per capita in EUR (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.1638,0.0882,not harmonised,reported,0.0013074323513612056,0.0007040020353483413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8571428571428572,2017676.0,currency,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Primary income per capita (log),income,F_neighbourhood,log,currency,0.6727,0.1092,,,***,,,high,District primary income per capita in EUR (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.6727,0.1092,not harmonised,reported,0.004702150238943901,0.0007633043051771578,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.16025641025641,1716307.0,currency,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Primary income per capita (log),income,F_neighbourhood,log,currency,0.5359,0.0998,,,***,,,high,District primary income per capita in EUR (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.5359,0.0998,not harmonised,reported,0.00488085363906938,0.0009089553893993732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.369739478957916,1210332.0,currency,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Students (log),other,other,log,count,-0.0092,0.0107,,,,,,high,Number of students in district (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.0092,0.0107,not harmonised,reported,-0.0004911900416970841,0.0005712753745824783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8598130841121495,3064142.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Students (log),other,other,log,count,-0.0071,0.0152,,,,,,high,Number of students in district (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.0071,0.0152,not harmonised,reported,-0.00032884331926398693,0.0007040025989876903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.46710526315789475,2017676.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Students (log),other,other,log,count,-0.0123,0.0159,,,,,,high,Number of students in district (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.0123,0.0159,not harmonised,reported,-0.0005904871139028432,0.000763312610654895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7735849056603773,1716307.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Students (log),other,other,log,count,0.0007,0.0129,,,,,,high,Number of students in district (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.0007,0.0129,not harmonised,reported,4.932374812180631e-05,0.0009089662153875733,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05426356589147287,1210332.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Unemployment rate (log),unemployment,F_neighbourhood,log,share,-0.2109,0.1154,,,*,,,high,District unemployment rate (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.2109,0.1154,not harmonised,reported,-0.0010440374815432164,0.0005712751321483509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8275563258232235,3064142.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Unemployment rate (log),unemployment,F_neighbourhood,log,share,-0.3313,0.1417,,,**,,,high,District unemployment rate (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.3313,0.1417,not harmonised,reported,-0.0016459827643383548,0.0007040016833889069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3380381086803106,2017676.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Unemployment rate (log),unemployment,F_neighbourhood,log,share,-0.2579,0.0826,,,***,,,high,District unemployment rate (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.2579,0.0826,not harmonised,reported,-0.0023832663139621408,0.0007633105759335899,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.12227602905569,1716307.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Unemployment rate (log),unemployment,F_neighbourhood,log,share,-0.3812,0.0744,,,***,,,high,District unemployment rate (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.3812,0.0744,not harmonised,reported,-0.004657179624386364,0.0009089563590093008,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.123655913978495,1210332.0,share,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,True,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Construction completions (log),other,other,log,count,0.0157,0.0029,,,***,,,high,Municipality construction completions (log); housing supply measure.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.0157,0.0029,not harmonised,reported,0.0030927522648027243,0.0005712727113329873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.413793103448276,3064142.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Construction completions (log),other,other,log,count,0.0113,0.0055,,,**,,,high,Municipality construction completions (log); housing supply measure.,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.0113,0.0055,not harmonised,reported,0.001446403904942198,0.0007040019006355829,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0545454545454547,2017676.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Construction completions (log),other,other,log,count,0.0272,0.0043,,,***,,,high,Municipality construction completions (log); housing supply measure.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.0272,0.0043,not harmonised,reported,0.0048283406083346955,0.0007633038461705584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.325581395348837,1716307.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Construction completions (log),other,other,log,count,0.0164,0.0079,,,**,,,high,Municipality construction completions (log); housing supply measure.,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.0164,0.0079,not harmonised,reported,0.0018869644824292765,0.0009089645982433709,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0759493670886076,1210332.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c1,6,1,23,Social assistance recipients (log),other,other,log,count,-0.0234,0.0527,,,,,,high,District number of social assistance recipients (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,3064152.0,0.6618,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,-0.0234,0.0527,not harmonised,reported,-0.0002536592969218321,0.0005712754251188271,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.44402277039848204,3064142.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c2,6,2,23,Social assistance recipients (log),other,other,log,count,-0.0577,0.0805,,,,,,high,District number of social assistance recipients (log).,"log rent price per sqm (EUR, net rent)",per_m2,OLS,2017686.0,0.5514,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,-0.0577,0.0805,not harmonised,reported,-0.0005046080370966528,0.0007040025474225399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7167701863354037,2017676.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c3,6,3,23,Social assistance recipients (log),other,other,log,count,0.1073,0.0625,,,*,,,high,District number of social assistance recipients (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1716317.0,0.6895,se,Full sample (regulated and unregulated municipalities); year FE + district FE; dependent variable is log of price per square metre,other,0.1073,0.0625,not harmonised,reported,0.0013104541932173525,0.0007633120883139285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7168,1716307.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7,d3cc2b5ffbf39645fc47562257a85c240474e3d7:T6-c4,6,4,23,Social assistance recipients (log),other,other,log,count,0.1747,0.0902,,,*,,,high,District number of social assistance recipients (log).,"log sale price per sqm (EUR, asking price)",per_m2,OLS,1210342.0,0.6125,se,Regulated municipalities only; year FE + district FE; dependent variable is log of price per square metre,other,0.1747,0.0902,not harmonised,reported,0.0017604894893624979,0.0009089648079021025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9368070953436807,1210332.0,count,,2024.0,Germany,Europe,High income,2010.0,2019.0,condo_apartment,listing,journal,True,True,clustered,,2014.5,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c1,3,1 (Prague),8,area_net,living_area,A_structural,level,m2,1.26e-05,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE); printed in scientific notation,log price per square metre (CZK),per_m2,OLS,4189.0,0.491,none,Prague: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,1.26e-05,,"b (currency per unit, as reported)",,,,,,4179.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c1,3,1 (Prague),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.019,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,4189.0,0.491,none,Prague: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.019,,"b (currency per unit, as reported)",,,,,,4179.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c1,3,1 (Prague),8,sqm_room,other,other,level,m2,0.015,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,4189.0,0.491,none,Prague: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.015,,"b (currency per unit, as reported)",,,,,,4179.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c1,3,1 (Prague),8,state,condition,A_structural,binary,binary,0.18,,,,***,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,4189.0,0.491,none,Prague: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.18,,"b (currency per unit, as reported)",,,,,,4179.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c1,3,1 (Prague),8,location,other,other,level,index,-0.056,,,,***,,,high,"Ordinal location attractiveness class 1 (most attractive) to 6 (least attractive), pooled Prague model only; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,4189.0,0.491,none,Prague: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.056,,"b (currency per unit, as reported)",,,,,,4179.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c1,3,1 (Prague),8,EPC,energy_efficiency,A_structural,level,index,-0.024,,,,***,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,4189.0,0.491,none,Prague: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.024,,"b (currency per unit, as reported)",,,,,,4179.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c2,3,2 (Location 1),8,area_net,living_area,A_structural,level,m2,7.83e-05,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE); printed in scientific notation,log price per square metre (CZK),per_m2,OLS,264.0,0.383,none,Location 1: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,7.83e-05,,"b (currency per unit, as reported)",,,,,,254.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c2,3,2 (Location 1),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.002,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,264.0,0.383,none,Location 1: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.002,,"b (currency per unit, as reported)",,,,,,254.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c2,3,2 (Location 1),8,sqm_room,other,other,level,m2,0.013,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,264.0,0.383,none,Location 1: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.013,,"b (currency per unit, as reported)",,,,,,254.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c2,3,2 (Location 1),8,state,condition,A_structural,binary,binary,0.266,,,,***,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,264.0,0.383,none,Location 1: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.266,,"b (currency per unit, as reported)",,,,,,254.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c2,3,2 (Location 1),8,EPC,energy_efficiency,A_structural,level,index,0.021,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,264.0,0.383,none,Location 1: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.021,,"b (currency per unit, as reported)",,,,,,254.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c3,3,3 (Location 2),8,area_net,living_area,A_structural,level,m2,0.0,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE); printed in scientific notation,log price per square metre (CZK),per_m2,OLS,364.0,0.359,none,Location 2: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.0,,"b (currency per unit, as reported)",,,,,,354.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c3,3,3 (Location 2),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.069,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,364.0,0.359,none,Location 2: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.069,,"b (currency per unit, as reported)",,,,,,354.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c3,3,3 (Location 2),8,sqm_room,other,other,level,m2,0.009,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,364.0,0.359,none,Location 2: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.009,,"b (currency per unit, as reported)",,,,,,354.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c3,3,3 (Location 2),8,state,condition,A_structural,binary,binary,0.073,,,,*,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,364.0,0.359,none,Location 2: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.073,,"b (currency per unit, as reported)",,,,,,354.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c3,3,3 (Location 2),8,EPC,energy_efficiency,A_structural,level,index,-0.001,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,364.0,0.359,none,Location 2: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.001,,"b (currency per unit, as reported)",,,,,,354.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c4,3,4 (Location 3),8,area_net,living_area,A_structural,level,m2,0.001,,,,**,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,914.0,0.482,none,Location 3: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.001,,"b (currency per unit, as reported)",,,,,,904.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c4,3,4 (Location 3),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.017,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,914.0,0.482,none,Location 3: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.017,,"b (currency per unit, as reported)",,,,,,904.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c4,3,4 (Location 3),8,sqm_room,other,other,level,m2,0.013,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,914.0,0.482,none,Location 3: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.013,,"b (currency per unit, as reported)",,,,,,904.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c4,3,4 (Location 3),8,state,condition,A_structural,binary,binary,0.18,,,,***,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,914.0,0.482,none,Location 3: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.18,,"b (currency per unit, as reported)",,,,,,904.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c4,3,4 (Location 3),8,EPC,energy_efficiency,A_structural,level,index,-0.016,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,914.0,0.482,none,Location 3: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.016,,"b (currency per unit, as reported)",,,,,,904.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c5,3,5 (Location 4),8,area_net,living_area,A_structural,level,m2,0.0,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE); printed in scientific notation,log price per square metre (CZK),per_m2,OLS,1281.0,0.548,none,Location 4: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.0,,"b (currency per unit, as reported)",,,,,,1271.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c5,3,5 (Location 4),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.035,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,1281.0,0.548,none,Location 4: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.035,,"b (currency per unit, as reported)",,,,,,1271.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c5,3,5 (Location 4),8,sqm_room,other,other,level,m2,0.015,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,1281.0,0.548,none,Location 4: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.015,,"b (currency per unit, as reported)",,,,,,1271.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c5,3,5 (Location 4),8,state,condition,A_structural,binary,binary,0.178,,,,***,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,1281.0,0.548,none,Location 4: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.178,,"b (currency per unit, as reported)",,,,,,1271.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c5,3,5 (Location 4),8,EPC,energy_efficiency,A_structural,level,index,-0.502,,,,,,,medium,"EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE); printed value -0.502 is an outlier vs other columns (possible typo for -0.052), recorded as printed",log price per square metre (CZK),per_m2,OLS,1281.0,0.548,none,Location 4: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.502,,"b (currency per unit, as reported)",,,,,,1271.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c6,3,6 (Location 5),8,area_net,living_area,A_structural,level,m2,-0.001,,,,***,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,1059.0,0.498,none,Location 5: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.001,,"b (currency per unit, as reported)",,,,,,1049.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c6,3,6 (Location 5),8,sep_kitch,kitchen_quality,B_interior,binary,binary,0.022,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,1059.0,0.498,none,Location 5: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.022,,"b (currency per unit, as reported)",,,,,,1049.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c6,3,6 (Location 5),8,sqm_room,other,other,level,m2,0.018,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,1059.0,0.498,none,Location 5: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.018,,"b (currency per unit, as reported)",,,,,,1049.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c6,3,6 (Location 5),8,state,condition,A_structural,binary,binary,0.128,,,,***,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,1059.0,0.498,none,Location 5: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.128,,"b (currency per unit, as reported)",,,,,,1049.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c6,3,6 (Location 5),8,EPC,energy_efficiency,A_structural,level,index,-0.064,,,,***,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,1059.0,0.498,none,Location 5: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.064,,"b (currency per unit, as reported)",,,,,,1049.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c7,3,7 (Location 6),8,area_net,living_area,A_structural,level,m2,-0.0,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE); printed in scientific notation,log price per square metre (CZK),per_m2,OLS,307.0,0.487,none,Location 6: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.0,,"b (currency per unit, as reported)",,,,,,297.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c7,3,7 (Location 6),8,sep_kitch,kitchen_quality,B_interior,binary,binary,0.036,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,307.0,0.487,none,Location 6: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.036,,"b (currency per unit, as reported)",,,,,,297.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c7,3,7 (Location 6),8,sqm_room,other,other,level,m2,0.016,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,307.0,0.487,none,Location 6: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.016,,"b (currency per unit, as reported)",,,,,,297.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c7,3,7 (Location 6),8,state,condition,A_structural,binary,binary,0.178,,,,***,,,high,"Technical condition dummy: 1 = after reconstruction, project, under construction, new construction; 0 = good, very good, under/before reconstruction; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,307.0,0.487,none,Location 6: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,0.178,,"b (currency per unit, as reported)",,,,,,297.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T3-c7,3,7 (Location 6),8,EPC,energy_efficiency,A_structural,level,index,-0.026,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,307.0,0.487,none,Location 6: full sample of sale ads (Table 3 spans pages 7-8); dependent variable is the natural log of asking price per m2,monetary,-0.026,,"b (currency per unit, as reported)",,,,,,297.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c1,4,1 (Prague),8,area_net,living_area,A_structural,level,m2,3.71e-05,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE); printed in scientific notation,log price per square metre (CZK),per_m2,OLS,2557.0,0.393,none,Prague: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,3.71e-05,,"b (currency per unit, as reported)",,,,,,2547.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c1,4,1 (Prague),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.055,,,,*,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,2557.0,0.393,none,Prague: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.055,,"b (currency per unit, as reported)",,,,,,2547.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c1,4,1 (Prague),8,sqm_room,other,other,level,m2,0.014,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,2557.0,0.393,none,Prague: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.014,,"b (currency per unit, as reported)",,,,,,2547.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c1,4,1 (Prague),8,location,other,other,level,index,-0.056,,,,***,,,high,"Ordinal location attractiveness class 1 (most attractive) to 6 (least attractive), pooled Prague model only; only point estimate and stars printed (no SE)",log price per square metre (CZK),per_m2,OLS,2557.0,0.393,none,Prague: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.056,,"b (currency per unit, as reported)",,,,,,2547.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c1,4,1 (Prague),8,EPC,energy_efficiency,A_structural,level,index,-0.08,,,,***,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,2557.0,0.393,none,Prague: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.08,,"b (currency per unit, as reported)",,,,,,2547.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c2,4,2 (Location 1),8,area_net,living_area,A_structural,level,m2,0.001,,,,**,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,131.0,0.253,none,Location 1: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.001,,"b (currency per unit, as reported)",,,,,,121.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c2,4,2 (Location 1),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.189,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,131.0,0.253,none,Location 1: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.189,,"b (currency per unit, as reported)",,,,,,121.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c2,4,2 (Location 1),8,sqm_room,other,other,level,m2,0.006,,,,**,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,131.0,0.253,none,Location 1: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.006,,"b (currency per unit, as reported)",,,,,,121.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c2,4,2 (Location 1),8,EPC,energy_efficiency,A_structural,level,index,-0.039,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,131.0,0.253,none,Location 1: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.039,,"b (currency per unit, as reported)",,,,,,121.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c3,4,3 (Location 2),8,area_net,living_area,A_structural,level,m2,0.001,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,237.0,0.301,none,Location 2: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.001,,"b (currency per unit, as reported)",,,,,,227.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c3,4,3 (Location 2),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.022,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,237.0,0.301,none,Location 2: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.022,,"b (currency per unit, as reported)",,,,,,227.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c3,4,3 (Location 2),8,sqm_room,other,other,level,m2,0.008,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,237.0,0.301,none,Location 2: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.008,,"b (currency per unit, as reported)",,,,,,227.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c3,4,3 (Location 2),8,EPC,energy_efficiency,A_structural,level,index,-0.028,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,237.0,0.301,none,Location 2: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.028,,"b (currency per unit, as reported)",,,,,,227.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c4,4,4 (Location 3),8,area_net,living_area,A_structural,level,m2,0.001,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,472.0,0.387,none,Location 3: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.001,,"b (currency per unit, as reported)",,,,,,462.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c4,4,4 (Location 3),8,sep_kitch,kitchen_quality,B_interior,binary,binary,0.034,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,472.0,0.387,none,Location 3: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.034,,"b (currency per unit, as reported)",,,,,,462.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c4,4,4 (Location 3),8,sqm_room,other,other,level,m2,0.01,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,472.0,0.387,none,Location 3: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.01,,"b (currency per unit, as reported)",,,,,,462.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c4,4,4 (Location 3),8,EPC,energy_efficiency,A_structural,level,index,-0.065,,,,***,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,472.0,0.387,none,Location 3: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.065,,"b (currency per unit, as reported)",,,,,,462.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c5,4,5 (Location 4),8,area_net,living_area,A_structural,level,m2,0.001,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,818.0,0.467,none,Location 4: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.001,,"b (currency per unit, as reported)",,,,,,808.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c5,4,5 (Location 4),8,sep_kitch,kitchen_quality,B_interior,binary,binary,-0.084,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,818.0,0.467,none,Location 4: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.084,,"b (currency per unit, as reported)",,,,,,808.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c5,4,5 (Location 4),8,sqm_room,other,other,level,m2,0.015,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,818.0,0.467,none,Location 4: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.015,,"b (currency per unit, as reported)",,,,,,808.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c5,4,5 (Location 4),8,EPC,energy_efficiency,A_structural,level,index,-0.139,,,,***,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,818.0,0.467,none,Location 4: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.139,,"b (currency per unit, as reported)",,,,,,808.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c6,4,6 (Location 5),8,area_net,living_area,A_structural,level,m2,-0.001,,,,***,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,678.0,0.408,none,Location 5: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.001,,"b (currency per unit, as reported)",,,,,,668.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c6,4,6 (Location 5),8,sep_kitch,kitchen_quality,B_interior,binary,binary,0.013,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,678.0,0.408,none,Location 5: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.013,,"b (currency per unit, as reported)",,,,,,668.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c6,4,6 (Location 5),8,sqm_room,other,other,level,m2,0.018,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,678.0,0.408,none,Location 5: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.018,,"b (currency per unit, as reported)",,,,,,668.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c6,4,6 (Location 5),8,EPC,energy_efficiency,A_structural,level,index,-0.142,,,,***,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,678.0,0.408,none,Location 5: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.142,,"b (currency per unit, as reported)",,,,,,668.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c7,4,7 (Location 6),8,area_net,living_area,A_structural,level,m2,-0.001,,,,,,,high,Total net area of the apartment in m2; dependent variable is price per m2; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,221.0,0.423,none,Location 6: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.001,,"b (currency per unit, as reported)",,,,,,211.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c7,4,7 (Location 6),8,sep_kitch,kitchen_quality,B_interior,binary,binary,0.145,,,,,,,high,Dummy = 1 if the apartment has a separate kitchen; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,221.0,0.423,none,Location 6: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.145,,"b (currency per unit, as reported)",,,,,,211.0,binary,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c7,4,7 (Location 6),8,sqm_room,other,other,level,m2,0.016,,,,***,,,high,Average square metres per room (area_net / rooms); printed name sqm_room; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,221.0,0.423,none,Location 6: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,0.016,,"b (currency per unit, as reported)",,,,,,211.0,m2,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e,d3e27cac15a096168d22a145ab49342c924dc96e:T4-c7,4,7 (Location 6),8,EPC,energy_efficiency,A_structural,level,index,-0.055,,,,,,,high,EPC class coded 1=A (most efficient) to 6=F (least efficient); class G excluded; one-step worse class effect; only point estimate and stars printed (no SE),log price per square metre (CZK),per_m2,OLS,221.0,0.423,none,Location 6: subsample with state = 1 (new construction / after reconstruction / project / under construction); dependent variable is the natural log of asking price per m2,monetary,-0.055,,"b (currency per unit, as reported)",,,,,,211.0,index,,2025.0,Czech Republic,Europe,High income,2023.0,2023.0,condo_apartment,listing,journal,False,False,OLS,,2023.0,ols,journal,False,False,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,Age,age,A_structural,level,years,-0.007867,,-12.55,,***,,,high,Age of house in years; quadratic with Age_2,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,semi_elasticity,-0.7867000000000001,0.06268525896414343,100*b/1 (per year),|b/t|,-0.12611630712227637,0.010049108137233176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.55,9745.0,years,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Age,age,A_structural,level,years,-0.007734,,-12.18,,***,,,high,Age of house in years; quadratic with Age_2,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,semi_elasticity,-0.7734000000000001,0.06349753694581281,100*b/1 (per year),|b/t|,-0.12245472543033066,0.010053754140421237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.18,9745.0,years,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Age,age,A_structural,level,years,-0.007778,,-12.36,,***,,,high,Age of house in years; quadratic with Age_2,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,semi_elasticity,-0.7778,0.06292880258899677,100*b/1 (per year),|b/t|,-0.1242366695759682,0.010051510483492572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.36,9745.0,years,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,Age_2,age,A_structural,squared,years,8.6e-05,,11.63,,***,,,high,Age squared,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,other,8.6e-05,7.3946689595872745e-06,not harmonised,|b/t|,0.11700262252599355,0.01006041466259618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.63,9745.0,years,nonstandard_x_transform,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,False,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Age_2,age,A_structural,squared,years,8.7e-05,,11.65,,***,,,high,Age squared,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,other,8.7e-05,7.467811158798283e-06,not harmonised,|b/t|,0.11720106934373048,0.010060177626071285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.65,9745.0,years,nonstandard_x_transform,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,False,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Age_2,age,A_structural,squared,years,8.5e-05,,11.41,,***,,,high,Age squared,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,other,8.5e-05,7.449605609114812e-06,not harmonised,|b/t|,0.1148187874236188,0.010062996268502962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.41,9745.0,years,nonstandard_x_transform,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,False,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,Bedrooms,bedrooms,A_structural,level,count,0.002218,,0.63,,,,,high,,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,semi_elasticity,0.2218,0.3520634920634921,100*b/1 (per unit count),|b/t|,0.00638176464692709,0.010129785153852523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.63,9745.0,count,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Bedrooms,bedrooms,A_structural,level,count,0.00225,,0.64,,,,,high,,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,semi_elasticity,0.22499999999999998,0.35156249999999994,100*b/1 (per unit count),|b/t|,0.006483058274173157,0.010129778553395558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.64,9745.0,count,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Bedrooms,bedrooms,A_structural,level,count,0.00354,,1.01,,,,,high,,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,semi_elasticity,0.35400000000000004,0.35049504950495053,100*b/1 (per unit count),|b/t|,0.0102307558917183,0.01012946127892901,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.01,9745.0,count,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,Bathrooms,bathrooms,A_structural,level,count,0.042084,,8.05,,***,,,high,,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,semi_elasticity,4.2084,0.5227826086956522,100*b/1 (per unit count),|b/t|,0.08127664130856393,0.010096477181188066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.05,9745.0,count,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Bathrooms,bathrooms,A_structural,level,count,0.045153,,8.55,,***,,,high,,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,semi_elasticity,4.5153,0.5281052631578946,100*b/1 (per unit count),|b/t|,0.08628838364162472,0.010092208613055523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.55,9745.0,count,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Bathrooms,bathrooms,A_structural,level,count,0.041481,,7.92,,***,,,high,,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,semi_elasticity,4.1480999999999995,0.52375,100*b/1 (per unit count),|b/t|,0.07997256222937982,0.010097545736032805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.92,9745.0,count,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,SqftStruc,living_area,A_structural,level,sqft,0.000226,,47.76,,***,,,high,Square footage of house,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,semi_elasticity,0.2432643754176397,0.005093475197186761,100*b/0.092903 (X per sqft->m2),|b/t|,0.43551528380396737,0.009118829225376202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.76,9745.0,sqft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,SqftStruc,living_area,A_structural,level,sqft,0.000225,,46.92,,***,,,high,Square footage of house,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,semi_elasticity,0.24218798437596872,0.005161721747143408,100*b/0.092903 (X per sqft->m2),|b/t|,0.42927728334424325,0.009149132211087877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),46.92,9745.0,sqft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,SqftStruc,living_area,A_structural,level,sqft,0.000226,,47.68,,***,,,high,Square footage of house,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,semi_elasticity,0.2432643754176397,0.005102021296510899,100*b/0.092903 (X per sqft->m2),|b/t|,0.43492386382327136,0.009121725331863914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.68,9745.0,sqft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,LotSqft,lot_size,A_structural,level,sqft,4.84e-08,,2.24,,**,,,high,Square footage of lot,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,semi_elasticity,5.2097326416875056e-05,2.3257735007533503e-05,100*b/0.092903 (X per sqft->m2),|b/t|,0.02268534134770862,0.010127384530227062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.24,9745.0,sqft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,LotSqft,lot_size,A_structural,level,sqft,4.73e-08,,2.16,,**,,,high,Square footage of lot,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,semi_elasticity,5.091329627103698e-05,2.3570970495850454e-05,100*b/0.092903 (X per sqft->m2),|b/t|,0.021875545469783056,0.010127567347121783,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.16,9745.0,sqft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,LotSqft,lot_size,A_structural,level,sqft,4.87e-08,,2.24,,**,,,high,Square footage of lot,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,semi_elasticity,5.2420243729376345e-05,2.3401894522043008e-05,100*b/0.092903 (X per sqft->m2),|b/t|,0.02268534134770862,0.010127384530227062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.24,9745.0,sqft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,LnDtownDist,cbd_distance,D_location,log,feet,0.175223,,7.66,,***,,,high,Log distance in feet to downtown San Diego,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,elasticity,0.175223,0.022875065274151434,b (log-log),|b/t|,0.07736317830859459,0.010099631632975793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.66,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,LnDtownDist,cbd_distance,D_location,log,feet,0.076875,,3.29,,***,,,high,Log distance in feet to downtown San Diego,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,elasticity,0.076875,0.02336626139817629,b (log-log),|b/t|,0.0333091781502552,0.010124370258436231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.29,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,LnDtownDist,cbd_distance,D_location,log,feet,0.075096,,3.25,,***,,,high,Log distance in feet to downtown San Diego,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,elasticity,0.075096,0.023106461538461538,b (log-log),|b/t|,0.03290464450780988,0.01012450600240304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.25,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,LnFreeDist,highway_distance,D_location,log,feet,0.010732,,3.06,,***,,,high,Log distance in feet to nearest freeway,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,elasticity,0.010732,0.00350718954248366,b (log-log),|b/t|,0.030982892231471992,0.010125128180219605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.06,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,LnFreeDist,highway_distance,D_location,log,feet,0.015182,,4.17,,***,,,high,Log distance in feet to nearest freeway,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,elasticity,0.015182,0.003640767386091127,b (log-log),|b/t|,0.04220442645721015,0.01012096557726862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.17,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,LnFreeDist,highway_distance,D_location,log,feet,0.009351,,2.57,,***,,,high,Log distance in feet to nearest freeway,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,elasticity,0.009351,0.00363852140077821,b (log-log),|b/t|,0.026025259876567508,0.01012656026325584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.57,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,Dist_API,school_quality,F_neighbourhood,level,index,0.000956,,5.87,,***,,,high,Elementary school district base Academic Performance Index (200-1000 scale),Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,semi_elasticity,0.0956,0.016286201022146507,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.05935820136394029,0.01011212970424877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.87,9745.0,index,unit_not_harmonisable,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,False,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Dist_API,school_quality,F_neighbourhood,level,index,0.001068,,6.48,,***,,,high,Elementary school district base Academic Performance Index (200-1000 scale),Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,semi_elasticity,0.10679999999999999,0.01648148148148148,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.06550137619820881,0.010108237067624816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.48,9745.0,index,unit_not_harmonisable,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,False,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Dist_API,school_quality,F_neighbourhood,level,index,0.001046,,6.41,,***,,,high,Elementary school district base Academic Performance Index (200-1000 scale),Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,semi_elasticity,0.1046,0.016318252730109204,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/t|,0.06479678660385922,0.010108703058324372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.41,9745.0,index,unit_not_harmonisable,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,False,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c1,2,Model 1,9,LnCoastDist,water_proximity,E_environment,log,feet,-0.146047,,-21.12,,***,,,high,Log distance in feet from house to coast,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7798,t,All sales; neighbourhood fixed effects; coastal specification: log coastal distance,elasticity,-0.146047,0.006915104166666667,b (log-log),|b/t|,-0.2092109222406618,0.009905820181849516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.12,9745.0,ft,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Coast_0to1miles,water_proximity,E_environment,dummy,binary,0.352211,,13.4,,***,,,high,Distance band dummy: residence within 1 mile of coast of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,pct_premium,42.17173824080649,3.7381903394866614,100*(exp(b - se^2/2) - 1),|b/t|,0.13450832592303835,0.010037934770375997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.4,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Coast_2miles,water_proximity,E_environment,dummy,binary,0.200634,,7.92,,***,,,high,Distance band dummy: residence 1 to 2 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,pct_premium,22.17852762614829,3.0960900891624643,100*(exp(b - se^2/2) - 1),|b/t|,0.07997256222937982,0.010097545736032805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.92,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Coast_3miles,water_proximity,E_environment,dummy,binary,0.118684,,5.24,,***,,,high,Distance band dummy: residence 2 to 3 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,pct_premium,12.572525312974946,2.550378826646916,100*(exp(b - se^2/2) - 1),|b/t|,0.05300653181966913,0.0101157503472651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.24,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Coast_4miles,water_proximity,E_environment,dummy,binary,0.082033,,4.13,,***,,,high,Distance band dummy: residence 3 to 4 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,pct_premium,8.527752352742368,2.1560807487097997,100*(exp(b - se^2/2) - 1),|b/t|,0.041800298613892284,0.010121137678908543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.13,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Coast_5miles,water_proximity,E_environment,dummy,binary,0.053282,,3.0,,***,,,high,Distance band dummy: residence 4 to 5 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,pct_premium,5.456069710394185,1.8732655314437352,100*(exp(b - se^2/2) - 1),|b/t|,0.030375950687208667,0.010125316895736222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c2,2,Model 2,9,Coast_6miles,water_proximity,E_environment,dummy,binary,0.035755,,2.59,,***,,,high,Distance band dummy: residence 5 to 6 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7749,t,All sales; neighbourhood fixed effects; coastal specification: 1-mile distance bands,pct_premium,3.6303143634247803,1.4307548194720268,100*(exp(b - se^2/2) - 1),|b/t|,0.026227652300238,0.010126506679628573,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.59,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_500ft,water_proximity,E_environment,dummy,binary,0.702698,,15.43,,***,,,high,Distance band dummy: residence within 500 ft of coast of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,101.71003454128935,9.195612357772582,100*(exp(b - se^2/2) - 1),|b/t|,0.15443066578674985,0.010008468294669465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.43,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,True
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_1000ft,water_proximity,E_environment,dummy,binary,0.487333,,11.11,,***,,,high,"Distance band dummy: residence 500 to 1,000 ft of coast; reference beyond 6 miles",Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,62.64032134202948,7.140979636998334,100*(exp(b - se^2/2) - 1),|b/t|,0.11183815150162023,0.010066440279173737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.11,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_2000ft,water_proximity,E_environment,dummy,binary,0.454806,,13.67,,***,,,high,"Distance band dummy: residence 1,000 to 2,000 ft of coast; reference beyond 6 miles",Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,57.49956984603286,5.242970414260189,100*(exp(b - se^2/2) - 1),|b/t|,0.13716806918622662,0.01003424061347671,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.67,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_3000ft,water_proximity,E_environment,dummy,binary,0.438393,,13.97,,***,,,high,"Distance band dummy: residence 2,000 to 3,000 ft of coast; reference beyond 6 miles",Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,54.945090829135545,4.864731393505186,100*(exp(b - se^2/2) - 1),|b/t|,0.14011986136541743,0.01003005450002988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.97,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_4000ft,water_proximity,E_environment,dummy,binary,0.293654,,9.38,,***,,,high,"Distance band dummy: residence 3,000 to 4,000 ft of coast; reference beyond 6 miles",Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,34.0662580433472,4.199188731739317,100*(exp(b - se^2/2) - 1),|b/t|,0.09459325353887618,0.010084568607556096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.38,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_5280ft,water_proximity,E_environment,dummy,binary,0.20111,,6.77,,***,,,high,"Distance band dummy: residence 4,000 to 5,280 ft of coast; reference beyond 6 miles",Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,22.221987513302576,3.632335544704507,100*(exp(b - se^2/2) - 1),|b/t|,0.06841933510448509,0.010106253338919511,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.77,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_2miles,water_proximity,E_environment,dummy,binary,0.193321,,7.71,,***,,,high,Distance band dummy: residence 1 to 2 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,21.28908540811516,3.042165931412079,100*(exp(b - se^2/2) - 1),|b/t|,0.07786510806736839,0.010099235806403165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.71,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_3miles,water_proximity,E_environment,dummy,binary,0.115352,,5.15,,***,,,high,Distance band dummy: residence 2 to 3 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,12.198692618929297,2.5137068976762715,100*(exp(b - se^2/2) - 1),|b/t|,0.05209860693534219,0.010116234356377124,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.15,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_4miles,water_proximity,E_environment,dummy,binary,0.082574,,4.2,,***,,,high,Distance band dummy: residence 3 to 4 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,8.58691571829917,2.1352831113077055,100*(exp(b - se^2/2) - 1),|b/t|,0.042507508756754346,0.010120835418274843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_5miles,water_proximity,E_environment,dummy,binary,0.053941,,3.07,,***,,,high,Distance band dummy: residence 4 to 5 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,5.525942930661798,1.8544148509491238,100*(exp(b - se^2/2) - 1),|b/t|,0.031084045841652815,0.010125096365359224,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.07,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1,d458e3973f160efbbd5c7fd28ec076d63dc933e1:T2-c3,2,Model 3,9,Coast_6miles,water_proximity,E_environment,dummy,binary,0.033207,,2.43,,**,,,high,Distance band dummy: residence 5 to 6 miles of coast; reference beyond 6 miles,Ln_Price (natural log of sales price),log,OLS,9755.0,0.7795,t,All sales; neighbourhood fixed effects; coastal specification: 500/1000-ft and mile bands,pct_premium,3.366798617668043,1.4126838667609076,100*(exp(b - se^2/2) - 1),|b/t|,0.024608424689525562,0.010126923740545498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.43,9745.0,binary,,2011.0,United States,North America,High income,2006.0,2006.0,single_family,transaction,journal,False,True,unknown,,2006.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Age,age,A_structural,level,years,-0.0055,,,0.006,,,,high,Age in years; model also includes Age-squared; p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,semi_elasticity,-0.5499999999999999,0.2001614840661458,100*b/1 (per year),|b/z(p)| (two-sided normal),-0.10003179876667992,0.0364045696271722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7477813854449926,747.0,years,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Age,age,A_structural,level,years,-0.0084,,,0.0,,,,high,Age in years; model also includes Age-squared; p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",semi_elasticity,-0.84,,100*b/1 (per year),,,,,,747.0,years,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Age-squared,age,A_structural,squared,years,0.0001,,,0.0,,,,high,Square of building age; p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,other,0.0001,,not harmonised,,,,,,747.0,years,nonstandard_x_transform,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Age-squared,age,A_structural,squared,years,0.0001,,,0.0,,,,high,Square of building age; p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",other,0.0001,,not harmonised,,,,,,747.0,years,nonstandard_x_transform,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Surface,living_area,A_structural,level,m2,0.0038,,,0.0,,,,high,Total built surface area (m2); p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,semi_elasticity,0.38,,100*b/1 (X per m2->m2),,,,,,747.0,m2,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Surface,living_area,A_structural,level,m2,0.0038,,,0.0,,,,high,Total built surface area (m2); p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",semi_elasticity,0.38,,100*b/1 (X per m2->m2),,,,,,747.0,m2,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Bedrooms,bedrooms,A_structural,level,count,0.0596,,,0.009,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,semi_elasticity,5.96,2.281729121125767,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.09513657936908908,0.036422131481668354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6120541412292777,747.0,count,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Bedrooms,bedrooms,A_structural,level,count,0.0719,,,0.0,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",semi_elasticity,7.19,,100*b/1 (per unit count),,,,,,747.0,count,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,WC,bathrooms,A_structural,level,count,0.1348,,,0.0,,,,high,Number of additional toilets apart from the main bathroom; p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,semi_elasticity,13.48,,100*b/1 (per unit count),,,,,,747.0,count,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,WC,bathrooms,A_structural,level,count,0.0768,,,0.001,,,,high,Number of additional toilets apart from the main bathroom; p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",semi_elasticity,7.68,2.3339728337408028,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.11953090945485606,0.03632576763801638,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,747.0,count,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Storage room,storage,B_interior,dummy,binary,0.0811,,,0.008,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,8.397239323564754,3.3163257670367465,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.09658054200383905,0.03641704366757784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.652069807902196,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Storage room,storage,B_interior,dummy,binary,0.0314,,,0.02,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,3.180418728475609,1.3928098730617402,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08480995483540665,0.036456265110554026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3263478740408408,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Air conditioned,air_conditioning,A_structural,dummy,binary,0.0789,,,0.005,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,8.166873236324612,3.041551683809494,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.10216657608391978,0.03639663235836301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Air conditioned,air_conditioning,A_structural,dummy,binary,0.0298,,,0.019,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,3.0165317026883853,1.3089319475642474,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08550420677015254,0.03645409411956984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3455309708066743,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,View,view,E_environment,dummy,binary,0.0963,,,0.0,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,10.108934125228885,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,View,view,E_environment,dummy,binary,0.0679,,,0.003,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,6.997819206044853,2.4486844477775755,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.10794933148787654,0.03637428041273032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Fireplace,fireplace,B_interior,dummy,binary,0.2052,,,0.0,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,22.77705945283084,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Fireplace,fireplace,B_interior,dummy,binary,0.1107,,,0.0,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,11.705973866014485,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Heat,heating,A_structural,dummy,binary,0.1758,,,0.015,,,,high,Has heating; p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,18.908983271958178,8.616612942842512,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08864573751918361,0.03644404732327045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4323790585844467,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Heat,heating,A_structural,dummy,binary,0.0984,,,0.08,,,,high,Has heating; p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,10.166251119874437,6.201852013132323,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.06392325106286231,0.03651325735238272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7506860712521697,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Elevator,accessibility,B_interior,dummy,binary,0.0761,,,0.005,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,7.8674002408143595,2.925410593032093,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.10216657608391978,0.03639663235836301,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Elevator,accessibility,B_interior,dummy,binary,0.0734,,,0.002,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,7.585738924754404,2.556125344297839,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11234983628649975,0.03635643704269741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.090232306167813,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,True,True,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Furnished,other,other,dummy,binary,0.1493,,,0.0,,,,high,Furnished dwelling; p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,16.102124334135624,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Furnished,other,other,dummy,binary,0.1279,,,0.0,,,,high,Furnished dwelling; p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,13.643935307936616,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-OLS,2,OLS,9,Garage,garage,A_structural,dummy,binary,0.1867,,,0.0,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,OLS,757.0,0.7504,p,All 757 apartments; RMSE 0.3490,pct_premium,20.526565111221263,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,ols,journal,False,False,False
d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3,d66dcd3e24d3aa705a9e17810be910446c18dce3:T2-RK,2,RK,9,Garage,garage,A_structural,dummy,binary,0.1775,,,0.0,,,,high,p-value printed in parentheses,log monthly rental price (EUR),log,other,757.0,0.8434,p,"All 757 apartments; regression-kriging (GLS, exponential variogram); RMSE 0.2768",pct_premium,19.422805790245956,,100*(exp(b) - 1),,,,,,747.0,binary,,2021.0,Greece,Europe,High income,2014.0,2014.0,condo_apartment,rent,journal,False,False,unknown,,2014.0,other,journal,False,False,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,numbed,bedrooms,A_structural,level,count,993.201,102.513,9.69,0.0,,,,high,,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,993.201,102.513,"b (currency per unit, as reported)",reported,0.4528353318926957,0.046732232393467056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.69,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,numbath,bathrooms,A_structural,level,count,-33.984,154.712,-0.22,0.826,,,,high,,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,-33.984,154.712,"b (currency per unit, as reported)",reported,-0.011530366650100609,0.05241075750045731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.22,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,numparking,parking,A_structural,level,count,458.98,120.699,3.8,0.0,,,,high,On-site parking spaces,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,458.98,120.699,"b (currency per unit, as reported)",reported,0.19533725466737573,0.05140454070194099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,age,age,A_structural,level,years,-2.048,3.591,-0.57,0.569,,,,high,Age of building in years,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,-2.048,3.591,"b (currency per unit, as reported)",reported,-0.029862793317094297,0.0523908654685865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.57,364.0,years,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,disbustop,transit_distance,D_location,level,km,-229.313,238.745,-0.96,0.337,,,,high,Distance to nearest bus stop,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,-229.313,238.745,"b (currency per unit, as reported)",reported,-0.05025409401052559,0.0523480145942975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.96,364.0,km,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,distrain,transit_distance,D_location,level,km,-139.868,56.781,-2.46,0.014,,,,high,Distance to nearest train station,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,-139.868,56.781,"b (currency per unit, as reported)",reported,-0.1278803934934635,0.051983899794090856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.46,364.0,km,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,discbd,cbd_distance,D_location,level,km,-85.066,15.166,-5.61,0.0,,,,high,Distance to Melbourne CBD,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,-85.066,15.166,"b (currency per unit, as reported)",reported,-0.28210120173641084,0.050285419204351305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.61,364.0,km,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,numbustp,transit_distance,D_location,level,count,6.29,1.054,5.97,0.0,,,,high,"Number of bus stops within 5 km (density, not distance)","price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,6.29,1.054,"b (currency per unit, as reported)",reported,0.29863407947164117,0.050022458872971715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.97,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,numhospital,hospital_distance,D_location,level,count,18.535,24.754,0.75,0.454,,,,high,"Number of hospitals within 5 km (count, not distance)","price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,18.535,24.754,"b (currency per unit, as reported)",reported,0.03928034255401342,0.0523737900720179,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,numschool,school_distance,D_location,level,count,8.267,4.282,1.93,0.054,,,,high,"Number of schools within 5 km (count, not distance)","price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,8.267,4.282,"b (currency per unit, as reported)",reported,0.10064583093000193,0.05214809892746214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.93,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,medianage,demographics,F_neighbourhood,level,years,23.657,20.37,1.16,0.246,,,,high,SA1 median age,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,23.657,20.37,"b (currency per unit, as reported)",reported,0.0606884504094742,0.05231762966333983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.16,364.0,years,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,medianincome,income,F_neighbourhood,level,AUD per week,2.133,0.378,5.64,0.0,,,,high,SA1 median weekly income,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,2.133,0.378,"b (currency per unit, as reported)",reported,0.2834888239557472,0.050263975878678584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.64,364.0,aud per week,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T4-c1,4,1,11,labor,other,other,level,share,-2713.769,1362.894,-1.99,0.047,,,,high,Labour-force participation rate of SA1,"price (unit transaction price, AUD per m2)",per_m2,OLS,374.0,0.738,se,"Complete-case sample; F(13,360)=77.96; adj R2 0.728",monetary,-2713.769,1362.894,"b (currency per unit, as reported)",reported,-0.10374154517798453,0.05213142973768067,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.99,364.0,share,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,ols,other,False,False,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T6-c1,6,1,14,numbed,bedrooms,A_structural,level,count,874.4881,80.2898,10.8916,,,,,medium,Single GWR 'Estimate/Standard Error/t(Est/SE)' reported; interpretation as a global estimate uncertain,"price (unit transaction price, AUD per m2)",per_m2,GWR,374.0,,se,"GWR with spatially varying covariates only (numbed, numbath, distrain, numbustp, numhospital, medianincome); single estimate per variable reported",monetary,874.4881,80.2898,"b (currency per unit, as reported)",reported,0.4957764556351468,0.045519157482385206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.8916,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,spatial,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T6-c1,6,1,14,numbath,bathrooms,A_structural,level,count,82.3344,121.2878,0.6788,,,,,medium,Single GWR 'Estimate/Standard Error/t(Est/SE)' reported; interpretation as a global estimate uncertain,"price (unit transaction price, AUD per m2)",per_m2,GWR,374.0,,se,"GWR with spatially varying covariates only (numbed, numbath, distrain, numbustp, numhospital, medianincome); single estimate per variable reported",monetary,82.3344,121.2878,"b (currency per unit, as reported)",reported,0.03555629000885084,0.05238109901127113,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6788,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,spatial,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T6-c1,6,1,14,distrain,transit_distance,D_location,level,km,-12.1714,8.2071,-1.483,,,,,medium,Single GWR 'Estimate/Standard Error/t(Est/SE)' reported; interpretation as a global estimate uncertain,"price (unit transaction price, AUD per m2)",per_m2,GWR,374.0,,se,"GWR with spatially varying covariates only (numbed, numbath, distrain, numbustp, numhospital, medianincome); single estimate per variable reported",monetary,-12.1714,8.2071,"b (currency per unit, as reported)",reported,-0.0774965560106533,0.05225661227960439,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.483,364.0,km,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,spatial,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T6-c1,6,1,14,numbustp,transit_distance,D_location,level,count,9.3065,0.9283,10.0254,,,,,medium,Single GWR 'Estimate/Standard Error/t(Est/SE)' reported; interpretation as a global estimate uncertain,"price (unit transaction price, AUD per m2)",per_m2,GWR,374.0,,se,"GWR with spatially varying covariates only (numbed, numbath, distrain, numbustp, numhospital, medianincome); single estimate per variable reported",monetary,9.3065,0.9283,"b (currency per unit, as reported)",reported,0.4651626208517077,0.04639841012345719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.0254,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,spatial,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T6-c1,6,1,14,numhospital,hospital_distance,D_location,level,count,56.8417,2.9904,19.0082,,,,,medium,Single GWR 'Estimate/Standard Error/t(Est/SE)' reported; interpretation as a global estimate uncertain,"price (unit transaction price, AUD per m2)",per_m2,GWR,374.0,,se,"GWR with spatially varying covariates only (numbed, numbath, distrain, numbustp, numhospital, medianincome); single estimate per variable reported",monetary,56.8417,2.9904,"b (currency per unit, as reported)",reported,0.7057951360575476,0.03713108742845444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.0082,364.0,count,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,spatial,other,False,True,False
d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0,d8941587e0ea17530b8486a47a4da824ad437bf0:T6-c1,6,1,14,medianincome,income,F_neighbourhood,level,AUD per week,1.6684,0.3339,4.9972,,,,,medium,Single GWR 'Estimate/Standard Error/t(Est/SE)' reported; interpretation as a global estimate uncertain,"price (unit transaction price, AUD per m2)",per_m2,GWR,374.0,,se,"GWR with spatially varying covariates only (numbed, numbath, distrain, numbustp, numhospital, medianincome); single estimate per variable reported",monetary,1.6684,0.3339,"b (currency per unit, as reported)",reported,0.25337721423124615,0.0507038369949664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.9972,364.0,aud per week,,2025.0,Australia,Oceania,High income,,,mixed,transaction,conference,False,False,OLS,,2022.0,spatial,other,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,area,living_area,A_structural,level,m2,0.0227,,,0.0001,,,,high,area of the dwelling in m2 (mean 52.86); entered with area2 (squared term); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,2.27,0.5834587811503436,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,m2,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,area2,living_area,A_structural,squared,m2,-0.0001,,,0.0001,,,,high,square of dwelling area (m2^2); pairs with 'area'; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,-0.0001,2.570303000662307e-05,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,m2,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,basement,basement,A_structural,dummy,binary,0.0294,,,0.0001,,,,high,1 if apartment has basement or storage room; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.980704342563767,0.7782155624717358,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,cons1960,age,A_structural,dummy,binary,0.0147,,,0.0001,,,,high,construction period dummy: built 1960-1969 (base cons1950 = built 1950-1959); vintage not age; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,1.4801332747850893,0.3834297327551238,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,cons1970,age,A_structural,dummy,binary,0.0341,,,0.0001,,,,high,construction period dummy: built 1970-1979 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,3.464832847252497,0.9068764915322208,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,cons1980,age,A_structural,dummy,binary,0.0608,,,0.0001,,,,high,construction period dummy: built 1980-1989 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,6.255660057230572,1.660706964660636,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,cons1990,age,A_structural,dummy,binary,0.1354,,,0.0001,,,,high,construction period dummy: built 1990-1999 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,14.430150755350102,3.984799373434541,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,cons2000,age,A_structural,dummy,binary,0.2273,,,0.0001,,,,high,construction period dummy: built after 2000 (base built 1950-1959); Table 3 also lists cons2010 so cons2000 likely 2000-2009; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,25.306603655556014,7.333290589131144,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,cons2010,age,A_structural,dummy,binary,0.2213,,,0.0001,,,,high,construction period dummy: built 2010+ (base built 1950-1959); cons2010 not described in Table 2 but reported; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,24.568089868024856,7.097004915199631,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,floor2,floor_level,A_structural,dummy,binary,0.0269,,,0.0001,,,,high,floor2 = 'other floors' (base floor1 = ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.7240517216530646,0.7102628910944769,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,floor3,floor_level,A_structural,dummy,binary,0.0284,,,0.0001,,,,high,floor3 = first and second floor (base ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.8779715326627153,0.7509942754830428,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,garage,garage,A_structural,other,ordinal,0.0291,,,0.0001,,,,high,"0 none, 1 parking space outside building, 2 individual space in garage (coded 0/1/2, entered as single variable); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,0.0291,0.007479581731927313,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,height,storeys,A_structural,other,ordinal,-0.0061,,,0.0001,,,,high,"building height: 1 = up to 4 floors, 2 = above 5 floors (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,-0.0061,0.0015678848304040071,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,technology,roof_materials,A_structural,other,ordinal,0.0497,,,0.0001,,,,high,"construction technology: 1 = prefabricated, 2 = traditional (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,0.0497,0.012774405913291664,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,dcc,cbd_distance,D_location,level,km,-0.0268,,,0.0001,,,,high,distance to city centre in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-2.68,0.6888412041774982,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,kindergarten,school_distance,D_location,level,km,-0.0119,,,0.0131,,,,high,distance to nearest kindergarten in km (mapped to school_distance; kindergarten not primary school),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-1.1900000000000002,0.4796377440464775,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.011954745856322333,0.004818443137122201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.481039106640214,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,school,school_distance,D_location,level,km,-0.0352,,,0.0001,,,,high,distance to nearest primary school in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-3.52,0.9047466562331319,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8905918864130946,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,subway,transit_distance,D_location,level,km,-0.016,,,0.0001,,,,high,distance to nearest subway station in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-1.6,0.41124848010596904,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8905918864130946,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,duga100,green_space,E_environment,dummy,binary,0.0452,,,0.0001,,,,high,urban green area within 0-100 m (base >500 m); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,4.616648213109631,1.2154941378083848,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,duga200,green_space,E_environment,dummy,binary,0.0145,,,0.0001,,,,high,urban green area 101-200 m (base >500 m); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,1.4598588500708276,0.3781373666606474,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,duga300,green_space,E_environment,dummy,binary,0.012,,,0.0001,,,,high,urban green area 201-300 m (base >500 m); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,1.2067474804470457,0.31215989291515783,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.018744641570233256,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130937,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,duga400,green_space,E_environment,dummy,binary,0.0074,,,0.0074,,,,high,urban green area 301-400 m (base >500 m),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,0.7423602342445879,0.2783482826634423,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.012905016387180519,0.004818386214406422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6782860096594163,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c1,4,1 (OLS),9,duga500,green_space,E_environment,dummy,binary,0.0056,,,0.051,,,,high,urban green area 401-500 m (base >500 m),ln(sale price),log,OLS,43075.0,0.8702,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,0.5611568845480974,0.2885732175490396,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.009403350556026174,0.004818574439681478,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9514797734758589,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,area,living_area,A_structural,level,m2,0.0256,,,0.0001,,,,high,area of the dwelling in m2 (mean 52.86); entered with area2 (squared term); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,2.56,0.6579975681695506,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,m2,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,area2,living_area,A_structural,squared,m2,-0.0001,,,0.0001,,,,high,square of dwelling area (m2^2); pairs with 'area'; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,-0.0001,2.570303000662307e-05,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,m2,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,basement,basement,A_structural,dummy,binary,0.0204,,,0.0001,,,,high,1 if apartment has basement or storage room; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.0595472256402347,0.5351482358604903,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,cons1960,age,A_structural,dummy,binary,0.0154,,,0.0001,,,,high,construction period dummy: built 1960-1969 (base cons1950 = built 1950-1959); vintage not age; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,1.5511235578909321,0.40196957169836794,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.018744641570233256,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130937,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,cons1970,age,A_structural,dummy,binary,0.0245,,,0.0001,,,,high,construction period dummy: built 1970-1979 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.4782271901407693,0.6453430278772883,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,cons1980,age,A_structural,dummy,binary,0.0371,,,0.0001,,,,high,construction period dummy: built 1980-1989 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,3.77496119625651,0.9896247725483827,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,cons1990,age,A_structural,dummy,binary,0.131,,,0.0001,,,,high,construction period dummy: built 1990-1999 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,13.93217542194658,3.8383820177435752,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,cons2000,age,A_structural,dummy,binary,0.2079,,,0.0001,,,,high,construction period dummy: built after 2000 (base built 1950-1959); Table 3 also lists cons2010 so cons2000 likely 2000-2009; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,22.93336380813078,6.578526604166228,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,cons2010,age,A_structural,dummy,binary,0.221,,,0.0001,,,,high,construction period dummy: built 2010+ (base built 1950-1959); cons2010 not described in Table 2 but reported; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,24.531270872186404,7.085258135589232,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,floor2,floor_level,A_structural,dummy,binary,0.0256,,,0.0001,,,,high,floor2 = 'other floors' (base floor1 = ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.590828504641429,0.6750597702882567,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,floor3,floor_level,A_structural,dummy,binary,0.0288,,,0.0001,,,,high,floor3 = first and second floor (base ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.9190531685493015,0.7618763492424905,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,garage,garage,A_structural,other,ordinal,0.025,,,0.0001,,,,high,"0 none, 1 parking space outside building, 2 individual space in garage (coded 0/1/2, entered as single variable); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,0.025,0.006425757501655767,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,height,storeys,A_structural,other,ordinal,-0.0037,,,0.0063,,,,high,"building height: 1 = up to 4 floors, 2 = above 5 floors (coded 1/2)",ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,-0.0037,0.0013544459110738424,not harmonised,|b/z(p)| (two-sided normal),-0.013162555251572821,0.004818370037777388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.731744375872889,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,technology,roof_materials,A_structural,other,ordinal,0.0482,,,0.0001,,,,high,"construction technology: 1 = prefabricated, 2 = traditional (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",other,0.0482,0.012388860463192318,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,dcc,cbd_distance,D_location,level,km,-0.0217,,,0.0001,,,,high,distance to city centre in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-2.17,0.5577557511437206,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,kindergarten,school_distance,D_location,level,km,-0.0029,,,0.5049,,,,high,distance to nearest kindergarten in km (mapped to school_distance; kindergarten not primary school),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-0.29,0.43491312690101536,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.0032131501025058477,0.004818762614769435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.666799832109926,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,school,school_distance,D_location,level,km,-0.0288,,,0.0001,,,,high,distance to nearest primary school in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-2.88,0.7402472641907443,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,subway,transit_distance,D_location,level,km,-0.0188,,,0.0001,,,,high,distance to nearest subway station in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",semi_elasticity,-1.8800000000000001,0.48321696412451365,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,duga100,green_space,E_environment,dummy,binary,0.0276,,,0.0001,,,,high,urban green area within 0-100 m (base >500 m); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,2.795854189581104,0.7292558690402241,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,duga200,green_space,E_environment,dummy,binary,0.0138,,,0.0001,,,,high,urban green area 101-200 m (base >500 m); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,1.3889281465544467,0.3596306297337358,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,duga300,green_space,E_environment,dummy,binary,0.0069,,,0.0044,,,,high,urban green area 201-300 m (base >500 m),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,0.6920904588038956,0.2439559057331214,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01372243767600837,0.004818333767255975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8479632874879175,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,duga400,green_space,E_environment,dummy,binary,0.0021,,,0.3788,,,,high,urban green area 301-400 m (base >500 m),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,0.20993539067992728,0.23910827729420642,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.004241022447538649,0.004818744154013719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8801094874493673,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c2,4,2 (WLS),9,duga500,green_space,E_environment,dummy,binary,0.0015,,,0.5372,,,,high,urban green area 401-500 m (base >500 m),ln(sale price),log,WLS,43075.0,0.8495,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted",pct_premium,0.1498166467892581,0.24345599887375519,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0029734324375420384,0.0048187661879972804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6170526482377062,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,area,living_area,A_structural,level,m2,0.0242,,,0.0001,,,,high,area of the dwelling in m2 (mean 52.86); entered with area2 (squared term); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",semi_elasticity,2.42,0.6220133261602782,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,m2,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,area2,living_area,A_structural,squared,m2,-0.0001,,,0.0001,,,,high,square of dwelling area (m2^2); pairs with 'area'; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",other,-0.0001,2.570303000662307e-05,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,m2,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,basement,basement,A_structural,dummy,binary,0.0216,,,0.0001,,,,high,1 if apartment has basement or storage room; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,2.181922079192544,0.5673079050399757,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,cons1960,age,A_structural,dummy,binary,0.018,,,0.0001,,,,high,construction period dummy: built 1960-1969 (base cons1950 = built 1950-1959); vintage not age; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,1.8152079598380944,0.47105772360803133,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,cons1970,age,A_structural,dummy,binary,0.0307,,,0.0001,,,,high,construction period dummy: built 1970-1979 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,3.114400195335043,0.8136835560479784,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,cons1980,age,A_structural,dummy,binary,0.0415,,,0.0001,,,,high,construction period dummy: built 1980-1989 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,4.2313863077830804,1.11187416927243,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,cons1990,age,A_structural,dummy,binary,0.1366,,,0.0001,,,,high,construction period dummy: built 1990-1999 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,14.566314128213165,4.02494220675223,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,cons2000,age,A_structural,dummy,binary,0.2139,,,0.0001,,,,high,construction period dummy: built after 2000 (base built 1950-1959); Table 3 also lists cons2010 so cons2000 likely 2000-2009; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,23.66284281128932,6.809115445172862,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,cons2010,age,A_structural,dummy,binary,0.2059,,,0.0001,,,,high,construction period dummy: built 2010+ (base built 1950-1959); cons2010 not described in Table 2 but reported; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,22.691096808027122,6.502223654518346,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.018744641570233256,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130937,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,floor2,floor_level,A_structural,dummy,binary,0.0269,,,0.0001,,,,high,floor2 = 'other floors' (base floor1 = ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,2.7240517216530646,0.7102628910944769,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,floor3,floor_level,A_structural,dummy,binary,0.0302,,,0.0001,,,,high,floor3 = first and second floor (base ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,3.0629595464792603,0.8000312652148589,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,garage,garage,A_structural,other,ordinal,0.0267,,,0.0001,,,,high,"0 none, 1 parking space outside building, 2 individual space in garage (coded 0/1/2, entered as single variable); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",other,0.0267,0.006862709011768359,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,height,storeys,A_structural,other,ordinal,-0.0026,,,0.0757,,,,high,"building height: 1 = up to 4 floors, 2 = above 5 floors (coded 1/2)",ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",other,-0.0026,0.0014637993304142272,not harmonised,|b/z(p)| (two-sided normal),-0.00855881562177748,0.004818610990883348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7761997467673725,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,technology,roof_materials,A_structural,other,ordinal,0.0484,,,0.0001,,,,high,"construction technology: 1 = prefabricated, 2 = traditional (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",other,0.0484,0.012440266523205564,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,dcc,cbd_distance,D_location,level,km,-0.0233,,,0.0001,,,,high,distance to city centre in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",semi_elasticity,-2.33,0.5988805991543175,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,kindergarten,school_distance,D_location,level,km,-0.0081,,,0.0718,,,,high,distance to nearest kindergarten in km (mapped to school_distance; kindergarten not primary school),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",semi_elasticity,-0.8099999999999999,0.44990398016112726,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.008675342195857355,0.004818606152069133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8003841613268434,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,school,school_distance,D_location,level,km,-0.0307,,,0.0001,,,,high,distance to nearest primary school in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",semi_elasticity,-3.0700000000000003,0.7890830212033282,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8905918864130946,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,subway,transit_distance,D_location,level,km,-0.0186,,,0.0001,,,,high,distance to nearest subway station in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",semi_elasticity,-1.8599999999999999,0.47807635812318905,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.018744641570233256,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8905918864130937,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,duga100,green_space,E_environment,dummy,binary,0.0304,,,0.0001,,,,high,urban green area within 0-100 m (base >500 m); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,3.0835329301478698,0.8054905675190613,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,duga200,green_space,E_environment,dummy,binary,0.0077,,,0.0042,,,,high,urban green area 101-200 m (base >500 m),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,0.7726075945751587,0.2710525224566353,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01379360529696232,0.004818329048610898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.862736263505904,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,duga300,green_space,E_environment,dummy,binary,0.0065,,,0.0126,,,,high,urban green area 201-300 m (base >500 m),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,0.6517754829801659,0.26223263570661526,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01202142418122752,0.004818439284969636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4948792482923805,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,duga400,green_space,E_environment,dummy,binary,0.0028,,,0.2807,,,,high,urban green area 301-400 m (base >500 m),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,0.2800545649090491,0.2602878995422016,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0051981880162170185,0.004818722385167946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0787481827583738,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T4-c3,4,3 (Median QR),9,duga500,green_space,E_environment,dummy,binary,0.0029,,,0.2778,,,,high,urban green area 401-500 m (base >500 m),ln(sale price),log,quantile,43075.0,,p,"general model, full sample of 43,075 apartment sales 2010-2015 (built after 1950); year dummies (base 2010) and 18 district dummies (base d9 Srodmiescie) included, not extracted; median (0.5) quantile regression",pct_premium,0.2900628554352247,0.2679894923086301,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.005229636719027631,0.00481872159501686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0852747177665767,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),10,area,living_area,A_structural,level,m2,0.0227,,,0.0001,,,,high,area of the dwelling in m2 (mean 52.86); entered with area2 (squared term); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,2.27,0.5834587811503436,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,m2,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),10,area2,living_area,A_structural,squared,m2,-0.0001,,,0.0001,,,,high,square of dwelling area (m2^2); pairs with 'area'; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,-0.0001,2.570303000662307e-05,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,m2,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),10,basement,basement,A_structural,dummy,binary,0.0287,,,0.0001,,,,high,1 if apartment has basement or storage room; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.908781323855325,0.7591550259535683,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),10,cons1960,age,A_structural,dummy,binary,0.0144,,,0.0002,,,,high,construction period dummy: built 1960-1969 (base cons1950 = built 1950-1959); vintage not age,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,1.4496574604773826,0.39281514995610456,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0179182729624692,0.00481801385730446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7190164854556804,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,cons1970,age,A_structural,dummy,binary,0.0342,,,0.0001,,,,high,construction period dummy: built 1970-1979 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,3.4751565027917097,0.9096269115273912,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,cons1980,age,A_structural,dummy,binary,0.0564,,,0.0001,,,,high,construction period dummy: built 1980-1989 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,5.790964238343044,1.5337608076395182,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,cons1990,age,A_structural,dummy,binary,0.1197,,,0.0001,,,,high,construction period dummy: built 1990-1999 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,12.662530702368159,3.467875704618173,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,cons2000,age,A_structural,dummy,binary,0.2116,,,0.0001,,,,high,construction period dummy: built after 2000 (base built 1950-1959); Table 3 also lists cons2010 so cons2000 likely 2000-2009; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,23.382733645532028,6.720424384820151,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,cons2010,age,A_structural,dummy,binary,0.2074,,,0.0001,,,,high,construction period dummy: built 2010+ (base built 1950-1959); cons2010 not described in Table 2 but reported; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,22.872755290660173,6.559424699447352,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,floor2,floor_level,A_structural,dummy,binary,0.0268,,,0.0001,,,,high,floor2 = 'other floors' (base floor1 = ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.7137980498035175,0.7075517498868367,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,floor3,floor_level,A_structural,dummy,binary,0.0281,,,0.0001,,,,high,floor3 = first and second floor (base ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.847170354082973,0.7428383524249466,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,garage,garage,A_structural,other,ordinal,0.0293,,,0.0001,,,,high,"0 none, 1 parking space outside building, 2 individual space in garage (coded 0/1/2, entered as single variable); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,0.0293,0.0075309877919405585,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,height,storeys,A_structural,other,ordinal,-0.0059,,,0.0002,,,,high,"building height: 1 = up to 4 floors, 2 = above 5 floors (coded 1/2)",ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,-0.0059,0.0015864409375633865,not harmonised,|b/z(p)| (two-sided normal),-0.0179182729624692,0.00481801385730446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7190164854556804,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,technology,roof_materials,A_structural,other,ordinal,0.0495,,,0.0001,,,,high,"construction technology: 1 = prefabricated, 2 = traditional (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,0.0495,0.012722999853278419,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,dcc,cbd_distance,D_location,level,km,-0.027,,,0.0001,,,,high,distance to city centre in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-2.7,0.6939818101788228,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,kindergarten,school_distance,D_location,level,km,-0.0117,,,0.0145,,,,high,distance to nearest kindergarten in km (mapped to school_distance; kindergarten not primary school),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-1.17,0.47859963757187485,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.011779344913983445,0.004818453168091097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4446320225729226,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,school,school_distance,D_location,level,km,-0.0354,,,0.0001,,,,high,distance to nearest primary school in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-3.54,0.9098872622344566,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,subway,transit_distance,D_location,level,km,-0.0146,,,0.0001,,,,high,distance to nearest subway station in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-1.46,0.3752642380966968,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period1 x duga100,green_space,E_environment,dummy,binary,-0.0037,,,0.4587,,,,high,interaction: period1 (built 1950-1989) x duga100 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,-0.37055839641697563,0.4974884098189279,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0035706466353106277,0.004818756771513019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7409891813629549,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period1 x duga200,green_space,E_environment,dummy,binary,-0.0015,,,0.6965,,,,high,interaction: period1 (built 1950-1989) x duga200 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,-0.1506259026802348,0.38399023103831925,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0018795619677703952,0.004818778978417436,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39004942459254965,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period1 x duga300,green_space,E_environment,dummy,binary,0.0009,,,0.807,,,,high,interaction: period1 (built 1950-1989) x duga300 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.08936130157128908,0.36873417329736663,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0011772195317214984,0.004818784151168973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.24429804174480832,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period1 x duga400,green_space,E_environment,dummy,binary,-0.0009,,,0.8015,,,,high,interaction: period1 (built 1950-1989) x duga400 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,-0.09059970447542476,0.3576642178076368,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0012114726281834507,0.004818783954031988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25140629663834246,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period1 x duga500,green_space,E_environment,dummy,binary,0.0042,,,0.2963,,,,high,interaction: period1 (built 1950-1989) x duga500 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.42007123524385737,0.4038370280195846,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.005032681708885172,0.004818726464983845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.044400786277468,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period2 x duga100,green_space,E_environment,dummy,binary,0.0993,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga100 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,10.40379093654229,2.8187659311750877,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period2 x duga200,green_space,E_environment,dummy,binary,0.031,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga200 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,3.145276090113369,0.8218813885915521,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period2 x duga300,green_space,E_environment,dummy,binary,0.022,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga300 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.222744134856236,0.5780447786592071,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period2 x duga400,green_space,E_environment,dummy,binary,0.0139,,,0.0008,,,,high,interaction: period2 (built after 1989) x duga400 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,1.3988340104072572,0.4203823966447002,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01615429730954375,0.004818158690616729,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3527947805048286,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c1,5,1 (OLS),11,period2 x duga500,green_space,E_environment,dummy,binary,0.004,,,0.3152,,,,high,interaction: period2 (built after 1989) x duga500 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,OLS,43075.0,0.8709,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.40000483975968226,0.3998555825896838,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.004839791954309844,0.004818731053245919,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0043706321915888,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),10,area,living_area,A_structural,level,m2,0.0257,,,0.0001,,,,high,area of the dwelling in m2 (mean 52.86); entered with area2 (squared term); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,2.5700000000000003,0.6605678711702128,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,m2,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),10,area2,living_area,A_structural,squared,m2,-0.0001,,,0.0001,,,,high,square of dwelling area (m2^2); pairs with 'area'; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,-0.0001,2.570303000662307e-05,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,m2,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),10,basement,basement,A_structural,dummy,binary,0.0198,,,0.0001,,,,high,1 if apartment has basement or storage room; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,1.9984111304066632,0.5190970301903235,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),10,cons1960,age,A_structural,dummy,binary,0.0143,,,0.0001,,,,high,construction period dummy: built 1960-1969 (base cons1950 = built 1950-1959); vintage not age; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,1.439588207863185,0.37284710196680587,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,cons1970,age,A_structural,dummy,binary,0.0238,,,0.0001,,,,high,construction period dummy: built 1970-1979 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.406631902295042,0.626465975949075,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,cons1980,age,A_structural,dummy,binary,0.0328,,,0.0001,,,,high,construction period dummy: built 1980-1989 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,3.3307128043722933,0.8711702296996617,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,cons1990,age,A_structural,dummy,binary,0.119,,,0.0001,,,,high,construction period dummy: built 1990-1999 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,12.584315918967338,3.4451832571904495,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,cons2000,age,A_structural,dummy,binary,0.196,,,0.0001,,,,high,construction period dummy: built after 2000 (base built 1950-1959); Table 3 also lists cons2010 so cons2000 likely 2000-2009; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,21.498415024366025,6.128611800127757,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,cons2010,age,A_structural,dummy,binary,0.2098,,,0.0001,,,,high,construction period dummy: built 2010+ (base built 1950-1959); cons2010 not described in Table 2 but reported; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,23.16393039889235,6.6512732401308705,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,floor2,floor_level,A_structural,dummy,binary,0.0254,,,0.0001,,,,high,floor2 = 'other floors' (base floor1 = ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.570346949573077,0.6696519220545372,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,floor3,floor_level,A_structural,dummy,binary,0.0288,,,0.0001,,,,high,floor3 = first and second floor (base ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.9190531685493015,0.7618763492424905,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,garage,garage,A_structural,other,ordinal,0.0256,,,0.0001,,,,high,"0 none, 1 parking space outside building, 2 individual space in garage (coded 0/1/2, entered as single variable); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,0.0256,0.006579975681695506,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,height,storeys,A_structural,other,ordinal,-0.0038,,,0.0046,,,,high,"building height: 1 = up to 4 floors, 2 = above 5 floors (coded 1/2)",ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,-0.0038,0.0013409618204426235,not harmonised,|b/z(p)| (two-sided normal),-0.013654143731790734,0.004818338272412458,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.833786870043548,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,technology,roof_materials,A_structural,other,ordinal,0.0476,,,0.0001,,,,high,"construction technology: 1 = prefabricated, 2 = traditional (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",other,0.0476,0.01223464228315258,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,dcc,cbd_distance,D_location,level,km,-0.0219,,,0.0001,,,,high,distance to city centre in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-2.19,0.5628963571450452,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,kindergarten,school_distance,D_location,level,km,-0.0024,,,0.569,,,,high,distance to nearest kindergarten in km (mapped to school_distance; kindergarten not primary school),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-0.24,0.4214038997935905,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.002744408969458219,0.004818769343159177,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5695248670398051,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,school,school_distance,D_location,level,km,-0.029,,,0.0001,,,,high,distance to nearest primary school in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-2.9000000000000004,0.745387870192069,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,subway,transit_distance,D_location,level,km,-0.0176,,,0.0001,,,,high,distance to nearest subway station in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",semi_elasticity,-1.76,0.45237332811656594,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8905918864130946,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period1 x duga100,green_space,E_environment,dummy,binary,-0.0057,,,0.1814,,,,high,interaction: period1 (built 1950-1989) x duga100 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,-0.5692829235066865,0.4240767281193459,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.006439967933088295,0.00481868756394502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3364568355238922,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period1 x duga200,green_space,E_environment,dummy,binary,0.002,,,0.5545,,,,high,interaction: period1 (built 1950-1989) x duga200 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.19962644299345023,0.3390694858440899,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.002848038722589163,0.004818767946804091,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5910304779208226,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period1 x duga300,green_space,E_environment,dummy,binary,-0.0001,,,0.9685,,,,high,interaction: period1 (built 1950-1989) x duga300 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,-0.010320096804328216,0.25320554634438863,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.00019029225930820953,0.004818787402971489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.03948965650380559,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period1 x duga400,green_space,E_environment,dummy,binary,-0.0046,,,0.1672,,,,high,interaction: period1 (built 1950-1989) x duga400 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,-0.45949561880188616,0.33150160216479024,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.006655835805683277,0.0048186807525312835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.381256851719617,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period1 x duga500,green_space,E_environment,dummy,binary,0.0015,,,0.6614,,,,high,interaction: period1 (built 1950-1989) x duga500 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.14952521300504173,0.34299465503509247,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0021105330401151572,0.004818776757924105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43798107821545157,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period2 x duga100,green_space,E_environment,dummy,binary,0.0768,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga100 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,7.961572500501735,2.131568822962557,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period2 x duga200,green_space,E_environment,dummy,binary,0.0273,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga200 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,2.7650759671543845,0.7211128085156497,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period2 x duga300,green_space,E_environment,dummy,binary,0.0136,,,0.0002,,,,high,interaction: period2 (built after 1989) x duga300 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,1.368612274997072,0.37069541108661447,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0179182729624692,0.00481801385730446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7190164854556804,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period2 x duga400,green_space,E_environment,dummy,binary,0.0084,,,0.0191,,,,high,interaction: period2 (built after 1989) x duga400 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.8428901332393268,0.36145046394761954,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01129246244936701,0.0048184802352355715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.343573470902684,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c2,5,2 (WLS),11,period2 x duga500,green_space,E_environment,dummy,binary,0.0003,,,0.9339,,,,high,interaction: period2 (built after 1989) x duga500 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,WLS,43075.0,0.8498,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11",pct_premium,0.029350130690053433,0.3618199113810957,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.00039966564636747696,0.004818787105359582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.08293905450252374,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,ols,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),10,area,living_area,A_structural,level,m2,0.0243,,,0.0001,,,,high,area of the dwelling in m2 (mean 52.86); entered with area2 (squared term); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",semi_elasticity,2.4299999999999997,0.6245836291609405,100*b/1 (X per m2->m2),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,m2,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),10,area2,living_area,A_structural,squared,m2,-0.0001,,,0.0001,,,,high,square of dwelling area (m2^2); pairs with 'area'; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",other,-0.0001,2.570303000662307e-05,not harmonised,|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,m2,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),10,basement,basement,A_structural,dummy,binary,0.0215,,,0.0001,,,,high,1 if apartment has basement or storage room; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,2.1717189439779183,0.5646250142290375,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),10,cons1960,age,A_structural,dummy,binary,0.0181,,,0.0001,,,,high,construction period dummy: built 1960-1969 (base cons1950 = built 1950-1959); vintage not age; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,1.8253778474005111,0.4737220808009581,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.018744641570233256,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130937,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,cons1970,age,A_structural,dummy,binary,0.0297,,,0.0001,,,,high,construction period dummy: built 1970-1979 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,3.0115428584505333,0.7863924199408604,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,cons1980,age,A_structural,dummy,binary,0.0408,,,0.0001,,,,high,construction period dummy: built 1980-1989 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,4.158648080785965,1.0923547491986034,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,cons1990,age,A_structural,dummy,binary,0.1237,,,0.0001,,,,high,construction period dummy: built 1990-1999 (base built 1950-1959); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,13.110445611332366,3.5981250288223574,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,cons2000,age,A_structural,dummy,binary,0.2026,,,0.0001,,,,high,construction period dummy: built after 2000 (base built 1950-1959); Table 3 also lists cons2010 so cons2000 likely 2000-2009; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,22.292328962638752,6.376932592541299,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,cons2010,age,A_structural,dummy,binary,0.1949,,,0.0001,,,,high,construction period dummy: built 2010+ (base built 1950-1959); cons2010 not described in Table 2 but reported; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,21.366564071658935,6.087516577287586,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,floor2,floor_level,A_structural,dummy,binary,0.0267,,,0.0001,,,,high,floor2 = 'other floors' (base floor1 = ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,2.703545333600599,0.7048411437784087,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,floor3,floor_level,A_structural,dummy,binary,0.0305,,,0.0001,,,,high,floor3 = first and second floor (base ground floor); p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,3.093821059836932,0.8082210256053853,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,garage,garage,A_structural,other,ordinal,0.0265,,,0.0001,,,,high,"0 none, 1 parking space outside building, 2 individual space in garage (coded 0/1/2, entered as single variable); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",other,0.0265,0.006811302951755112,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,height,storeys,A_structural,other,ordinal,-0.0023,,,0.1029,,,,high,"building height: 1 = up to 4 floors, 2 = above 5 floors (coded 1/2)",ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",other,-0.0023,0.0014102174711528464,not harmonised,|b/z(p)| (two-sided normal),-0.007858978618347235,0.0048186386752217085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6309541237776328,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,technology,roof_materials,A_structural,other,ordinal,0.0492,,,0.0001,,,,high,"construction technology: 1 = prefabricated, 2 = traditional (coded 1/2); p printed as 0.0001 (likely <0.0001)",ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",other,0.0492,0.012645890763258548,not harmonised,|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,ordinal,nonstandard_x_transform,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,False,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,dcc,cbd_distance,D_location,level,km,-0.0237,,,0.0001,,,,high,distance to city centre in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",semi_elasticity,-2.37,0.6091618111569667,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,kindergarten,school_distance,D_location,level,km,-0.0082,,,0.0596,,,,high,distance to nearest kindergarten in km (mapped to school_distance; kindergarten not primary school),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",semi_elasticity,-0.8200000000000001,0.43530397395740283,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.009076974238617828,0.004818588972657697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8837411304686185,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,school,school_distance,D_location,level,km,-0.0294,,,0.0001,,,,high,distance to nearest primary school in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",semi_elasticity,-2.94,0.7556690821947181,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,subway,transit_distance,D_location,level,km,-0.0175,,,0.0001,,,,high,distance to nearest subway station in km; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",semi_elasticity,-1.7500000000000002,0.4498030251159037,100*b/1 (X per km->km),|b/z(p)| (two-sided normal),-0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.890591886413094,43065.0,km,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period1 x duga100,green_space,E_environment,dummy,binary,0.0022,,,0.6303,,,,high,interaction: period1 (built 1950-1989) x duga100 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,0.2191952215190307,0.4580976674699132,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0023192985388399583,0.004818774529720932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4813046397035462,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period1 x duga200,green_space,E_environment,dummy,binary,-0.0034,,,0.3284,,,,high,interaction: period1 (built 1950-1989) x duga200 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,-0.34002570967021084,0.3467016217228182,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.004709549875612127,0.004818734049906042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9773417305949799,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period1 x duga300,green_space,E_environment,dummy,binary,-0.0017,,,0.6198,,,,high,interaction: period1 (built 1950-1989) x duga300 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,-0.1704416263666353,0.3420674804796843,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.002390756608910239,0.004818773718785535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4961338191893303,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period1 x duga400,green_space,E_environment,dummy,binary,-0.0043,,,0.194,,,,high,interaction: period1 (built 1950-1989) x duga400 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,-0.4296224925920189,0.32964497527839964,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.006258695133111661,0.004818693110293816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2988366326425052,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period1 x duga500,green_space,E_environment,dummy,binary,0.0002,,,0.9484,,,,high,interaction: period1 (built 1950-1989) x duga500 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,0.019524371443990773,0.3091036606991046,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0003118533827021776,0.0048187872558987455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.06471615494550698,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period2 x duga100,green_space,E_environment,dummy,binary,0.0826,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga100 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,8.586252883163038,2.305882082799255,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period2 x duga200,green_space,E_environment,dummy,binary,0.0208,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga200 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,2.1003236298003714,0.545859638716959,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8905918864130946,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period2 x duga300,green_space,E_environment,dummy,binary,0.0147,,,0.0001,,,,high,interaction: period2 (built after 1989) x duga300 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions; p printed as 0.0001 (likely <0.0001),ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,1.4801332747850893,0.3834297327551238,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01874464157023326,0.004817940847430995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.890591886413094,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period2 x duga400,green_space,E_environment,dummy,binary,0.0111,,,0.0032,,,,high,interaction: period2 (built after 1989) x duga400 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,1.115466510157903,0.3807495194862094,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.014203593865508306,0.004818301389597884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9478425521849063,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995,d956659c01d343fed17ef54a0b1e248a34a32995:T5-c3,5,3 (Median QR),11,period2 x duga500,green_space,E_environment,dummy,binary,0.0051,,,0.1665,,,,high,interaction: period2 (built after 1989) x duga500 distance band dummy (base >500 m); Table 5 replaces main duga effects by period interactions,ln(sale price),log,quantile,43075.0,,p,"same sample, duga distance bands interacted with construction period (period1 = 1950-1989, period2 = after 1989); year and district dummies included, not extracted; table spans pages 10-11; median (0.5) quantile regression",pct_premium,0.5106198379491778,0.3705049571187617,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0066668268768206385,0.004818680399715127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3835378825320666,43065.0,binary,,2018.0,Poland,Europe,High income,2010.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2012.5,quantile,journal,True,True,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Living area,living_area,A_structural,level,sqft,414.48,,,,,,,medium,"Living area in square feet; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,414.48,,"b (currency per unit, as reported)",,,,,,,sqft,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Full bath,bathrooms,A_structural,level,count,155785.0,,,,,,,medium,"Number of full bathrooms; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,155785.0,,"b (currency per unit, as reported)",,,,,,,count,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Land value,other,other,level,USD (assessed land value),1.04,,,,,,,medium,"Assessed land value component entered as regressor; taxonomy other; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,1.04,,"b (currency per unit, as reported)",,,,,,,usd,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Half bath,bathrooms,A_structural,level,count (half bathrooms),132309.92,,,,,,,medium,"Number of half bathrooms; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,132309.92,,"b (currency per unit, as reported)",,,,,,,count,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Num bedroom,bedrooms,A_structural,level,count,-7783.65,,,,,,,medium,"Number of bedrooms; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,-7783.65,,"b (currency per unit, as reported)",,,,,,,count,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Num kitchen,kitchen_quality,B_interior,level,count (number of kitchens),-232968.69,,,,,,,medium,"Number of kitchens (count, proxy for multi-unit), not kitchen quality; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,-232968.69,,"b (currency per unit, as reported)",,,,,,,count,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Gross-living area,other,other,level,"sqft (gross area minus living area, non-livable space)",-268.59,,,,,,,medium,"Constructed variable gross area minus living area; taxonomy other; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,-268.59,,"b (currency per unit, as reported)",,,,,,,sqft,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Land sf,lot_size,A_structural,level,sqft,0.05,,,,,,,medium,"Land area in square feet; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,0.05,,"b (currency per unit, as reported)",,,,,,,sqft,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8,db3684d67741de25ae92064ef093bf9c1d604ef8:T1-c1,1,1,5,Owner occupied,other,other,binary,binary,27603.03,,,,,,,medium,"Owner-occupancy indicator; taxonomy other; slope coefficient in dollars, no SE/t/p reported","Total assessed (appraisal) value, USD, FY2024 City of Boston assessment",level,OLS,,0.736,none,"Multiple linear regression fitted on a 70% training split; ZIP-code dummies (one-hot) included, not extracted; outliers below 1st/above 99th percentile removed; N not stated; RMSE 279,830 USD",monetary,27603.03,,"b (currency per unit, as reported)",,,,,,,binary,,2025.0,United States,North America,High income,2024.0,2024.0,mixed,appraisal,conference,False,True,unknown,"K-means clustering (descriptive segmentation, not used for coefficients)",2024.0,ols,other,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,DAGP,green_space,E_environment,level,km,-0.016,,,,***,,,high,"Straight-line distance from community to the Asian Games Park (sports park), km",ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",semi_elasticity,-1.6,,100*b/1 (X per km->km),,,,,,31319.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,DAGP,green_space,E_environment,level,km,-0.003,,,,,,,high,"Straight-line distance from community to the Asian Games Park (sports park), km",ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",semi_elasticity,-0.3,,100*b/1 (X per km->km),,,,,,3758.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,DAGP,green_space,E_environment,level,km,-0.019,,,,***,,,high,"Straight-line distance from community to the Asian Games Park (sports park), km",ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",semi_elasticity,-1.9,,100*b/1 (X per km->km),,,,,,9562.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,DAGP,green_space,E_environment,level,km,-0.017,,,,***,,,high,"Straight-line distance from community to the Asian Games Park (sports park), km",ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",semi_elasticity,-1.7000000000000002,,100*b/1 (X per km->km),,,,,,11044.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,DAGP,green_space,E_environment,level,km,-0.02,,,,***,,,high,"Straight-line distance from community to the Asian Games Park (sports park), km",ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",semi_elasticity,-2.0,,100*b/1 (X per km->km),,,,,,6925.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,lnArea,living_area,A_structural,log,m2,1.084,,,,***,,,high,ln(floor area m2),ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",elasticity,1.084,,b (log-log),,,,,,31319.0,m2,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,lnArea,living_area,A_structural,log,m2,1.082,,,,***,,,high,ln(floor area m2),ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",elasticity,1.082,,b (log-log),,,,,,3758.0,m2,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,lnArea,living_area,A_structural,log,m2,1.053,,,,***,,,high,ln(floor area m2),ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",elasticity,1.053,,b (log-log),,,,,,9562.0,m2,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,lnArea,living_area,A_structural,log,m2,1.054,,,,***,,,high,ln(floor area m2),ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",elasticity,1.054,,b (log-log),,,,,,11044.0,m2,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,lnArea,living_area,A_structural,log,m2,1.177,,,,***,,,high,ln(floor area m2),ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",elasticity,1.177,,b (log-log),,,,,,6925.0,m2,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,lnAge,age,A_structural,log,years,-0.03,,,,***,,,high,"ln(building age, years)",ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",elasticity,-0.03,,b (log-log),,,,,,31319.0,years,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,lnAge,age,A_structural,log,years,0.03,,,,***,,,high,"ln(building age, years)",ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",elasticity,0.03,,b (log-log),,,,,,3758.0,years,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,lnAge,age,A_structural,log,years,-0.03,,,,***,,,high,"ln(building age, years)",ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",elasticity,-0.03,,b (log-log),,,,,,9562.0,years,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,lnAge,age,A_structural,log,years,-0.037,,,,***,,,high,"ln(building age, years)",ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",elasticity,-0.037,,b (log-log),,,,,,11044.0,years,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,lnAge,age,A_structural,log,years,-0.044,,,,***,,,high,"ln(building age, years)",ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",elasticity,-0.044,,b (log-log),,,,,,6925.0,years,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,Green,green_space,E_environment,binary,binary,0.046,,,,***,,,high,Green space available within 1 km of the community =1,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",pct_premium,4.707441095693721,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,Green,green_space,E_environment,binary,binary,0.068,,,,***,,,high,Green space available within 1 km of the community =1,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",pct_premium,7.036530847877431,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,Green,green_space,E_environment,binary,binary,0.042,,,,***,,,high,Green space available within 1 km of the community =1,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,Green,green_space,E_environment,binary,binary,0.034,,,,***,,,high,Green space available within 1 km of the community =1,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",pct_premium,3.4584606728117917,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,Green,green_space,E_environment,binary,binary,0.06,,,,***,,,high,Green space available within 1 km of the community =1,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",pct_premium,6.183654654535964,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,Bus,transit_distance,D_location,level,count (bus routes within 1 km),-0.001,,,,***,,,high,Number of bus routes within 1 km,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",semi_elasticity,-0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,31319.0,count,unit_not_harmonisable,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,Bus,transit_distance,D_location,level,count (bus routes within 1 km),-0.001,,,,***,,,high,Number of bus routes within 1 km,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",semi_elasticity,-0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,3758.0,count,unit_not_harmonisable,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,Bus,transit_distance,D_location,level,count (bus routes within 1 km),-0.001,,,,***,,,high,Number of bus routes within 1 km,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",semi_elasticity,-0.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,9562.0,count,unit_not_harmonisable,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,Bus,transit_distance,D_location,level,count (bus routes within 1 km),-0.0,,,,*,,,high,Number of bus routes within 1 km,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",semi_elasticity,-0.0,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,11044.0,count,unit_not_harmonisable,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,Bus,transit_distance,D_location,level,count (bus routes within 1 km),0.0,,,,,,,high,Number of bus routes within 1 km,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",semi_elasticity,0.0,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,6925.0,count,unit_not_harmonisable,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,Metro,transit_distance,D_location,binary,binary,0.054,,,,***,,,high,Metro station within 1 km =1,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",pct_premium,5.548460215508011,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,Metro,transit_distance,D_location,binary,binary,0.067,,,,***,,,high,Metro station within 1 km =1,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",pct_premium,6.9295478174600245,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,Metro,transit_distance,D_location,binary,binary,0.055,,,,***,,,high,Metro station within 1 km =1,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",pct_premium,5.654061467549432,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,Metro,transit_distance,D_location,binary,binary,0.042,,,,***,,,high,Metro station within 1 km =1,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,Metro,transit_distance,D_location,binary,binary,0.054,,,,***,,,high,Metro station within 1 km =1,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",pct_premium,5.548460215508011,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,PriSch,school_distance,D_location,binary,binary,0.009,,,,***,,,high,Key primary school within 1 km =1,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",pct_premium,0.9040621773867885,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,PriSch,school_distance,D_location,binary,binary,0.02,,,,**,,,high,Key primary school within 1 km =1,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,PriSch,school_distance,D_location,binary,binary,0.013,,,,**,,,high,Key primary school within 1 km =1,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,PriSch,school_distance,D_location,binary,binary,0.003,,,,,,,high,Key primary school within 1 km =1,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",pct_premium,0.3004504503377081,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,PriSch,school_distance,D_location,binary,binary,0.006,,,,,,,high,Key primary school within 1 km =1,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",pct_premium,0.6018036054064924,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,MidSch,school_distance,D_location,binary,binary,0.009,,,,**,,,high,Key middle school within 1 km =1,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",pct_premium,0.9040621773867885,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,MidSch,school_distance,D_location,binary,binary,0.001,,,,,,,high,Key middle school within 1 km =1,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",pct_premium,0.10005001667083846,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,MidSch,school_distance,D_location,binary,binary,0.007,,,,,,,high,Key middle school within 1 km =1,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",pct_premium,0.7024557266848586,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,MidSch,school_distance,D_location,binary,binary,0.012,,,,**,,,high,Key middle school within 1 km =1,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",pct_premium,1.2072288866077807,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,MidSch,school_distance,D_location,binary,binary,0.015,,,,*,,,high,Key middle school within 1 km =1,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",pct_premium,1.511306461571893,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,HighSch,school_distance,D_location,binary,binary,0.045,,,,***,,,high,High school within 1 km =1,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",pct_premium,4.602785990871694,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,HighSch,school_distance,D_location,binary,binary,0.07,,,,***,,,high,High school within 1 km =1,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",pct_premium,7.250818125421654,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,HighSch,school_distance,D_location,binary,binary,0.063,,,,***,,,high,High school within 1 km =1,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",pct_premium,6.502683923130537,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,HighSch,school_distance,D_location,binary,binary,0.0,,,,,,,high,High school within 1 km =1,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",pct_premium,0.0,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,HighSch,school_distance,D_location,binary,binary,0.061,,,,***,,,high,High school within 1 km =1,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",pct_premium,6.289891418719518,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,Univ,university_distance,D_location,binary,binary,0.002,,,,,,,high,University within 1 km =1,ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",pct_premium,0.2002001334000303,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,Univ,university_distance,D_location,binary,binary,-0.027,,,,***,,,high,University within 1 km =1,ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",pct_premium,-2.6638758475663216,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,Univ,university_distance,D_location,binary,binary,-0.015,,,,**,,,high,University within 1 km =1,ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",pct_premium,-1.4888060396937353,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,Univ,university_distance,D_location,binary,binary,0.021,,,,***,,,high,University within 1 km =1,ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,Univ,university_distance,D_location,binary,binary,0.01,,,,,,,high,University within 1 km =1,ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",pct_premium,1.005016708416795,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,lnDHosp,hospital_distance,D_location,log,km,-0.009,,,,***,,,high,"ln(distance to nearest tertiary hospital, km)",ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",elasticity,-0.009,,b (log-log),,,,,,31319.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,lnDHosp,hospital_distance,D_location,log,km,-0.003,,,,,,,high,"ln(distance to nearest tertiary hospital, km)",ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",elasticity,-0.003,,b (log-log),,,,,,3758.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,lnDHosp,hospital_distance,D_location,log,km,-0.009,,,,**,,,high,"ln(distance to nearest tertiary hospital, km)",ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",elasticity,-0.009,,b (log-log),,,,,,9562.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,lnDHosp,hospital_distance,D_location,log,km,-0.013,,,,***,,,high,"ln(distance to nearest tertiary hospital, km)",ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",elasticity,-0.013,,b (log-log),,,,,,11044.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,lnDHosp,hospital_distance,D_location,log,km,-0.006,,,,,,,high,"ln(distance to nearest tertiary hospital, km)",ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",elasticity,-0.006,,b (log-log),,,,,,6925.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,lnDWL,cbd_distance,D_location,log,km,-0.164,,,,***,,,high,"ln(distance to West Lake, traditional cultural/commercial centre, km)",ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",elasticity,-0.164,,b (log-log),,,,,,31319.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,lnDWL,cbd_distance,D_location,log,km,-0.132,,,,***,,,high,"ln(distance to West Lake, traditional cultural/commercial centre, km)",ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",elasticity,-0.132,,b (log-log),,,,,,3758.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,lnDWL,cbd_distance,D_location,log,km,-0.168,,,,***,,,high,"ln(distance to West Lake, traditional cultural/commercial centre, km)",ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",elasticity,-0.168,,b (log-log),,,,,,9562.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,lnDWL,cbd_distance,D_location,log,km,-0.178,,,,***,,,high,"ln(distance to West Lake, traditional cultural/commercial centre, km)",ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",elasticity,-0.178,,b (log-log),,,,,,11044.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,lnDWL,cbd_distance,D_location,log,km,-0.147,,,,***,,,high,"ln(distance to West Lake, traditional cultural/commercial centre, km)",ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",elasticity,-0.147,,b (log-log),,,,,,6925.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c1,2,(1) T,7,lnDQJ,cbd_distance,D_location,log,km,-0.013,,,,***,,,high,"ln(distance to Qianjiang CBD, km)",ln(total transaction price),log,OLS,31329.0,0.77,none,"Baseline OLS, full period Jan 2018-Mar 2023; month FE + region FE",elasticity,-0.013,,b (log-log),,,,,,31319.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c2,2,(2) T1,7,lnDQJ,cbd_distance,D_location,log,km,-0.029,,,,***,,,high,"ln(distance to Qianjiang CBD, km)",ln(total transaction price),log,OLS,3768.0,0.785,none,"Baseline OLS, T1 pre-announcement (1 Jan 2018-4 Apr 2019); month FE + region FE",elasticity,-0.029,,b (log-log),,,,,,3758.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c3,2,(3) T2,7,lnDQJ,cbd_distance,D_location,log,km,-0.065,,,,***,,,high,"ln(distance to Qianjiang CBD, km)",ln(total transaction price),log,OLS,9572.0,0.791,none,"Baseline OLS, T2 post-announcement (5 Apr 2019-31 Jul 2020); month FE + region FE",elasticity,-0.065,,b (log-log),,,,,,9562.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c4,2,(4) T3,7,lnDQJ,cbd_distance,D_location,log,km,0.013,,,,**,,,high,"ln(distance to Qianjiang CBD, km)",ln(total transaction price),log,OLS,11054.0,0.769,none,"Baseline OLS, T3 pre-opening (1 Aug 2020-30 Nov 2021); month FE + region FE",elasticity,0.013,,b (log-log),,,,,,11044.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T2-c5,2,(5) T4,7,lnDQJ,cbd_distance,D_location,log,km,0.0,,,,,,,high,"ln(distance to Qianjiang CBD, km)",ln(total transaction price),log,OLS,6935.0,0.759,none,"Baseline OLS, T4 post-opening (1 Dec 2021-31 Mar 2023); month FE + region FE",elasticity,0.0,,b (log-log),,,,,,6925.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D1,green_space,E_environment,dummy,binary (distance ring),0.349,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,41.76491903419861,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D1,green_space,E_environment,dummy,binary (distance ring),0.18,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,19.721736312181015,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D1,green_space,E_environment,dummy,binary (distance ring),0.345,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,41.19899196676591,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D1,green_space,E_environment,dummy,binary (distance ring),0.368,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,44.4842038973879,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D1,green_space,E_environment,dummy,binary (distance ring),0.452,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,57.145194857764906,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D2,green_space,E_environment,dummy,binary (distance ring),0.115,,,,***,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,12.187343757193837,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D2,green_space,E_environment,dummy,binary (distance ring),-0.003,,,,,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D2,green_space,E_environment,dummy,binary (distance ring),0.154,,,,***,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,16.64908867784396,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D2,green_space,E_environment,dummy,binary (distance ring),0.12,,,,***,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,12.749685157937574,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D2,green_space,E_environment,dummy,binary (distance ring),0.16,,,,***,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,17.35108709918103,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D3,green_space,E_environment,dummy,binary (distance ring),0.054,,,,***,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,5.548460215508011,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D3,green_space,E_environment,dummy,binary (distance ring),-0.025,,,,,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-2.4690087971667385,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D3,green_space,E_environment,dummy,binary (distance ring),0.084,,,,***,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,8.76288938088261,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D3,green_space,E_environment,dummy,binary (distance ring),0.069,,,,***,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,7.143620914834625,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D3,green_space,E_environment,dummy,binary (distance ring),0.066,,,,***,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,6.822671716599338,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D4,green_space,E_environment,dummy,binary (distance ring),0.047,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,4.812200907965569,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D4,green_space,E_environment,dummy,binary (distance ring),0.02,,,,,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D4,green_space,E_environment,dummy,binary (distance ring),0.058,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,5.97149957102876,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D4,green_space,E_environment,dummy,binary (distance ring),0.056,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,5.759768373661123,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D4,green_space,E_environment,dummy,binary (distance ring),0.062,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,6.396234472803375,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D5,green_space,E_environment,dummy,binary (distance ring),0.028,,,,***,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,2.839568442142504,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D5,green_space,E_environment,dummy,binary (distance ring),-0.018,,,,,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-1.7838967641699233,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D5,green_space,E_environment,dummy,binary (distance ring),0.03,,,,***,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D5,green_space,E_environment,dummy,binary (distance ring),0.017,,,,,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,1.7145322325240686,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D5,green_space,E_environment,dummy,binary (distance ring),0.086,,,,***,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,8.980632830512869,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D6,green_space,E_environment,dummy,binary (distance ring),-0.01,,,,,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D6,green_space,E_environment,dummy,binary (distance ring),-0.03,,,,,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D6,green_space,E_environment,dummy,binary (distance ring),0.012,,,,,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,1.2072288866077807,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D6,green_space,E_environment,dummy,binary (distance ring),-0.023,,,,**,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-2.273751622672293,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D6,green_space,E_environment,dummy,binary (distance ring),0.022,,,,,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,2.2243784470438177,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c1,3,(1) T,8,D7,green_space,E_environment,dummy,binary (distance ring),-0.018,,,,***,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,31329.0,0.777,none,"Distance-ring dummies (reference D8 = 9-12 km), full period Jan 2018-Mar 2023; controls of Table 2 included but not printed; month FE + region FE",pct_premium,-1.7838967641699233,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c2,3,(2) T1,8,D7,green_space,E_environment,dummy,binary (distance ring),0.0,,,,,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,3768.0,0.789,none,"Distance-ring dummies (reference D8 = 9-12 km), T1 pre-announcement (1 Jan 2018-4 Apr 2019); controls of Table 2 included but not printed; month FE + region FE",pct_premium,0.0,,100*(exp(b) - 1),,,,,,3758.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c3,3,(3) T2,8,D7,green_space,E_environment,dummy,binary (distance ring),-0.017,,,,,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,9572.0,0.798,none,"Distance-ring dummies (reference D8 = 9-12 km), T2 post-announcement (5 Apr 2019-31 Jul 2020); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-1.6856315365090357,,100*(exp(b) - 1),,,,,,9562.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c4,3,(4) T3,8,D7,green_space,E_environment,dummy,binary (distance ring),-0.008,,,,,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,11054.0,0.778,none,"Distance-ring dummies (reference D8 = 9-12 km), T3 pre-opening (1 Aug 2020-30 Nov 2021); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-0.7968085162939342,,100*(exp(b) - 1),,,,,,11044.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T3-c5,3,(5) T4,8,D7,green_space,E_environment,dummy,binary (distance ring),-0.024,,,,*,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km,ln(total transaction price),log,OLS,6935.0,0.767,none,"Distance-ring dummies (reference D8 = 9-12 km), T4 post-opening (1 Dec 2021-31 Mar 2023); controls of Table 2 included but not printed; month FE + region FE",pct_premium,-2.3714290242090708,,100*(exp(b) - 1),,,,,,6925.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T4-c1,4,(1) T,9,DAGP,green_space,E_environment,level,km,-0.009,,,,***,,,high,Distance to Asian Games Park (km); main effect = pre-announcement/pre-opening baseline in DID specification,ln(total transaction price),log,OLS,31329.0,0.77,none,"DID: DAGP plus DAGP x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",semi_elasticity,-0.8999999999999999,,100*b/1 (X per km->km),,,,,,31319.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T4-c2,4,(2) T12,9,DAGP,green_space,E_environment,level,km,-0.01,,,,***,,,high,Distance to Asian Games Park (km); main effect = pre-announcement/pre-opening baseline in DID specification,ln(total transaction price),log,OLS,13340.0,0.789,none,"DID: DAGP plus DAGP x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",semi_elasticity,-1.0,,100*b/1 (X per km->km),,,,,,13330.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T4-c3,4,(3) T34,9,DAGP,green_space,E_environment,level,km,-0.018,,,,***,,,high,Distance to Asian Games Park (km); main effect = pre-announcement/pre-opening baseline in DID specification,ln(total transaction price),log,OLS,17989.0,0.762,none,"DID: DAGP plus DAGP x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",semi_elasticity,-1.7999999999999998,,100*b/1 (X per km->km),,,,,,17979.0,km,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D1,green_space,E_environment,dummy,binary (distance ring),0.195,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,21.531098648973067,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D1,green_space,E_environment,dummy,binary (distance ring),0.214,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,23.862265479345226,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D1,green_space,E_environment,dummy,binary (distance ring),0.375,,,,***,,,high,Community within 0-1 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,45.49914146182012,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D2,green_space,E_environment,dummy,binary (distance ring),0.021,,,,,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,2.122205163752855,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D2,green_space,E_environment,dummy,binary (distance ring),0.03,,,,,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,3.045453395351694,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D2,green_space,E_environment,dummy,binary (distance ring),0.124,,,,***,,,high,Community within 1-2 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,13.201587099917521,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D3,green_space,E_environment,dummy,binary (distance ring),0.005,,,,,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,0.501252085940096,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D3,green_space,E_environment,dummy,binary (distance ring),0.008,,,,,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,0.8032085504273478,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D3,green_space,E_environment,dummy,binary (distance ring),0.073,,,,***,,,high,Community within 2-3 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,7.573053691470344,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D4,green_space,E_environment,dummy,binary (distance ring),0.033,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,3.3550539241305577,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D4,green_space,E_environment,dummy,binary (distance ring),0.045,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,4.602785990871694,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D4,green_space,E_environment,dummy,binary (distance ring),0.061,,,,***,,,high,Community within 3-4 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,6.289891418719518,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D5,green_space,E_environment,dummy,binary (distance ring),0.009,,,,,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,0.9040621773867885,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D5,green_space,E_environment,dummy,binary (distance ring),0.005,,,,,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,0.501252085940096,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D5,green_space,E_environment,dummy,binary (distance ring),0.033,,,,***,,,high,Community within 4-5 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,3.3550539241305577,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D6,green_space,E_environment,dummy,binary (distance ring),-0.038,,,,**,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,-3.728705910880048,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D6,green_space,E_environment,dummy,binary (distance ring),-0.03,,,,**,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,-2.9554466451491845,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D6,green_space,E_environment,dummy,binary (distance ring),-0.006,,,,,,,high,Community within 5-6 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c1,5,(1) T,9,D7,green_space,E_environment,dummy,binary (distance ring),-0.006,,,,,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,31329.0,0.778,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), full period Jan 2018-Mar 2023; controls, month FE + region FE",pct_premium,-0.5982035946064723,,100*(exp(b) - 1),,,,,,31319.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c2,5,(2) T12,9,D7,green_space,E_environment,dummy,binary (distance ring),-0.003,,,,,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,13340.0,0.795,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T12 = T1+T2 (pre- and post-announcement); controls, month FE + region FE",pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,13330.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0,dbfc419fae85b2cf607f68db238ec77b3593a5e0:T5-c3,5,(3) T34,9,D7,green_space,E_environment,dummy,binary (distance ring),0.011,,,,,,,high,Community within 6-9 km of the Asian Games Park =1; reference 9-12 km; main effect = baseline period in DID specification,ln(total transaction price),log,OLS,17989.0,0.771,none,"DID with distance rings: D1-D7 plus Dj x T_announced / x T_open interactions (interactions not extracted), T34 = T3+T4 (pre- and post-opening); controls, month FE + region FE",pct_premium,1.106072244471945,,100*(exp(b) - 1),,,,,,17979.0,binary,,2025.0,China,Asia,Upper middle income,2018.0,2023.0,condo_apartment,transaction,journal,True,True,unknown,,2020.5,ols,journal,False,False,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M1,3,M1,14,X1,cbd_distance,D_location,other,"ordinal rank 1-3 (distance to city centre, higher = closer)",-0.0684,0.0121,-5.6485,0.0,,,,high,"overall location OL; rank 1 >4 km, 2 2.1-4 km, 3 <2 km; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,146.0,0.6345,none,"Full sample S1 (150 developments, outliers removed); SE, t and p in separate columns",other,-0.0684,0.0121,not harmonised,reported,-0.43591378632216626,0.07717337104048265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.6485,136.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M1,3,M1,14,X3,floor_level,A_structural,other,"ordinal rank 1-3 (1 ground/top floor, 2 other floors, 3 first/second floor)",0.0211,0.0097,2.174,0.0314,,,,high,floor level FL; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,146.0,0.6345,none,"Full sample S1 (150 developments, outliers removed); SE, t and p in separate columns",other,0.0211,0.0097,not harmonised,reported,0.18326179757402083,0.08429705500184952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.174,136.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M1,3,M1,14,X4,living_area,A_structural,other,"ordinal rank 1-3 (1 >55 m2, 2 40-55 m2, 3 <40 m2; higher = smaller)",0.0516,0.0111,4.656,0.0,,,,high,dwelling area DA; dependent variable is price per m2 so smaller dwellings have higher unit price; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,146.0,0.6345,none,"Full sample S1 (150 developments, outliers removed); SE, t and p in separate columns",other,0.0516,0.0111,not harmonised,reported,0.37078909894969986,0.07963683396685996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.656,136.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M1,3,M1,14,X5,condition,A_structural,other,"ordinal rank 1-3 (1 poor, 3 good)",0.0518,0.0152,3.3989,0.0009,,,,high,"building condition BC, technical condition of common parts; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,146.0,0.6345,none,"Full sample S1 (150 developments, outliers removed); SE, t and p in separate columns",other,0.0518,0.0152,not harmonised,reported,0.27981118450504033,0.0823240414560712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3989,136.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M1,3,M1,14,X6,accessibility,B_interior,other,"ordinal rank 1-3 (1 no amenities, 2 ramp+elevator, 3 ramp+elevator+disabled parking)",0.0329,0.0138,2.3773,0.0188,,,,high,building accessibility BA; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,146.0,0.6345,none,"Full sample S1 (150 developments, outliers removed); SE, t and p in separate columns",other,0.0329,0.0138,not harmonised,reported,0.19974380409978865,0.08402128637521081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3773,136.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M1,3,M1,14,X7,other,other,other,"ordinal rank 1-3 (1 bad, 3 good)",0.0495,0.0092,5.3782,0.0,,,,high,cleanliness and orderliness of common property CO; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,146.0,0.6345,none,"Full sample S1 (150 developments, outliers removed); SE, t and p in separate columns",other,0.0495,0.0092,not harmonised,reported,0.41878744720088534,0.07786758528892294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.3782,136.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,False,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M2,3,M2,14,X2,neighbourhood_amenities,F_neighbourhood,other,"ordinal rank 1-3 (mean distance to nearest school, store and transit stop, higher = closer)",0.0347,0.0095,3.6467,0.0007,,,,high,"neighbourhood location NL; rank 1 >0.5 km, 2 0.25-0.5 km, 3 <0.25 km; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,47.0,0.6752,none,"Subsample S2: developments managed by municipal companies; SE, t and p in separate columns",other,0.0347,0.0095,not harmonised,reported,0.5141890948664816,0.14100120516260772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6467,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M2,3,M2,14,X3,floor_level,A_structural,other,"ordinal rank 1-3 (1 ground/top floor, 2 other floors, 3 first/second floor)",0.019,0.0076,2.4687,0.0178,,,,high,floor level FL; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,47.0,0.6752,none,"Subsample S2: developments managed by municipal companies; SE, t and p in separate columns",other,0.019,0.0076,not harmonised,reported,0.376060307022436,0.15233131082044635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4687,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M2,3,M2,14,X5,condition,A_structural,other,"ordinal rank 1-3 (1 poor, 3 good)",0.0217,0.0098,2.2129,0.0325,,,,high,"building condition BC, technical condition of common parts; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,47.0,0.6752,none,"Subsample S2: developments managed by municipal companies; SE, t and p in separate columns",other,0.0217,0.0098,not harmonised,reported,0.34187764745326377,0.15449303965532277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.2129,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M2,3,M2,14,X7,other,other,other,"ordinal rank 1-3 (1 bad, 3 good)",0.0354,0.0122,2.8845,0.0062,,,,high,cleanliness and orderliness of common property CO; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,47.0,0.6752,none,"Subsample S2: developments managed by municipal companies; SE, t and p in separate columns",other,0.0354,0.0122,not harmonised,reported,0.4284734954088516,0.1485434201452077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8845,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,False,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M2,3,M2,14,X8,landscaping,C_exterior,other,"ordinal rank 1-3 (1 poor, 3 good)",0.0293,0.0123,2.3769,0.0222,,,,high,"land development LD of common areas: green areas, footpaths, roads, parking, recreation facilities; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,47.0,0.6752,none,"Subsample S2: developments managed by municipal companies; SE, t and p in separate columns",other,0.0293,0.0123,not harmonised,reported,0.36395958109776466,0.15312364049718738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3769,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M3,3,M3,14,X4,living_area,A_structural,other,"ordinal rank 1-3 (1 >55 m2, 2 40-55 m2, 3 <40 m2; higher = smaller)",0.0454,0.0106,4.2847,0.0001,,,,high,dwelling area DA; dependent variable is price per m2 so smaller dwellings have higher unit price; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,46.0,0.6496,none,"Subsample S3: developments managed by housing cooperatives; SE, t and p in separate columns",other,0.0454,0.0106,not harmonised,reported,0.5811470959619872,0.13563308888883405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2847,36.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M3,3,M3,14,X6,accessibility,B_interior,other,"ordinal rank 1-3 (1 no amenities, 2 ramp+elevator, 3 ramp+elevator+disabled parking)",0.0328,0.0141,2.3329,0.0249,,,,high,building accessibility BA; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,46.0,0.6496,none,"Subsample S3: developments managed by housing cooperatives; SE, t and p in separate columns",other,0.0328,0.0141,not harmonised,reported,0.3623877900895384,0.15533790136291242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3329,36.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M3,3,M3,14,X7,other,other,other,"ordinal rank 1-3 (1 bad, 3 good)",0.0456,0.0141,3.2439,0.0024,,,,high,cleanliness and orderliness of common property CO; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,46.0,0.6496,none,"Subsample S3: developments managed by housing cooperatives; SE, t and p in separate columns",other,0.0456,0.0141,not harmonised,reported,0.4755915481965012,0.14661103862526623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2439,36.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,False,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M3,3,M3,14,X8,landscaping,C_exterior,other,"ordinal rank 1-3 (1 poor, 3 good)",0.033,0.0158,2.0799,0.0441,,,,high,"land development LD of common areas: green areas, footpaths, roads, parking, recreation facilities; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,46.0,0.6496,none,"Subsample S3: developments managed by housing cooperatives; SE, t and p in separate columns",other,0.033,0.0158,not harmonised,reported,0.3275291575396849,0.15747351196676998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0799,36.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M4,3,M4,14,X2,neighbourhood_amenities,F_neighbourhood,other,"ordinal rank 1-3 (mean distance to nearest school, store and transit stop, higher = closer)",0.0597,0.0234,2.5526,0.0145,,,,high,"neighbourhood location NL; rank 1 >0.5 km, 2 0.25-0.5 km, 3 <0.25 km; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,47.0,0.7317,none,"Subsample S4: developments managed by private firms; SE, t and p in separate columns",other,0.0597,0.0234,not harmonised,reported,0.38695409298283534,0.15159213859705215,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5526,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M4,3,M4,14,X4,living_area,A_structural,other,"ordinal rank 1-3 (1 >55 m2, 2 40-55 m2, 3 <40 m2; higher = smaller)",0.0529,0.0141,3.7544,0.0005,,,,high,dwelling area DA; dependent variable is price per m2 so smaller dwellings have higher unit price; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,47.0,0.7317,none,"Subsample S4: developments managed by private firms; SE, t and p in separate columns",other,0.0529,0.0141,not harmonised,reported,0.525229526128386,0.13989706108256605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7544,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M4,3,M4,14,X5,condition,A_structural,other,"ordinal rank 1-3 (1 poor, 3 good)",0.0461,0.019,2.4304,0.0195,,,,high,"building condition BC, technical condition of common parts; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,47.0,0.7317,none,"Subsample S4: developments managed by private firms; SE, t and p in separate columns",other,0.0461,0.019,not harmonised,reported,0.37103465074193537,0.15266402680296878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4304,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M4,3,M4,14,X7,other,other,other,"ordinal rank 1-3 (1 bad, 3 good)",0.057,0.0264,2.1616,0.0365,,,,high,cleanliness and orderliness of common property CO; ordinal rank regressor entered as level,"ln(price per m2, PLN)",log,OLS,47.0,0.7317,none,"Subsample S4: developments managed by private firms; SE, t and p in separate columns",other,0.057,0.0264,not harmonised,reported,0.33485013485644805,0.15490846357163585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1616,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,False,False
ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976,ddac707c3201e8fd8ee39f4a8176310e5a638976:T3-M4,3,M4,14,X8,landscaping,C_exterior,other,"ordinal rank 1-3 (1 poor, 3 good)",0.0447,0.0176,2.536,0.0151,,,,high,"land development LD of common areas: green areas, footpaths, roads, parking, recreation facilities; ordinal rank regressor entered as level","ln(price per m2, PLN)",log,OLS,47.0,0.7317,none,"Subsample S4: developments managed by private firms; SE, t and p in separate columns",other,0.0447,0.0176,not harmonised,reported,0.38481133374017884,0.15173948491331973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.536,37.0,ordinal rank 1-3,nonstandard_x_transform,2023.0,Poland,Europe,High income,2018.0,2019.0,condo_apartment,transaction,journal,False,False,OLS,,2018.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Metro: 0 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.131,0.025,,,***,,,high,metro station within 0-400 m; reference >1500 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,13.961159703702197,2.849919453279976,100*(exp(b - se^2/2) - 1),reported,0.07006338436718308,0.01337087487923341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.24,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Metro: 0 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.131,0.02,,,***,,,high,metro station within 0-400 m; reference >1500 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,13.973981055356365,2.279935562623981,100*(exp(b - se^2/2) - 1),reported,0.08745856648591326,0.013352452898612713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.55,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Metro: 0 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.084,0.014,,,***,,,high,metro station within 0-400 m; reference >1500 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,8.752231139985621,1.5226804513323566,100*(exp(b - se^2/2) - 1),reported,0.08016405884111773,0.013360676473519621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Metro: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.132,0.026,,,***,,,high,reference metro >1500 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,14.072268983036885,2.966881630094811,100*(exp(b - se^2/2) - 1),reported,0.06789311476144134,0.01337288624088996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0769230769230775,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Metro: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.133,0.017,,,***,,,high,reference metro >1500 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,14.208495513079388,1.9418249971625203,100*(exp(b - se^2/2) - 1),reported,0.10429326179969203,0.013330717673644845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.823529411764706,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Metro: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.088,0.012,,,***,,,high,reference metro >1500 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,9.190950171377676,1.310385746433837,100*(exp(b - se^2/2) - 1),reported,0.0978231976089037,0.013339526946668685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.333333333333333,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Metro: 800 - 1500 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.108,0.021,,,***,,,high,reference metro >1500 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,11.380212493925935,2.339500265311582,100*(exp(b - se^2/2) - 1),reported,0.06877070007477712,0.013372080570095554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.142857142857142,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Metro: 800 - 1500 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.11,0.017,,,***,,,high,reference metro >1500 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,11.611677993121127,1.8976727197800813,100*(exp(b - se^2/2) - 1),reported,0.08640618939333616,0.013353683815333773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.470588235294117,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Metro: 800 - 1500 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.069,0.013,,,***,,,high,reference metro >1500 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,7.134567661372659,1.3928670718928502,100*(exp(b - se^2/2) - 1),reported,0.07096396065922295,0.013370021573476786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.307692307692308,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Centrum: 0 - 3 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.25,0.051,,,***,,,high,distance to city centre 0-3 km; reference >10 km,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,28.235662699903607,6.548529625107481,100*(exp(b - se^2/2) - 1),reported,0.0655636002359697,0.013374974448137818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.901960784313726,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Centrum: 0 - 3 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.251,0.028,,,***,,,high,distance to city centre 0-3 km; reference >10 km,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,28.48063415181794,3.598868236127347,100*(exp(b - se^2/2) - 1),reported,0.11929753463425197,0.013308091512984283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.964285714285714,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Centrum: 0 - 3 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.173,0.021,,,***,,,high,distance to city centre 0-3 km; reference >10 km,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,18.860398900722064,2.4966188206764675,100*(exp(b - se^2/2) - 1),reported,0.10975480527750653,0.013322837634841836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.238095238095237,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Centrum: 3 - 5 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.212,0.047,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,23.47833134628314,5.809895059868967,100*(exp(b - se^2/2) - 1),reported,0.06034955791052257,0.013379383121672455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.51063829787234,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Centrum: 3 - 5 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.209,0.023,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,23.211905993542302,2.834623496619586,100*(exp(b - se^2/2) - 1),reported,0.12090634306888264,0.013305482730068423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.08695652173913,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Centrum: 3 - 5 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.152,0.017,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,16.39920274313409,1.9790724019345913,100*(exp(b - se^2/2) - 1),reported,0.1189943549117088,0.013308579167756908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.941176470588234,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Centrum: 5- 7 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.126,0.033,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,13.366471975801097,3.743131155333983,100*(exp(b - se^2/2) - 1),reported,0.05111130785303759,0.013386294913890798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.818181818181818,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Centrum: 5- 7 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.118,0.02,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,12.501908504845073,2.2504882227346847,100*(exp(b - se^2/2) - 1),reported,0.07883636501178166,0.01336209576470876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8999999999999995,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Centrum: 5- 7 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.092,0.014,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,9.625738359282487,1.5349107509131437,100*(exp(b - se^2/2) - 1),reported,0.08774249124497477,0.013352118232930945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.571428571428571,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Centrum: 7 - 10 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.103,0.034,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,10.785088617051342,3.7688707908584247,100*(exp(b - se^2/2) - 1),reported,0.040572238021567335,0.013392777599352328,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.029411764705882,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Centrum: 7 - 10 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.104,0.016,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,10.945843514681242,1.775360727864932,100*(exp(b - se^2/2) - 1),reported,0.08679599260340812,0.013353229631293557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Centrum: 7 - 10 km,cbd_distance,D_location,dummy,"binary (distance band, km)",0.078,0.011,,,***,,,high,reference >10 km,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,8.10572527277833,1.1892349245370912,100*(exp(b - se^2/2) - 1),reported,0.09461881473796693,0.013343679001508155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.090909090909092,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Tram: 0 - 200 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.049,0.018,,,***,,,high,tram stop within 0-200 m; reference >800 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-4.797311115066016,1.7139260334573088,100*(exp(b - se^2/2) - 1),reported,-0.036463895183025834,0.013394900271315614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7222222222222223,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Tram: 0 - 200 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.032,0.016,,,*,,,high,tram stop within 0-200 m; reference >800 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,-3.1617378829641107,1.5496105313267163,100*(exp(b - se^2/2) - 1),reported,-0.02679800094369162,0.01339900047184581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Tram: 0 - 200 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.03,0.012,,,***,,,high,tram stop within 0-200 m; reference >800 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,-2.9624336014572816,1.16453464025821,100*(exp(b - se^2/2) - 1),reported,-0.03349073757537233,0.013396295030148931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Tram: 200 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.006,0.018,,,,,,high,reference tram >800 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-0.6143053813442045,1.7892323352970834,100*(exp(b - se^2/2) - 1),reported,-0.004467893470426545,0.013403680411279633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33333333333333337,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Tram: 200 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.003,0.015,,,,,,high,reference tram >800 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,0.289167284352021,1.5045067567550656,100*(exp(b - se^2/2) - 1),reported,0.002680753206595003,0.013403766032975013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Tram: 200 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.002,0.011,,,,,,high,reference tram >800 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,0.19413820466716025,1.1022022014674002,100*(exp(b - se^2/2) - 1),reported,0.002437049889416815,0.013403774391792482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18181818181818182,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Tram: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.045,0.019,,,**,,,high,reference tram >800 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,4.583906891890277,1.987452933826562,100*(exp(b - se^2/2) - 1),reported,0.03172989105143109,0.013397065110604239,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3684210526315788,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Tram: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.044,0.014,,,***,,,high,reference tram >800 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,4.487995163550607,1.4629752968438214,100*(exp(b - se^2/2) - 1),reported,0.0420889437396518,0.013391936644434665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.142857142857143,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Tram: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.035,0.01,,,***,,,high,reference tram >800 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,3.5567929108686336,1.0356197087996233,100*(exp(b - se^2/2) - 1),reported,0.04686180983096696,0.013389088523133415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5000000000000004,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Bus: 0 -100 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.044,0.023,,,**,,,high,bus stop within 0-100 m; reference >400 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-4.329912337728703,2.2009941021880075,100*(exp(b - se^2/2) - 1),reported,-0.025633653444480056,0.013399409755069121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9130434782608694,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Bus: 0 -100 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.027,0.02,,,,,,high,bus stop within 0-100 m; reference >400 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,-2.683341125804095,1.9467224830486738,100*(exp(b - se^2/2) - 1),reported,-0.018092187404415993,0.013401620299567403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3499999999999999,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Bus: 0 -100 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.031,0.016,,,**,,,high,bus stop within 0-100 m; reference >400 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,-3.0648511855722704,1.5511609169216416,100*(exp(b - se^2/2) - 1),reported,-0.025961136928035256,0.01339929647898594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9375,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Bus: 100 - 250 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.019,0.02,,,,,,high,reference bus >400 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-1.9016854007363526,1.962358724485612,100*(exp(b - se^2/2) - 1),reported,-0.01273259126387656,0.013402727646185851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.95,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Bus: 100 - 250 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.001,0.019,,,,,,high,reference bus >400 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,-0.11798034839810567,1.8981009496834125,100*(exp(b - se^2/2) - 1),reported,-0.0007054637295000216,0.01340381086050041,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.052631578947368425,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Bus: 100 - 250 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.007,0.015,,,,,,high,reference bus >400 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,-0.7087266032847706,1.4895366643998524,100*(exp(b - se^2/2) - 1),reported,-0.006254990924846091,0.01340355198181305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4666666666666667,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Bus: 250 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",-0.006,0.018,,,,,,high,reference bus >400 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-0.6143053813442045,1.7892323352970834,100*(exp(b - se^2/2) - 1),reported,-0.004467893470426545,0.013403680411279633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33333333333333337,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Bus: 250 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.007,0.019,,,,,,high,reference bus >400 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,0.6842805737830915,1.9133466588070123,100*(exp(b - se^2/2) - 1),reported,0.004938187123674966,0.013403650764260623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Bus: 250 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.003,0.016,,,,,,high,reference bus >400 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,0.2876128143062928,1.6048072072054034,100*(exp(b - se^2/2) - 1),reported,0.002513207224720603,0.01340377186517655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.1875,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Square meters: 0-35,living_area,A_structural,dummy,"binary (area band, m2)",0.054,0.011,,,***,,,high,usable area 0-35 m2; reference 35-60 m2; dependent variable is price per m2,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,5.54207472682795,1.161033062370588,100*(exp(b - se^2/2) - 1),reported,0.06565855464605322,0.013374890761233065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.909090909090909,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Square meters: 0-35,living_area,A_structural,dummy,"binary (area band, m2)",0.055,0.01,,,***,,,high,usable area 0-35 m2; reference 35-60 m2; dependent variable is price per m2,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,5.648778896541429,1.0565406146754943,100*(exp(b - se^2/2) - 1),reported,0.07352146220938077,0.013367538583523778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Square meters: 0-35,living_area,A_structural,dummy,"binary (area band, m2)",0.052,0.01,,,***,,,high,usable area 0-35 m2; reference 35-60 m2; dependent variable is price per m2,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,5.332307504293676,1.0533757425133647,100*(exp(b - se^2/2) - 1),reported,0.06953114497121082,0.013371374032925161,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.199999999999999,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Square meters: 60-90,living_area,A_structural,dummy,"binary (area band, m2)",0.0,0.01,,,,,,high,reference 35-60 m2,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-0.004999875002087428,1.0,100*(exp(b - se^2/2) - 1),reported,0.0,0.01340381419589974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Square meters: 60-90,living_area,A_structural,dummy,"binary (area band, m2)",-0.009,0.008,,,,,,high,reference 35-60 m2,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,-0.8991334011829832,0.7928323030183069,100*(exp(b - se^2/2) - 1),reported,-0.01507757686029332,0.01340229054248295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.125,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Square meters: 60-90,living_area,A_structural,dummy,"binary (area band, m2)",-0.005,0.008,,,,,,high,reference 35-60 m2,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,-0.5019360697210873,0.796009983354146,100*(exp(b - se^2/2) - 1),reported,-0.00837708992316135,0.013403343877058162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.625,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Square meters: >90,living_area,A_structural,dummy,"binary (area band, m2)",0.011,0.043,,,,,,high,reference 35-60 m2,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,1.0126428750624639,4.347561106512293,100*(exp(b - se^2/2) - 1),reported,0.003428862544361859,0.013403735400687268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2558139534883721,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Square meters: >90,living_area,A_structural,dummy,"binary (area band, m2)",-0.002,0.011,,,,,,high,reference 35-60 m2,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,-0.20583786271547444,1.0978021985340662,100*(exp(b - se^2/2) - 1),reported,-0.002437049889416815,0.013403774391792482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.18181818181818182,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Square meters: >90,living_area,A_structural,dummy,"binary (area band, m2)",0.003,0.01,,,,,,high,reference 35-60 m2,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,0.2954355531886499,1.003004504503377,100*(exp(b - se^2/2) - 1),reported,0.004021111749014758,0.013403705830049194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor 1,floor_level,A_structural,dummy,binary (floor),0.047,0.018,,,***,,,high,reference ground floor (Floor 0),"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,4.795222706689906,1.8866196163433802,100*(exp(b - se^2/2) - 1),reported,0.0349774324669682,0.013395612434158035,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.611111111111111,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor 1,floor_level,A_structural,dummy,binary (floor),0.038,0.011,,,***,,,high,reference ground floor (Floor 0),"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,3.866839153987822,1.1426043561663473,100*(exp(b - se^2/2) - 1),reported,0.04625452552320246,0.013389467914611236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4545454545454546,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor 1,floor_level,A_structural,dummy,binary (floor),0.038,0.011,,,***,,,high,reference ground floor (Floor 0),"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,3.866839153987822,1.1426043561663473,100*(exp(b - se^2/2) - 1),reported,0.04625452552320246,0.013389467914611236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4545454545454546,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor 2,floor_level,A_structural,dummy,binary (floor),0.036,0.022,,,*,,,high,reference ground floor,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,3.64050061289809,2.280642862280032,100*(exp(b - se^2/2) - 1),reported,0.021928240164577607,0.013400591211686316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6363636363636362,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor 2,floor_level,A_structural,dummy,binary (floor),0.02,0.011,,,*,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,2.0139619712742585,1.1222214740294312,100*(exp(b - se^2/2) - 1),reported,0.02436333734476866,0.013399835539622764,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8181818181818183,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor 2,floor_level,A_structural,dummy,binary (floor),0.025,0.011,,,**,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,2.5253090836054204,1.1278466325768717,100*(exp(b - se^2/2) - 1),reported,0.030449088867824405,0.013397599101842739,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.272727272727273,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor 3,floor_level,A_structural,dummy,binary (floor),0.034,0.02,,,*,,,high,reference ground floor,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,3.4377710497085046,2.069169213456236,100*(exp(b - se^2/2) - 1),reported,0.022780570792576588,0.01340033576033917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7000000000000002,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor 3,floor_level,A_structural,dummy,binary (floor),0.019,0.011,,,*,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,1.911998999285891,1.1210998134791488,100*(exp(b - se^2/2) - 1),reported,0.023145840250185037,0.013400223302738705,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7272727272727273,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor 3,floor_level,A_structural,dummy,binary (floor),0.024,0.011,,,**,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,2.4228350200930615,1.1267193496796837,100*(exp(b - se^2/2) - 1),reported,0.029232187751812223,0.013398086052913934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.181818181818182,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor 4,floor_level,A_structural,dummy,binary (floor),0.041,0.024,,,*,,,high,reference ground floor,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,4.155209534262538,2.500445053309151,100*(exp(b - se^2/2) - 1),reported,0.022892181879299513,0.013400301587882642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7083333333333333,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor 4,floor_level,A_structural,dummy,binary (floor),0.033,0.012,,,***,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,3.347612628137875,1.2402606470895667,100*(exp(b - se^2/2) - 1),reported,0.03683547343418786,0.01339471761243195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor 4,floor_level,A_structural,dummy,binary (floor),0.034,0.012,,,***,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,3.45101193180124,1.2415015280737416,100*(exp(b - se^2/2) - 1),reported,0.03795011590807103,0.013394158555789773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8333333333333335,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor 5-6,floor_level,A_structural,dummy,binary (floor),0.044,0.022,,,**,,,high,reference ground floor,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,4.472949975526586,2.2989611807545764,100*(exp(b - se^2/2) - 1),reported,0.02679800094369162,0.01339900047184581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor 5-6,floor_level,A_structural,dummy,binary (floor),0.034,0.013,,,***,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,3.4497188022341696,1.3449599887465533,100*(exp(b - se^2/2) - 1),reported,0.03503460846593142,0.013395585589914952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.615384615384616,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor 5-6,floor_level,A_structural,dummy,binary (floor),0.035,0.013,,,***,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,3.553220263141732,1.3463056214395102,100*(exp(b - se^2/2) - 1),reported,0.036063717079506674,0.013395094915245334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6923076923076925,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor 7-9,floor_level,A_structural,dummy,binary (floor),0.024,0.024,,,,,,high,reference ground floor,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,2.399536475435937,2.4582967629374917,100*(exp(b - se^2/2) - 1),reported,0.01340261027851321,0.01340261027851321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor 7-9,floor_level,A_structural,dummy,binary (floor),0.024,0.017,,,,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,2.414231863289018,1.7412935404140566,100*(exp(b - se^2/2) - 1),reported,0.018919644725254352,0.013401415013721834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4117647058823528,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor 7-9,floor_level,A_structural,dummy,binary (floor),0.021,0.017,,,,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,2.1074495712239294,1.7360774877837986,100*(exp(b - se^2/2) - 1),reported,0.016555383607973262,0.013401977206454543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2352941176470589,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Floor >=10,floor_level,A_structural,dummy,binary (floor),0.053,0.033,,,,,,high,reference ground floor,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,5.38556644580046,3.479617828893875,100*(exp(b - se^2/2) - 1),reported,0.021522351516992527,0.013400709435108557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.606060606060606,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Floor >=10,floor_level,A_structural,dummy,binary (floor),0.068,0.024,,,***,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,7.005708765586149,2.5688767403490584,100*(exp(b - se^2/2) - 1),reported,0.03795011590807103,0.013394158555789773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8333333333333335,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Floor >=10,floor_level,A_structural,dummy,binary (floor),0.056,0.024,,,**,,,high,reference ground floor,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,5.729313946017589,2.5382344409678668,100*(exp(b - se^2/2) - 1),reported,0.03126028139904691,0.013397263456734388,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3333333333333335,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Perpetual ownership,other,other,binary,binary,0.033,0.012,,,***,,,high,perpetual usufruct ownership vs private ownership (reference),"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,3.347612628137875,1.2402606470895667,100*(exp(b - se^2/2) - 1),reported,0.03683547343418786,0.01339471761243195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Perpetual ownership,other,other,binary,binary,0.033,0.009,,,***,,,high,perpetual usufruct ownership vs private ownership (reference),"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,3.35086812920955,0.930195485317175,100*(exp(b - se^2/2) - 1),reported,0.0490880693673816,0.013387655282013163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.666666666666667,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Perpetual ownership,other,other,binary,binary,0.032,0.008,,,***,,,high,perpetual usufruct ownership vs private ownership (reference),"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,3.2484465273592056,0.8260140042440947,100*(exp(b - se^2/2) - 1),reported,0.0535383614313231,0.013384590357830777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Other type of ownership,other,other,binary,binary,-0.001,0.044,,,,,,high,other ownership type vs private ownership,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-0.1966064757727093,4.3956021992668495,100*(exp(b - se^2/2) - 1),reported,-0.0003046321266808599,0.013403813573957837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.022727272727272728,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Other type of ownership,other,other,binary,binary,0.019,0.052,,,,,,high,other ownership type vs private ownership,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,1.780464609193677,5.299744572810521,100*(exp(b - se^2/2) - 1),reported,0.004897488759495067,0.013403653447039126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3653846153846154,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Other type of ownership,other,other,binary,binary,0.018,0.05,,,,,,high,other ownership type vs private ownership,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,1.6891067777802027,5.090814881948969,100*(exp(b - se^2/2) - 1),reported,0.004825316933966735,0.013403658149907599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35999999999999993,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Parking,parking,A_structural,binary,binary,0.001,0.011,,,,,,high,parking place,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,0.09399414683672891,1.101100550183379,100*(exp(b - se^2/2) - 1),reported,0.0012185276586217891,0.013403804244839682,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09090909090909091,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Parking,parking,A_structural,binary,binary,0.007,0.008,,,,,,high,parking place,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,0.6992332996607065,0.8056196458134789,100*(exp(b - se^2/2) - 1),reported,0.011727530865351398,0.013402892417544453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.875,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Parking,parking,A_structural,binary,binary,0.005,0.008,,,,,,high,parking place,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,0.4980360973294573,0.8040100166875208,100*(exp(b - se^2/2) - 1),reported,0.00837708992316135,0.013403343877058162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.625,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Age of the building: 0-2,age,A_structural,dummy,"binary (age band, years)",0.129,0.02,,,***,,,high,building age 0-2 years; reference >50 years,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,13.746260889318407,2.275380248331463,100*(exp(b - se^2/2) - 1),reported,0.08613330352178025,0.013354000546012443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.45,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Age of the building: 0-2,age,A_structural,dummy,"binary (age band, years)",0.135,0.014,,,***,,,high,building age 0-2 years; reference >50 years,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,14.442462524233424,1.6023514980918403,100*(exp(b - se^2/2) - 1),reported,0.12818478319383847,0.01329323677565732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.642857142857144,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Age of the building: 0-2,age,A_structural,dummy,"binary (age band, years)",0.119,0.013,,,***,,,high,building age 0-2 years; reference >50 years,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,12.627474405142557,1.4642808937748588,100*(exp(b - se^2/2) - 1),reported,0.12178318972829749,0.01330404593670477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.153846153846153,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,15,Age of the building: 2-10,age,A_structural,dummy,"binary (age band, years)",0.177,0.02,,,***,,,high,reference >50 years; SEs printed at top of p.16,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,19.339239077954385,2.3872621862542776,100*(exp(b - se^2/2) - 1),reported,0.1177978491037063,0.013310491424147605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.85,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,15,Age of the building: 2-10,age,A_structural,dummy,"binary (age band, years)",0.171,0.013,,,***,,,high,reference >50 years; SEs printed at top of p.16,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,18.639049478922033,1.5424379737282252,100*(exp(b - se^2/2) - 1),reported,0.17363359218660418,0.013200214610677509,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.153846153846155,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,15,Age of the building: 2-10,age,A_structural,dummy,"binary (age band, years)",0.157,0.013,,,***,,,high,reference >50 years; SEs printed at top of p.16,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,16.98967534484519,1.5209942980711877,100*(exp(b - se^2/2) - 1),reported,0.15979670666160803,0.013231574436948437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.076923076923077,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Age of the building: 10-50,age,A_structural,dummy,"binary (age band, years)",-0.014,0.016,,,,,,high,reference >50 years,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-1.4028668145037337,1.5777560708205791,100*(exp(b - se^2/2) - 1),reported,-0.011727530865351398,0.013402892417544453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.875,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Age of the building: 10-50,age,A_structural,dummy,"binary (age band, years)",0.002,0.01,,,,,,high,reference >50 years,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,0.19519024864151913,1.0020020013340003,100*(exp(b - se^2/2) - 1),reported,0.002680753206595003,0.013403766032975013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Age of the building: 10-50,age,A_structural,dummy,"binary (age band, years)",-0.005,0.01,,,,,,high,reference >50 years,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,-0.5037270187532417,0.9950124791926824,100*(exp(b - se^2/2) - 1),reported,-0.006701756593068863,0.013403513186137726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Primary market,other,other,binary,binary,-0.082,0.019,,,***,,,high,primary (developer/new-build) market vs secondary market,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,pct_premium,-7.889431588546358,1.7504167215230626,100*(exp(b - se^2/2) - 1),reported,-0.05775149124656311,0.013381443093715842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.315789473684211,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Primary market,other,other,binary,binary,-0.069,0.011,,,***,,,high,primary (developer/new-build) market vs secondary market,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,pct_premium,-6.67297844778727,1.0266593480860222,100*(exp(b - se^2/2) - 1),reported,-0.08378285396225754,0.013356686863548302,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.272727272727273,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Primary market,other,other,binary,binary,-0.065,0.011,,,***,,,high,primary (developer/new-build) market vs secondary market,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,pct_premium,-6.298922748921488,1.0307742097151438,100*(exp(b - se^2/2) - 1),reported,-0.07895708189532166,0.013361967705362125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.90909090909091,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Living condition index,other,other,level,index (0-1),0.169,0.11,,,,,,high,district-level living condition index (GUS/MOBP),"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,semi_elasticity,16.900000000000002,11.0,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.020588767569317993,0.013400972974112304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5363636363636364,5566.0,index,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Living condition index,other,other,level,index (0-1),0.219,0.065,,,***,,,high,district-level living condition index (GUS/MOBP),"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,semi_elasticity,21.9,6.5,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.04511456164378708,0.01339016669792767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3692307692307693,5566.0,index,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Living condition index,other,other,level,index (0-1),0.122,0.047,,,***,,,high,district-level living condition index (GUS/MOBP),"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,semi_elasticity,12.2,4.7,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.03477183933899248,0.01339570859780858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.595744680851064,5566.0,index,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Population density (thousands/m2),density,F_neighbourhood,level,thousand inhabitants per km2 (printed as thousands/m2),-0.009,0.006,,,,,,high,district level,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,semi_elasticity,-0.8999999999999999,0.6,100*b (unit as reported: thousand inhabitants per km2; no harmonised unit defined for this characteristic),reported,-0.0201016587568113,0.0134011058378742,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4999999999999998,5566.0,thousand inhabitants per km2,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Population density (thousands/m2),density,F_neighbourhood,level,thousand inhabitants per km2 (printed as thousands/m2),-0.013,0.004,,,***,,,high,district level,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,semi_elasticity,-1.3,0.4,100*b (unit as reported: thousand inhabitants per km2; no harmonised unit defined for this characteristic),reported,-0.04352112107731466,0.013391114177635279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.25,5566.0,thousand inhabitants per km2,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Population density (thousands/m2),density,F_neighbourhood,level,thousand inhabitants per km2 (printed as thousands/m2),-0.008,0.003,,,***,,,high,district level,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,semi_elasticity,-0.8,0.3,100*b (unit as reported: thousand inhabitants per km2; no harmonised unit defined for this characteristic),reported,-0.03572069346059052,0.013395260047721447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6666666666666665,5566.0,thousand inhabitants per km2,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Share of green areas,green_space,E_environment,level,percent,-0.001,0.001,,,,,,high,district share of green areas,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,semi_elasticity,-0.1,0.1,"100*b (unit as reported: percent; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.01340261027851321,0.01340261027851321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5566.0,percent,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Share of green areas,green_space,E_environment,level,percent,-0.001,0.001,,,***,,,high,district share of green areas,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,semi_elasticity,-0.1,0.1,"100*b (unit as reported: percent; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.01340261027851321,0.01340261027851321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5566.0,percent,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Share of green areas,green_space,E_environment,level,percent,-0.001,0.0,,,**,,,high,district share of green areas,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,semi_elasticity,-0.1,,"100*b (unit as reported: percent; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,5566.0,percent,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Share of recreational areas,neighbourhood_amenities,F_neighbourhood,level,percent,0.006,0.002,,,***,,,high,district share of recreational areas,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,semi_elasticity,0.6,0.2,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),reported,0.040178971810945506,0.013392990603648501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,5566.0,percent,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Share of recreational areas,neighbourhood_amenities,F_neighbourhood,level,percent,0.006,0.001,,,***,,,high,district share of recreational areas,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,semi_elasticity,0.6,0.1,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),reported,0.08016405884111773,0.013360676473519621,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0,5566.0,percent,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Share of recreational areas,neighbourhood_amenities,F_neighbourhood,level,percent,0.005,0.001,,,***,,,high,district share of recreational areas,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,semi_elasticity,0.5,0.1,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),reported,0.06686906615607269,0.013373813231214537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,5566.0,percent,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Crimes per 1000 inhabitants,crime,F_neighbourhood,level,crimes per 1000 inhabitants,-0.0,0.001,,,,,,high,district level,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,semi_elasticity,-0.0,0.1,100*b (unit as reported: crimes per 1000 inhabitants; no harmonised unit defined for this characteristic),reported,-0.0,0.01340381419589974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.0,5566.0,crimes per 1000 inhabitants,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Crimes per 1000 inhabitants,crime,F_neighbourhood,level,crimes per 1000 inhabitants,-0.001,0.001,,,**,,,high,district level,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,semi_elasticity,-0.1,0.1,100*b (unit as reported: crimes per 1000 inhabitants; no harmonised unit defined for this characteristic),reported,-0.01340261027851321,0.01340261027851321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,5566.0,crimes per 1000 inhabitants,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Crimes per 1000 inhabitants,crime,F_neighbourhood,level,crimes per 1000 inhabitants,-0.001,0.0,,,**,,,high,district level,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,semi_elasticity,-0.1,,100*b (unit as reported: crimes per 1000 inhabitants; no harmonised unit defined for this characteristic),,,,,,5566.0,crimes per 1000 inhabitants,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c1,A.1,OLS,16,Children per 1000 children in kindergartens,public_services,F_neighbourhood,level,children in kindergartens per 1000 children,0.0,0.0,,,,,,high,district childcare availability; coefficient rounds to 0.000,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Full sample; year/quarter FE; OLS,semi_elasticity,0.0,,100*b (unit as reported: children in kindergartens per 1000 children; no harmonised unit defined for this characteristic),,,,,,5566.0,children in kindergartens per 1000 children,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c2,A.1,SEM,16,Children per 1000 children in kindergartens,public_services,F_neighbourhood,level,children in kindergartens per 1000 children,0.0,0.0,,,,,,high,district childcare availability; coefficient rounds to 0.000,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Full sample; year/quarter FE; SEM,semi_elasticity,0.0,,100*b (unit as reported: children in kindergartens per 1000 children; no harmonised unit defined for this characteristic),,,,,,5566.0,children in kindergartens per 1000 children,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:TA1-c3,A.1,SAR,16,Children per 1000 children in kindergartens,public_services,F_neighbourhood,level,children in kindergartens per 1000 children,0.0,0.0,,,*,,,high,district childcare availability; coefficient rounds to 0.000,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Full sample; year/quarter FE; SAR,semi_elasticity,0.0,,100*b (unit as reported: children in kindergartens per 1000 children; no harmonised unit defined for this characteristic),,,,,,5566.0,children in kindergartens per 1000 children,unit_not_harmonisable,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,False,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c1,6,OLS,11,Metro: 0 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.13,0.026,,,***,,,high,main effect in interaction model; reference >1500 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,13.844352437588437,2.9609537966440165,100*(exp(b - se^2/2) - 1),reported,0.06686906615607269,0.013373813231214537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c2,6,SEM,11,Metro: 0 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.132,0.021,,,***,,,high,main effect in interaction model; reference >1500 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,14.085673262128374,2.396327470461194,100*(exp(b - se^2/2) - 1),reported,0.0839550960322227,0.013356492550580884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.285714285714286,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c3,6,SAR,11,Metro: 0 - 400 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.084,0.015,,,***,,,high,main effect in interaction model; reference >1500 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,8.750654244066624,1.6314433407132392,100*(exp(b - se^2/2) - 1),reported,0.0748507932166282,0.013366213074397889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.6000000000000005,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c1,6,OLS,11,Metro: 0 - 400 m x MOT,transit_distance,D_location,other,binary x binary interaction,0.001,0.025,,,,,,high,interaction with MOT dummy (bus/tram closer than metro by >250 m),"ln(price per m2, PLN)",log,OLS,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),other,0.001,0.025,not harmonised,reported,0.0005361524907749113,0.013403812269372782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.04,5566.0,binary x binary interaction,nonstandard_x_transform,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c2,6,SEM,11,Metro: 0 - 400 m x MOT,transit_distance,D_location,other,binary x binary interaction,-0.006,0.038,,,,,,high,interaction with MOT dummy (bus/tram closer than metro by >250 m),"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),other,-0.006,0.038,not harmonised,reported,-0.002116386975378221,0.013403784177395397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15789473684210528,5566.0,binary x binary interaction,nonstandard_x_transform,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c3,6,SAR,11,Metro: 0 - 400 m x MOT,transit_distance,D_location,other,binary x binary interaction,-0.002,0.036,,,,,,high,interaction with MOT dummy (bus/tram closer than metro by >250 m),"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),other,-0.002,0.036,not harmonised,reported,-0.0007446561377558999,0.013403810479606199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.05555555555555556,5566.0,binary x binary interaction,nonstandard_x_transform,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c1,6,OLS,11,Metro: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.181,0.034,,,***,,,high,main effect in interaction model; reference >1500 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,19.77226954666731,4.074611609596879,100*(exp(b - se^2/2) - 1),reported,0.0711746320190544,0.013369820379269887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.323529411764706,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c2,6,SEM,11,Metro: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.143,0.032,,,***,,,high,main effect in interaction model; reference >1500 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,15.313924320342466,3.6919353653312337,100*(exp(b - se^2/2) - 1),reported,0.059791131241338696,0.013379833564495375,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.468749999999999,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c3,6,SAR,11,Metro: 400 - 800 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.115,0.028,,,***,,,high,main effect in interaction model; reference >1500 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,12.143374936892837,3.141245625201427,100*(exp(b - se^2/2) - 1),reported,0.05496814801698132,0.01338354908239545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.107142857142858,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c1,6,OLS,11,Metro: 400 - 800 m x MOT,transit_distance,D_location,other,binary x binary interaction,-0.055,0.025,,,**,,,high,interaction with MOT dummy,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),other,-0.055,0.025,not harmonised,reported,-0.029475578546806904,0.013397990248548593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1999999999999997,5566.0,binary x binary interaction,nonstandard_x_transform,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c2,6,SEM,11,Metro: 400 - 800 m x MOT,transit_distance,D_location,other,binary x binary interaction,-0.011,0.032,,,,,,high,interaction with MOT dummy,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),other,-0.011,0.032,not harmonised,reported,-0.004607512222234296,0.013403671919227044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34375,5566.0,binary x binary interaction,nonstandard_x_transform,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c3,6,SAR,11,Metro: 400 - 800 m x MOT,transit_distance,D_location,other,binary x binary interaction,-0.03,0.013,,,,,,high,interaction with MOT dummy,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),other,-0.03,0.013,not harmonised,reported,-0.030917092004567517,0.013397406535312587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.307692307692308,5566.0,binary x binary interaction,nonstandard_x_transform,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,False,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c1,6,OLS,11,Metro: 800 - 1500 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.108,0.021,,,***,,,high,reference >1500 m,"ln(price per m2, PLN)",log,OLS,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,11.380212493925935,2.339500265311582,100*(exp(b - se^2/2) - 1),reported,0.06877070007477712,0.013372080570095554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.142857142857142,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,ols,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c2,6,SEM,11,Metro: 800 - 1500 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.11,0.017,,,***,,,high,reference >1500 m,"ln(price per m2, PLN)",log,spatial_error,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,11.611677993121127,1.8976727197800813,100*(exp(b - se^2/2) - 1),reported,0.08640618939333616,0.013353683815333773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.470588235294117,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa,dde22a8458111f4d97568d09ce7efc13ea6e96aa:T6-c3,6,SAR,11,Metro: 800 - 1500 m,transit_distance,D_location,dummy,"binary (distance band, m)",0.069,0.029,,,***,,,high,reference >1500 m,"ln(price per m2, PLN)",log,spatial_lag,5576.0,,se,Model with metro x MOT interactions; same controls as Table 4/A.1 (other coefficients not printed in dump; Appendix Table A.3),pct_premium,7.098576493493258,3.1071650065302046,100*(exp(b - se^2/2) - 1),reported,0.031875627720011304,0.01339700295478736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3793103448275863,5566.0,binary,,2020.0,Poland,Europe,High income,2006.0,2013.0,condo_apartment,transaction,journal,True,False,clustered,,2009.5,spatial,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Number of bathrooms,bathrooms,A_structural,level,count,0.1104,,21.19,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,11.04,0.5210004719207173,100*b/1 (per unit count),|b/t|,0.22078887470063896,0.01041948441248886,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.19,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Number of bathrooms,bathrooms,A_structural,level,count,0.1103,,21.14,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,11.03,0.5217596972563859,100*b/1 (per unit count),|b/t|,0.22029321133407645,0.01042068170927514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.14,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Number of bathrooms,bathrooms,A_structural,level,count,0.1132,,22.08,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,11.32,0.5126811594202899,100*b/1 (per unit count),|b/t|,0.22958278554681366,0.01039777108454772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.08,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Number of bathrooms,bathrooms,A_structural,level,count,0.1121,,21.88,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,11.21,0.5123400365630714,100*b/1 (per unit count),|b/t|,0.2276114333829094,0.010402716333770996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.88,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Total number of rooms,bedrooms,A_structural,level,count,0.013,,6.15,,*,,,high,"total number of rooms, not bedrooms; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,1.3,0.21138211382113817,100*b/1 (per unit count),|b/t|,0.06555988146898893,0.010660143328290884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.15,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Total number of rooms,bedrooms,A_structural,level,count,0.0131,,6.16,,*,,,high,"total number of rooms, not bedrooms; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,1.31,0.2126623376623377,100*b/1 (per unit count),|b/t|,0.06566602360555103,0.010660068767134906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.16,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Total number of rooms,bedrooms,A_structural,level,count,0.011,,5.28,,*,,,high,"total number of rooms, not bedrooms; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,1.0999999999999999,0.20833333333333334,100*b/1 (per unit count),|b/t|,0.05631738605289554,0.010666171600927185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.28,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Total number of rooms,bedrooms,A_structural,level,count,0.0114,,5.48,,*,,,high,"total number of rooms, not bedrooms; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,1.1400000000000001,0.20802919708029197,100*b/1 (per unit count),|b/t|,0.058443466540159854,0.010664866156963475,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.48,8762.0,count,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Floor space (100s sq ft),living_area,A_structural,level,100 sqft,0.0211,,34.06,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,0.2271185097925751,0.00666818877840796,100*b/9.2903 (X per 100sqft->m2),|b/t|,0.34193472112611656,0.010039187349563022,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.06,8762.0,100sqft,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Floor space (100s sq ft),living_area,A_structural,level,100 sqft,0.021,,34.04,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,0.22604211875090416,0.0066404852747034135,100*b/9.2903 (X per 100sqft->m2),|b/t|,0.3417573946268759,0.010039876457898823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.04,8762.0,100sqft,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Floor space (100s sq ft),living_area,A_structural,level,100 sqft,0.0214,,35.23,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,0.230347682917588,0.006538395768310759,100*b/9.2903 (X per 100sqft->m2),|b/t|,0.3522444747960612,0.009998423922681273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.23,8762.0,100sqft,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Floor space (100s sq ft),living_area,A_structural,level,100 sqft,0.0214,,35.21,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,0.230347682917588,0.006542109710809089,100*b/9.2903 (X per 100sqft->m2),|b/t|,0.35206929913534496,0.009999128064054104,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.21,8762.0,100sqft,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Has basement,basement,A_structural,binary,binary,0.0974,,15.44,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,10.227927337296784,0.6953635845704899,100*(exp(b - se^2/2) - 1),|b/t|,0.162748331518725,0.010540695046549546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.44,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Has basement,basement,A_structural,binary,binary,0.097,,15.13,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,10.183772938258784,0.7064141192414939,100*(exp(b - se^2/2) - 1),|b/t|,0.15956474268227508,0.010546248690170198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.13,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Has basement,basement,A_structural,binary,binary,0.1087,,17.43,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,11.480617288050187,0.6952483504999537,100*(exp(b - se^2/2) - 1),|b/t|,0.18306031393080896,0.010502599766540962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.43,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Has basement,basement,A_structural,binary,binary,0.1036,,16.43,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,10.913465377485299,0.6993830461445232,100*(exp(b - se^2/2) - 1),|b/t|,0.17288086374399667,0.010522268030675392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.43,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Age,age,A_structural,level,years,-0.0024,,15.71,,*,,,high,t-values printed as absolute values; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,-0.24,0.015276893698281347,100*b/1 (per year),|b/t|,0.16551700280185855,0.010535773571092205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.71,8762.0,years,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Age,age,A_structural,level,years,-0.0024,,14.65,,*,,,high,t-values printed as absolute values; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,-0.24,0.016382252559726963,100*b/1 (per year),|b/t|,0.15462550952910878,0.010554642288676367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.65,8762.0,years,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Age,age,A_structural,level,years,-0.0021,,14.17,,*,,,high,t-values printed as absolute values; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,-0.21,0.014820042342978122,100*b/1 (per year),|b/t|,0.14967465522767434,0.010562784419737075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.17,8762.0,years,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Age,age,A_structural,level,years,-0.0024,,15.07,,*,,,high,t-values printed as absolute values; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,-0.24,0.0159256801592568,100*b/1 (per year),|b/t|,0.15894798549663772,0.010547311579073506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.07,8762.0,years,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Lot area <= 0.5 acre,lot_size,A_structural,dummy,binary,-0.0492,,6.23,,*,,,high,dummy for lot <= 0.5 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,-4.80389733739065,0.7518128901401923,100*(exp(b - se^2/2) - 1),|b/t|,0.06640895595195097,0.010659543491484907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.23,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Lot area <= 0.5 acre,lot_size,A_structural,dummy,binary,-0.0493,,6.24,,*,,,high,dummy for lot <= 0.5 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,-4.813419005327524,0.7520584783242094,100*(exp(b - se^2/2) - 1),|b/t|,0.06651508015760503,0.010659467973975163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.24,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Lot area <= 0.5 acre,lot_size,A_structural,dummy,binary,-0.0416,,5.35,,*,,,high,dummy for lot <= 0.5 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,-4.077559331903069,0.7458867599347334,100*(exp(b - se^2/2) - 1),|b/t|,0.057061602864305616,0.010665720161552452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.35,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Lot area <= 0.5 acre,lot_size,A_structural,dummy,binary,-0.0426,,5.49,,*,,,high,dummy for lot <= 0.5 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,-4.173421815016354,0.7435947412010854,100*(exp(b - se^2/2) - 1),|b/t|,0.05854974993413822,0.010664799623704596,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.49,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Lot area >= 1.0 acre,lot_size,A_structural,dummy,binary,0.0956,,6.1,,*,,,high,dummy for lot >= 1.0 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,10.018372892522699,1.7244341296508234,100*(exp(b - se^2/2) - 1),|b/t|,0.0650291374754857,0.010660514340243557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.1,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Lot area >= 1.0 acre,lot_size,A_structural,dummy,binary,0.0959,,6.11,,*,,,high,dummy for lot >= 1.0 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,10.05134288054419,1.727532544769153,100*(exp(b - se^2/2) - 1),|b/t|,0.0651352907038068,0.010660440377055122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.11,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Lot area >= 1.0 acre,lot_size,A_structural,dummy,binary,0.0951,,6.18,,*,,,high,dummy for lot >= 1.0 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,9.963862087412245,1.692362708755823,100*(exp(b - se^2/2) - 1),|b/t|,0.06587830118820924,0.010659919286118001,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.18,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Lot area >= 1.0 acre,lot_size,A_structural,dummy,binary,0.0996,,6.47,,*,,,high,dummy for lot >= 1.0 acre (reference 0.5-1.0 acre); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,10.459804700930553,1.7006337285283808,100*(exp(b - se^2/2) - 1),|b/t|,0.06895530672122023,0.010657698102197874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.47,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,True
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Has garage,garage,A_structural,binary,binary,0.0855,,19.81,,*,,,high,"Table 1 and Table 5 label this variable 'Garage bays' (mean 1.622), so it may be a count of bays; Table 2 prints 'Has garage'; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,8.925141607806374,0.4701255098178578,100*(exp(b - se^2/2) - 1),|b/t|,0.20704687345963874,0.010451634197861622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.81,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Has garage,garage,A_structural,binary,binary,0.0855,,19.81,,*,,,high,"Table 1 and Table 5 label this variable 'Garage bays' (mean 1.622), so it may be a count of bays; Table 2 prints 'Has garage'; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,8.925141607806374,0.4701255098178578,100*(exp(b - se^2/2) - 1),|b/t|,0.20704687345963874,0.010451634197861622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.81,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Has garage,garage,A_structural,binary,binary,0.0842,,19.83,,*,,,high,"Table 1 and Table 5 label this variable 'Garage bays' (mean 1.622), so it may be a count of bays; Table 2 prints 'Has garage'; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,8.783663483339389,0.46190958326255205,100*(exp(b - se^2/2) - 1),|b/t|,0.20724693225329172,0.010451181656746935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.83,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Has garage,garage,A_structural,binary,binary,0.0843,,19.88,,*,,,high,"Table 1 and Table 5 label this variable 'Garage bays' (mean 1.622), so it may be a count of bays; Table 2 prints 'Has garage'; * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,8.794545001510269,0.46134117710835665,100*(exp(b - se^2/2) - 1),|b/t|,0.20774696545266655,0.010450048564017433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.88,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Has porch or deck,terrace_balcony,C_exterior,binary,binary,0.0477,,8.66,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,4.8840040846703126,0.577718578276203,100*(exp(b - se^2/2) - 1),|b/t|,0.09212246975172794,0.010637698585649877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.66,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Has porch or deck,terrace_balcony,C_exterior,binary,binary,0.0478,,8.67,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,4.8944900147013914,0.5783198184081324,100*(exp(b - se^2/2) - 1),|b/t|,0.09222794241303883,0.010637594280627317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.67,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Has porch or deck,terrace_balcony,C_exterior,binary,binary,0.0456,,8.42,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,4.664031608642771,0.5668348969311007,100*(exp(b - se^2/2) - 1),|b/t|,0.08959020351833574,0.01064016668863845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.42,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Has porch or deck,terrace_balcony,C_exterior,binary,binary,0.047,,8.68,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,4.810664399606868,0.567531502612256,100*(exp(b - se^2/2) - 1),|b/t|,0.09233341197007104,0.010637489858303114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.68,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Exterior brick or stone,roof_materials,A_structural,binary,binary,0.084,,13.95,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,8.760917607675612,0.6549163231537019,100*(exp(b - se^2/2) - 1),|b/t|,0.14740172209873026,0.010566431691665252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.95,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Exterior brick or stone,roof_materials,A_structural,binary,binary,0.0841,,13.96,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,8.77179237620247,0.6552918150836846,100*(exp(b - se^2/2) - 1),|b/t|,0.14750508820327207,0.010566267063271637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.96,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Exterior brick or stone,roof_materials,A_structural,binary,binary,0.0777,,13.12,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,8.077941720032022,0.6400764740035004,100*(exp(b - se^2/2) - 1),|b/t|,0.13880579386876996,0.010579709898534295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.12,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Exterior brick or stone,roof_materials,A_structural,binary,binary,0.0784,,13.25,,*,,,high,* = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,8.153626141692992,0.6399541677586107,100*(exp(b - se^2/2) - 1),|b/t|,0.1401542695681686,0.010577680722125931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.25,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,No air conditioning,air_conditioning,A_structural,binary,binary,-0.2511,,29.12,,*,,,high,reverse-coded: dummy = 1 if NO air conditioning; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,pct_premium,-22.20843483186774,0.6708169036177157,100*(exp(b - se^2/2) - 1),|b/t|,0.29705047779769483,0.01020090926503073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.12,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,No air conditioning,air_conditioning,A_structural,binary,binary,-0.2512,,29.11,,*,,,high,reverse-coded: dummy = 1 if NO air conditioning; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,pct_premium,-22.21621789195164,0.6712474606670792,100*(exp(b - se^2/2) - 1),|b/t|,0.2969574656567822,0.010201218332421236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.11,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,True
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,No air conditioning,air_conditioning,A_structural,binary,binary,-0.2444,,28.8,,*,,,high,reverse-coded: dummy = 1 if NO air conditioning; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,pct_premium,-21.685389740161465,0.6646104145118957,100*(exp(b - se^2/2) - 1),|b/t|,0.2940699045385413,0.010210760574254909,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.8,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,No air conditioning,air_conditioning,A_structural,binary,binary,-0.2451,,28.91,,*,,,high,reverse-coded: dummy = 1 if NO air conditioning; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,pct_premium,-21.74018542396058,0.6635133144033968,100*(exp(b - se^2/2) - 1),|b/t|,0.29509544980919195,0.010207383251788028,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.91,8762.0,binary,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Effective tax rate,public_services,F_neighbourhood,level,"annual property tax per $1,000 of sales price",-0.2393,,70.28,,*,,,high,annual property tax payment divided by sales price in thousands (Table 5 says per $100); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,-23.93,0.3404951622083096,100*b (unit as reported: annual property tax per $1000 of sales price; no harmonised unit defined for this characteristic),|b/t|,0.6004145489946521,0.008543177987971714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),70.28,8762.0,annual property tax per $1000 of sales price,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Effective tax rate,public_services,F_neighbourhood,level,"annual property tax per $1,000 of sales price",-0.2393,,70.2,,*,,,high,annual property tax payment divided by sales price in thousands (Table 5 says per $100); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,-23.93,0.34088319088319086,100*b (unit as reported: annual property tax per $1000 of sales price; no harmonised unit defined for this characteristic),|b/t|,0.5999772092758165,0.008546683892817899,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),70.2,8762.0,annual property tax per $1000 of sales price,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Effective tax rate,public_services,F_neighbourhood,level,"annual property tax per $1,000 of sales price",-0.2385,,71.24,,*,,,high,annual property tax payment divided by sales price in thousands (Table 5 says per $100); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,-23.849999999999998,0.3347838293093768,100*b (unit as reported: annual property tax per $1000 of sales price; no harmonised unit defined for this characteristic),|b/t|,0.6056207977477659,0.00850113416265814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.24,8762.0,annual property tax per $1000 of sales price,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Effective tax rate,public_services,F_neighbourhood,level,"annual property tax per $1,000 of sales price",-0.2376,,71.01,,*,,,high,annual property tax payment divided by sales price in thousands (Table 5 says per $100); * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,-23.76,0.33460076045627374,100*b (unit as reported: annual property tax per $1000 of sales price; no harmonised unit defined for this characteristic),|b/t|,0.6043804795830968,0.008511202359992911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),71.01,8762.0,annual property tax per $1000 of sales price,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,School district SAT,school_quality,F_neighbourhood,level,SAT points,0.0004,,5.56,,*,,,high,mean SAT score of school district; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,0.04,0.007194244604316548,100*b (unit as reported: sat points; no harmonised unit defined for this characteristic),|b/t|,0.05929367774581819,0.010664330529823416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.56,8762.0,sat points,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,School district SAT,school_quality,F_neighbourhood,level,SAT points,0.0004,,5.45,,*,,,high,mean SAT score of school district; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,0.04,0.007339449541284404,100*b (unit as reported: sat points; no harmonised unit defined for this characteristic),|b/t|,0.058124604448171815,0.0106650650363618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.45,8762.0,sat points,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,School district SAT,school_quality,F_neighbourhood,level,SAT points,0.0003,,4.6,,*,,,high,mean SAT score of school district; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,0.03,0.006521739130434782,100*b (unit as reported: sat points; no harmonised unit defined for this characteristic),|b/t|,0.04908315090235576,0.010670250196164296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6,8762.0,sat points,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,School district SAT,school_quality,F_neighbourhood,level,SAT points,0.0003,,3.66,,*,,,high,mean SAT score of school district; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,0.03,0.008196721311475409,100*b (unit as reported: sat points; no harmonised unit defined for this characteristic),|b/t|,0.039070388840599254,0.01067496962857903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.66,8762.0,sat points,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Neighborhood median income,income,F_neighbourhood,level,1000 USD,0.0051,,22.36,,*,,,medium,"1-mile radius neighbourhood, Census 2000; unit $1,000 inferred from Table 5 (unit change = +0.51%); * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,0.51,0.022808586762075138,100*b (unit as reported: 1000 usd; no harmonised unit defined for this characteristic),|b/t|,0.23233793422319424,0.010390784178139278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.36,8762.0,1000 usd,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Neighborhood median income,income,F_neighbourhood,level,1000 USD,0.0051,,17.88,,*,,,medium,"1-mile radius neighbourhood, Census 2000; unit $1,000 inferred from Table 5 (unit change = +0.51%); * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,0.51,0.02852348993288591,100*b (unit as reported: 1000 usd; no harmonised unit defined for this characteristic),|b/t|,0.18762213455959525,0.010493407973131726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.88,8762.0,1000 usd,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Neighborhood median income,income,F_neighbourhood,level,1000 USD,0.0049,,22.15,,*,,,medium,"1-mile radius neighbourhood, Census 2000; unit $1,000 inferred from Table 5 (unit change = +0.51%); * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,0.49,0.022121896162528215,100*b (unit as reported: 1000 usd; no harmonised unit defined for this characteristic),|b/t|,0.23027209318337324,0.010396031294960417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.15,8762.0,1000 usd,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Neighborhood median income,income,F_neighbourhood,level,1000 USD,0.0058,,20.54,,*,,,medium,"1-mile radius neighbourhood, Census 2000; unit $1,000 inferred from Table 5 (unit change = +0.51%); * = p<0.001; t-value in parentheses",log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,0.58,0.028237585199610515,100*b (unit as reported: 1000 usd; no harmonised unit defined for this characteristic),|b/t|,0.21433202381127947,0.010434859971337853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.54,8762.0,1000 usd,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Neighborhood percent black,demographics,F_neighbourhood,level,percent,-0.0005,,3.31,,*,,,high,percent African-American within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,-0.05,0.015105740181268881,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.03533906182084822,0.01067645372231064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.31,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Neighborhood percent black,demographics,F_neighbourhood,level,percent,-0.0005,,3.31,,*,,,high,percent African-American within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,-0.05,0.015105740181268881,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.03533906182084822,0.01067645372231064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.31,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Neighborhood percent black,demographics,F_neighbourhood,level,percent,-0.0009,,6.73,,*,,,high,percent African-American within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,-0.09,0.01337295690936107,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.0717123318176456,0.010655621369635304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.73,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Neighborhood percent black,demographics,F_neighbourhood,level,percent,-0.001,,6.99,,*,,,high,percent African-American within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,-0.1,0.014306151645207441,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.07446771414677752,0.010653464112557584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.99,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c1,2,1,8,Neighborhood percent vacant,vacancy,F_neighbourhood,level,percent,-0.0216,,22.34,,*,,,high,percent housing units vacant within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,Base Model,semi_elasticity,-2.16,0.09668755595344673,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.2321413223850021,0.010391285693151392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.34,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Neighborhood percent vacant,vacancy,F_neighbourhood,level,percent,-0.0216,,22.34,,*,,,high,percent housing units vacant within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,-2.16,0.09668755595344673,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.2321413223850021,0.010391285693151392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.34,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c3,2,3,8,Neighborhood percent vacant,vacancy,F_neighbourhood,level,percent,-0.0135,,12.74,,*,,,high,percent housing units vacant within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8627,t,Center Township,semi_elasticity,-1.35,0.10596546310832025,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.13485969169913165,0.010585533100402797,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.74,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Neighborhood percent vacant,vacancy,F_neighbourhood,level,percent,-0.0129,,12.08,,*,,,high,percent housing units vacant within 1-mile radius; * = p<0.001; t-value in parentheses,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,-1.29,0.10678807947019867,100*b (unit as reported: percent; no harmonised unit defined for this characteristic),|b/t|,0.12799076187139963,0.010595261744321162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.08,8762.0,percent,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c2,2,2,8,Distance to CBD,cbd_distance,D_location,level,km,-0.0004,,0.35,,,,,high,"not significant; t printed as absolute value; Table 3 reports SE 0.0011, p 0.7285",log sales price,log,OLS,8772.0,0.8579,t,CBD Distance,semi_elasticity,-0.04,0.1142857142857143,100*b/1 (X per km->km),|b/t|,0.003739068177313053,0.010683051935180151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35,8762.0,km,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T2-c4,2,4,8,Distance to CBD,cbd_distance,D_location,level,km,-0.0055,,5.1,,*,,,high,model includes Center Township dummy; * = p<0.001,log sales price,log,OLS,8772.0,0.8631,t,Center Township + CBD,semi_elasticity,-0.5499999999999999,0.10784313725490195,100*b/1 (X per km->km),|b/t|,0.05440325752279412,0.010667305396626298,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.1,8762.0,km,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m2,3,Free-flow travel time to CBD,9,Free-flow travel time to CBD,cbd_distance,D_location,level,minutes,-0.0033,0.0013,-2.6385,0.0083,,,,high,free-flow road travel time to CBD,log sales price,log,OLS,8772.0,0.858,none,"Base model (Table 2 Model 1 covariates) + Free-flow travel time to CBD; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.33,0.13,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.02817623834315735,0.010678885102580006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6385,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m3,3,Congested travel time to CBD,9,Congested travel time to CBD,cbd_distance,D_location,level,minutes,-0.0066,0.0012,-5.2895,0.0,,,,high,congested (peak) road travel time to CBD,log sales price,log,OLS,8772.0,0.8583,none,"Base model (Table 2 Model 1 covariates) + Congested travel time to CBD; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.66,0.12,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.0564183924392431,0.010666110679505264,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.2895,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m4,3,Distance to 3 TAZ employment centers,9,Distance to CBD,cbd_distance,D_location,level,km,0.0005,0.0012,0.4542,0.6497,,,,high,jointly with distances to 2 other employment centres,log sales price,log,OLS,8772.0,0.8605,none,"Base model (Table 2 Model 1 covariates) + Distance to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,0.05,0.12,100*b/1 (X per km->km),reported,0.0048522189866167156,0.010683000851203689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4542,8762.0,km,,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,True,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m4,3,Distance to 3 TAZ employment centers,9,Distance to Keystone/Castleton,employment_access,D_location,level,km,-0.0008,0.0006,-1.2444,0.2134,,,,high,airline distance to Keystone/Castleton employment centre,log sales price,log,OLS,8772.0,0.8605,none,"Base model (Table 2 Model 1 covariates) + Distance to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.08,0.06,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.013292908164837013,0.010682182710412259,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2444,8762.0,km,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m4,3,Distance to 3 TAZ employment centers,9,Distance to West 86th Street,employment_access,D_location,level,km,-0.0048,0.0006,-7.9205,0.0,,,,high,airline distance to West 86th Street employment centre,log sales price,log,OLS,8772.0,0.8605,none,"Base model (Table 2 Model 1 covariates) + Distance to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.48,0.06,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.08431440482332933,0.01064508614649698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.9205,8762.0,km,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m5,3,Free-flow travel time to 3 TAZ employment centers,9,Free-flow travel time to CBD,cbd_distance,D_location,level,minutes,-0.0032,0.0014,-2.3359,0.0195,,,,high,,log sales price,log,OLS,8772.0,0.8608,none,"Base model (Table 2 Model 1 covariates) + Free-flow travel time to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.32,0.13999999999999999,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.024946948961913195,0.010679801773155181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3359,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m5,3,Free-flow travel time to 3 TAZ employment centers,9,Free-flow time to Keyston/Castleton,employment_access,D_location,level,minutes,-0.0015,0.0006,-2.3661,0.018,,,,high,free-flow travel time to Keystone/Castleton employment centre,log sales price,log,OLS,8772.0,0.8608,none,"Base model (Table 2 Model 1 covariates) + Free-flow travel time to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.15,0.06,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,-0.025269274341851552,0.010679715287541336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3661,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m5,3,Free-flow travel time to 3 TAZ employment centers,9,Free-flow time to West 86th Street,employment_access,D_location,level,minutes,-0.006,0.0006,-9.2329,0.0,,,,high,free-flow travel time to West 86th Street employment centre,log sales price,log,OLS,8772.0,0.8608,none,"Base model (Table 2 Model 1 covariates) + Free-flow travel time to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.6,0.06,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,-0.09815989143768548,0.010631534126621697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.2329,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m6,3,Congested travel time to 3 TAZ employment centers,9,Congested travel time to CBD,cbd_distance,D_location,level,minutes,-0.005,0.0013,-3.8608,0.0001,,,,high,,log sales price,log,OLS,8772.0,0.861,none,"Base model (Table 2 Model 1 covariates) + Congested travel time to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.5,0.13,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",reported,-0.04121037690351354,0.010674051207913786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8608,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m6,3,Congested travel time to 3 TAZ employment centers,9,Congested time to Keystone/Castleton,employment_access,D_location,level,minutes,-0.001,0.0006,-1.7612,0.0782,,,,high,congested travel time to Keystone/Castleton employment centre,log sales price,log,OLS,8772.0,0.861,none,"Base model (Table 2 Model 1 covariates) + Congested travel time to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.1,0.06,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,-0.018811793116467936,0.010681236155160083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7612,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m6,3,Congested travel time to 3 TAZ employment centers,9,Congested time to West 86th Street,employment_access,D_location,level,minutes,-0.0063,0.0007,-9.4799,0.0,,,,high,congested travel time to West 86th Street employment centre,log sales price,log,OLS,8772.0,0.861,none,"Base model (Table 2 Model 1 covariates) + Congested travel time to 3 TAZ employment centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.63,0.06999999999999999,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,-0.1007595639246582,0.010628758101315222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.4799,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m7,3,Mean distance to 7 ZIP centers,9,Mean distance to 7 ZIP centers,employment_access,D_location,level,km,-0.0096,0.0008,-11.6072,0.0,,,,high,mean airline distance to 7 ZIP-code employment centres,log sales price,log,OLS,8772.0,0.8601,none,"Base model (Table 2 Model 1 covariates) + Mean distance to 7 ZIP centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.96,0.08,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.12305870296561963,0.010601928369082952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.6072,8762.0,km,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m8,3,Mean distance to 5 TAZ centers,9,Mean distance to 5 TAZ centers,employment_access,D_location,level,km,-0.0089,0.0008,-11.8679,0.0,,,,high,mean airline distance to 5 TAZ employment centres,log sales price,log,OLS,8772.0,0.8601,none,"Base model (Table 2 Model 1 covariates) + Mean distance to 5 TAZ centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-0.89,0.08,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,-0.12577937202044362,0.010598283775600032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.8679,8762.0,km,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m9,3,Mean free-flow time to 5 TAZ centers,9,Mean free-flow time to 5 TAZ centers,employment_access,D_location,level,minutes,-0.0129,0.001,-13.4577,0.0,,,,high,,log sales price,log,OLS,8772.0,0.8608,none,"Base model (Table 2 Model 1 covariates) + Mean free-flow time to 5 TAZ centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-1.29,0.1,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,-0.14230709852864612,0.01057439967666437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.4577,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T3-m10,3,Mean congested time to 5 TAZ centers,9,Mean congested time to 5 TAZ centers,employment_access,D_location,level,minutes,-0.0138,0.001,-14.0707,0.0,,,,high,,log sales price,log,OLS,8772.0,0.861,none,"Base model (Table 2 Model 1 covariates) + Mean congested time to 5 TAZ centers; only location coefficients reported with SE, t and p in columns",semi_elasticity,-1.38,0.1,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),reported,-0.14864902740794522,0.010564437263813828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0707,8762.0,minutes,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m1,4,Distance accessibility to ZIP employment,12,Distance accessibility to ZIP employment (coefficient = 1.1),employment_access,D_location,level,"index (gravity accessibility to employment, distance decay exponent 1.1)",0.0059,0.0006,10.1156,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8595,none,"Base model covariates + Distance accessibility to ZIP employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.59,0.06,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.10744069366045986,0.010621287285031027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.1156,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m2,4,Distance accessibility to TAZ employment,12,Distance accessibility to TAZ employment (coefficient = 1.6),employment_access,D_location,level,"index (gravity accessibility to employment, distance decay exponent 1.6)",0.0205,0.0014,14.5388,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8612,none,"Base model covariates + Distance accessibility to TAZ employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,2.0500000000000003,0.13999999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.15347958362347722,0.010556550996194819,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.5388,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m3,4,Free-flow travel time accessibility to TAZ employment,12,Free-flow travel time accessibility to TAZ employment (coefficient = 2.2),employment_access,D_location,level,"index (gravity accessibility, travel-time decay exponent 2.2)",0.0273,0.0019,14.5371,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8612,none,"Base model covariates + Free-flow travel time accessibility to TAZ employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,2.73,0.19,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.15346206015327093,0.010556580071215781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.5371,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m4,4,Congested travel time accessibility to TAZ employment,12,Congested travel time accessibility to TAZ employment (coefficient = 2.1),employment_access,D_location,level,"index (gravity accessibility, travel-time decay exponent 2.1)",0.0232,0.0017,13.8811,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.861,none,"Base model covariates + Congested travel time accessibility to TAZ employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,2.32,0.16999999999999998,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14668939852074994,0.010567562982814757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.8811,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m5,4,Distance accessibility to change in ZIP employment,12,Distance accessibility to change in ZIP employment (coefficient = 0.081),employment_access,D_location,level,"index (accessibility to 1995-2000 employment change, decay 0.081)",0.0081,0.0006,13.5473,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8608,none,"Base model covariates + Distance accessibility to change in ZIP employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.8099999999999999,0.06,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14323518919181322,0.010572969461945423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.5473,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m6,4,Accessibility to employment and change in employment,12,Distance accessibility to ZIP employment (coefficient = 1.3),employment_access,D_location,level,"index (gravity accessibility to employment, decay 1.3)",0.0048,0.0007,6.9897,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8616,none,"Base model covariates + Accessibility to employment and change in employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.48,0.06999999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.07446453582985435,0.010653466648047033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9897,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m6,4,Accessibility to employment and change in employment,12,Distance accessibility to change in ZIP employment (coefficient = 0.067),employment_access,D_location,level,"index (accessibility to 1995-2000 employment change, decay 0.067)",0.0069,0.0006,11.4801,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8616,none,"Base model covariates + Accessibility to employment and change in employment; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.69,0.06,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.12173127475889006,0.010603677211774293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.4801,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m7,4,"Accessibility, change in employment, mean distance to ZIP centers",12,Distance accessibility to ZIP employment (coefficient = 1.3),employment_access,D_location,level,"index (gravity accessibility to employment, decay 1.3)",0.0048,0.0007,6.9896,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8616,none,"Base model covariates + Accessibility, change in employment, mean distance to ZIP centers; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.48,0.06999999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.07446347639037568,0.010653467493186401,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.9896,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m7,4,"Accessibility, change in employment, mean distance to ZIP centers",12,Distance accessibility to change in ZIP employment (coefficient = 0.067),employment_access,D_location,level,"index (accessibility to 1995-2000 employment change, decay 0.067)",0.0075,0.0012,6.0354,0.0,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8616,none,"Base model covariates + Accessibility, change in employment, mean distance to ZIP centers; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.75,0.12,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.06434333457900197,0.010660989259867111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0354,8762.0,index,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23,ddfcdefd81b294b9edfe4b97744d2d4725aeed23:T4-m7,4,"Accessibility, change in employment, mean distance to ZIP centers",12,Mean distance to 7 ZIP employment centers,employment_access,D_location,level,km,0.0008,0.0017,0.4736,0.6358,,,,high,accessibility index = sum of employment weighted by negative exponential of distance/time; scale not standardised,log sales price,log,OLS,8772.0,0.8616,none,"Base model covariates + Accessibility, change in employment, mean distance to ZIP centers; accessibility measures use best-fit negative-exponential decay coefficients (nonlinear search); SE, t, p in columns",semi_elasticity,0.08,0.16999999999999998,100*b (unit as reported: km; no harmonised unit defined for this characteristic),reported,0.005059464006558462,0.010682989878712966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4736,8762.0,km,unit_not_harmonisable,2008.0,United States,North America,High income,1999.0,1999.0,single_family,transaction,journal,False,False,unknown,,1999.0,ols,journal,False,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,PT (Villa),dwelling_type,A_structural,dummy,binary,0.381,0.038,9.903,0.0,,,,high,"Villa vs other property types (condominium, detached); z-value column","log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,46.2691161060925,5.562240900556969,100*(exp(b - se^2/2) - 1),reported,0.2809919293154019,0.028374424852610505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.903,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,BC (Building Class-Economic),condition,A_structural,dummy,binary,-0.114,0.035,-3.281,0.001,,,,high,Building category dummy (economic/medium/noble; Italian cadastral building class),"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,-10.828838478166103,3.1229028455884293,100*(exp(b - se^2/2) - 1),reported,-0.09655159033136701,0.02942748867155349,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.281,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,BC (Building Class-Medium),condition,A_structural,dummy,binary,-0.065,0.031,-2.079,0.037,,,,high,Building category dummy,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,-6.338268938088831,2.9049091364699504,100*(exp(b - se^2/2) - 1),reported,-0.06135113492953777,0.029509925411033075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.079,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,BC (Building Class-Noble),condition,A_structural,dummy,binary,0.135,0.047,2.862,0.004,,,,high,Building category dummy,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,14.327334133784419,5.379322886451178,100*(exp(b - se^2/2) - 1),reported,0.08431549264034657,0.02946033984638245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.862,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,FA (Floor area),living_area,A_structural,level,m2,0.004,0.001,22.816,0.0,,,,high,"Floor area, m2 (mean 139)","log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,semi_elasticity,0.4,0.1,100*b/1 (X per m2->m2),reported,0.5592274686613316,0.024510320330528208,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.816,1144.0,m2,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,TR (Terrace),terrace_balcony,C_exterior,binary,binary,0.105,0.025,4.126,0.0,,,,high,,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,11.03635675182366,2.776776525889263,100*(exp(b - se^2/2) - 1),reported,0.12109010287891078,0.029348061773851374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.126,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,US (Unit Status-Partially renovated),renovation,A_structural,dummy,binary,0.271,0.038,7.105,0.0,,,,high,"Unit status dummy, ref. not renovated","log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,31.03286714748885,4.982845267461434,100*(exp(b - se^2/2) - 1),reported,0.20557696715827797,0.02893412627139732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.105,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,US (Unit Status-Renovated),renovation,A_structural,dummy,binary,0.466,0.042,10.962,0.0,,,,high,"Unit status dummy, ref. not renovated","log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,59.220205764542875,6.693149397263636,100*(exp(b - se^2/2) - 1),reported,0.3083101626674636,0.028125356930073314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.962,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,True
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,EPC (Energy Performance Certification -B and over),energy_efficiency,A_structural,dummy,binary,0.162,0.066,2.45,0.014,,,,high,EPC class B or better dummy,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,17.330200464643,7.760677592718599,100*(exp(b - se^2/2) - 1),reported,0.07224647998715969,0.02948835917843252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,HT (Heating Type-independent),heating,A_structural,dummy,binary,0.106,0.021,5.013,0.0,,,,high,Independent (autonomous) heating vs central,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,11.157674680930342,2.334825940663715,100*(exp(b - se^2/2) - 1),reported,0.1466109037527516,0.029246140784510592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.013,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,E (Elevator),accessibility,B_interior,binary,binary,0.101,0.033,3.054,0.002,,,,high,,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,pct_premium,10.567443809680999,3.650712917819298,100*(exp(b - se^2/2) - 1),reported,0.08992756187328262,0.02944582903512856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.054,1144.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,BN (Bathrooms number),bathrooms,A_structural,level,count,0.958,0.017,5.728,0.0,,,,medium,"Printed coefficient 0.958 with SE 0.017 is inconsistent with printed z=5.728 (implies beta ~0.098); probable misprint, recorded as printed","log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,semi_elasticity,95.8,1.7000000000000002,100*b/1 (per unit count),reported,0.16697437908640433,0.029150555008101316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.728,1144.0,count,implausible_magnitude,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,False,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,HD (Distance from the nearest highway exit),highway_distance,D_location,level,m,-1.42e-05,5.628e-05,-2.517,0.011,,,,medium,Distance unit not stated; sample mean 6151 implies metres. Printed SE 5.628e-05 inconsistent with z=-2.517 (implies SE 5.6e-06); recorded as printed,"log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,semi_elasticity,-1.42,5.628,100*b/0.001 (X per m->km),reported,-0.07421146313103469,0.029484093417177074,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.517,1144.0,m,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T4-DS,4,Coefficient,15,RE (Retail services),commercial_distance,D_location,other,index,0.248,0.083,2.98,0.002,,,,high,"Retail services indicator (numeric, mean 0.21; presence/density of retail services, not a distance)","log total listing price (logTLP), EUR",log,spatial_error,1154.0,0.664,se,DS: Ivrea + 31 municipalities of the Eporediese area,other,0.248,0.083,not harmonised,reported,0.08776556159664985,0.029451530737130818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.98,1144.0,index,nonstandard_x_transform,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,False,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,BC (Building Class-Noble),condition,A_structural,dummy,binary,0.35,0.078,4.454,0.0,,,,high,Building category dummy,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,41.47573043158139,11.068726879027405,100*(exp(b - se^2/2) - 1),reported,0.27738096397411,0.062276821727460706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.454,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,FA,living_area,A_structural,level,m2,0.005,0.001,12.813,0.0,,,,high,"Floor area, m2 (mean 112)","log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,semi_elasticity,0.5,0.1,100*b/1 (X per m2->m2),reported,0.6389169172032393,0.04986474027965654,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.813,238.0,m2,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,US1 (Unit Status-Partially renovated),renovation,A_structural,dummy,binary,0.237,0.073,3.248,0.001,,,,high,ref. not renovated,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,26.406851606093753,9.25232015975957,100*(exp(b - se^2/2) - 1),reported,0.20602008886547743,0.0634298303157258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.248,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,US2 (Unit Status-Renovated),renovation,A_structural,dummy,binary,0.398,0.085,4.68,0.0,,,,high,ref. not renovated,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,48.34752839851393,12.65517425421186,100*(exp(b - se^2/2) - 1),reported,0.2902957703360835,0.062029010755573405,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.68,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,True
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,HT (Heating Type-independent),heating,A_structural,dummy,binary,0.135,0.046,2.933,0.003,,,,high,,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,14.332650478425712,5.264869208016047,100*(exp(b - se^2/2) - 1),reported,0.1867726733847835,0.06367973862420168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.933,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,PG (Private Garden),garden,C_exterior,binary,binary,0.152,0.065,2.343,0.019,,,,high,,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,16.170354372793284,7.567041536808731,100*(exp(b - se^2/2) - 1),reported,0.15015231651540714,0.06408549573854339,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.343,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,OL (Building Olivettian Connotation),other,other,binary,binary,-0.174,0.066,-2.658,0.008,,,,high,Modern-heritage (Olivetti company) architectural connotation dummy,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,-16.153127737936213,5.545959524545648,100*(exp(b - se^2/2) - 1),reported,-0.16979088166323442,0.06387918798466306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.658,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,False,False,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,UD (Distance from the UNESCO site),other,other,level,m,-9.921e-05,3.49e-05,-2.845,0.004,,,,medium,"Distance to centre of UNESCO site 'Ivrea, industrial city of the 20th century'; unit not stated, mean 1520 implies metres; SE printed as '-3.49e-05' (sign misprint), consistent with z","log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,semi_elasticity,-0.009921,0.00349,100*b (unit as reported: m; no harmonised unit defined for this characteristic),reported,-0.18135591966067854,0.06374549021464974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.845,238.0,m,unit_not_harmonisable,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,False,False,False
ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb,ddfdca9d11843f81e9f91bacfe723acfa0d83bbb:T5-SS,5,Coefficient,16,EPC (Energy Performance Certification-B and over),energy_efficiency,A_structural,dummy,binary,0.311,0.14,2.219,0.027,,,,high,,"log total listing price (logTLP), EUR",log,spatial_error,248.0,0.62,se,SS: city of Ivrea only,pct_premium,35.14796104323117,19.10704909660626,100*(exp(b - se^2/2) - 1),reported,0.14237119334591083,0.06416006910586337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.219,238.0,binary,,2022.0,Italy,Europe,High income,2019.0,2020.0,mixed,listing,journal,False,False,unknown,,2019.5,spatial,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Launch × Near,other,other,other,binary interaction (post-launch x within 100 m of bus route),0.0129,0.0054,,,**,,,high,DiD interaction: net effect of new bus service within 100 m,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,other,0.0129,0.0054,not harmonised,reported,0.06282837685987264,0.026300250778551335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.388888888888889,1440.0,binary interaction,nonstandard_x_transform,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,False,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Near,transit_distance,D_location,dummy,binary (within 100 m of bus route vs 100-200 m),-0.0097,0.0038,,,**,,,high,pre-launch proximity to bus route,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,pct_premium,-0.966025702276585,0.37633181943727656,100*(exp(b - se^2/2) - 1),reported,-0.06711607142878724,0.026292893961792935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5526315789473686,1440.0,binary,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Launch × Distance,other,other,other,interaction (post-launch x distance to bus route in 100 m),-0.0134,0.005,,,***,,,high,DiD interaction,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,other,-0.0134,0.005,not harmonised,reported,-0.070448728308962,0.02628683892125448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.68,1440.0,interaction,nonstandard_x_transform,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,False,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Distance,transit_distance,D_location,level,100 m (distance to bus route),0.0104,0.0037,,,***,,,high,pre-launch distance to bus route,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,10.4,3.6999999999999997,100*b/0.1 (X per 100m->km),reported,0.07386900217237996,0.02628031808055826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8108108108108105,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Prime lending rate,other,other,level,"share (prime lending rate, %)",-2.8766,0.3558,,,***,,,high,macro market variable,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,-287.65999999999997,35.58,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.20837834581191306,0.025773835583632995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.084879145587408,1440.0,share,unit_not_harmonisable,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,False,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Prime lending rate,other,other,level,"share (prime lending rate, %)",-2.8815,0.3563,,,***,,,high,macro market variable,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,-288.15,35.63,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.2084376848007311,0.025773502375325524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.087285994948077,1440.0,share,unit_not_harmonisable,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,False,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Floor,floor_level,A_structural,level,count,0.007,0.0003,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,0.7000000000000001,0.03,100*b/1 (per unit count),reported,0.5237901326137371,0.02244814854058873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.333333333333336,1440.0,count,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Floor,floor_level,A_structural,level,count,0.007,0.0003,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,0.7000000000000001,0.03,100*b/1 (per unit count),reported,0.5237901326137371,0.02244814854058873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.333333333333336,1440.0,count,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Area,living_area,A_structural,level,m2,0.003,0.0008,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,0.3,0.08,100*b/1 (X per m2->m2),reported,0.09834215720608248,0.02622457525495533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.75,1440.0,m2,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Area,living_area,A_structural,level,m2,0.003,0.0008,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,0.3,0.08,100*b/1 (X per m2->m2),reported,0.09834215720608248,0.02622457525495533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.75,1440.0,m2,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Building age,age,A_structural,level,years,-0.0009,0.0006,,,,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,-0.09,0.06,100*b/1 (per year),reported,-0.03949762527666821,0.026331750184445477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,1440.0,years,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Building age,age,A_structural,level,years,-0.0009,0.0006,,,,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,-0.09,0.06,100*b/1 (per year),reported,-0.03949762527666821,0.026331750184445477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5,1440.0,years,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Distance to train station,transit_distance,D_location,level,100 m,-0.0035,0.0015,,,**,,,high,distance to MRT train station,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,-3.5,1.4999999999999998,100*b/0.1 (X per 100m->km),reported,-0.06137282058924549,0.026302637395390918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3333333333333335,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Distance to train station,transit_distance,D_location,level,100 m,-0.0036,0.0015,,,**,,,high,distance to MRT train station,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,-3.5999999999999996,1.4999999999999998,100*b/0.1 (X per 100m->km),reported,-0.06311944030978031,0.026299766795741798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Distance to bus stop,transit_distance,D_location,level,100 m,-0.0042,0.0031,,,,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,-4.199999999999999,3.0999999999999996,100*b/0.1 (X per 100m->km),reported,-0.035680400964909376,0.02633553404552835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3548387096774193,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Distance to bus stop,transit_distance,D_location,level,100 m,-0.0046,0.0032,,,,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,-4.6,3.1999999999999997,100*b/0.1 (X per 100m->km),reported,-0.03785430033168579,0.026333426317694465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4375,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Distance to expressway,highway_distance,D_location,level,100 m,0.0025,0.0008,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,2.5,0.7999999999999999,100*b/0.1 (X per 100m->km),reported,0.08207315387277983,0.02626340923928954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.125,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Distance to expressway,highway_distance,D_location,level,100 m,0.0024,0.0008,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,2.4,0.7999999999999999,100*b/0.1 (X per 100m->km),reported,0.07881104062391006,0.02627034687463669,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9999999999999996,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Distance to major road,highway_distance,D_location,level,100 m,-0.0014,0.0016,,,,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,-1.3999999999999997,1.5999999999999999,100*b/0.1 (X per 100m->km),reported,-0.023052147190514766,0.02634531107487402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.875,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Distance to major road,highway_distance,D_location,level,100 m,-0.001,0.0017,,,,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,-1.0,1.6999999999999997,100*b/0.1 (X per 100m->km),reported,-0.015499498986792747,0.026349148277547668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5882352941176471,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c1,5,1,20,Distance to shopping center,commercial_distance,D_location,level,100 m,0.0037,0.0009,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,binary closeness indicator (Near = within 100 m of bus route; control 100-200 m); block and time FE,semi_elasticity,3.6999999999999997,0.8999999999999999,100*b/0.1 (X per 100m->km),reported,0.10770705690763097,0.026199013842396715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.111111111111112,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T5-c2,5,2,20,Distance to shopping center,commercial_distance,D_location,level,100 m,0.0037,0.0009,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,1450.0,0.941,se,continuous distance to bus route; block and time FE,semi_elasticity,3.6999999999999997,0.8999999999999999,100*b/0.1 (X per 100m->km),reported,0.10770705690763097,0.026199013842396715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.111111111111112,1440.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c1,6,1,21,Noise sentiment in previous 12 months,noise,E_environment,level,index (sentiment score 0-1),-0.03,0.0149,,,**,,,high,"NLP sentiment score (0-1, higher = more negative) of noise complaints in the building over the previous 12 months; AI-derived noise measure","log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,transactions matched with 12-month lagged noise sentiment; block and time FE,semi_elasticity,-3.0,1.49,100*b (unit as reported: index; noise unit not dB),reported,-0.06760206907783607,0.03357569430865859,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.013422818791946,883.0,index,unit_not_harmonisable,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Noise sentiment in previous 12 months,noise,E_environment,level,index (sentiment score 0-1),-0.0262,0.0132,,,**,,,high,"NLP sentiment score (0-1, higher = more negative) of noise complaints in the building over the previous 12 months; AI-derived noise measure; main effect for medium floors (5-8) given interaction","log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,semi_elasticity,-2.62,1.32,100*b (unit as reported: index; noise unit not dB),reported,-0.06664695800579842,0.03357785670521142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9848484848484849,883.0,index,unit_not_harmonisable,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Noise sentiment in previous 12 months × Low/high floors,noise,E_environment,other,interaction (sentiment index x low/high floor dummy),-0.0053,0.0084,,,,,,high,interaction term,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,other,-0.0053,0.0084,not harmonised,reported,-0.0212284533080275,0.03364509580894924,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.630952380952381,883.0,interaction,nonstandard_x_transform,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c1,6,1,21,Distance to nearest bus stop connecting CBD,transit_distance,D_location,level,100 m,-0.0027,0.0016,,,*,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,transactions matched with 12-month lagged noise sentiment; block and time FE,semi_elasticity,-2.7,1.5999999999999999,100*b/0.1 (X per 100m->km),reported,-0.056697545899837436,0.03359854571842218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6875,883.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Distance to nearest bus stop connecting CBD,transit_distance,D_location,level,100 m,-0.0027,0.0016,,,*,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,semi_elasticity,-2.7,1.5999999999999999,100*b/0.1 (X per 100m->km),reported,-0.056697545899837436,0.03359854571842218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6875,883.0,100m,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c1,6,1,21,Prime lending rate,other,other,level,"share (prime lending rate, %)",-3.5344,0.4423,,,***,,,high,macro market variable,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,transactions matched with 12-month lagged noise sentiment; block and time FE,semi_elasticity,-353.44,44.230000000000004,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.25969102033918634,0.03249811518108367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.990956364458512,883.0,share,unit_not_harmonisable,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,False,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Prime lending rate,other,other,level,"share (prime lending rate, %)",-3.5089,0.4638,,,***,,,high,macro market variable,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,semi_elasticity,-350.89,46.379999999999995,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.2467297211379063,0.032612284380791966,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.5655454937473054,883.0,share,unit_not_harmonisable,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,False,False,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c1,6,1,21,Floor,floor_level,A_structural,level,count,0.0064,0.0005,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,transactions matched with 12-month lagged noise sentiment; block and time FE,semi_elasticity,0.64,0.05,100*b/1 (per unit count),reported,0.39561232683082437,0.030907213033658155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.8,883.0,count,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Floor,floor_level,A_structural,level,count,0.0065,0.0006,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,semi_elasticity,0.65,0.06,100*b/1 (per unit count),reported,0.34251824186221946,0.031617068479589484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.833333333333334,883.0,count,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c1,6,1,21,Area,living_area,A_structural,level,m2,0.0075,0.0002,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,transactions matched with 12-month lagged noise sentiment; block and time FE,semi_elasticity,0.75,0.02,100*b/1 (X per m2->m2),reported,0.7837628175659862,0.020900341801759634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.5,883.0,m2,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Area,living_area,A_structural,level,m2,0.0075,0.0002,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,semi_elasticity,0.75,0.02,100*b/1 (X per m2->m2),reported,0.7837628175659862,0.020900341801759634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.5,883.0,m2,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c1,6,1,21,Building age,age,A_structural,level,years,-0.0035,0.0009,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,transactions matched with 12-month lagged noise sentiment; block and time FE,semi_elasticity,-0.35000000000000003,0.09,100*b/1 (per year),reported,-0.12976498333138178,0.03336813857092674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8888888888888893,883.0,years,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6,de48aa9a311d709ef0a33453d7042001c2e721a6:T6-c2,6,2,21,Building age,age,A_structural,level,years,-0.0035,0.0009,,,***,,,high,,"log(price), HDB resale price (SGD)",log,OLS,893.0,0.919,se,as column 1 plus interaction of noise sentiment with low/high floor dummy,semi_elasticity,-0.35000000000000003,0.09,100*b/1 (per year),reported,-0.12976498333138178,0.03336813857092674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8888888888888893,883.0,years,,2021.0,Singapore,Asia,High income,2010.0,2018.0,condo_apartment,transaction,journal,True,True,clustered,NLP sentiment analysis of noise complaint texts (noise sentiment score 0-1),2014.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,NO2,air_pollution,E_environment,level,ug/m3,-0.0079,0.0008,,,***,,,high,nitrogen dioxide concentration near the property (mean 24.36),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-0.79,0.08,100*b/1 (per ug/m3),reported,-0.03096760057205867,0.003135959551600878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.875,101588.0,ug/m3,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,NO2,air_pollution,E_environment,level,ug/m3,-0.0014,0.0008,,,*,,,high,nitrogen dioxide concentration near the property (mean 24.36),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-0.13999999999999999,0.08,100*b/1 (per ug/m3),reported,-0.006513228758737801,0.003721845004993029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.75,72188.0,ug/m3,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,NO2,air_pollution,E_environment,level,ug/m3,-0.0243,0.0017,,,***,,,high,nitrogen dioxide concentration near the property (mean 24.36),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-2.4299999999999997,0.16999999999999998,100*b/1 (per ug/m3),reported,-0.08366816207904579,0.005853328211291269,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.294117647058824,28983.0,ug/m3,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,True
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,Ruido,noise,E_environment,level,db,-0.0013,0.0006,,,**,,,high,day/night noise level in decibels,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-0.13,0.06,100*b/1 (per dB),reported,-0.00679768228731536,0.003137391824914781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.166666666666667,101588.0,db,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,Ruido,noise,E_environment,level,db,0.0,0.0006,,,,,,high,day/night noise level in decibels,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,0.0,0.06,100*b/1 (per dB),reported,0.0,0.0037219239518362836,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.0,72188.0,db,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,Ruido,noise,E_environment,level,db,-0.0094,0.0015,,,***,,,high,day/night noise level in decibels,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-0.9400000000000001,0.15,100*b/1 (per dB),reported,-0.03678501158638858,0.0058699486574024335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.266666666666667,28983.0,db,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,antigüedad,age,A_structural,log,years,-0.3089,0.0021,,,***,,,high,Table 1 defines as logarithm of building age,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,-0.3089,0.0021,b (log-log),reported,-0.41903392431072367,0.0028487252866705066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-147.0952380952381,101588.0,years,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,antigüedad,age,A_structural,log,years,-0.2807,0.0024,,,***,,,high,Table 1 defines as logarithm of building age,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,-0.2807,0.0024,b (log-log),reported,-0.3991327729760523,0.0034126065377361073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-116.95833333333334,72188.0,years,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,antigüedad,age,A_structural,log,years,-0.37,0.0038,,,***,,,high,Table 1 defines as logarithm of building age,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,-0.37,0.0038,b (log-log),reported,-0.4964701129493348,0.005098882241101276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-97.36842105263158,28983.0,years,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,True
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,accidentes,other,other,level,index -3 to 3 (traffic accident frequency),-0.032,0.0013,,,***,,,high,"frequency of car accidents in the zone, scale -3 (least) to 3 (most)","ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-3.2,0.13,100*b (unit as reported: index -3 to 3; no harmonised unit defined for this characteristic),reported,-0.07700059886696692,0.003128149328970531,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.615384615384617,101588.0,index -3 to 3,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,accidentes,other,other,level,index -3 to 3 (traffic accident frequency),-0.014,0.0012,,,***,,,high,"frequency of car accidents in the zone, scale -3 (least) to 3 (most)","ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-1.4000000000000001,0.12,100*b (unit as reported: index -3 to 3; no harmonised unit defined for this characteristic),reported,-0.04338156720114976,0.0037184200458128362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.666666666666668,72188.0,index -3 to 3,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,accidentes,other,other,level,index -3 to 3 (traffic accident frequency),-0.0977,0.0039,,,***,,,high,"frequency of car accidents in the zone, scale -3 (least) to 3 (most)","ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-9.77,0.38999999999999996,100*b (unit as reported: index -3 to 3; no harmonised unit defined for this characteristic),reported,-0.14558163644476926,0.005811344750610032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.05128205128205,28983.0,index -3 to 3,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,robos,crime,F_neighbourhood,level,index -3 to 3 (robbery frequency),0.0014,0.0016,,,,,,high,"(personal robbery + house burglary)/2, scale -3 to 3","ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,0.13999999999999999,0.16,100*b (unit as reported: index -3 to 3; no harmonised unit defined for this characteristic),reported,0.002745270930233984,0.0031374524916959814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.875,101588.0,index -3 to 3,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,robos,crime,F_neighbourhood,level,index -3 to 3 (robbery frequency),-0.0205,0.0015,,,***,,,high,"(personal robbery + house burglary)/2, scale -3 to 3","ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-2.0500000000000003,0.15,100*b (unit as reported: index -3 to 3; no harmonised unit defined for this characteristic),reported,-0.05080061621853639,0.003717118259892907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.666666666666666,72188.0,index -3 to 3,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,robos,crime,F_neighbourhood,level,index -3 to 3 (robbery frequency),0.0624,0.0042,,,***,,,high,"(personal robbery + house burglary)/2, scale -3 to 3","ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,6.239999999999999,0.42,100*b (unit as reported: index -3 to 3; no harmonised unit defined for this characteristic),reported,0.08693929259218279,0.005851683155243072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.857142857142858,28983.0,index -3 to 3,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,distancia al centro,cbd_distance,D_location,level,km,-0.0126,0.0011,,,***,,,high,distance to historic centre in thousands of metres,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-1.26,0.11,100*b/1 (X per km->km),reported,-0.03591504193869332,0.0031354401692510045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.454545454545453,101588.0,km,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,distancia al centro,cbd_distance,D_location,level,km,0.0029,0.001,,,***,,,high,distance to historic centre in thousands of metres,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,0.29,0.1,100*b/1 (X per km->km),reported,0.01079295078192604,0.003721707166181393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9,72188.0,km,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,distancia al centro,cbd_distance,D_location,level,km,-0.0149,0.0027,,,***,,,high,distance to historic centre in thousands of metres,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-1.49,0.27,100*b/1 (X per km->km),reported,-0.03239834210561633,0.005870840515782825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.518518518518518,28983.0,km,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,río,waterfront,C_exterior,binary,binary,0.0442,0.0038,,,***,,,high,= 1 if property fronts the river,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,4.518382600599624,0.39717272145897553,100*(exp(b - se^2/2) - 1),reported,0.03646938721160598,0.0031353771810882967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.631578947368421,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,río,waterfront,C_exterior,binary,binary,0.0184,0.0041,,,***,,,high,= 1 if property fronts the river,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,1.856176199666204,0.41761383244814226,100*(exp(b - se^2/2) - 1),reported,0.016700938855154255,0.003721404853594155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.48780487804878,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,río,waterfront,C_exterior,binary,binary,0.0694,0.0068,,,***,,,high,= 1 if property fronts the river,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,7.184008812902465,0.7288681111636658,100*(exp(b - se^2/2) - 1),reported,0.059841145391382806,0.005863397531144136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.205882352941178,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,edificio,dwelling_type,A_structural,binary,binary,0.0304,0.0051,,,***,,,high,= 1 if the property is a building (apartment) vs villa,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,3.085339187891778,0.5257420670893713,100*(exp(b - se^2/2) - 1),reported,0.018698478409823156,0.0031369157858584904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.9607843137254894,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,registro municipal,neighbourhood_amenities,F_neighbourhood,level,count (municipal business registrations in the zone),0.0046,0.0001,,,***,,,high,number of municipally registered businesses in the property's zone (RMO),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,0.45999999999999996,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.1428433667220606,0.0031052905809143615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),46.0,101588.0,count,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,registro municipal,neighbourhood_amenities,F_neighbourhood,level,count (municipal business registrations in the zone),0.001,0.0002,,,***,,,high,number of municipally registered businesses in the property's zone (RMO),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,0.1,0.02,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.018606398173266907,0.0037212796346533807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,72188.0,count,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,registro municipal,neighbourhood_amenities,F_neighbourhood,level,count (municipal business registrations in the zone),0.0055,0.0001,,,***,,,high,number of municipally registered businesses in the property's zone (RMO),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,0.5499999999999999,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.3074209216924353,0.005589471303498824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),54.99999999999999,28983.0,count,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,Zona industrial,hazard_site,E_environment,binary,binary,-0.1084,0.0142,,,***,,,high,= 1 if located in an industrial zone,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-10.282184086942115,1.2741214364133453,100*(exp(b - se^2/2) - 1),reported,-0.023943917311496116,0.0031365648138675724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.633802816901408,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,13,Zona industrial,hazard_site,E_environment,binary,binary,-0.1337,0.0138,,,***,,,high,= 1 if located in an industrial zone,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-12.52308185040838,1.2072964237568213,100*(exp(b - se^2/2) - 1),reported,-0.03603608855145888,0.003719506522140108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.688405797101451,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,13,Servicios 250,neighbourhood_amenities,F_neighbourhood,level,count within 250 m,-0.0107,0.001,,,***,,,high,number of services within 250 m; blank for villas,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-1.0699999999999998,0.1,100*b (unit as reported: count within 250 m; no harmonised unit defined for this characteristic),reported,-0.03355196690211122,0.003135697841318806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.7,101588.0,count within 250 m,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,13,Servicios 250,neighbourhood_amenities,F_neighbourhood,level,count within 250 m,-0.0135,0.0022,,,***,,,high,number of services within 250 m; blank for villas,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,semi_elasticity,-1.35,0.22,100*b (unit as reported: count within 250 m; no harmonised unit defined for this characteristic),reported,-0.036021142450560206,0.0058701121030542555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.136363636363636,28983.0,count within 250 m,unit_not_harmonisable,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Terreno,lot_size,A_structural,log,m2,-0.0234,0.0022,,,***,,,high,ln land area; full sample only,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,-0.0234,0.0022,b (log-log),reported,-0.03335264513029345,0.0031357187729335726,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.636363636363637,101588.0,m2,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Frentes,other,other,log,m (property frontage),0.0121,0.002,,,***,,,high,ln frontage of the property,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,0.0121,0.002,b (log-log),reported,0.01897824044844952,0.003136899247677606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.05,101588.0,m,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Número-pisos,storeys,A_structural,log,count,-0.0132,0.0037,,,***,,,high,ln number of floors,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,-0.0132,0.0037,b (log-log),reported,-0.011192414829155246,0.0031372677930207884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5675675675675675,101588.0,count,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Número-pisos,storeys,A_structural,log,count,0.102,0.0041,,,***,,,high,ln number of floors,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,0.102,0.0041,b (log-log),reported,0.09219980307513505,0.0037060705157652327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.878048780487802,72188.0,count,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Número-pisos,storeys,A_structural,log,count,-0.1596,0.0071,,,***,,,high,ln number of floors,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,-0.1596,0.0071,b (log-log),reported,-0.13090302181732968,0.005823380043252136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.478873239436616,28983.0,count,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Baños,bathrooms,A_structural,log,count,0.0074,0.0006,,,***,,,high,ln number of bathrooms,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,0.0074,0.0006,b (log-log),reported,0.03866645575131317,0.003135118033890256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.333333333333336,101588.0,count,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Baños,bathrooms,A_structural,log,count,0.0196,0.0008,,,***,,,high,ln number of bathrooms,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,0.0196,0.0008,b (log-log),reported,0.09081037003347109,0.003706545715651882,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.499999999999996,72188.0,count,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Baños,bathrooms,A_structural,log,count,0.0118,0.001,,,***,,,high,ln number of bathrooms,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,elasticity,0.0118,0.001,b (log-log),reported,0.0691464071373723,0.005859865011641721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.799999999999999,28983.0,count,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Tipo-piso: 1. Mezanine,floor_level,A_structural,dummy,binary,0.0803,0.0198,,,***,,,high,floor type mezzanine vs normal (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,8.339971372062172,2.145551963045148,100*(exp(b - se^2/2) - 1),reported,0.012723130908060001,0.0031372103608915073,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.055555555555555,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Tipo-piso: 1. Mezanine,floor_level,A_structural,dummy,binary,0.0256,0.0183,,,,,,high,floor type mezzanine vs normal (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,2.575872164041093,1.8774528043683996,100*(exp(b - se^2/2) - 1),reported,0.005206555284468237,0.003721873504131591,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3989071038251366,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Tipo-piso: 1. Mezanine,floor_level,A_structural,dummy,binary,0.1643,0.0446,,,***,,,high,floor type mezzanine vs normal (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,17.739623513762236,5.256412532558397,100*(exp(b - se^2/2) - 1),reported,0.021633629347046754,0.005872549414962174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6838565022421523,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Tipo-piso: 2. Subsuelo,floor_level,A_structural,dummy,binary,-0.0353,0.0076,,,***,,,high,floor type basement vs normal,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-3.47120998365239,0.7336399913412469,100*(exp(b - se^2/2) - 1),reported,-0.01457114898453436,0.0031371312261320432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.644736842105263,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Tipo-piso: 2. Subsuelo,floor_level,A_structural,dummy,binary,-0.016,0.0075,,,**,,,high,floor type basement vs normal,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-1.5900358136365034,0.7380954900414638,100*(exp(b - se^2/2) - 1),reported,-0.007939854149450639,0.003721806632554987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1333333333333333,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Tipo-piso: 2. Subsuelo,floor_level,A_structural,dummy,binary,-0.0994,0.0151,,,***,,,high,floor type basement vs normal,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-9.472272862845854,1.3671245299190442,100*(exp(b - se^2/2) - 1),reported,-0.038637885428182875,0.005869537927218928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.582781456953643,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Tipo-piso: 3. Ático,floor_level,A_structural,dummy,binary,-0.1202,0.0488,,,**,,,high,floor type attic vs normal,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-11.43121639727135,4.3273061833911575,100*(exp(b - se^2/2) - 1),reported,-0.007727703893915846,0.003137370632471658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4631147540983607,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Tipo-piso: 3. Ático,floor_level,A_structural,dummy,binary,-0.0956,0.0597,,,,,,high,floor type attic vs normal,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-9.279065487682125,5.425700021940819,100*(exp(b - se^2/2) - 1),reported,-0.005959959970665851,0.003721857847790286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6013400335008374,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Tipo-piso: 3. Ático,floor_level,A_structural,dummy,binary,-0.1507,0.0805,,,*,,,high,floor type attic vs normal,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-14.267664828958704,6.9238508179098455,100*(exp(b - se^2/2) - 1),reported,-0.010995613050821048,0.005873569015203015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8720496894409937,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Estado construcción: 1. Regular,condition,A_structural,dummy,binary,-0.1481,0.0096,,,***,,,high,construction state regular vs good (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-13.769485979380658,0.8278510810968605,100*(exp(b - se^2/2) - 1),reported,-0.04834532614946513,0.0031337956180612094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.427083333333336,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Estado construcción: 1. Regular,condition,A_structural,dummy,binary,-0.1368,0.009,,,***,,,high,construction state regular vs good (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-12.789068352466249,0.7849301738561127,100*(exp(b - se^2/2) - 1),reported,-0.056482928566001504,0.003715982142500098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.200000000000001,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Estado construcción: 1. Regular,condition,A_structural,dummy,binary,-0.3479,0.0535,,,***,,,high,construction state regular vs good (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-29.48404043635947,3.778006769534506,100*(exp(b - se^2/2) - 1),reported,-0.03816914128282438,0.005869643744268766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.502803738317757,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Estado construcción: 2. Malo,condition,A_structural,dummy,binary,-0.1847,0.0301,,,***,,,high,construction state bad vs good,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-16.90228695310254,2.50237449414755,100*(exp(b - se^2/2) - 1),reported,-0.019248581269021046,0.003136883033013176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.136212624584718,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Estado construcción: 2. Malo,condition,A_structural,dummy,binary,-0.1208,0.0278,,,***,,,high,construction state bad vs good,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-11.413119946291783,2.4636670918165002,100*(exp(b - se^2/2) - 1),reported,-0.01617084978602101,0.003721437285193577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.345323741007195,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Estado construcción: 2. Malo,condition,A_structural,dummy,binary,-0.2844,0.1636,,,*,,,high,construction state bad vs good,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-25.753717730255477,12.310337187560046,100*(exp(b - se^2/2) - 1),reported,-0.010210616951828325,0.005873617908998291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7383863080684596,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Columnas: 1. Madera,roof_materials,A_structural,dummy,binary,0.2212,0.0244,,,***,,,high,wooden columns vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,24.720159796176855,3.044077925283875,100*(exp(b - se^2/2) - 1),reported,0.028431416021512543,0.003136195980673174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.065573770491802,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Columnas: 1. Madera,roof_materials,A_structural,dummy,binary,0.1627,0.0236,,,***,,,high,wooden columns vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,17.63559944075015,2.77697337067728,100*(exp(b - se^2/2) - 1),reported,0.025650753291803326,0.0037206993096899716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.89406779661017,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Columnas: 1. Madera,roof_materials,A_structural,dummy,binary,0.7823,0.3472,,,**,,,high,wooden columns vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,105.85998762568951,75.9151211952057,100*(exp(b - se^2/2) - 1),reported,0.013233780053651154,0.005873409733641417,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.253168202764977,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,"Columnas: 2. Hierro, hormigón armado",roof_materials,A_structural,dummy,binary,0.7626,0.0053,,,***,,,high,iron/reinforced-concrete columns vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,114.38128622484349,1.1362367753251124,100*(exp(b - se^2/2) - 1),reported,0.41145558039752667,0.0028595785157446776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),143.88679245283018,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,"Columnas: 2. Hierro, hormigón armado",roof_materials,A_structural,dummy,binary,0.7326,0.0053,,,***,,,high,iron/reinforced-concrete columns vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,108.04536169328371,1.1026559036678152,100*(exp(b - se^2/2) - 1),reported,0.4574763401108892,0.0033096158921481207,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),138.22641509433961,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,"Columnas: 2. Hierro, hormigón armado",roof_materials,A_structural,dummy,binary,0.8373,0.022,,,***,,,high,iron/reinforced-concrete columns vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,130.95622398116063,5.0822666873169355,100*(exp(b - se^2/2) - 1),reported,0.2181708735235273,0.00573242471935698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),38.05909090909091,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Vigas (Beams): 1. Madera,roof_materials,A_structural,dummy,binary,-0.135,0.0631,,,**,,,high,wooden beams vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-12.802175606479949,5.513147402751498,100*(exp(b - se^2/2) - 1),reported,-0.006712331863203884,0.0031373936338382597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.13946117274168,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Vigas (Beams): 1. Madera,roof_materials,A_structural,dummy,binary,-0.1612,0.0583,,,***,,,high,wooden beams vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-15.03233766480756,4.962040267253818,100*(exp(b - se^2/2) - 1),reported,-0.010290606733928755,0.003721726877097062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7650085763293313,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Vigas (Beams): 1. Madera,roof_materials,A_structural,dummy,binary,0.2148,0.1883,,,,,,high,wooden beams vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,21.783117949831208,23.341930666867835,100*(exp(b - se^2/2) - 1),reported,0.006700427916487337,0.005873792256399281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1407328730748805,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,"Vigas (Beams): 2. Hierro, hormigón armado",roof_materials,A_structural,dummy,binary,0.1275,0.0626,,,**,,,high,iron/reinforced-concrete beams vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,13.376122140600867,7.111265275189674,100*(exp(b - se^2/2) - 1),reported,0.0063900724099618025,0.003137400257753795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.036741214057508,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,"Vigas (Beams): 2. Hierro, hormigón armado",roof_materials,A_structural,dummy,binary,0.0015,0.0577,,,,,,high,iron/reinforced-concrete beams vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-0.01646314467558252,5.778661494496843,100*(exp(b - se^2/2) - 1),reported,9.675712134525305e-05,0.003721923934414068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.025996533795493933,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,"Vigas (Beams): 2. Hierro, hormigón armado",roof_materials,A_structural,dummy,binary,0.2526,0.119,,,**,,,high,iron/reinforced-concrete beams vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,27.82852099093345,15.319681895835789,100*(exp(b - se^2/2) - 1),reported,0.012467545461202533,0.005873467576734369,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.122689075630252,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,Mezanines: 1. Madera,roof_materials,A_structural,dummy,binary,0.2027,0.0205,,,***,,,high,wooden mezzanine vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,22.444768752631596,2.510645253341036,100*(exp(b - se^2/2) - 1),reported,0.031007717567929877,0.0031359556494453015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.88780487804878,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,Mezanines: 1. Madera,roof_materials,A_structural,dummy,binary,0.1018,0.0167,,,***,,,high,wooden mezzanine vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,10.70076397361266,1.8489605686902337,100*(exp(b - se^2/2) - 1),reported,0.022682298105170685,0.0037209663885692575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.095808383233533,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,Mezanines: 1. Madera,roof_materials,A_structural,dummy,binary,0.3019,0.0894,,,***,,,high,wooden mezzanine vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,34.70322240896184,12.090688237503564,100*(exp(b - se^2/2) - 1),reported,0.019832090812781644,0.005872768859432523,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.376957494407159,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,19,"Mezanines: 2. Hormigón armado, simple",roof_materials,A_structural,dummy,binary,0.5772,0.0214,,,***,,,high,concrete mezzanine vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,78.06367409991262,3.8114352682732022,100*(exp(b - se^2/2) - 1),reported,0.08432218694510615,0.003126290368373651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.971962616822434,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,19,"Mezanines: 2. Hormigón armado, simple",roof_materials,A_structural,dummy,binary,0.2919,0.0175,,,***,,,high,concrete mezzanine vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,33.87641000840165,2.343195949557438,100*(exp(b - se^2/2) - 1),reported,0.06196240057457273,0.003714772216701003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.68,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,19,"Mezanines: 2. Hormigón armado, simple",roof_materials,A_structural,dummy,binary,0.7122,0.0889,,,***,,,high,concrete mezzanine vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,103.04316278318302,18.122006881394874,100*(exp(b - se^2/2) - 1),reported,0.047005450531255176,0.005867431272435531,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.011248593925758,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Paredes: 1. Madera, bahareque",roof_materials,A_structural,dummy,binary,-0.2604,0.0521,,,***,,,high,walls wood/bahareque vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-23.030212018047614,4.015572211863221,100*(exp(b - se^2/2) - 1),reported,-0.015679371882978806,0.003137078629428555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.998080614203455,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Paredes: 1. Madera, bahareque",roof_materials,A_structural,dummy,binary,-0.159,0.0511,,,***,,,high,walls wood/bahareque vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-14.81165906477332,4.358811394332261,100*(exp(b - se^2/2) - 1),reported,-0.01158016101229521,0.0037216743882282086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.111545988258317,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Paredes: 1. Madera, bahareque",roof_materials,A_structural,dummy,binary,0.4258,0.1666,,,**,,,high,walls wood/bahareque vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,50.9717005973213,25.50337092943259,100*(exp(b - se^2/2) - 1),reported,0.015011014905304564,0.00587326229033288,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5558223289315727,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Paredes: 2. Adobe, Tierra apisonada",roof_materials,A_structural,dummy,binary,-0.1672,0.0447,,,***,,,high,walls adobe/rammed earth vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-15.481441202447744,3.781755835869898,100*(exp(b - se^2/2) - 1),reported,-0.011734852634302814,0.003137248282017558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7404921700223714,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Paredes: 2. Adobe, Tierra apisonada",roof_materials,A_structural,dummy,binary,-0.1346,0.0442,,,***,,,high,walls adobe/rammed earth vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-12.678791980013637,3.8633693683801207,100*(exp(b - se^2/2) - 1),reported,-0.011333456758248532,0.003721684908726487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0452488687782804,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Paredes: 2. Adobe, Tierra apisonada",roof_materials,A_structural,dummy,binary,-1.007,0.231,,,***,,,high,walls adobe/rammed earth vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-64.43045884905416,8.438736701838398,100*(exp(b - se^2/2) - 1),reported,-0.025597850003968265,0.00587199935542867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.359307359307358,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,True
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Paredes: 3. Hormigón armado, bloque, ladrillo",roof_materials,A_structural,dummy,binary,0.0834,0.0363,,,**,,,high,walls concrete/block/brick vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,8.626059903021343,3.9457247393099326,100*(exp(b - se^2/2) - 1),reported,0.007208201816380732,0.0031373828049714694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.297520661157025,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Paredes: 3. Hormigón armado, bloque, ladrillo",roof_materials,A_structural,dummy,binary,0.2119,0.0355,,,***,,,high,walls concrete/block/brick vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,23.524567200814907,4.387886181470378,100*(exp(b - se^2/2) - 1),reported,0.022210735997644882,0.0037210057947918507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.969014084507043,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Paredes: 3. Hormigón armado, bloque, ladrillo",roof_materials,A_structural,dummy,binary,-0.1391,0.0424,,,***,,,high,walls concrete/block/brick vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-13.064079091942526,3.6893978824271394,100*(exp(b - se^2/2) - 1),reported,-0.019266773112212112,0.00587283378833784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2806603773584904,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Cubierta: 1. Teja,roof_materials,A_structural,dummy,binary,0.0148,0.006,,,**,,,high,roof cover tile vs none/straw (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,1.489179408699659,0.6089460373822223,100*(exp(b - se^2/2) - 1),reported,0.007738846893185842,0.003137370362102368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.466666666666667,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Cubierta: 1. Teja,roof_materials,A_structural,dummy,binary,0.0856,0.0075,,,***,,,high,roof cover tile vs none/straw (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,8.93398548324258,0.817027869710017,100*(exp(b - se^2/2) - 1),reported,0.0424412830415407,0.003718570359948075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.413333333333334,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Cubierta: 1. Teja,roof_materials,A_structural,dummy,binary,0.1007,0.0117,,,***,,,high,roof cover tile vs none/straw (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,10.586911474640504,1.2939554260015877,100*(exp(b - se^2/2) - 1),reported,0.05049142662778283,0.005866431892205156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.606837606837606,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Cubierta: 2. Zinc, madera, ladrillo",roof_materials,A_structural,dummy,binary,-0.0763,0.0189,,,***,,,high,roof cover zinc/wood/brick vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-7.362726617855797,1.7511572044259498,100*(exp(b - se^2/2) - 1),reported,-0.01266504376013582,0.003137212674529056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.037037037037037,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Cubierta: 2. Zinc, madera, ladrillo",roof_materials,A_structural,dummy,binary,0.0528,0.0183,,,***,,,high,roof cover zinc/wood/brick vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,5.404227139203321,1.9292203679078601,100*(exp(b - se^2/2) - 1),reported,0.010738046695996283,0.003721709366225984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8852459016393444,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Cubierta: 2. Zinc, madera, ladrillo",roof_materials,A_structural,dummy,binary,-0.1581,0.1007,,,,,,high,roof cover zinc/wood/brick vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-15.055343792597641,8.597407520682072,100*(exp(b - se^2/2) - 1),reported,-0.00922172705739462,0.0058736743496498305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5700099304865938,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Cubierta: 3. Hormigón armado, asbesto",roof_materials,A_structural,dummy,binary,0.1344,0.0031,,,***,,,high,roof cover reinforced concrete/asbestos vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,14.384477206879343,0.3545935831593996,100*(exp(b - se^2/2) - 1),reported,0.13478305594315065,0.003108835367736362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),43.354838709677416,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Cubierta: 3. Hormigón armado, asbesto",roof_materials,A_structural,dummy,binary,0.0758,0.0029,,,***,,,high,roof cover reinforced concrete/asbestos vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,7.874226709949506,0.31283657293387696,100*(exp(b - se^2/2) - 1),reported,0.09682628573551331,0.0037044357339444402,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.13793103448276,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Cubierta: 3. Hormigón armado, asbesto",roof_materials,A_structural,dummy,binary,0.3149,0.0096,,,***,,,high,roof cover reinforced concrete/asbestos vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,37.00591580963728,1.3153174002018901,100*(exp(b - se^2/2) - 1),reported,0.18919703754361988,0.00576783601276199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.802083333333336,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Piso: 1. Ladrillo, madera, piedra",flooring,B_interior,dummy,binary,-0.0564,0.0132,,,***,,,high,flooring brick/wood/stone vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-5.492134297014195,1.247612514547194,100*(exp(b - se^2/2) - 1),reported,-0.013404324964157532,0.0031371824384198482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.2727272727272725,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Piso: 1. Ladrillo, madera, piedra",flooring,B_interior,dummy,binary,0.0703,0.0122,,,***,,,high,flooring brick/wood/stone vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,7.275014493980181,1.3088525780110711,100*(exp(b - se^2/2) - 1),reported,0.02144189337626004,0.0037210682672883717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7622950819672125,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Piso: 1. Ladrillo, madera, piedra",flooring,B_interior,dummy,binary,-0.5387,0.0388,,,***,,,high,flooring brick/wood/stone vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-41.69327338754909,2.26400451282267,100*(exp(b - se^2/2) - 1),reported,-0.08128382200853994,0.005854487272937349,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.884020618556699,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,True
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Piso: 2. Cerámica, parquet, vinilo, moqueta, baldosa, mármol",flooring,B_interior,dummy,binary,0.0013,0.0132,,,,,,high,flooring ceramic/parquet/vinyl/carpet/tile/marble vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,0.12136158364108862,1.321717115883497,100*(exp(b - se^2/2) - 1),reported,0.00030899268288817636,0.003137464164710714,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09848484848484848,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Piso: 2. Cerámica, parquet, vinilo, moqueta, baldosa, mármol",flooring,B_interior,dummy,binary,0.1249,0.0122,,,***,,,high,flooring ceramic/parquet/vinyl/carpet/tile/marble vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,13.295082654915191,1.382302875542229,100*(exp(b - se^2/2) - 1),reported,0.03807632743961881,0.0037192249380572414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.237704918032787,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Piso: 2. Cerámica, parquet, vinilo, moqueta, baldosa, mármol",flooring,B_interior,dummy,binary,-0.4724,0.0373,,,***,,,high,flooring ceramic/parquet/vinyl/carpet/tile/marble vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-37.69295252479045,2.325670149045176,100*(exp(b - se^2/2) - 1),reported,-0.07418753731754685,0.005857737387689453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.664879356568365,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Puerta: 1. Madera,roof_materials,A_structural,dummy,binary,0.1995,0.019,,,***,,,high,wooden doors vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,22.05718763228508,2.3195051979188843,100*(exp(b - se^2/2) - 1),reported,0.0329255136770877,0.0031357632073416857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.5,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Puerta: 1. Madera,roof_materials,A_structural,dummy,binary,0.3527,0.0255,,,***,,,high,wooden doors vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,42.24416616001947,3.6284057307418753,100*(exp(b - se^2/2) - 1),reported,0.05141123887316226,0.003717001959925256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.831372549019608,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Puerta: 1. Madera,roof_materials,A_structural,dummy,binary,0.5784,0.0351,,,***,,,high,wooden doors vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,78.20849427497858,6.258972520141204,100*(exp(b - se^2/2) - 1),reported,0.09634395964943113,0.005846599211090996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.47863247863248,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Puerta: 2. Hierro, aluminio",roof_materials,A_structural,dummy,binary,0.163,0.0192,,,***,,,high,iron/aluminium doors vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,17.681975827883644,2.2599104442018563,100*(exp(b - se^2/2) - 1),reported,0.026626321220168184,0.0031363519474063135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.489583333333334,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Puerta: 2. Hierro, aluminio",roof_materials,A_structural,dummy,binary,0.3292,0.0252,,,***,,,high,iron/aluminium doors vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,38.941456199334155,3.502436613359388,100*(exp(b - se^2/2) - 1),reported,0.048563954492466296,0.0037175323609056816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.063492063492063,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Puerta: 2. Hierro, aluminio",roof_materials,A_structural,dummy,binary,0.5806,0.0485,,,***,,,high,iron/aluminium doors vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,78.50097457131943,8.667485320494777,100*(exp(b - se^2/2) - 1),reported,0.07014433038073369,0.005859455775862183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.971134020618557,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Ventana: 1. Madera,roof_materials,A_structural,dummy,binary,-0.019,0.0192,,,,,,high,wooden windows vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-1.9001472071040304,1.8838643755061872,100*(exp(b - se^2/2) - 1),reported,-0.003104767430108724,0.003137449192530921,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9895833333333334,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Ventana: 1. Madera,roof_materials,A_structural,dummy,binary,0.1068,0.0252,,,***,,,high,wooden windows vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,11.235843775513455,2.8040334585117903,100*(exp(b - se^2/2) - 1),reported,0.01577190615610475,0.003721461003125839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.238095238095238,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Ventana: 1. Madera,roof_materials,A_structural,dummy,binary,-0.2119,0.0572,,,***,,,high,wooden windows vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-19.22768537859364,4.627740821024548,100*(exp(b - se^2/2) - 1),reported,-0.02175506889986676,0.0058725339361603525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.7045454545454546,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Ventana: 2. Hierro, aluminio",roof_materials,A_structural,dummy,binary,-0.0057,0.016,,,,,,high,iron/aluminium windows vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-0.5811050151890651,1.590905942685493,100*(exp(b - se^2/2) - 1),reported,-0.0011177209638511172,0.003137462354669803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35625,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Ventana: 2. Hierro, aluminio",roof_materials,A_structural,dummy,binary,0.1266,0.023,,,***,,,high,iron/aluminium windows vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,13.466278379516595,2.610414766129938,100*(exp(b - se^2/2) - 1),reported,0.020482466141739594,0.0037211431379147765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.504347826086956,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Ventana: 2. Hierro, aluminio",roof_materials,A_structural,dummy,binary,-0.0052,0.027,,,,,,high,iron/aluminium windows vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-0.5549046846186179,2.6859964408085704,100*(exp(b - se^2/2) - 1),reported,-0.00113127355009862,0.005873920356281296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1925925925925926,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Enlucido: 1. Barro,roof_materials,A_structural,dummy,binary,0.0872,0.0259,,,***,,,high,mud plaster vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,9.074897685343931,2.8259875414626485,100*(exp(b - se^2/2) - 1),reported,0.010562611039528283,0.003137289288116772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3667953667953667,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Enlucido: 1. Barro,roof_materials,A_structural,dummy,binary,0.1161,0.0244,,,***,,,high,mud plaster vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,12.277390024967527,2.7403839526989486,100*(exp(b - se^2/2) - 1),reported,0.017706869845553384,0.003721340432657214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.758196721311475,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Enlucido: 1. Barro,roof_materials,A_structural,dummy,binary,1.0755,0.193,,,***,,,high,mud plaster vs none (reference),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,187.73665975484008,56.5771444815767,100*(exp(b - se^2/2) - 1),reported,0.03271514912510937,0.005870779898787642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.572538860103626,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,True
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Enlucido: 2. Arena, cal, cemento, granito",roof_materials,A_structural,dummy,binary,-0.0011,0.0102,,,,,,high,sand/lime/cement/granite plaster vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-0.11513566797036745,1.0188786168737922,100*(exp(b - se^2/2) - 1),reported,-0.00033835397533169757,0.003137464134893923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.10784313725490197,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Enlucido: 2. Arena, cal, cemento, granito",roof_materials,A_structural,dummy,binary,0.0747,0.0097,,,***,,,high,sand/lime/cement/granite plaster vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,7.7510141482288075,1.0452340091151238,100*(exp(b - se^2/2) - 1),reported,0.028650884847669953,0.0037203960243962324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.701030927835052,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Enlucido: 2. Arena, cal, cemento, granito",roof_materials,A_structural,dummy,binary,-0.0132,0.0452,,,,,,high,sand/lime/cement/granite plaster vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-1.4120871872478902,4.460728055460099,100*(exp(b - se^2/2) - 1),reported,-0.0017153912442494609,0.0058739154727330024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2920353982300885,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Techo: 1. barro, latón, cartón",roof_materials,A_structural,dummy,binary,0.4513,0.2123,,,**,,,high,ceiling mud/tin/cardboard vs none (reference); blank for edificios,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,53.535919204904324,33.33857969266041,100*(exp(b - se^2/2) - 1),reported,0.006669364832889325,0.0031373945358351514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.125765426283561,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Techo: 1. barro, latón, cartón",roof_materials,A_structural,dummy,binary,0.4456,0.1971,,,**,,,high,ceiling mud/tin/cardboard vs none (reference); blank for edificios,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,53.13899621700864,30.775721654332322,100*(exp(b - se^2/2) - 1),reported,0.0084141583104317,0.003721792197006481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.26078132927448,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Techo: 2. Fundición, madera, fibra mineral",roof_materials,A_structural,dummy,binary,0.1235,0.0102,,,***,,,high,ceiling cast/wood/mineral fibre vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,13.139114804371332,1.1540790046329203,100*(exp(b - se^2/2) - 1),reported,0.03796054554341064,0.00313520295176347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.107843137254902,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Techo: 2. Fundición, madera, fibra mineral",roof_materials,A_structural,dummy,binary,0.0894,0.01,,,***,,,high,ceiling cast/wood/mineral fibre vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,9.346330151504635,1.093517976046974,100*(exp(b - se^2/2) - 1),reported,0.03325559560479727,0.0037198652801786656,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.94,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Techo: 2. Fundición, madera, fibra mineral",roof_materials,A_structural,dummy,binary,0.1116,0.0469,,,**,,,high,ceiling cast/wood/mineral fibre vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,11.68365668352276,5.243727405908767,100*(exp(b - se^2/2) - 1),reported,0.013975818931695945,0.005873350429180463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.37953091684435,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,"Techo: 3. Arena, cal, cemento",roof_materials,A_structural,dummy,binary,0.2264,0.0128,,,***,,,high,ceiling sand/lime/cement vs none,"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,25.397446604771055,1.6052188106799672,100*(exp(b - se^2/2) - 1),reported,0.055408648160038064,0.0031326444189420815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.6875,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,"Techo: 3. Arena, cal, cemento",roof_materials,A_structural,dummy,binary,0.1841,0.015,,,***,,,high,ceiling sand/lime/cement vs none,"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,20.200079799241987,1.8032040460333358,100*(exp(b - se^2/2) - 1),reported,0.04563282710000797,0.003718046749050079,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.273333333333335,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,"Techo: 3. Arena, cal, cemento",roof_materials,A_structural,dummy,binary,0.3722,0.0454,,,***,,,high,ceiling sand/lime/cement vs none,"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,44.9428614782162,6.5871910415442105,100*(exp(b - se^2/2) - 1),reported,0.04810008785172625,0.005867125170522225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.198237885462554,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,True,True,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Tipo de calzada: 2. Adoquín de piedra,other,other,dummy,binary,-0.1331,0.0122,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-12.46875931965219,1.0679606109716224,100*(exp(b - se^2/2) - 1),reported,-0.03420918678041098,0.003135627939301383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.909836065573769,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Tipo de calzada: 2. Adoquín de piedra,other,other,dummy,binary,-0.1442,0.0118,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-13.434568112542422,1.0215432136349085,100*(exp(b - se^2/2) - 1),reported,-0.04543619927791163,0.0037180801073464437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.220338983050848,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Tipo de calzada: 2. Adoquín de piedra,other,other,dummy,binary,0.3126,0.0349,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,36.614239086185975,4.770741464990135,100*(exp(b - se^2/2) - 1),reported,0.052540187787798974,0.005865811112585362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.95702005730659,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Tipo de calzada: 3. Hormigón asfáltico,other,other,dummy,binary,-0.0643,0.0101,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-6.232418713766775,0.9471008766392432,100*(exp(b - se^2/2) - 1),reported,-0.019970170681571652,0.0031368386296092335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.366336633663366,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Tipo de calzada: 3. Hormigón asfáltico,other,other,dummy,binary,-0.0674,0.0094,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-6.522010142633439,0.8787319261781705,100*(exp(b - se^2/2) - 1),reported,-0.026677488533821517,0.0037205992910670956,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.170212765957447,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Tipo de calzada: 3. Hormigón asfáltico,other,other,dummy,binary,0.1602,0.0304,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,17.320335756106076,3.568186613778884,100*(exp(b - se^2/2) - 1),reported,0.030939215625674314,0.0058711120787796455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.269736842105264,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Tipo de calzada: 4. Hormigón hidráulico,other,other,dummy,binary,-0.033,0.0101,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-3.2510788456690642,0.9772139451849223,100*(exp(b - se^2/2) - 1),reported,-0.010250582447982739,0.003137299476503808,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2673267326732676,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Tipo de calzada: 4. Hormigón hidráulico,other,other,dummy,binary,-0.0258,0.0093,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-2.551216652290933,0.9063128777866449,100*(exp(b - se^2/2) - 1),reported,-0.010324787053281539,0.0037217255657177642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.774193548387097,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Tipo de calzada: 4. Hormigón hidráulico,other,other,dummy,binary,0.182,0.0303,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,19.90636433556392,3.6348310074560013,100*(exp(b - se^2/2) - 1),reported,0.03526037649559906,0.005870271471520063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.006600660066006,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Tipo de calzada: 5. Lastre,other,other,dummy,binary,-0.0434,0.0103,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-4.252248860797891,0.9862541511977093,100*(exp(b - se^2/2) - 1),reported,-0.013218840200386996,0.0031371901858061307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.213592233009709,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Tipo de calzada: 5. Lastre,other,other,dummy,binary,-0.0109,0.0096,,,,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-1.088638965659361,0.9495928221587638,100*(exp(b - se^2/2) - 1),reported,-0.004225896753013572,0.0037218907182504854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1354166666666667,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Tipo de calzada: 5. Lastre,other,other,dummy,binary,0.2173,0.0312,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,24.211215330242997,3.8772766071919804,100*(exp(b - se^2/2) - 1),reported,0.04087618323463134,0.005869014804051992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.964743589743589,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Tipo de calzada: 6. Piedra,other,other,dummy,binary,-0.031,0.0143,,,**,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-3.062354588664773,1.3863500694987172,100*(exp(b - se^2/2) - 1),reported,-0.006801338752040067,0.003137391746908805,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.167832167832168,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Tipo de calzada: 6. Piedra,other,other,dummy,binary,-0.0582,0.0132,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-5.662095444357451,1.2453688319411487,100*(exp(b - se^2/2) - 1),reported,-0.016408091876085017,0.0037214228997306214,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.409090909090909,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Tipo de calzada: 6. Piedra,other,other,dummy,binary,0.1529,0.1052,,,,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,15.87785543875948,12.257992917893805,100*(exp(b - se^2/2) - 1),reported,0.008536979743029706,0.005873710065184597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4534220532319393,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c1,3,Muestra total,20,Tipo de calzada: 7. Tierra,other,other,dummy,binary,-0.0664,0.0107,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,101598.0,0.7385,se,Full sample; robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-6.429707902049831,1.001259440904234,100*(exp(b - se^2/2) - 1),reported,-0.019466182776720253,0.0031368698149232934,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.205607476635515,101588.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c2,3,Villas,20,Tipo de calzada: 7. Tierra,other,other,dummy,binary,-0.053,0.0098,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,72198.0,0.758,se,Villas (detached houses); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,-5.166552763247434,0.9294124122326521,100*(exp(b - se^2/2) - 1),reported,-0.020124695869325058,0.003721170179611048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.408163265306123,72188.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82,dfd836c56fd33dc021acfdf9971e3bbd78377b82:T3-c3,3,Edificios,20,Tipo de calzada: 7. Tierra,other,other,dummy,binary,0.2046,0.0326,,,***,,,high,street surface type vs concrete pavement (reference 1),"ln(cadastral value per m2 built, USD)",log,OLS,28993.0,0.5837,se,Edificios (buildings/apartments); robust SE in parentheses; Table A1 (pp.19-20) continues the same model,pct_premium,22.638230492270452,4.000131339191103,100*(exp(b - se^2/2) - 1),reported,0.03684015503125055,0.005869936725409423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.2760736196319025,28983.0,binary,,2025.0,Ecuador,South America,Upper middle income,2018.0,2018.0,mixed,appraisal,journal,False,False,robust,,2018.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,CERTIFED,energy_efficiency,A_structural,dummy,binary,0.044,,,,**,,,high,"BEAM Plus green building certification (Gold/Silver/Bronze) vs non-registered building; printed as CERTIFED (sic); no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.044,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,CERTIFED,energy_efficiency,A_structural,dummy,binary,0.062,,,,**,,,high,"BEAM Plus green building certification (Gold/Silver/Bronze) vs non-registered building; printed as CERTIFED (sic); no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.062,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,UNCL,energy_efficiency,A_structural,dummy,binary,-0.059,,,,**,,,high,"building obtained 'unclassified' rating under BEAM Plus vs non-registered; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,-0.059,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,AREA,living_area,A_structural,level,m2,-0.005,,,,**,,,medium,"saleable area of the unit, defined in Table 6 in square metres (level); the text interprets the coefficient as a 1% change in area, so functional form is ambiguous; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,-0.005,,"b (currency per unit, as reported)",,,,,,636.0,m2,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,AREA,living_area,A_structural,level,m2,-0.005,,,,**,,,medium,"saleable area of the unit, defined in Table 6 in square metres (level); the text interprets the coefficient as a 1% change in area, so functional form is ambiguous; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,-0.005,,"b (currency per unit, as reported)",,,,,,636.0,m2,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,5 years or less,age,A_structural,dummy,binary,-0.185,,,,**,,,high,"building-age category dummy (0-5 years) vs pre-sale (uncompleted) flats; not age in years; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,-0.185,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,5 years or less,age,A_structural,dummy,binary,-0.157,,,,**,,,high,"building-age category dummy (0-5 years) vs pre-sale (uncompleted) flats; not age in years; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,-0.157,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,6 to 10 years,age,A_structural,dummy,binary,-0.238,,,,**,,,high,"building-age category dummy (6-10 years) vs pre-sale flats; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,-0.238,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,6 to 10 years,age,A_structural,dummy,binary,-0.203,,,,**,,,high,"building-age category dummy (6-10 years) vs pre-sale flats; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,-0.203,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,More than 10 years,age,A_structural,dummy,binary,-0.324,,,,**,,,high,"building-age category dummy (>10 years) vs pre-sale flats; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,-0.324,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,More than 10 years,age,A_structural,dummy,binary,-0.295,,,,**,,,high,"building-age category dummy (>10 years) vs pre-sale flats; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,-0.295,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,11th to 20th floor,floor_level,A_structural,dummy,binary,0.03,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.03,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,11th to 20th floor,floor_level,A_structural,dummy,binary,0.031,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.031,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,21st to 30th floor,floor_level,A_structural,dummy,binary,0.09,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.09,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,21st to 30th floor,floor_level,A_structural,dummy,binary,0.091,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.091,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,31st to 40th floor,floor_level,A_structural,dummy,binary,0.154,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.154,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,31st to 40th floor,floor_level,A_structural,dummy,binary,0.153,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.153,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,41st floor or above,floor_level,A_structural,dummy,binary,0.199,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.199,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,41st floor or above,floor_level,A_structural,dummy,binary,0.188,,,,**,,,high,"floor-level category vs 10th floor or below; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.188,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,MTR,transit_distance,D_location,dummy,binary,0.028,,,,**,,,high,"=1 if within 5 minutes walk (about 400 m) of Sham Shui Po or Cheung Sha Wan MTR station; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.028,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,MTR,transit_distance,D_location,dummy,binary,0.051,,,,**,,,high,"=1 if within 5 minutes walk (about 400 m) of Sham Shui Po or Cheung Sha Wan MTR station; no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.051,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c1,7,Model 1,13,NSSP,other,other,dummy,binary,0.242,,,,**,,,high,"=1 if located in Cheung Sha Wan, 0 if Sham Shui Po (sub-district location dummy); no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.8,none,"full sample, CERTIFIED and UNCL included; r2 = adjusted R-square; F = 216.095",monetary,0.242,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4,e06a4d3e776b1e6666c940f56997612ef11873e4:T7-c2,7,Model 2,13,NSSP,other,other,dummy,binary,0.25,,,,**,,,high,"=1 if located in Cheung Sha Wan, 0 if Sham Shui Po (sub-district location dummy); no SE/t printed; ** = significant at 1% level, * at 5% level",ln of price-index-adjusted transaction price per square metre (HKD),per_m2,OLS,646.0,0.796,none,"full sample, UNCL omitted (unclassified buildings treated as non-registered); r2 = adjusted R-square; F = 229.644",monetary,0.25,,"b (currency per unit, as reported)",,,,,,636.0,binary,,2017.0,Hong Kong,Asia,High income,2012.0,2014.0,condo_apartment,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,AREA,living_area,A_structural,level,m2,0.001,,,0.023,**,,,low,House area (m2); table row 'AREA' and text agree,"House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,0.001,0.0004398630941889216,not harmonised,|b/z(p)| (two-sided normal),0.16485919648862465,0.07251547627298585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.273434650942775,185.0,m2,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,BEDROOMS (table row labelled DIENTICH),bedrooms,A_structural,level,count,0.065,,,0.0,***,,,low,Text/equation attribute 0.065 to BEDROOMS; table row label is 'DIENTICH',"House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,0.065,,not harmonised,,,,,,185.0,count,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,BATHROOMS (table row labelled BEDROOMS),bathrooms,A_structural,level,count,0.003,,,0.64,,,,low,Text says BATHROOMS not significant; table row label is 'BEDROOMS',"House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,0.003,0.006414384654568036,not harmonised,|b/z(p)| (two-sided normal),0.034365588820674586,0.07347803519217663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46769879911450823,185.0,count,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,CITY_CENTER (table row labelled BATHROOMS),cbd_distance,D_location,level,km,-0.02,,,0.0,***,,,low,Distance to city centre (km); text/equation attribute -0.020 to CITY_CENTER; table row label is 'BATHROOMS',"House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,-0.02,,not harmonised,,,,,,185.0,km,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,TYPE (table row labelled CENTER),other,other,binary,binary,0.148,,,0.036,**,,,low,"Street-frontage house dummy (1 = street front, 0 = alley); table row label is 'CENTER'","House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,0.148,0.07057945732484422,not harmonised,|b/z(p)| (two-sided normal),0.15236904053253372,0.07266300131009071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0969274291643423,185.0,binary,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,AMENITIES (table row labelled TYPE),neighbourhood_amenities,F_neighbourhood,binary,binary,0.087,,,0.0,***,,,low,"Dummy: local amenities nearby (parks, plazas, schools, shopping centres); table row label is 'TYPE'","House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,0.087,,not harmonised,,,,,,185.0,binary,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a,e075c6fe2099973f79200c3fb2110ef7bfef767a:T4-c1,4,Coef.,6,STRUCTURE (table row labelled AMENITIES),condition,A_structural,binary,binary,0.05,,,0.0,***,,,low,Dummy: modern structure (newly built / modern design vs outdated); table row label is 'AMENITIES',"House price (PRICE), declared 'billion [VND] per square meter'",other,OLS,195.0,0.8559,p,"All 195 listings; F(8,196)=276.08",other,0.05,,not harmonised,,,,,,185.0,binary,,2024.0,Vietnam,Asia,Lower middle income,2023.0,2024.0,unknown,listing,journal,False,False,unknown,,2023.5,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,LF,floor_level,A_structural,dummy,count,0.004,,,0.277,,,,high,Low floor dummy (floors 1-2 vs middle floors 3-4 reference); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,pct_premium,0.40012139468215135,0.36943186074858003,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.00831649462161826,0.00765028279230872,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0870832944867503,17085.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,HF,floor_level,A_structural,dummy,count,-0.063,,,0.0,***,,,high,High floor dummy (floors 5-7 vs middle floors 3-4 reference); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,pct_premium,-6.105652631086677,,100*(exp(b) - 1),,,,,,17085.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,lnAR,living_area,A_structural,log,m2,0.776,,,0.0,***,,,high,Log building area (m2); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,elasticity,0.776,,b (log-log),,,,,,17085.0,m2,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,lnAG,age,A_structural,log,years,-0.04,,,0.0,***,,,high,Log building age (years); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,elasticity,-0.04,,b (log-log),,,,,,17085.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,S,sunlight,E_environment,binary,binary,0.032,,,0.003,***,,,high,South-facing orientation dummy; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,pct_premium,3.245748424417827,1.1133247274844402,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.02269896956910266,0.007648576168156257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,17085.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,D,condition,A_structural,binary,binary,0.052,,,0.0,***,,,high,Well-decorated (fit-out) dummy; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,pct_premium,5.337574251336474,,100*(exp(b) - 1),,,,,,17085.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,BN,bedrooms,A_structural,level,count,0.034,,,0.0,***,,,high,Number of bedrooms; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,semi_elasticity,3.4000000000000004,,100*b/1 (per unit count),,,,,,17085.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,LN,other,other,level,count,0.014,,,0.015,**,,,high,Number of living rooms; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,semi_elasticity,1.4000000000000001,0.5755681850076227,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.018605809925359474,0.007649223035239974,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4323790585844467,17085.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,EF,school_distance,D_location,level,count,0.007,,,0.0,***,,,high,"Count score of primary/secondary/senior high schools within 1 km (0-3), not a distance; p-value printed in adjacent column","ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,semi_elasticity,0.7000000000000001,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,17085.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.018,,,0.0,***,,,high,"Living facilities: count score of shopping malls, 3A hospitals, post offices, banks within 1 km (0-4); second variable printed as LF in table; p-value printed in adjacent column","ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,semi_elasticity,1.7999999999999998,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,17085.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,TF,transit_distance,D_location,level,count,0.071,,,0.0,***,,,high,"Traffic facilities: count score of bus stops and subway stations within 1 km (0-2), not a distance; p-value printed in adjacent column","ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,semi_elasticity,7.1,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,17085.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,LP,green_space,E_environment,level,count,0.023,,,0.0,***,,,high,Landscapes and parks within 1 km count score (0-2); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,semi_elasticity,2.3,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,17085.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c1,2,1,6,lnD_TAM,cbd_distance,D_location,log,m,-0.159,,,0.0,***,,,high,Log straight-line distance to Tiananmen Square (m); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,17095.0,0.82,p,Without-elevator sample (adj. R2); year FE + district FE,elasticity,-0.159,,b (log-log),,,,,,17085.0,m,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,LF,floor_level,A_structural,dummy,count,-0.055,,,0.048,**,,,high,Low floor dummy (floors 1-2 vs middle floors 3-4 reference); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,pct_premium,-5.38809101278831,2.6326243706290042,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.06778690188642908,0.03428137160496409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9773684281819468,847.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,HF,floor_level,A_structural,dummy,count,0.014,,,0.477,,,,high,High floor dummy (floors 5-7 vs middle floors 3-4 reference); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,pct_premium,1.390196060919635,1.996436631671782,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.024427628746899476,0.03435015352470729,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7111359409013772,847.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,lnAR,living_area,A_structural,log,m2,0.827,,,0.0,***,,,high,Log building area (m2); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,elasticity,0.827,,b (log-log),,,,,,847.0,m2,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,lnAG,age,A_structural,log,years,-0.034,,,0.083,*,,,high,Log building age (years); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,elasticity,-0.034,0.019613063064160757,b (log-log),|b/z(p)| (two-sided normal),-0.0594596998112308,0.03429961300510991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7335385038417948,847.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,S,sunlight,E_environment,binary,binary,0.027,,,0.282,,,,high,South-facing orientation dummy; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,pct_premium,2.7044312021136996,2.578357257251985,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.03694097788291456,0.03433695391205722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0758373610404317,847.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,D,condition,A_structural,binary,binary,0.064,,,0.001,***,,,high,Well-decorated (fit-out) dummy; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,pct_premium,6.589076981519337,2.0735255808057675,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11234802033629712,0.03414286814967146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491894,847.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,BN,bedrooms,A_structural,level,count,0.058,,,0.001,***,,,high,Number of bedrooms; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,semi_elasticity,5.800000000000001,1.7626357338146694,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.11234802033629712,0.03414286814967146,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491894,847.0,count,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,True,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,LN,other,other,level,count,0.02,,,0.371,,,,high,Number of living rooms; p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,semi_elasticity,2.0,2.2356315902781048,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.030724368962574532,0.03434418492204587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8946017799610745,847.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,EF,school_distance,D_location,level,count,0.006,,,0.0,***,,,high,"Count score of primary/secondary/senior high schools within 1 km (0-3), not a distance; p-value printed in adjacent column","ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,semi_elasticity,0.6,,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,847.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,LF,neighbourhood_amenities,F_neighbourhood,level,count,0.033,,,0.007,***,,,high,"Living facilities: count score of shopping malls, 3A hospitals, post offices, banks within 1 km (0-4); second variable printed as LF in table; p-value printed in adjacent column","ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,semi_elasticity,3.3000000000000003,1.2236524177059742,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.09226936567450068,0.03421382799630285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6968442608781262,847.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,TF,transit_distance,D_location,level,count,0.055,,,0.011,**,,,high,"Traffic facilities: count score of bus stops and subway stations within 1 km (0-2), not a distance; p-value printed in adjacent column","ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,semi_elasticity,5.5,2.163056024582531,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/z(p)| (two-sided normal),0.08703661311098683,0.034230012790723456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.542698819399048,847.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,LP,green_space,E_environment,level,count,0.003,,,0.826,,,,high,Landscapes and parks within 1 km count score (0-2); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,semi_elasticity,0.3,1.364662566283323,"100*b (unit as reported: count; distance unit not harmonisable (counts, shares, indices excluded))",|b/z(p)| (two-sided normal),0.0075533895216491065,0.03435942642917077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21983456380506872,847.0,count,unit_not_harmonisable,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,True,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T2-c2,2,2,6,lnD_TAM,cbd_distance,D_location,log,m,-0.158,,,0.0,***,,,high,Log straight-line distance to Tiananmen Square (m); p-value printed in adjacent column,"ln housing price (total transaction price, CNY)",log,OLS,857.0,0.713,p,Installed (retrofitted) elevator sample (adj. R2); year FE + district FE,elasticity,-0.158,,b (log-log),,,,,,847.0,m,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T3-c1,3,1,6,E,accessibility,B_interior,binary,binary,0.054,,,,***,,,high,Retrofitted (additional) elevator dummy; PSM-matched sample; only stars printed,"ln housing price (total transaction price, CNY)",log,OLS,17750.0,0.814,none,"Whole PSM-matched sample, Model 2 (no interaction); controls + year FE + district FE; adj. R2",pct_premium,5.548460215508011,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T3-c3,3,3,6,E,accessibility,B_interior,binary,binary,0.024,,,,,,,high,Retrofitted (additional) elevator dummy; PSM-matched sample; only stars printed,"ln housing price (total transaction price, CNY)",log,OLS,2617.0,0.794,none,"Low-floor (LF) sample, Model 2 (no interaction); controls + year FE + district FE; adj. R2",pct_premium,2.4290317890621527,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T3-c5,3,5,6,E,accessibility,B_interior,binary,binary,0.07,,,,***,,,high,Retrofitted (additional) elevator dummy; PSM-matched sample; only stars printed,"ln housing price (total transaction price, CNY)",log,OLS,8643.0,0.818,none,"Middle-floor (MF) sample, Model 2 (no interaction); controls + year FE + district FE; adj. R2",pct_premium,7.250818125421654,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T3-c7,3,7,6,E,accessibility,B_interior,binary,binary,0.119,,,,***,,,high,Retrofitted (additional) elevator dummy; PSM-matched sample; only stars printed,"ln housing price (total transaction price, CNY)",log,OLS,6124.0,0.824,none,"High-floor (HF) sample, Model 2 (no interaction); controls + year FE + district FE; adj. R2",pct_premium,12.636991828835287,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T4-c1,4,1,7,lnAG,age,A_structural,log,years,-0.065,,,,***,,,high,Log building age; model also contains elevator x single-orientation interaction (not extracted),"ln housing price (total transaction price, CNY)",log,OLS,17750.0,0.815,none,"Whole sample, Model 4 (includes E, Single and E x Single interaction); controls + year FE + district FE; adj. R2",elasticity,-0.065,,b (log-log),,,,,,17740.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T4-c3,4,3,7,lnAG,age,A_structural,log,years,-0.055,,,,***,,,high,Log building age; model also contains elevator x single-orientation interaction (not extracted),"ln housing price (total transaction price, CNY)",log,OLS,2617.0,0.794,none,"Low-floor sample, Model 4 (includes E, Single and E x Single interaction); controls + year FE + district FE; adj. R2",elasticity,-0.055,,b (log-log),,,,,,2607.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T4-c5,4,5,7,lnAG,age,A_structural,log,years,-0.083,,,,***,,,high,Log building age; model also contains elevator x single-orientation interaction (not extracted),"ln housing price (total transaction price, CNY)",log,OLS,8643.0,0.82,none,"Middle-floor sample, Model 4 (includes E, Single and E x Single interaction); controls + year FE + district FE; adj. R2",elasticity,-0.083,,b (log-log),,,,,,8633.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T4-c7,4,7,7,lnAG,age,A_structural,log,years,-0.083,,,,***,,,high,Log building age; model also contains elevator x single-orientation interaction (not extracted),"ln housing price (total transaction price, CNY)",log,OLS,6124.0,0.826,none,"High-floor sample, Model 4 (includes E, Single and E x Single interaction); controls + year FE + district FE; adj. R2",elasticity,-0.083,,b (log-log),,,,,,6114.0,years,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-OLS,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.052,,,,***,,,high,"Retrofitted elevator dummy, OLS; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,OLS,5996.0,,none,AH sample (affordable housing); OLS row; controls + year FE + district FE,pct_premium,5.337574251336474,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-OLS,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.047,,,,***,,,high,"Retrofitted elevator dummy, OLS; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,OLS,11754.0,,none,CH sample (commercial housing); OLS row; controls + year FE + district FE,pct_premium,4.812200907965569,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,ols,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.1,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.01,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.1; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.1 row; controls + year FE + district FE,pct_premium,-0.9950166250831893,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.1,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.099,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.1; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.1 row; controls + year FE + district FE,pct_premium,-9.42572919764515,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.1,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.009,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.1; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.1 row; controls + year FE + district FE,pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.1,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.064,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.1; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.1 row; controls + year FE + district FE,pct_premium,6.609239876150519,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.1,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.019,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.1; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.1 row; controls + year FE + district FE,pct_premium,1.918164861740812,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.1,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.015,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.1; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.1 row; controls + year FE + district FE,pct_premium,-1.4888060396937353,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.2,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.031,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.2; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.2 row; controls + year FE + district FE,pct_premium,3.1485503886522714,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.2,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.027,,,,*,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.2; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.2 row; controls + year FE + district FE,pct_premium,-2.6638758475663216,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.2,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.014,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.2; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.2 row; controls + year FE + district FE,pct_premium,1.4098458938492264,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.2,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.083,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.2; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.2 row; controls + year FE + district FE,pct_premium,8.654180854823812,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.2,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.05,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.2; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.2 row; controls + year FE + district FE,pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.2,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.027,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.2; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.2 row; controls + year FE + district FE,pct_premium,2.736780276348938,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.3,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.038,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.3; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.3 row; controls + year FE + district FE,pct_premium,3.873123287849767,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.3,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.009,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.3; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.3 row; controls + year FE + district FE,pct_premium,-0.8959621227116354,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.3,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.024,,,,**,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.3; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.3 row; controls + year FE + district FE,pct_premium,2.4290317890621527,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.3,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.091,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.3; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.3 row; controls + year FE + district FE,pct_premium,9.52690052584655,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.3,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.054,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.3; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.3 row; controls + year FE + district FE,pct_premium,5.548460215508011,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.3,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.032,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.3; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.3 row; controls + year FE + district FE,pct_premium,3.251750530511832,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.4,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.053,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.4; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.4 row; controls + year FE + district FE,pct_premium,5.4429645119356,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.4,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,-0.003,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.4; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.4 row; controls + year FE + district FE,pct_premium,-0.29955044966269995,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.4,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.031,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.4; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.4 row; controls + year FE + district FE,pct_premium,3.1485503886522714,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.4,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.125,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.4; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.4 row; controls + year FE + district FE,pct_premium,13.314845306682631,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.4,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.058,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.4; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.4 row; controls + year FE + district FE,pct_premium,5.97149957102876,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.4,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.056,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.4; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.4 row; controls + year FE + district FE,pct_premium,5.759768373661123,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.5,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.087,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.5; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.5 row; controls + year FE + district FE,pct_premium,9.089667971827776,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.5,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,0.013,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.5; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.5 row; controls + year FE + district FE,pct_premium,1.3084867359809182,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.5,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.058,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.5; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.5 row; controls + year FE + district FE,pct_premium,5.97149957102876,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.5,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.148,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.5; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.5 row; controls + year FE + district FE,pct_premium,15.951289636297394,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.5,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.059,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.5; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.5 row; controls + year FE + district FE,pct_premium,6.077524074015894,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.5,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.08,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.5; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.5 row; controls + year FE + district FE,pct_premium,8.328706767495863,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.6,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.091,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.6; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.6 row; controls + year FE + district FE,pct_premium,9.52690052584655,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.6,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,0.016,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.6; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.6 row; controls + year FE + district FE,pct_premium,1.6128685406094911,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.6,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.087,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.6; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.6 row; controls + year FE + district FE,pct_premium,9.089667971827776,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.6,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.155,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.6; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.6 row; controls + year FE + district FE,pct_premium,16.765796110512497,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.6,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.08,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.6; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.6 row; controls + year FE + district FE,pct_premium,8.328706767495863,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.6,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.095,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.6; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.6 row; controls + year FE + district FE,pct_premium,9.965885512610285,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.7,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.098,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.7; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.7 row; controls + year FE + district FE,pct_premium,10.296278510850776,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.7,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,0.042,,,,,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.7; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.7 row; controls + year FE + district FE,pct_premium,4.289447875076324,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.7,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.09,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.7; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.7 row; controls + year FE + district FE,pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.7,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.156,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.7; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.7 row; controls + year FE + district FE,pct_premium,16.88262030898691,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.7,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.082,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.7; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.7 row; controls + year FE + district FE,pct_premium,8.545580982954903,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.7,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.099,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.7; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.7 row; controls + year FE + district FE,pct_premium,10.406629955888192,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.8,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.102,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.8; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.8 row; controls + year FE + district FE,pct_premium,10.73834717279334,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.8,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,0.05,,,,*,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.8; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.8 row; controls + year FE + district FE,pct_premium,5.127109637602412,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.8,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.091,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.8; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.8 row; controls + year FE + district FE,pct_premium,9.52690052584655,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.8,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.158,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.8; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.8 row; controls + year FE + district FE,pct_premium,17.11661947076695,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.8,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.073,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.8; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.8 row; controls + year FE + district FE,pct_premium,7.573053691470344,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.8,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.103,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.8; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.8 row; controls + year FE + district FE,pct_premium,10.849140907600718,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c1-Q0.9,5,1,9,E (additional elevator),accessibility,B_interior,binary,binary,0.102,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.9; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,17750.0,,none,Whole sample; Q0.9 row; controls + year FE + district FE,pct_premium,10.73834717279334,,100*(exp(b) - 1),,,,,,17740.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c2-Q0.9,5,2,9,E (additional elevator),accessibility,B_interior,binary,binary,0.055,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.9; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,2617.0,,none,LF sample; Q0.9 row; controls + year FE + district FE,pct_premium,5.654061467549432,,100*(exp(b) - 1),,,,,,2607.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c3-Q0.9,5,3,9,E (additional elevator),accessibility,B_interior,binary,binary,0.093,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.9; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,8643.0,,none,MF sample; Q0.9 row; controls + year FE + district FE,pct_premium,9.7461735268082,,100*(exp(b) - 1),,,,,,8633.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c4-Q0.9,5,4,9,E (additional elevator),accessibility,B_interior,binary,binary,0.159,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.9; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,6124.0,,none,HF sample; Q0.9 row; controls + year FE + district FE,pct_premium,17.233794668071756,,100*(exp(b) - 1),,,,,,6114.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c5-Q0.9,5,5,9,E (additional elevator),accessibility,B_interior,binary,binary,0.062,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.9; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,5996.0,,none,AH sample (affordable housing); Q0.9 row; controls + year FE + district FE,pct_premium,6.396234472803375,,100*(exp(b) - 1),,,,,,5986.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f,e1b44941a0d59a0ba33dc85660a7305568feae7f:T5-c6-Q0.9,5,6,9,E (additional elevator),accessibility,B_interior,binary,binary,0.108,,,,***,,,high,"Retrofitted elevator dummy, quantile regression at tau=0.9; only stars printed; PSM-matched sample","ln housing price (total transaction price, CNY)",log,quantile,11754.0,,none,CH sample (commercial housing); Q0.9 row; controls + year FE + district FE,pct_premium,11.404774538646766,,100*(exp(b) - 1),,,,,,11744.0,binary,,2024.0,China,Asia,Upper middle income,2017.0,2021.0,condo_apartment,transaction,journal,True,True,unknown,,2019.0,quantile,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,BEDS,bedrooms,A_structural,level,count,-0.074,,,0.0,,,,high,"Number of bedrooms; main effect, interacted with property-type dummies; only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,-7.3999999999999995,,100*b/1 (per unit count),,,,,,1680.0,count,,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,FLATBED,dwelling_type,A_structural,other,count,0.07,,,0.0,,,,high,"Interaction FLAT dummy x number of bedrooms (terraced x bedrooms is the omitted base); only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",other,0.07,,not harmonised,,,,,,1680.0,count,nonstandard_x_transform,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,SEMIBED,dwelling_type,A_structural,other,count,0.082,,,0.0,,,,high,"Interaction SEMI-detached dummy x number of bedrooms; only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",other,0.082,,not harmonised,,,,,,1680.0,count,nonstandard_x_transform,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,DETABED,dwelling_type,A_structural,other,count,0.092,,,0.0,,,,high,"Interaction DETAched dummy x number of bedrooms; text: extra bedroom in detached house = -0.074+0.092 = +1.8%; only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",other,0.092,,not harmonised,,,,,,1680.0,count,nonstandard_x_transform,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,SP_AVE,school_quality,F_neighbourhood,level,index,-0.009,,,0.085,,,,high,"Average point score of nearest primary school (2003 DfES performance statistics); only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,-0.8999999999999999,0.5225315942010819,100*b (unit as reported: index; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.041984811285749275,0.02437598930374905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7223838902526911,1680.0,index,unit_not_harmonisable,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,True,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,ETHNM,demographics,F_neighbourhood,level,share,0.001,,,0.685,,,,high,"Percentage ethnic minority in 2001 census output area (percentage points); only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,0.1,0.24651811181440078,100*b (unit as reported: share; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.009896354833970411,0.024396307075157036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4056497076989145,1680.0,share,unit_not_harmonisable,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,True,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,HPROF,other,other,level,share,0.029,,,0.0,,,,high,"Percentage of higher professionals / large employers / higher managerial occupations in census output area (percentage points); socio-economic status proxy; only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,2.9000000000000004,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,1680.0,share,unit_not_harmonisable,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,UNEM,unemployment,F_neighbourhood,level,share,-0.074,,,0.0,,,,high,"Percentage long-term unemployed in census output area (percentage points); only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,-7.3999999999999995,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,1680.0,share,unit_not_harmonisable,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,CAR_ACC,cbd_distance,D_location,level,minutes,0.021,,,0.0,,,,high,"Car travel time (minutes) to Newcastle city centre, half-congested network; only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,2.1,,"100*b (unit as reported: minutes; distance unit not harmonisable (counts, shares, indices excluded))",,,,,,1680.0,minutes,unit_not_harmonisable,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,False,False
e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af,e2459715fae0e20316482cb1778f7b7fac58b6af:T2-c1,2,Estimate,9,PT_ACC,employment_access,D_location,level,minutes,-0.012,,,0.003,,,,high,"Public transport travel time (minutes) to employment, gravity-based Hansen measure (Metro + bus); only estimate and p-value printed, no SE",ln asking price (GBP),log,OLS,1690.0,0.328,p,"Global regression, all properties; p-values in a separate column",semi_elasticity,-1.2,0.40434837246007854,100*b (unit as reported: minutes; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),-0.07221634034684019,0.02433379973688994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9677379253417833,1680.0,minutes,unit_not_harmonisable,2012.0,United Kingdom,Europe,High income,2005.0,2005.0,mixed,listing,journal,False,False,unknown,,2005.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,DIST_TO_ST,transit_distance,D_location,level,m,14.93413,,0.521,,,,,high,Distance to nearest urban rail station; semi-log column prints positive coefficient with negative t (as printed),Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,14.93413,28.664357005758156,"b (currency per unit, as reported)",|b/t|,0.07743290702970962,0.1486236219380223,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.521,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,DIST_TO_ST,transit_distance,D_location,level,m,1e-05,,-0.355,,,,,medium,Distance to nearest urban rail station; semi-log column prints positive coefficient with negative t (as printed),ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,1.0,2.816901408450705,100*b/0.001 (X per m->km),|b/t|,-0.05284632766675901,0.14886289483594087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.355,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,DIST_TO_ST,transit_distance,D_location,log,m,-0.00781,,-0.267,,,,,high,Distance to nearest urban rail station; semi-log column prints positive coefficient with negative t (as printed),ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,-0.00781,0.029250936329588015,b (log-log),|b/t|,-0.039770520237001526,0.14895325931461245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.267,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,CORE_CBD,cbd_distance,D_location,binary,binary,2640.75367,,0.191,,,,,high,Dummy = 1 if located in core CBD,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,2640.75367,13825.935445026178,"b (currency per unit, as reported)",|b/t|,0.028461064716219436,0.14901081003256247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.191,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,CORE_CBD,cbd_distance,D_location,binary,binary,-0.04,,-0.258,,,,,high,Dummy = 1 if located in core CBD,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,pct_premium,-5.068870365077805,14.895960296935243,100*(exp(b - se^2/2) - 1),|b/t|,-0.03843195544228092,0.14896106760573996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.258,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,CORE_CBD,cbd_distance,D_location,binary,binary,-0.00833,,-0.216,,,,,high,Dummy = 1 if located in core CBD,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,pct_premium,-0.9032581382727778,3.82449041850322,100*(exp(b - se^2/2) - 1),|b/t|,-0.032182699686605316,0.1489939800305802,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.216,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,UNITS,other,other,level,count,69.28454,,1.786,,*,,,high,Number of units in the project,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,69.28454,38.793135498320275,"b (currency per unit, as reported)",|b/t|,0.2572787462122422,0.1440530493909531,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.786,45.0,count,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,UNITS,other,other,level,count,8e-05,,1.767,,*,,,high,Number of units in the project,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,0.008,0.004527447651386531,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/t|,0.254720214063425,0.144154054365266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.767,45.0,count,unit_not_harmonisable,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,UNITS,other,other,log,count,0.03358,,0.513,,,,,high,Number of units in the project,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,0.03358,0.06545808966861598,b (log-log),|b/t|,0.07625088465521387,0.14863720205694708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.513,45.0,count,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,FLOORS,storeys,A_structural,level,count,-483.23396,,-0.861,,,,,high,Number of floors of the building,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,-483.23396,561.2473403019745,"b (currency per unit, as reported)",|b/t|,-0.12730597871624574,0.14785827957752118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.861,45.0,count,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,FLOORS,storeys,A_structural,level,count,-0.00038,,-0.6,,,,,high,Number of floors of the building,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,-0.038,0.06333333333333334,100*b/1 (per unit count),|b/t|,-0.0890870806374748,0.14847846772912465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6,45.0,count,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,FLOORS,storeys,A_structural,log,count,-0.04395,,-0.935,,,,,high,Number of floors of the building,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,-0.04395,0.04700534759358289,b (log-log),|b/t|,-0.1380470850585011,0.14764394123903862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.935,45.0,count,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,AREA,lot_size,A_structural,level,m2,-4.16441,,-1.378,,,,,high,"Project (site) area in sq.m, not unit size",Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,-4.16441,3.0220682148040643,"b (currency per unit, as reported)",|b/t|,-0.20121852602457446,0.1460221524126085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.378,45.0,m2,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,AREA,lot_size,A_structural,level,m2,-1e-05,,-1.522,,,,,high,"Project (site) area in sq.m, not unit size",ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,-0.001,0.000657030223390276,100*b/1 (X per m2->m2),|b/t|,-0.22126280801747927,0.14537635217968414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.522,45.0,m2,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,AREA,lot_size,A_structural,log,m2,-0.01511,,-0.329,,,,,high,"Project (site) area in sq.m, not unit size",ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,-0.01511,0.04592705167173252,b (log-log),|b/t|,-0.04898554586368482,0.14889223666773502,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.329,45.0,m2,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,FACILITY,recreation_private,C_exterior,binary,binary,-13592.16403,,-0.921,,,,,high,"Common facilities (pool, exercise room, sauna)",Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,-13592.16403,14758.049978284473,"b (currency per unit, as reported)",|b/t|,-0.1360185970480064,0.14768577312487122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.921,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,FACILITY,recreation_private,C_exterior,binary,binary,-0.01804,,-1.089,,,,,high,"Common facilities (pool, exercise room, sauna)",ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,pct_premium,-1.8013001457486522,1.626949156154009,100*(exp(b - se^2/2) - 1),|b/t|,-0.1602407893254032,0.1471448937790663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.089,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,FACILITY,recreation_private,C_exterior,binary,binary,-0.00618,,-0.136,,,,,high,"Common facilities (pool, exercise room, sauna)",ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,pct_premium,-0.7186502958823171,4.516121596897968,100*(exp(b - se^2/2) - 1),|b/t|,-0.020269517812822435,0.14904057215310612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.136,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,DEVELOPER,other,other,binary,binary,13892.27443,,0.881,,,,,high,Developer listed on Stock Exchange of Thailand,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,13892.27443,15768.756447219068,"b (currency per unit, as reported)",|b/t|,0.1302135635685052,0.14780200178036915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.881,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,DEVELOPER,other,other,binary,binary,0.01976,,1.117,,,,,high,Developer listed on Stock Exchange of Thailand,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,pct_premium,1.9796938600734393,1.8043277400744537,100*(exp(b - se^2/2) - 1),|b/t|,0.16425105023974526,0.14704659824507185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.117,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,DEVELOPER,other,other,binary,binary,0.05353,,1.163,,,,,high,Developer listed on Stock Exchange of Thailand,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,pct_premium,5.38717189429605,4.855850554614344,100*(exp(b - se^2/2) - 1),|b/t|,0.17082160976570282,0.14688014597222943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.163,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,CAR_PARK,parking,A_structural,level,share,528.34177,,3.07,,***,,,high,Percentage of car parking lots per unit,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,528.34177,172.09829641693813,"b (currency per unit, as reported)",|b/t|,0.41614008889200693,0.13555051755439967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.07,45.0,share,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,CAR_PARK,parking,A_structural,level,share,0.0006,,3.085,,***,,,high,Percentage of car parking lots per unit,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,0.06,0.01944894651539708,100*b (unit as reported: share; count expected),|b/t|,0.41781910802982775,0.13543569141971723,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.085,45.0,share,unit_not_harmonisable,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,CAR_PARK,parking,A_structural,log,share,0.10543,,1.447,,,,,high,Percentage of car parking lots per unit,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,0.10543,0.07286109191430545,b (log-log),|b/t|,0.2108563197153313,0.14571964043906793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.447,45.0,share,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,PARK,green_space,E_environment,binary,binary,46997.97505,,3.115,,***,,,high,Private recreation park inside the project,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,46997.97505,15087.632439807383,"b (currency per unit, as reported)",|b/t|,0.4211643335109552,0.13520524350271437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.115,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,PARK,green_space,E_environment,binary,binary,0.069,,4.076,,***,,,high,Private recreation park inside the project,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,pct_premium,7.128269972478574,1.813765908519036,100*(exp(b - se^2/2) - 1),|b/t|,0.5192724272222081,0.12739755329298533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.076,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,PARK,green_space,E_environment,binary,binary,0.1503,,3.525,,***,,,high,Private recreation park inside the project,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,pct_premium,16.112688688971954,4.9553498339551005,100*(exp(b - se^2/2) - 1),|b/t|,0.46516417096089224,0.1319614669392602,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.525,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,FEE,other,other,level,THB per m2,1530.39732,,4.336,,***,,,high,Common area fee per sq.m per month,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,1530.39732,352.9514114391144,"b (currency per unit, as reported)",|b/t|,0.5428450532324198,0.1251948923506503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.336,45.0,thb per m2,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,FEE,other,other,level,THB per m2,0.00189,,4.773,,***,,,high,Common area fee per sq.m per month,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,0.189,0.03959773727215588,100*b (unit as reported: thb per m2; no harmonised unit defined for this characteristic),|b/t|,0.5797433206051711,0.12146308833127406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.773,45.0,thb per m2,unit_not_harmonisable,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,FEE,other,other,log,THB per m2,0.3847,,4.36,,***,,,high,Common area fee per sq.m per month,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,0.3847,0.08823394495412844,b (log-log),|b/t|,0.5449591295978627,0.12499062605455569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.36,45.0,thb per m2,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,CORNER,other,other,binary,binary,25253.07083,,1.59,,,,,high,Building located at street corner,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,25253.07083,15882.43448427673,"b (currency per unit, as reported)",|b/t|,0.2306332387369144,0.1450523514068644,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.59,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,CORNER,other,other,binary,binary,0.04489,,2.277,,**,,,high,Building located at street corner,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,pct_premium,4.57095691394076,2.0619686312871055,100*(exp(b - se^2/2) - 1),|b/t|,0.32142321367839016,0.1411608316549803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.277,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,CORNER,other,other,binary,binary,0.07695,,1.519,,,,,high,Building located at street corner,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,pct_premium,7.860319588452969,5.471039000685089,100*(exp(b - se^2/2) - 1),|b/t|,0.22084797062287553,0.14539036907365077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.519,45.0,binary,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,False,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,MAIN_RD,highway_distance,D_location,level,m,15.79588,,0.542,,,,,high,Distance to nearest main road,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,15.79588,29.143690036900367,"b (currency per unit, as reported)",|b/t|,0.0805341501518571,0.14858699290010535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.542,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,MAIN_RD,highway_distance,D_location,level,m,1e-05,,0.288,,,,,high,Distance to nearest main road,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,1.0,3.4722222222222228,100*b/0.001 (X per m->km),|b/t|,0.04289299318421739,0.14893400411186594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.288,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,MAIN_RD,highway_distance,D_location,log,m,-0.00219,,-0.193,,,,,high,Distance to nearest main road,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,-0.00219,0.011347150259067358,b (log-log),|b/t|,-0.028758841127341325,0.14900953951990326,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.193,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,EXPWAY,highway_distance,D_location,level,m,-24.97502,,-2.734,,***,,,high,Distance to nearest expressway,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,-24.97502,9.134974396488662,"b (currency per unit, as reported)",|b/t|,-0.37741859737840416,0.13804630482019173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.734,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,EXPWAY,highway_distance,D_location,level,m,-3e-05,,-2.76,,***,,,high,Distance to nearest expressway,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,-3.0,1.0869565217391304,100*b/0.001 (X per m->km),|b/t|,-0.38049027802218993,0.13785879638485143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.76,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,True
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,EXPWAY,highway_distance,D_location,log,m,-0.08183,,-2.855,,***,,,high,Distance to nearest expressway,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,-0.08183,0.02866199649737303,b (log-log),|b/t|,-0.39160685956517555,0.1371652748039144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.855,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,DEPART,commercial_distance,D_location,level,m,0.55753,,0.041,,,,,high,Distance to nearest department store/mall,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,0.55753,13.598292682926829,"b (currency per unit, as reported)",|b/t|,0.006111804984630162,0.14906841425927222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.041,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,DEPART,commercial_distance,D_location,level,m,1e-05,,0.361,,,,,high,Distance to nearest department store/mall,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,1.0,2.770083102493075,100*b/0.001 (X per m->km),|b/t|,0.05373694721652093,0.14885580946404692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.361,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,DEPART,commercial_distance,D_location,log,m,0.00861,,0.35,,,,,high,Distance to nearest department store/mall,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,0.00861,0.0246,b (log-log),|b/t|,0.05210404827496326,0.14886870935703791,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,SCHOOL,school_distance,D_location,level,m,5.20411,,0.261,,,,,high,Distance to nearest school/university,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,5.20411,19.93911877394636,"b (currency per unit, as reported)",|b/t|,0.038878167052037824,0.14895849445225218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.261,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,SCHOOL,school_distance,D_location,level,m,1e-05,,0.298,,,,,high,Distance to nearest school/university,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,1.0,3.3557046979865777,100*b/0.001 (X per m->km),|b/t|,0.044379449040121935,0.1489243256379931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.298,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,SCHOOL,school_distance,D_location,log,m,0.00813,,0.308,,,,,high,Distance to nearest school/university,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,0.00813,0.026396103896103897,b (log-log),|b/t|,0.04586561019812378,0.14891431882507722,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.308,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c1,3,Model 1 Linear,12,HOSP,hospital_distance,D_location,level,m,6.90188,,0.421,,,,,high,Distance to nearest hospital,Price (THB per sq.m),per_m2,OLS,55.0,0.805,t,55 luxury condominium projects within 1 km of rail stations,monetary,6.90188,16.394014251781474,"b (currency per unit, as reported)",|b/t|,0.06263574443520949,0.14877849034491564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.421,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,False,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c2,3,Model 2 Semi-log,12,HOSP,hospital_distance,D_location,level,m,2e-05,,0.961,,,,,high,Distance to nearest hospital,ln(Price per sq.m),log,OLS,55.0,0.861,t,Dependent variable is log of price per sq.m,semi_elasticity,2.0,2.081165452653486,100*b/0.001 (X per m->km),|b/t|,0.14180965434514142,0.14756467673792031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.961,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139,e2aec14baa7ef3008564e4685e54665957939139:T3-c3,3,Model 3 Double-log,12,HOSP,hospital_distance,D_location,log,m,0.03615,,1.176,,,,,high,Distance to nearest hospital,ln(Price per sq.m),log,OLS,55.0,0.83,t,Continuous regressors in logs; dummies unchanged,elasticity,0.03615,0.03073979591836735,b (log-log),|b/t|,0.17267442426880303,0.14683199342585293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.176,45.0,m,,2022.0,Thailand,Asia,Upper middle income,2014.0,2018.0,condo_apartment,listing,journal,False,False,unknown,,2016.0,ols,journal,True,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln Size,living_area,A_structural,log,m2,-0.093,,-19.351,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,-0.093,0.004805953180714175,not harmonised,|b/t|,-0.19731068949177624,0.010196407911310849,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-19.351,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Ln Size,living_area,A_structural,log,m2,-0.112,,-24.94,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,other,-0.112,0.004490777866880513,not harmonised,|b/t|,-0.2510879172906458,0.010067679121517471,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-24.94,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Ln Size,living_area,A_structural,log,m2,-0.115,,-25.243,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",other,-0.115,0.004555718416986888,not harmonised,|b/t|,-0.25394281153427667,0.010059929942331604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-25.243,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln Building’s age,age,A_structural,log,years,0.017,,3.326,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,0.017,0.005111244738424534,not harmonised,|b/t|,0.034572642247186806,0.010394660928198077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.326,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Ln Building’s age,age,A_structural,log,years,0.011,,5.166,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,other,0.011,0.0021293070073557874,not harmonised,|b/t|,0.053653545935217005,0.010385897393576655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.166,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Ln Building’s age,age,A_structural,log,years,0.012,,5.332,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",other,0.012,0.0022505626406601653,not harmonised,|b/t|,0.05537240080220317,0.010384921380758283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.332,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln Floor/Level No.,floor_level,A_structural,log,count,0.01,,4.0,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,0.01,0.0025,not harmonised,|b/t|,0.04156755671263727,0.010391889178159317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.0,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Ln Floor/Level No.,floor_level,A_structural,log,count,-0.073,,-15.675,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,other,-0.073,0.0046570972886762355,not harmonised,|b/t|,-0.1609093124677528,0.010265346887894915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.675,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Ln Floor/Level No.,floor_level,A_structural,log,count,-0.074,,-15.837,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",other,-0.074,0.004672602134242596,not harmonised,|b/t|,-0.16252858878345028,0.010262586903040367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.837,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln. Total floors,storeys,A_structural,log,count,-0.033,,-7.127,,***,,,high,total number of floors of the building (building height),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,-0.033,0.004630279219868107,not harmonised,|b/t|,-0.07392424050550767,0.010372420444157102,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.127,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Ln. Total floors,storeys,A_structural,log,count,-0.047,,-8.464,,***,,,high,total number of floors of the building (building height),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,other,-0.047,0.005552930056710775,not harmonised,|b/t|,-0.08769388743307746,0.010360809006743556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.464,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Ln. Total floors,storeys,A_structural,log,count,-0.046,,-8.473,,***,,,high,total number of floors of the building (building height),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",other,-0.046,0.005429009795822022,not harmonised,|b/t|,-0.08778641648688297,0.010360724240160858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.473,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln Distance to CBD,cbd_distance,D_location,log,km,-0.235,,-17.77,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,-0.235,0.013224535734383792,not harmonised,|b/t|,-0.1817454889243815,0.01022765835252569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-17.77,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Ln Distance to CBD,cbd_distance,D_location,log,km,-0.338,,-11.65,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,other,-0.338,0.029012875536480687,not harmonised,|b/t|,-0.12029038929725183,0.010325355304485135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.65,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Ln Distance to CBD,cbd_distance,D_location,log,km,-0.342,,-11.665,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",other,-0.342,0.029318474067723966,not harmonised,|b/t|,-0.12044302427552855,0.010325162818305063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.665,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln Distance to West Intime,commercial_distance,D_location,log,km,-0.109,,-20.037,,***,,,high,road distance to the West Intime shopping mall (study object),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,-0.109,0.005439936118181365,not harmonised,|b/t|,-0.20401906125788655,0.01018211614802049,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.037,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Ln Distance to Xicheng Square,commercial_distance,D_location,log,km,-0.048,,-9.958,,***,,,high,distance to Xicheng Square commercial centre,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,other,-0.048,0.004820245029122314,not harmonised,|b/t|,-0.10302086400977303,0.010345537659145716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.958,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Ln Distance to Xicheng Square,commercial_distance,D_location,log,km,-0.039,,-4.025,,***,,,high,distance to Xicheng Square commercial centre,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,other,-0.039,0.00968944099378882,not harmonised,|b/t|,-0.04182690083860696,0.010391776605865082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.025,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Ln Distance to Xicheng Square,commercial_distance,D_location,log,km,-0.039,,-3.947,,***,,,high,distance to Xicheng Square commercial centre,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",other,-0.039,0.009880922219407144,not harmonised,|b/t|,-0.04101771944206561,0.0103921255237055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.947,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Bus route,transit_distance,D_location,level,count,-0.002,,-3.998,,***,,,high,"number of bus routes serving the community (count), not a distance",ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,-0.002,0.0005002501250625312,"b (currency per unit, as reported)",|b/t|,-0.04154680881889439,0.010391898153800497,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.998,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Bus route,transit_distance,D_location,level,count,-0.004,,-7.507,,***,,,high,"number of bus routes serving the community (count), not a distance",ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,-0.004,0.0005328360197149328,"b (currency per unit, as reported)",|b/t|,-0.07784247782931997,0.010369319012830688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.507,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Bus route,transit_distance,D_location,level,count,-0.003,,-7.344,,***,,,high,"number of bus routes serving the community (count), not a distance",ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,-0.003,0.0004084967320261438,"b (currency per unit, as reported)",|b/t|,-0.0761621912907737,0.010370668748743697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.344,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,External environment,other,other,level,index,0.048,,11.484,,***,,,high,survey score 1-5 of surrounding environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,0.048,0.0041797283176593526,"b (currency per unit, as reported)",|b/t|,0.11860066153113064,0.010327469656141645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.484,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,External environment,other,other,level,index,0.07,,15.541,,***,,,high,survey score 1-5 of surrounding environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,0.07,0.004504214658001416,"b (currency per unit, as reported)",|b/t|,0.15956892785863744,0.010267610054606358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.541,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,External environment,other,other,level,index,0.069,,15.134,,***,,,high,survey score 1-5 of surrounding environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,0.069,0.004559270516717326,"b (currency per unit, as reported)",|b/t|,0.15549237280685013,0.01027437378134334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.134,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Inner environment,other,other,level,index,-0.022,,-6.345,,***,,,high,survey score 1-5 of inner (community) environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,-0.022,0.0034672970843183607,"b (currency per unit, as reported)",|b/t|,-0.065850337046267,0.010378303710995587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.345,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Inner environment,other,other,level,index,-0.031,,-7.815,,***,,,high,survey score 1-5 of inner (community) environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,-0.031,0.003966730646193218,"b (currency per unit, as reported)",|b/t|,-0.08101567624906514,0.010366689219330151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.815,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Inner environment,other,other,level,index,-0.029,,-7.247,,***,,,high,survey score 1-5 of inner (community) environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,-0.029,0.004001655857596247,"b (currency per unit, as reported)",|b/t|,-0.07516195719797002,0.010371458147919142,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.247,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,-0.007,,-3.089,,***,,,high,survey score 1-5 of living facilities (small commercial facilities),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,-0.007,0.0022661055357720947,"b (currency per unit, as reported)",|b/t|,-0.03211174525869514,0.010395514813433197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.089,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,-0.031,,-12.806,,***,,,high,survey score 1-5 of living facilities (small commercial facilities),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,-0.031,0.00242074027799469,"b (currency per unit, as reported)",|b/t|,-0.1320276794269623,0.010309829722548985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.806,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,-0.031,,-12.824,,***,,,high,survey score 1-5 of living facilities (small commercial facilities),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,-0.031,0.0024173424828446664,"b (currency per unit, as reported)",|b/t|,-0.1322100148089404,0.010309576950166906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.824,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.006,,2.587,,**,,,high,survey score 1-5,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,0.006,0.0023192887514495554,"b (currency per unit, as reported)",|b/t|,0.026897338257375697,0.01039711567737754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.587,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.022,,9.695,,***,,,high,survey score 1-5,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,0.022,0.002269210933470861,"b (currency per unit, as reported)",|b/t|,0.10032774264533327,0.010348400479147321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.695,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.022,,9.451,,***,,,high,survey score 1-5,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,0.022,0.0023277960004232353,"b (currency per unit, as reported)",|b/t|,0.09782720655448371,0.010350990006822951,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.451,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Property management,other,other,level,index,0.084,,19.028,,***,,,high,survey score 1-5 of property management quality,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,0.084,0.004414546983392895,"b (currency per unit, as reported)",|b/t|,0.1941423968881931,0.01020298491108856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.028,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Property management,other,other,level,index,0.071,,16.67,,***,,,high,survey score 1-5 of property management quality,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,0.071,0.004259148170365926,"b (currency per unit, as reported)",|b/t|,0.17083389578859456,0.010247984150485577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.67,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Property management,other,other,level,index,0.072,,16.702,,***,,,high,survey score 1-5 of property management quality,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,0.072,0.004310860974733564,"b (currency per unit, as reported)",|b/t|,0.1711522339718412,0.010247409530106645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.702,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Quality of primary school,school_quality,F_neighbourhood,level,index,0.084,,34.341,,***,,,high,survey score 1-3,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,0.084,0.0024460557351271076,"b (currency per unit, as reported)",|b/t|,0.33636455850001645,0.009794838778719794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.341,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Quality of primary school,school_quality,F_neighbourhood,level,index,0.063,,21.108,,***,,,high,survey score 1-3,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,0.063,0.002984650369528141,"b (currency per unit, as reported)",|b/t|,0.21443483292019083,0.010158936560554806,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.108,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Quality of primary school,school_quality,F_neighbourhood,level,index,0.063,,20.962,,***,,,high,survey score 1-3,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,0.063,0.0030054384123652323,"b (currency per unit, as reported)",|b/t|,0.2130191554797626,0.010162157975372704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.962,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Quality of junior high school,school_quality,F_neighbourhood,level,index,0.041,,5.725,,***,,,high,survey score 1-2,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,0.041,0.007161572052401748,"b (currency per unit, as reported)",|b/t|,0.059439748712712646,0.010382488858115747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.725,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Quality of junior high school,school_quality,F_neighbourhood,level,index,0.005,,0.386,,,,,high,survey score 1-2,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,0.005,0.012953367875647668,"b (currency per unit, as reported)",|b/t|,0.004014706824839183,0.010400794883003064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.386,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Quality of junior high school,school_quality,F_neighbourhood,level,index,0.005,,0.34,,,,,high,survey score 1-2,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,0.005,0.014705882352941176,"b (currency per unit, as reported)",|b/t|,0.0035362766479156613,0.01040081367034018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.34,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Distance to primary school,school_distance,D_location,level,km,-0.032,,-9.596,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,-0.032,0.003334722801167153,"b (currency per unit, as reported)",|b/t|,-0.09931340732043013,0.010349458870407476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.596,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Distance to primary school,school_distance,D_location,level,km,-0.028,,-7.14,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,-0.028,0.00392156862745098,"b (currency per unit, as reported)",|b/t|,-0.07405834308421015,0.01037231695857285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.14,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Distance to primary school,school_distance,D_location,level,km,-0.028,,-7.027,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,-0.028,0.003984630710118116,"b (currency per unit, as reported)",|b/t|,-0.07289254866693738,0.010373210284180642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.027,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M1,5,Model 1,8,Distance to junior high school,school_distance,D_location,level,km,0.033,,11.329,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.642,t,Model 1: simple hedonic with ln distance to West Intime and year dummies; 9-zone partition,monetary,0.033,0.0029128784535263482,"b (currency per unit, as reported)",|b/t|,0.11702197261609917,0.010329417655229867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.329,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M2,5,Model 2,8,Distance to junior high school,school_distance,D_location,level,km,-0.047,,-10.44,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.701,t,Model 2: zone x year spatio-temporal interaction dummies (44) instead of distance to West Intime and year dummies,monetary,-0.047,0.004501915708812261,"b (currency per unit, as reported)",|b/t|,-0.10795062971681267,0.01034009863187861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.44,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:T5-M3,5,Model 3,8,Distance to junior high school,school_distance,D_location,level,km,-0.046,,-10.061,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.696,t,"Model 3: extended price-gradient model with zone dummies, year dummies and zone x period interactions",monetary,-0.046,0.004572110128217871,"b (currency per unit, as reported)",|b/t|,-0.1040749707880773,0.010344396261611898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.061,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Size,living_area,A_structural,log,m2,-0.09,,-21.711,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.09,0.004145364101146885,not harmonised,|b/t|,-0.22026739229594133,0.01014542822974259,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.711,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Size,living_area,A_structural,log,m2,-0.085,,-20.923,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,-0.085,0.004062514935716676,not harmonised,|b/t|,-0.21264076749497057,0.010163015222242057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.923,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Size,living_area,A_structural,log,m2,-0.085,,-20.976,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,-0.085,0.0040522501906941275,not harmonised,|b/t|,-0.21315496369806863,0.010161849909328216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-20.976,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Floor/Level No.,floor_level,A_structural,log,count,-0.033,,-8.278,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.033,0.003986470161874849,not harmonised,|b/t|,-0.08578111552019707,0.01036254113556379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.278,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Floor/Level No.,floor_level,A_structural,log,count,-0.036,,-9.047,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,-0.036,0.003979219630816845,not harmonised,|b/t|,-0.09368292006734173,0.010355136516783655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.047,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Floor/Level No.,floor_level,A_structural,log,count,-0.036,,-9.12,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,-0.036,0.003947368421052632,not harmonised,|b/t|,-0.09443213086684392,0.010354400314346923,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.12,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Total floors,storeys,A_structural,log,count,0.009,,3.961,,***,,,high,total number of floors of the building (building height),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,0.009,0.0022721534965917697,not harmonised,|b/t|,0.041162963117991155,0.01039206339762463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.961,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Total floors,storeys,A_structural,log,count,0.009,,4.567,,***,,,high,total number of floors of the building (building height),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,0.009,0.0019706590759798553,not harmonised,|b/t|,0.04744731486406745,0.010389164629749825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.567,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Total floors,storeys,A_structural,log,count,0.01,,4.599,,***,,,high,total number of floors of the building (building height),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,0.01,0.0021743857360295715,not harmonised,|b/t|,0.04777901183096928,0.010389000180684775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.599,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Building’s age,age,A_structural,log,years,-0.001,,-1.162,,,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.001,0.0008605851979345956,not harmonised,|b/t|,-0.012084938479740601,0.010400119173614978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.162,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Building’s age,age,A_structural,log,years,-0.003,,-3.579,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,-0.003,0.0008382229673093043,not harmonised,|b/t|,-0.03719898082917617,0.010393680030504658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.579,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Building’s age,age,A_structural,log,years,-0.004,,-4.426,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,-0.004,0.0009037505648441031,not harmonised,|b/t|,-0.045985589613909514,0.01038987564706496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.426,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Distance to CBD,cbd_distance,D_location,log,km,-0.431,,-39.491,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.431,0.010913879111696336,not harmonised,|b/t|,-0.37994007798297214,0.009620928261704494,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-39.491,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Distance to CBD,cbd_distance,D_location,log,km,-0.538,,-46.694,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,-0.538,0.01152182293228252,not harmonised,|b/t|,-0.43686345500101864,0.009355879877522138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-46.694,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Distance to CBD,cbd_distance,D_location,log,km,-0.542,,-46.896,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,-0.542,0.011557488911634255,not harmonised,|b/t|,-0.4383907643318681,0.009348148335292308,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-46.896,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Distance to West Intime,commercial_distance,D_location,log,km,-0.053,,-15.442,,***,,,high,road distance to the West Intime shopping mall (study object),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.053,0.003432197901826188,not harmonised,|b/t|,-0.1585780760948883,0.010269270566953006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.442,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Distance to Xicheng Square,commercial_distance,D_location,log,km,-0.016,,-4.077,,***,,,high,distance to Xicheng Square commercial centre,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.016,0.003924454255580083,not harmonised,|b/t|,-0.04236630947989079,0.010391540220723767,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.077,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Distance to Xicheng Square,commercial_distance,D_location,log,km,-0.036,,-7.412,,***,,,high,distance to Xicheng Square commercial centre,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,-0.036,0.004856988667026443,not harmonised,|b/t|,-0.07686324944027524,0.010370109206728987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.412,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Distance to Xicheng Square,commercial_distance,D_location,log,km,-0.038,,-7.848,,***,,,high,distance to Xicheng Square commercial centre,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,-0.038,0.004841997961264016,not harmonised,|b/t|,-0.0813555174533175,0.010366401306487957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.848,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Bus route,transit_distance,D_location,level,count,-0.003,,-9.935,,***,,,high,"number of bus routes serving the community (count), not a distance",ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,-0.003,0.0003019627579265224,"b (currency per unit, as reported)",|b/t|,-0.10278543339979035,0.010345790981357858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.935,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Bus route,transit_distance,D_location,level,count,-0.004,,-14.169,,***,,,high,"number of bus routes serving the community (count), not a distance",ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,-0.004,0.00028230644364457617,"b (currency per unit, as reported)",|b/t|,-0.14579536722309444,0.010289742905151701,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.169,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Bus route,transit_distance,D_location,level,count,-0.004,,-14.187,,***,,,high,"number of bus routes serving the community (count), not a distance",ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,-0.004,0.0002819482624938324,"b (currency per unit, as reported)",|b/t|,-0.1459766382583708,0.010289464880409585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.187,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Surrounding environment,other,other,level,index,0.09,,25.051,,***,,,high,survey score 1-5 of surrounding environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,0.09,0.0035926709512594307,"b (currency per unit, as reported)",|b/t|,0.25213453480160064,0.010064849099900231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.051,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Surrounding environment,other,other,level,index,0.051,,12.635,,***,,,high,survey score 1-5 of surrounding environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,0.051,0.004036406806489909,"b (currency per unit, as reported)",|b/t|,0.13029482726342118,0.010312214266990201,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.635,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Surrounding environment,other,other,level,index,0.051,,12.472,,***,,,high,survey score 1-5 of surrounding environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,0.051,0.004089159717767799,"b (currency per unit, as reported)",|b/t|,0.1286419317006528,0.010314458924042078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.472,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Inner environment,other,other,level,index,-0.019,,-6.597,,***,,,high,survey score 1-5 of inner (community) environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,-0.019,0.0028800970137941484,"b (currency per unit, as reported)",|b/t|,-0.06845364740352178,0.010376481340536876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.597,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Inner environment,other,other,level,index,-0.003,,-1.11,,,,,high,survey score 1-5 of inner (community) environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,-0.003,0.0027027027027027024,"b (currency per unit, as reported)",|b/t|,-0.011544206043197624,0.010400185624502364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.11,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Inner environment,other,other,level,index,-0.003,,-1.16,,,,,high,survey score 1-5 of inner (community) environment,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,-0.003,0.0025862068965517245,"b (currency per unit, as reported)",|b/t|,-0.012064141271342352,0.010400121785639961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.16,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,-0.006,,-2.113,,**,,,high,survey score 1-5 of living facilities (small commercial facilities),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,-0.006,0.002839564600094652,"b (currency per unit, as reported)",|b/t|,-0.021971751261433158,0.010398367847341768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.113,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.004,,1.347,,,,,high,survey score 1-5 of living facilities (small commercial facilities),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,0.004,0.0029695619896065333,"b (currency per unit, as reported)",|b/t|,0.014008608878508685,0.010399858113220998,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.347,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.003,,1.12,,,,,high,survey score 1-5 of living facilities (small commercial facilities),ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,0.003,0.002678571428571428,"b (currency per unit, as reported)",|b/t|,0.011648193851414244,0.01040017308161986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.12,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.035,,17.221,,***,,,high,survey score 1-5,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,0.035,0.0020324022995180306,"b (currency per unit, as reported)",|b/t|,0.17630773595735744,0.010237949942358599,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.221,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.031,,15.402,,***,,,high,survey score 1-5,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,0.031,0.0020127256200493443,"b (currency per unit, as reported)",|b/t|,0.15817759583153201,0.01026993869832048,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.402,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.03,,15.225,,***,,,high,survey score 1-5,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,0.03,0.0019704433497536944,"b (currency per unit, as reported)",|b/t|,0.15640453596858328,0.010272875925686915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.225,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Property management,other,other,level,index,0.063,,22.996,,***,,,high,survey score 1-5 of property management quality,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,0.063,0.0027396068881544618,"b (currency per unit, as reported)",|b/t|,0.2326175292685601,0.010115564849041579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.996,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Property management,other,other,level,index,0.061,,22.294,,***,,,high,survey score 1-5 of property management quality,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,0.061,0.0027361621961065757,"b (currency per unit, as reported)",|b/t|,0.22588413276847294,0.010132059422646136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.294,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Property management,other,other,level,index,0.062,,22.615,,***,,,high,survey score 1-5 of property management quality,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,0.062,0.00274154322352421,"b (currency per unit, as reported)",|b/t|,0.22896716164229128,0.010124570490483807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.615,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Quality of primary school,school_quality,F_neighbourhood,level,index,0.054,,25.602,,***,,,high,survey score 1-3,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,0.054,0.0021092102179517225,"b (currency per unit, as reported)",|b/t|,0.25731676804565534,0.010050651044670547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.602,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Quality of primary school,school_quality,F_neighbourhood,level,index,0.059,,25.861,,***,,,high,survey score 1-3,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,0.059,0.0022814276323421365,"b (currency per unit, as reported)",|b/t|,0.2597450814142015,0.010043891628869784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.861,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Quality of primary school,school_quality,F_neighbourhood,level,index,0.059,,25.83,,***,,,high,survey score 1-3,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,0.059,0.002284165698799845,"b (currency per unit, as reported)",|b/t|,0.2594546922860323,0.010044703534108877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.83,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Quality of junior high school,school_quality,F_neighbourhood,level,index,0.003,,0.513,,,,,high,survey score 1-2,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,monetary,0.003,0.005847953216374269,"b (currency per unit, as reported)",|b/t|,0.005335574825668317,0.010400730654324205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.513,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Quality of junior high school,school_quality,F_neighbourhood,level,index,-0.009,,-1.262,,,,,high,survey score 1-2,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),monetary,-0.009,0.007131537242472266,"b (currency per unit, as reported)",|b/t|,-0.01312477834167245,0.010399982838092273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.262,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Quality of junior high school,school_quality,F_neighbourhood,level,index,-0.014,,-1.872,,,,,high,survey score 1-2,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,monetary,-0.014,0.007478632478632478,"b (currency per unit, as reported)",|b/t|,-0.019466755376102436,0.010398907786379506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.872,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Distance to primary school,school_distance,D_location,log,km,-0.005,,-2.339,,**,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,-0.005,0.0021376656690893546,not harmonised,|b/t|,-0.024320459504372394,0.010397802267794952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.339,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Distance to primary school,school_distance,D_location,log,km,0.009,,4.219,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,0.009,0.0021332069210713437,not harmonised,|b/t|,0.043839119920411694,0.010390879336433205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.219,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Distance to primary school,school_distance,D_location,log,km,0.009,,4.206,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,0.009,0.0021398002853067043,not harmonised,|b/t|,0.043704296901403344,0.010390940775416867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.206,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M1,A1,Model 1,14,Ln Distance to junior high school,school_distance,D_location,log,km,0.038,,16.232,,***,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.557,t,Appendix 1 robustness: 8 concentric distance rings around West Intime instead of 9 zones; Model 1 specification,other,0.038,0.0023410547067520947,not harmonised,|b/t|,0.1664713009410095,0.0102557479633446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),16.232,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M2,A1,Model 2,14,Ln Distance to junior high school,school_distance,D_location,log,km,-0.002,,-0.812,,,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.596,t,Appendix 1 robustness: distance rings; Model 2 specification (ring x year interactions),other,-0.002,0.0024630541871921183,not harmonised,|b/t|,-0.008445212327935015,0.010400507793023416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.812,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA1-M3,A1,Model 3,14,Ln Distance to junior high school,school_distance,D_location,log,km,-0.002,,-0.738,,,,,high,,ln sale price per m2 (yuan/m2),per_m2,OLS,9254.0,0.592,t,Appendix 1 robustness: distance rings; Model 3 specification,other,-0.002,0.0027100271002710027,not harmonised,|b/t|,-0.007675622367749825,0.010400572314024154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.738,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,True,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Size,living_area,A_structural,level,m2,-22.814,,,,***,,,high,,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-22.814,,"b (currency per unit, as reported)",,,,,,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Size,living_area,A_structural,log,m2,-1740.935,,,,***,,,high,linear-log column: level price per m2 on ln(x),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-1740.935,,not harmonised,,,,,,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Size,living_area,A_structural,level,m2,0.001,,,,***,,,high,,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.001,,"b (currency per unit, as reported)",,,,,,9244.0,m2,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Size,living_area,A_structural,other,m2,-0.096,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.096,,not harmonised,,,,,,9244.0,m2,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Size,living_area,A_structural,other,m2,-0.122,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.122,,not harmonised,,,,,,9244.0,m2,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Size,living_area,A_structural,other,m2,-0.122,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.122,,not harmonised,,,,,,9244.0,m2,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Size,living_area,A_structural,other,m2,-0.124,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.124,,not harmonised,,,,,,9244.0,m2,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Floor/Level No.,floor_level,A_structural,level,count,20.424,,,,**,,,high,,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,20.424,,"b (currency per unit, as reported)",,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Floor/Level No.,floor_level,A_structural,log,count,190.989,,,,**,,,high,linear-log column: level price per m2 on ln(x),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,190.989,,not harmonised,,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Floor/Level No.,floor_level,A_structural,level,count,-0.001,,,,***,,,high,,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.001,,"b (currency per unit, as reported)",,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Floor/Level No.,floor_level,A_structural,other,count,0.011,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.011,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Floor/Level No.,floor_level,A_structural,other,count,0.012,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.012,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Floor/Level No.,floor_level,A_structural,other,count,0.012,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,0.012,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Floor/Level No.,floor_level,A_structural,other,count,0.013,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,0.013,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Total floors,storeys,A_structural,level,count,-32.672,,,,***,,,high,total number of floors of the building (building height),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-32.672,,"b (currency per unit, as reported)",,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Total floors,storeys,A_structural,log,count,-652.257,,,,***,,,high,linear-log column: level price per m2 on ln(x); total number of floors of the building (building height),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-652.257,,not harmonised,,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Total floors,storeys,A_structural,level,count,-0.002,,,,***,,,high,total number of floors of the building (building height),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.002,,"b (currency per unit, as reported)",,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Total floors,storeys,A_structural,other,count,-0.052,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; total number of floors of the building (building height),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.052,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Total floors,storeys,A_structural,other,count,-0.102,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; total number of floors of the building (building height),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.102,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Total floors,storeys,A_structural,other,count,-0.099,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; total number of floors of the building (building height),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.099,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Total floors,storeys,A_structural,other,count,-0.1,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; total number of floors of the building (building height),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.1,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Building’s age,age,A_structural,level,years,-30.326,,,,***,,,high,,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-30.326,,"b (currency per unit, as reported)",,,,,,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Building’s age,age,A_structural,log,years,235.921,,,,**,,,high,linear-log column: level price per m2 on ln(x),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,235.921,,not harmonised,,,,,,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Building’s age,age,A_structural,level,years,-0.001,,,,,,,high,,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.001,,"b (currency per unit, as reported)",,,,,,9244.0,years,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Building’s age,age,A_structural,other,years,-0.013,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.013,,not harmonised,,,,,,9244.0,years,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Building’s age,age,A_structural,other,years,0.028,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.028,,not harmonised,,,,,,9244.0,years,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Building’s age,age,A_structural,other,years,-0.036,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.036,,not harmonised,,,,,,9244.0,years,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Building’s age,age,A_structural,other,years,-0.03,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.03,,not harmonised,,,,,,9244.0,years,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Distance to CBD,cbd_distance,D_location,level,km,-789.131,,,,***,,,high,,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-789.131,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Distance to CBD,cbd_distance,D_location,log,km,-4095.293,,,,***,,,high,linear-log column: level price per m2 on ln(x),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-4095.293,,not harmonised,,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Distance to CBD,cbd_distance,D_location,level,km,-0.046,,,,***,,,high,,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.046,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Distance to CBD,cbd_distance,D_location,other,km,-0.046,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.046,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Distance to CBD,cbd_distance,D_location,other,km,-0.219,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.219,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Distance to CBD,cbd_distance,D_location,other,km,-0.291,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.291,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Distance to CBD,cbd_distance,D_location,other,km,-0.282,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.282,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Distance to West Intime,commercial_distance,D_location,level,km,-305.48,,,,***,,,high,road distance to the West Intime shopping mall (study object),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-305.48,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Distance to West Intime,commercial_distance,D_location,log,km,-1893.405,,,,***,,,high,linear-log column: level price per m2 on ln(x); road distance to the West Intime shopping mall (study object),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-1893.405,,not harmonised,,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Distance to West Intime,commercial_distance,D_location,level,km,-0.018,,,,***,,,high,road distance to the West Intime shopping mall (study object),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.018,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Distance to West Intime,commercial_distance,D_location,other,km,-0.037,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; road distance to the West Intime shopping mall (study object),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.037,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Distance to West Intime,commercial_distance,D_location,other,km,-0.136,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; road distance to the West Intime shopping mall (study object),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.136,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Distance to Xicheng Square,commercial_distance,D_location,level,km,-327.487,,,,***,,,high,distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-327.487,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Distance to Xicheng Square,commercial_distance,D_location,log,km,-959.592,,,,***,,,high,linear-log column: level price per m2 on ln(x); distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-959.592,,not harmonised,,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Distance to Xicheng Square,commercial_distance,D_location,level,km,-0.015,,,,***,,,high,distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.015,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Distance to Xicheng Square,commercial_distance,D_location,other,km,-0.008,,,,*,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.008,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Distance to Xicheng Square,commercial_distance,D_location,other,km,-0.03,,,,*,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.03,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Distance to Xicheng Square,commercial_distance,D_location,other,km,-0.019,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.019,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Distance to Xicheng Square,commercial_distance,D_location,other,km,-0.013,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; distance to Xicheng Square commercial centre,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.013,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,External environment,other,other,level,index,718.103,,,,***,,,high,survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,718.103,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,External environment,other,other,log,index,730.509,,,,***,,,high,linear-log column: level price per m2 on ln(x); survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,730.509,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,External environment,other,other,level,index,0.047,,,,***,,,high,survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.047,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,External environment,other,other,other,index,0.015,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.015,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,External environment,other,other,other,index,0.068,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.068,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,External environment,other,other,other,index,0.076,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,0.076,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,External environment,other,other,other,index,0.073,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of surrounding environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,0.073,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Inner environment,other,other,level,index,-284.909,,,,***,,,high,survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-284.909,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Inner environment,other,other,log,index,-366.97,,,,***,,,high,linear-log column: level price per m2 on ln(x); survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-366.97,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Inner environment,other,other,level,index,-0.016,,,,***,,,high,survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.016,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Inner environment,other,other,other,index,-0.002,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.002,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Inner environment,other,other,other,index,-0.005,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.005,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Inner environment,other,other,other,index,-0.018,,,,*,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.018,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Inner environment,other,other,other,index,-0.014,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of inner (community) environment,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.014,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,-181.152,,,,***,,,high,survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-181.152,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Living facilities,neighbourhood_amenities,F_neighbourhood,log,index,-92.52,,,,*,,,high,linear-log column: level price per m2 on ln(x); survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-92.52,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Living facilities,neighbourhood_amenities,F_neighbourhood,level,index,-0.013,,,,***,,,high,survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.013,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Living facilities,neighbourhood_amenities,F_neighbourhood,other,index,-0.003,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.003,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Living facilities,neighbourhood_amenities,F_neighbourhood,other,index,-0.004,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.004,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Living facilities,neighbourhood_amenities,F_neighbourhood,other,index,-0.025,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.025,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Living facilities,neighbourhood_amenities,F_neighbourhood,other,index,-0.026,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of living facilities (small commercial facilities),"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.026,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,300.827,,,,***,,,high,survey score 1-5,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,300.827,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,log,index,131.78,,,,***,,,high,linear-log column: level price per m2 on ln(x); survey score 1-5,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,131.78,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,level,index,0.014,,,,***,,,high,survey score 1-5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.014,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,other,index,0.004,,,,*,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.004,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,other,index,0.008,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.008,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,other,index,0.024,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,0.024,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Sports facilities,neighbourhood_amenities,F_neighbourhood,other,index,0.023,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,0.023,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Property management,other,other,level,index,1170.15,,,,***,,,high,survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,1170.15,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Property management,other,other,log,index,1656.332,,,,***,,,high,linear-log column: level price per m2 on ln(x); survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,1656.332,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Property management,other,other,level,index,0.061,,,,***,,,high,survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.061,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Property management,other,other,other,index,0.03,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.03,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Property management,other,other,other,index,0.081,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.081,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Property management,other,other,other,index,0.06,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,0.06,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Property management,other,other,other,index,0.061,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-5 of property management quality,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,0.061,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Bus route,transit_distance,D_location,level,count,-14.807,,,,*,,,high,"number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-14.807,,"b (currency per unit, as reported)",,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Bus route,transit_distance,D_location,log,count,-18.432,,,,**,,,high,"linear-log column: level price per m2 on ln(x); number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-18.432,,not harmonised,,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Bus route,transit_distance,D_location,level,count,-0.001,,,,***,,,high,"number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.001,,"b (currency per unit, as reported)",,,,,,9244.0,count,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Bus route,transit_distance,D_location,other,count,-0.001,,,,***,,,medium,"functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.001,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Bus route,transit_distance,D_location,other,count,-0.005,,,,***,,,medium,"functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.005,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Bus route,transit_distance,D_location,other,count,-0.005,,,,***,,,medium,"functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.005,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Bus route,transit_distance,D_location,other,count,-0.005,,,,***,,,medium,"functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; number of bus routes serving the community (count), not a distance","sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.005,,not harmonised,,,,,,9244.0,count,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,15,Distance to primary school,school_distance,D_location,level,km,-763.224,,,,***,,,high,,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,-763.224,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,15,Distance to primary school,school_distance,D_location,log,km,-548.397,,,,***,,,high,linear-log column: level price per m2 on ln(x),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,-548.397,,not harmonised,,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,15,Distance to primary school,school_distance,D_location,level,km,-0.045,,,,***,,,high,,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,-0.045,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,15,Distance to primary school,school_distance,D_location,other,km,-0.014,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,-0.014,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,15,Distance to primary school,school_distance,D_location,other,km,-0.026,,,,**,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,-0.026,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,15,Distance to primary school,school_distance,D_location,other,km,-0.019,,,,**,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.019,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,15,Distance to primary school,school_distance,D_location,other,km,-0.017,,,,*,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.017,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,16,Distance to junior high school,school_distance,D_location,level,km,835.816,,,,***,,,high,,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,835.816,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,16,Distance to junior high school,school_distance,D_location,log,km,613.014,,,,***,,,high,linear-log column: level price per m2 on ln(x),"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,613.014,,not harmonised,,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,16,Distance to junior high school,school_distance,D_location,level,km,0.044,,,,***,,,high,,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.044,,"b (currency per unit, as reported)",,,,,,9244.0,km,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,16,Distance to junior high school,school_distance,D_location,other,km,0.008,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.008,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,16,Distance to junior high school,school_distance,D_location,other,km,0.023,,,,**,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.023,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,16,Distance to junior high school,school_distance,D_location,other,km,-0.047,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,-0.047,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,16,Distance to junior high school,school_distance,D_location,other,km,-0.046,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,-0.046,,not harmonised,,,,,,9244.0,km,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,16,Quality of primary school,school_quality,F_neighbourhood,level,index,1534.241,,,,***,,,high,survey score 1-3,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,1534.241,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,16,Quality of primary school,school_quality,F_neighbourhood,log,index,1790.623,,,,***,,,high,linear-log column: level price per m2 on ln(x); survey score 1-3,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,1790.623,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,16,Quality of primary school,school_quality,F_neighbourhood,level,index,0.071,,,,***,,,high,survey score 1-3,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.071,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,16,Quality of primary school,school_quality,F_neighbourhood,other,index,0.026,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-3,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.026,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,16,Quality of primary school,school_quality,F_neighbourhood,other,index,0.093,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-3,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.093,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,16,Quality of primary school,school_quality,F_neighbourhood,other,index,0.067,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-3,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,0.067,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,16,Quality of primary school,school_quality,F_neighbourhood,other,index,0.067,,,,***,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-3,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,0.067,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c1,A2,Model 1 Linear,16,Quality of junior high school,school_quality,F_neighbourhood,level,index,139.638,,,,,,,high,survey score 1-2,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.591,none,"Model 1, linear form (level price per m2 on level regressors)",monetary,139.638,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c2,A2,Model 1 Linear-log,16,Quality of junior high school,school_quality,F_neighbourhood,log,index,186.55,,,,,,,high,linear-log column: level price per m2 on ln(x); survey score 1-2,"sale price per m2 (yuan/m2), level",per_m2,OLS,9254.0,0.587,none,"Model 1, linear-log form (level price per m2 on ln regressors)",other,186.55,,not harmonised,,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c3,A2,Model 1 Log-linear,16,Quality of junior high school,school_quality,F_neighbourhood,level,index,0.038,,,,***,,,high,survey score 1-2,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,OLS,9254.0,0.644,none,"Model 1, log-linear form (ln price per m2 on level regressors)",monetary,0.038,,"b (currency per unit, as reported)",,,,,,9244.0,index,,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,ols,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c4,A2,Model 1 SAR,16,Quality of junior high school,school_quality,F_neighbourhood,other,index,0.008,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-2,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_lag,9254.0,0.709,none,"Model 1, spatial autoregressive model (W_lnprice = 0.755***)",other,0.008,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c5,A2,Model 1 SEM,16,Quality of junior high school,school_quality,F_neighbourhood,other,index,0.056,,,,**,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-2,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.722,none,"Model 1, spatial error model (lambda = 0.803***)",other,0.056,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c6,A2,Model 2 SEM,16,Quality of junior high school,school_quality,F_neighbourhood,other,index,0.037,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-2,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.734,none,"Model 2, spatial error model (lambda = 0.685***)",other,0.037,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89,e4f33aca0968c7c930129cd6f3fbf32507e5ac89:TA2-c7,A2,Model 3 SEM,16,Quality of junior high school,school_quality,F_neighbourhood,other,index,0.038,,,,,,,medium,functional form of regressors not stated for the SAR/SEM columns of Table A2 (variable names without 'Ln'); magnitudes are consistent with the log-log form of Table 5; survey score 1-2,"sale price per m2 (yuan/m2), ln for log-linear/SAR/SEM columns",per_m2,spatial_error,9254.0,0.728,none,"Model 3, spatial error model (lambda = 0.681***)",other,0.038,,not harmonised,,,,,,9244.0,index,nonstandard_x_transform,2019.0,China,Asia,Upper middle income,2011.0,2015.0,condo_apartment,transaction,journal,True,True,unknown,,2013.0,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Newly built dummy,age,A_structural,binary,binary,-0.112,0.106,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,-0.112,0.106,"b (currency per unit, as reported)",reported,-0.031472248425914534,0.029786235117383396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0566037735849056,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Property age [years]/10,age,A_structural,level,10 years,-0.744,0.087,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,-0.744,0.087,"b (currency per unit, as reported)",reported,-0.24695634515755416,0.02887795971600431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.551724137931036,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,(Property age [years]/10)2,age,A_structural,squared,10 years,0.076,0.017,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,other,0.076,0.017,not harmonised,reported,0.13206112259423078,0.02953998794870952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.470588235294117,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.289,0.024,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,0.289,0.024,"b (currency per unit, as reported)",reported,0.33776413220875,0.02804961651560554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.041666666666666,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.003,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,other,-0.003,0.001,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.141,0.027,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,0.141,0.027,"b (currency per unit, as reported)",reported,0.1537763501736158,0.02944653513962856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.222222222222221,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,-0.004,0.023,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,-0.004,0.023,"b (currency per unit, as reported)",reported,-0.005182712611551395,0.029800597516420526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.17391304347826086,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.025,0.004,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,-0.025,0.004,"b (currency per unit, as reported)",reported,-0.18310719317631438,0.0292971509082103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-OLS,2,s1-OLS,14,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.393,0.071,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.617,se,,monetary,-0.393,0.071,"b (currency per unit, as reported)",reported,-0.16275538531664918,0.029403644675526943,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.535211267605635,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Newly built dummy,age,A_structural,binary,binary,0.079,0.085,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,0.079,0.085,"b (currency per unit, as reported)",reported,0.02768678004939008,0.029789573470862748,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9294117647058823,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Property age [years]/10,age,A_structural,level,10 years,-0.661,0.069,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,-0.661,0.069,"b (currency per unit, as reported)",reported,-0.2745172137758068,0.028656108548457897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.579710144927535,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,(Property age [years]/10)2,age,A_structural,squared,10 years,0.062,0.013,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,other,0.062,0.013,not harmonised,reported,0.14071371008510644,0.029504487598490056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.769230769230769,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.205,0.021,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,0.205,0.021,"b (currency per unit, as reported)",reported,0.27933433102746513,0.028614736349154967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.761904761904761,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.003,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,other,-0.003,0.001,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.164,0.022,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,0.164,0.022,"b (currency per unit, as reported)",reported,0.21686596236373926,0.029091775439038188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.454545454545455,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,-0.01,0.018,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,-0.01,0.018,"b (currency per unit, as reported)",reported,-0.016553841275380874,0.02979691429568557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5555555555555556,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.02,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,-0.02,0.006,"b (currency per unit, as reported)",reported,-0.09885014112597598,0.02965504233779279,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3333333333333335,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s1-GEO,2,s1-GEO,14,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.421,0.128,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.99,se,,monetary,-0.421,0.128,"b (currency per unit, as reported)",reported,-0.0975498640386577,0.029658866026005197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2890625,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Newly built dummy,age,A_structural,binary,binary,-0.067,0.099,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.067,0.099,"b (currency per unit, as reported)",reported,-0.020164251402478094,0.02979493863948256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6767676767676768,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Property age [years]/10,age,A_structural,level,10 years,-0.736,0.082,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.736,0.082,"b (currency per unit, as reported)",reported,-0.2583980414638362,0.02878891222830784,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.97560975609756,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,(Property age [years]/10)2,age,A_structural,squared,10 years,0.077,0.016,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,other,0.077,0.016,not harmonised,reported,0.1419647306111455,0.02949916480231595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8125,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.234,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.234,0.023,"b (currency per unit, as reported)",reported,0.29014978458621327,0.028518995920867114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.173913043478262,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.144,0.025,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.144,0.025,"b (currency per unit, as reported)",reported,0.16917940503557805,0.02937142448534342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.759999999999999,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,-0.008,0.017,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.008,0.017,"b (currency per unit, as reported)",reported,-0.014022620078703535,0.02979806766724501,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.47058823529411764,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.031,0.004,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.031,0.004,"b (currency per unit, as reported)",reported,-0.22503388349787978,0.029036630128758678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.636,0.077,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.636,0.077,"b (currency per unit, as reported)",reported,-0.2390141533433594,0.028937248124903576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.25974025974026,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,14,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.01,0.022,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.01,0.022,"b (currency per unit, as reported)",reported,0.01354466546070762,0.02979826401355676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4545454545454546,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Included in DID in 1960s or before,other,other,binary,binary,-0.596,0.105,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.596,0.105,"b (currency per unit, as reported)",reported,-0.16678676770384693,0.029383574847154238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.6761904761904765,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Included in DID in 1970s,other,other,binary,binary,-0.662,0.106,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.662,0.106,"b (currency per unit, as reported)",reported,-0.1829736274056222,0.029297892001504466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.245283018867925,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Included in DID in 1980s,other,other,binary,binary,-0.6,0.095,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.6,0.095,"b (currency per unit, as reported)",reported,-0.18496902445668667,0.029286762205642053,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.315789473684211,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Included in DID in 1990s,other,other,binary,binary,-0.649,0.148,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.649,0.148,"b (currency per unit, as reported)",reported,-0.12957962989858454,0.02954974610938445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.385135135135135,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Included in DID in 2000s,other,other,binary,binary,-0.523,0.169,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.523,0.169,"b (currency per unit, as reported)",reported,-0.0918346739386398,0.029675066722046128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.094674556213018,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Included in DID in 2010s,other,other,binary,binary,-0.466,0.167,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.466,0.167,"b (currency per unit, as reported)",reported,-0.08287123603260547,0.02969849016619123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7904191616766467,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.35,0.098,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.35,0.098,"b (currency per unit, as reported)",reported,0.10583438750787989,0.029633628502206372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.571428571428571,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Sediment disaster risk dummy,other,other,binary,binary,-0.233,0.047,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.233,0.047,"b (currency per unit, as reported)",reported,-0.14615051668990278,0.02948100551255549,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.957446808510639,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.089,0.045,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.089,0.045,"b (currency per unit, as reported)",reported,-0.05883764184282536,0.029749369471091473,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9777777777777779,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Hillside area dummy,other,other,binary,binary,-0.158,0.069,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.158,0.069,"b (currency per unit, as reported)",reported,-0.06808163235682974,0.029731852105197798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.289855072463768,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-OLS,2,s2-OLS,15,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.287,0.047,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.287,0.047,"b (currency per unit, as reported)",reported,0.17903602664206958,0.0293194886835445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.106382978723404,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Newly built dummy,age,A_structural,binary,binary,0.068,0.086,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,0.068,0.086,"b (currency per unit, as reported)",reported,0.023557040594568954,0.029792727810778383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7906976744186047,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Property age [years]/10,age,A_structural,level,10 years,-0.664,0.069,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.664,0.069,"b (currency per unit, as reported)",reported,-0.2756686479588971,0.028646290224644433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.623188405797102,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,(Property age [years]/10)2,age,A_structural,squared,10 years,0.063,0.013,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,other,0.063,0.013,not harmonised,reported,0.1429372768121185,0.029494993627897464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.846153846153847,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.198,0.02,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,0.198,0.02,"b (currency per unit, as reported)",reported,0.28297150955813144,0.02858298076344762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.9,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.162,0.022,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,0.162,0.022,"b (currency per unit, as reported)",reported,0.21434347677790214,0.029108373389591647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.363636363636364,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,-0.006,0.023,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.006,0.023,"b (currency per unit, as reported)",reported,-0.00777393841095346,0.029800097241988257,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2608695652173913,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.026,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.026,0.006,"b (currency per unit, as reported)",reported,-0.1280741559431766,0.02955557444842537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.333333333333333,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.563,0.125,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.563,0.125,"b (currency per unit, as reported)",reported,-0.13303070256325894,0.02953612401493316,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.504,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,14,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.004,0.042,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,0.004,0.042,"b (currency per unit, as reported)",reported,0.002838178831093994,0.02980087772648694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09523809523809523,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Included in DID in 1960s or before,other,other,binary,binary,-0.269,0.175,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.269,0.175,"b (currency per unit, as reported)",reported,-0.04576040398002712,0.02976977954090983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5371428571428574,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Included in DID in 1970s,other,other,binary,binary,-0.288,0.173,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.288,0.173,"b (currency per unit, as reported)",reported,-0.04954996978784497,0.029764391573948543,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6647398843930636,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Included in DID in 1980s,other,other,binary,binary,-0.213,0.164,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.213,0.164,"b (currency per unit, as reported)",reported,-0.03867599549985538,0.02977870076045203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2987804878048779,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Included in DID in 1990s,other,other,binary,binary,-0.311,0.199,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.311,0.199,"b (currency per unit, as reported)",reported,-0.0465229898112873,0.029768729814939464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5628140703517588,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Included in DID in 2000s,other,other,binary,binary,-0.117,0.225,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.117,0.225,"b (currency per unit, as reported)",reported,-0.015494658483934262,0.02979742016141204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.52,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Included in DID in 2010s,other,other,binary,binary,-0.137,0.262,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.137,0.262,"b (currency per unit, as reported)",reported,-0.015581072822839498,0.029797380142948526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5229007633587787,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.072,0.124,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,0.072,0.124,"b (currency per unit, as reported)",reported,0.017301215162015448,0.029796537223471052,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5806451612903225,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Sediment disaster risk dummy,other,other,binary,binary,-0.14,0.046,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.14,0.046,"b (currency per unit, as reported)",reported,-0.09032791964356691,0.02967917359717198,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0434782608695654,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.026,0.06,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.026,0.06,"b (currency per unit, as reported)",reported,-0.012912689043814006,0.029798513178032317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.43333333333333335,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Hillside area dummy,other,other,binary,binary,-0.173,0.086,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,-0.173,0.086,"b (currency per unit, as reported)",reported,-0.05984108598258509,0.029747591875735935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.011627906976744,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s2-GEO,2,s2-GEO,15,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.262,0.074,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.992,se,,monetary,0.262,0.074,"b (currency per unit, as reported)",reported,0.10492918439166607,0.029636487194592703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.540540540540541,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Newly built dummy,age,A_structural,binary,binary,0.039,0.08,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,0.039,0.08,"b (currency per unit, as reported)",reported,0.014526453492019437,0.02979785331696295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4875,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Property age [years]/10,age,A_structural,level,10 years,-0.675,0.067,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.675,0.067,"b (currency per unit, as reported)",reported,-0.28755343157328267,0.02854234061542213,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.074626865671641,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,(Property age [years]/10)2,age,A_structural,squared,10 years,0.065,0.013,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,other,0.065,0.013,not harmonised,reported,0.14737789273403454,0.029475578546806904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.16,0.019,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,0.16,0.019,"b (currency per unit, as reported)",reported,0.24340802087256241,0.028904702478616786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.421052631578947,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.001,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,other,-0.001,0.001,not harmonised,reported,-0.029787773436724965,0.029787773436724965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.204,0.036,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,0.204,0.036,"b (currency per unit, as reported)",reported,0.16651468973554912,0.029384945247449844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.666666666666667,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.009,0.002,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,other,-0.009,0.002,not harmonised,reported,-0.1329146461549154,0.029536588034425645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,0.007,0.014,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,0.007,0.014,"b (currency per unit, as reported)",reported,0.014898845011808954,0.029797690023617908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.033,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.033,0.006,"b (currency per unit, as reported)",reported,-0.1617472168606377,0.02940858488375231,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.685,0.132,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.685,0.132,"b (currency per unit, as reported)",reported,-0.152832320150174,0.0294508996493766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.1893939393939394,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,14,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.002,0.043,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,0.002,0.043,"b (currency per unit, as reported)",reported,0.0013860915872796252,0.029800969126511936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.04651162790697675,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Included in DID in 1960s or before,other,other,binary,binary,-0.177,0.205,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.177,0.205,"b (currency per unit, as reported)",reported,-0.025722104133607444,0.029791137555873027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8634146341463415,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Included in DID in 1970s,other,other,binary,binary,-0.191,0.198,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.191,0.198,"b (currency per unit, as reported)",reported,-0.028735555837623245,0.02978869139188169,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9646464646464646,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Included in DID in 1980s,other,other,binary,binary,-0.14,0.19,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.14,0.19,"b (currency per unit, as reported)",reported,-0.021953337816051056,0.029793815607497855,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.736842105263158,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Included in DID in 1990s,other,other,binary,binary,-0.201,0.188,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.201,0.188,"b (currency per unit, as reported)",reported,-0.03184554485290914,0.02978588274799462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.069148936170213,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Included in DID in 2000s,other,other,binary,binary,-0.164,0.216,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.164,0.216,"b (currency per unit, as reported)",reported,-0.022620893647599248,0.029793372121228276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7592592592592593,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Included in DID in 2010s,other,other,binary,binary,-0.035,0.245,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.035,0.245,"b (currency per unit, as reported)",reported,-0.004257246813428672,0.029800727694000696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.14285714285714288,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,-0.096,0.108,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.096,0.108,"b (currency per unit, as reported)",reported,-0.02648048662313369,0.029790547451025395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.888888888888889,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Sediment disaster risk dummy,other,other,binary,binary,-0.101,0.041,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.101,0.041,"b (currency per unit, as reported)",reported,-0.07321518783179722,0.02972101684261075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4634146341463414,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-OLS,2,s3-OLS,15,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.344,0.388,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.85,se,,monetary,-0.344,0.388,"b (currency per unit, as reported)",reported,-0.0264122856186194,0.029790601221000957,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8865979381443297,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Newly built dummy,age,A_structural,binary,binary,0.078,0.092,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,0.078,0.092,"b (currency per unit, as reported)",reported,0.025258002573997605,0.029791490215484356,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8478260869565217,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Property age [years]/10,age,A_structural,level,10 years,-0.655,0.076,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.655,0.076,"b (currency per unit, as reported)",reported,-0.24876365711651932,0.028864180062374758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.618421052631579,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,(Property age [years]/10)2,age,A_structural,squared,10 years,0.062,0.015,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,other,0.062,0.015,not harmonised,reported,0.12225349202456401,0.029577457747878382,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.133333333333334,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.178,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,0.178,0.023,"b (currency per unit, as reported)",reported,0.22473421889208864,0.02903869120515752,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.739130434782608,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.181,0.041,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,0.181,0.041,"b (currency per unit, as reported)",reported,0.13043653626821214,0.029546397718213803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.414634146341463,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.007,0.002,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,other,-0.007,0.002,not harmonised,reported,-0.10374070973411938,0.02964020278117696,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,0.007,0.019,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,0.007,0.019,"b (currency per unit, as reported)",reported,0.010978653269140952,0.029799201730525443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.368421052631579,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.031,0.008,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.031,0.008,"b (currency per unit, as reported)",reported,-0.1147165060247919,0.029604259619301136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.875,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.666,0.166,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.666,0.166,"b (currency per unit, as reported)",reported,-0.11871749794839842,0.02959024723638759,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0120481927710845,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,14,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.009,0.059,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,0.009,0.059,"b (currency per unit, as reported)",reported,0.004545867940927466,0.02980068983496894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.15254237288135594,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Included in DID in 1960s or before,other,other,binary,binary,-0.213,0.276,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.213,0.276,"b (currency per unit, as reported)",reported,-0.022992516118605175,0.029793119477629245,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7717391304347825,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Included in DID in 1970s,other,other,binary,binary,-0.223,0.268,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.223,0.268,"b (currency per unit, as reported)",reported,-0.024789478571723738,0.029791839718484137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.832089552238806,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Included in DID in 1980s,other,other,binary,binary,-0.163,0.259,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.163,0.259,"b (currency per unit, as reported)",reported,-0.018751770382804477,0.029795757847523675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6293436293436293,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Included in DID in 1990s,other,other,binary,binary,-0.239,0.263,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.239,0.263,"b (currency per unit, as reported)",reported,-0.02707158962243375,0.029790075609623753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9087452471482889,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Included in DID in 2000s,other,other,binary,binary,-0.096,0.286,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.096,0.286,"b (currency per unit, as reported)",reported,-0.010002631680858324,0.029799506882557093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3356643356643357,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Included in DID in 2010s,other,other,binary,binary,-0.107,0.329,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.107,0.329,"b (currency per unit, as reported)",reported,-0.009691662617724845,0.02979959814235023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.32522796352583583,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,-0.002,0.135,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.002,0.135,"b (currency per unit, as reported)",reported,-0.00044149621999582683,0.029800994849718317,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.014814814814814814,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Sediment disaster risk dummy,other,other,binary,binary,-0.11,0.049,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.11,0.049,"b (currency per unit, as reported)",reported,-0.06675098921940821,0.02973453156137275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2448979591836733,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s3-GEO,2,s3-GEO,15,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.172,0.512,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.998,se,,monetary,-0.172,0.512,"b (currency per unit, as reported)",reported,-0.010010771027919904,0.02979950445520344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33593749999999994,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Newly built dummy,age,A_structural,binary,binary,-0.071,0.099,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.071,0.099,"b (currency per unit, as reported)",reported,-0.02136755314503143,0.029794193821945238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7171717171717171,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Property age [years]/10,age,A_structural,level,10 years,-0.757,0.082,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.757,0.082,"b (currency per unit, as reported)",reported,-0.265258749552075,0.028733444469313276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.23170731707317,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,(Property age [years]/10)2,age,A_structural,squared,10 years,0.081,0.016,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,other,0.081,0.016,not harmonised,reported,0.14917936616581368,0.02946752911917307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.0625,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.24,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.24,0.023,"b (currency per unit, as reported)",reported,0.2969410246927785,0.0284568481997246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.434782608695652,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.145,0.025,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.145,0.025,"b (currency per unit, as reported)",reported,0.17032029472135884,0.029365568055406698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.799999999999999,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.031,0.004,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.031,0.004,"b (currency per unit, as reported)",reported,-0.22503388349787978,0.029036630128758678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.75,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.646,0.077,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.646,0.077,"b (currency per unit, as reported)",reported,-0.24255275456113554,0.028911086843974358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.38961038961039,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.002,0.022,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.002,0.022,"b (currency per unit, as reported)",reported,0.0027091716718217417,0.02980088839003916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09090909090909091,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Included in DID in 1960s or before,other,other,binary,binary,-0.61,0.105,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.61,0.105,"b (currency per unit, as reported)",reported,-0.17059183437784609,0.029364168212580068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.809523809523809,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,17,Included in DID in 1970s,other,other,binary,binary,-0.669,0.106,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.669,0.106,"b (currency per unit, as reported)",reported,-0.1848426232316034,0.02928747094551563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.311320754716982,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Included in DID in 1980s,other,other,binary,binary,-0.613,0.095,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.613,0.095,"b (currency per unit, as reported)",reported,-0.1888352408412781,0.029264841565940322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.4526315789473685,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Included in DID in 1990s,other,other,binary,binary,-0.651,0.148,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.651,0.148,"b (currency per unit, as reported)",reported,-0.12997221534069725,0.029548214854720724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.398648648648649,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Included in DID in 2000s,other,other,binary,binary,-0.495,0.168,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.495,0.168,"b (currency per unit, as reported)",reported,-0.08746996270916942,0.029686775222505988,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.946428571428571,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Included in DID in 2010s,other,other,binary,binary,-0.489,0.167,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.489,0.167,"b (currency per unit, as reported)",reported,-0.08693125902798932,0.029688180486041344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.92814371257485,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.39,0.098,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.39,0.098,"b (currency per unit, as reported)",reported,0.11777048043056708,0.029593607903065568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.979591836734694,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Sediment disaster risk dummy,other,other,binary,binary,-0.216,0.047,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.216,0.047,"b (currency per unit, as reported)",reported,-0.13569108114771844,0.02952537413862392,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.595744680851063,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.074,0.045,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.074,0.045,"b (currency per unit, as reported)",reported,-0.048947344559414914,0.029765277096941503,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6444444444444444,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Hillside area dummy,other,other,binary,binary,-0.16,0.069,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,-0.16,0.069,"b (currency per unit, as reported)",reported,-0.06893935486359476,0.029730096784925247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3188405797101446,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-OLS,2,s4-OLS,18,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.276,0.047,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.667,se,,monetary,0.276,0.047,"b (currency per unit, as reported)",reported,0.1723818646311912,0.02935488274516662,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.872340425531915,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Newly built dummy,age,A_structural,binary,binary,0.066,0.088,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,0.066,0.088,"b (currency per unit, as reported)",reported,0.022345167681715763,0.029793556908954348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7500000000000001,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Property age [years]/10,age,A_structural,level,10 years,-0.685,0.071,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.685,0.071,"b (currency per unit, as reported)",reported,-0.2763222213132604,0.02864069739159341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.647887323943664,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,(Property age [years]/10)2,age,A_structural,squared,10 years,0.067,0.013,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,other,0.067,0.013,not harmonised,reported,0.15180961451454736,0.0294555968461062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.153846153846154,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.206,0.021,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,0.206,0.021,"b (currency per unit, as reported)",reported,0.28058989854637767,0.02860382460909676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.809523809523808,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.003,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,other,-0.003,0.001,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.158,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,0.158,0.023,"b (currency per unit, as reported)",reported,0.2005602689062985,0.02919548218256244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.869565217391305,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.027,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.027,0.006,"b (currency per unit, as reported)",reported,-0.1329146461549154,0.029536588034425645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.5,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.575,0.129,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.575,0.129,"b (currency per unit, as reported)",reported,-0.1316772719508738,0.02954150970723952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.457364341085271,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",-0.005,0.042,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.005,0.042,"b (currency per unit, as reported)",reported,-0.003547715501379489,0.02980081021158771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11904761904761904,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Included in DID in 1960s or before,other,other,binary,binary,-0.279,0.177,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.279,0.177,"b (currency per unit, as reported)",reported,-0.046922712800853576,0.02976817263710065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5762711864406782,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,17,Included in DID in 1970s,other,other,binary,binary,-0.301,0.175,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.301,0.175,"b (currency per unit, as reported)",reported,-0.05119051246494515,0.0297619258517123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.72,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Included in DID in 1980s,other,other,binary,binary,-0.22,0.166,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.22,0.166,"b (currency per unit, as reported)",reported,-0.039464530284451405,0.02977778194190424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3253012048192772,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Included in DID in 1990s,other,other,binary,binary,-0.313,0.201,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.313,0.201,"b (currency per unit, as reported)",reported,-0.04635663964681188,0.0297689602843744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5572139303482586,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Included in DID in 2000s,other,other,binary,binary,-0.123,0.228,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.123,0.228,"b (currency per unit, as reported)",reported,-0.01607477680028239,0.02979714723954785,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5394736842105263,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Included in DID in 2010s,other,other,binary,binary,-0.183,0.265,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.183,0.265,"b (currency per unit, as reported)",reported,-0.02057520041529674,0.029794689125976152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.690566037735849,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.098,0.125,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,0.098,0.125,"b (currency per unit, as reported)",reported,0.02335760793410164,0.02979286726288474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.784,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Sediment disaster risk dummy,other,other,binary,binary,-0.123,0.047,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.123,0.047,"b (currency per unit, as reported)",reported,-0.07775373837763051,0.029710778079257188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.617021276595745,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.021,0.061,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.021,0.061,"b (currency per unit, as reported)",reported,-0.01025882000346087,0.029799429533862526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34426229508196726,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Hillside area dummy,other,other,binary,binary,-0.173,0.088,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,-0.173,0.088,"b (currency per unit, as reported)",reported,-0.05848576728576114,0.02974998567136983,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9659090909090908,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s4-GEO,2,s4-GEO,18,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.254,0.075,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.993,se,,monetary,0.254,0.075,"b (currency per unit, as reported)",reported,0.10041591985101876,0.029650370034749633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3866666666666667,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Newly built dummy,age,A_structural,binary,binary,0.037,0.08,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,0.037,0.08,"b (currency per unit, as reported)",reported,0.013781652472995155,0.029798167509178717,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.46249999999999997,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Property age [years]/10,age,A_structural,level,10 years,-0.694,0.067,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.694,0.067,"b (currency per unit, as reported)",reported,-0.2949521891064022,0.028475211340243438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.35820895522388,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,(Property age [years]/10)2,age,A_structural,squared,10 years,0.068,0.013,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,other,0.068,0.013,not harmonised,reported,0.15402206417517988,0.02944539462172556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.230769230769232,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.169,0.019,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,0.169,0.019,"b (currency per unit, as reported)",reported,0.2562232971553038,0.028806169502667286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.894736842105264,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.001,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,other,-0.001,0.001,not harmonised,reported,-0.029787773436724965,0.029787773436724965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.189,0.036,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,0.189,0.036,"b (currency per unit, as reported)",reported,0.15457481614972995,0.029442822123758084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.25,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.008,0.002,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,other,-0.008,0.002,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.035,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.035,0.006,"b (currency per unit, as reported)",reported,-0.17127051338469956,0.029360659437377066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.833333333333334,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.724,0.133,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.724,0.133,"b (currency per unit, as reported)",reported,-0.16013157691875027,0.02941643609142788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.443609022556391,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.011,0.043,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,0.011,0.043,"b (currency per unit, as reported)",reported,0.007623289531726335,0.02980013180583931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2558139534883721,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Included in DID in 1960s or before,other,other,binary,binary,-0.227,0.204,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.227,0.204,"b (currency per unit, as reported)",reported,-0.033142696553784635,0.02978462597785051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1127450980392157,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,17,Included in DID in 1970s,other,other,binary,binary,-0.234,0.198,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.234,0.198,"b (currency per unit, as reported)",reported,-0.035197538174500664,0.029782532301500558,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1818181818181819,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Included in DID in 1980s,other,other,binary,binary,-0.147,0.19,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.147,0.19,"b (currency per unit, as reported)",reported,-0.023050435370704368,0.029793079730842383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7736842105263158,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Included in DID in 1990s,other,other,binary,binary,-0.226,0.186,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.226,0.186,"b (currency per unit, as reported)",reported,-0.03618609953211968,0.02978148014590381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2150537634408602,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Included in DID in 2000s,other,other,binary,binary,-0.163,0.215,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.163,0.215,"b (currency per unit, as reported)",reported,-0.02258755031554067,0.02979339458798309,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.758139534883721,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Included in DID in 2010s,other,other,binary,binary,-0.085,0.244,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.085,0.244,"b (currency per unit, as reported)",reported,-0.010380935729367824,0.02979939197606763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34836065573770497,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,-0.011,0.108,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.011,0.108,"b (currency per unit, as reported)",reported,-0.003035272826388241,0.02980086047726637,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.10185185185185185,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Sediment disaster risk dummy,other,other,binary,binary,-0.088,0.041,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.088,0.041,"b (currency per unit, as reported)",reported,-0.06383267173886109,0.029740222060151197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.146341463414634,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-OLS,2,s5-OLS,18,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.341,0.39,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.857,se,,monetary,-0.341,0.39,"b (currency per unit, as reported)",reported,-0.02604792864258945,0.02979088613082078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8743589743589744,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Newly built dummy,age,A_structural,binary,binary,0.063,0.097,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,0.063,0.097,"b (currency per unit, as reported)",reported,0.01935166271149417,0.02979541719071325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6494845360824743,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Property age [years]/10,age,A_structural,level,10 years,-0.684,0.081,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.684,0.081,"b (currency per unit, as reported)",reported,-0.24404393794834772,0.02889994002019907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.444444444444445,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,(Property age [years]/10)2,age,A_structural,squared,10 years,0.067,0.016,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,other,0.067,0.016,not harmonised,reported,0.12383119375155795,0.029571628358581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.1875,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.189,0.024,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,0.189,0.024,"b (currency per unit, as reported)",reported,0.22847542095165385,0.029012751866876678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.875,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.167,0.043,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,0.167,0.043,"b (currency per unit, as reported)",reported,0.11497127380752342,0.02960338187858387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.883720930232559,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.006,0.002,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,other,-0.006,0.002,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.031,0.008,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.031,0.008,"b (currency per unit, as reported)",reported,-0.1147165060247919,0.029604259619301136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.875,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.677,0.17,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.677,0.17,"b (currency per unit, as reported)",reported,-0.1178510569865834,0.029593323024695976,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9823529411764707,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.007,0.062,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,0.007,0.062,"b (currency per unit, as reported)",reported,0.003364609733829632,0.029800829071062455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11290322580645161,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Included in DID in 1960s or before,other,other,binary,binary,-0.243,0.282,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.243,0.282,"b (currency per unit, as reported)",reported,-0.025671120271551634,0.02979117661143029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8617021276595745,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,17,Included in DID in 1970s,other,other,binary,binary,-0.254,0.275,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.254,0.275,"b (currency per unit, as reported)",reported,-0.02751486397285109,0.029789714931236416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9236363636363636,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Included in DID in 1980s,other,other,binary,binary,-0.16,0.264,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.16,0.264,"b (currency per unit, as reported)",reported,-0.01805826563088111,0.029796138290953834,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6060606060606061,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Included in DID in 1990s,other,other,binary,binary,-0.227,0.269,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.227,0.269,"b (currency per unit, as reported)",reported,-0.025140105512580734,0.029791578779225628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8438661710037174,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Included in DID in 2000s,other,other,binary,binary,-0.102,0.292,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.102,0.292,"b (currency per unit, as reported)",reported,-0.010409373571666332,0.02979938316594675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3493150684931507,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Included in DID in 2010s,other,other,binary,binary,-0.098,0.341,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.098,0.341,"b (currency per unit, as reported)",reported,-0.00856419552883831,0.02979990485034555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.28739002932551316,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.034,0.139,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,0.034,0.139,"b (currency per unit, as reported)",reported,0.007289259029154573,0.02980020603095546,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2446043165467626,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Sediment disaster risk dummy,other,other,binary,binary,-0.095,0.052,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.095,0.052,"b (currency per unit, as reported)",reported,-0.054363618807479704,0.029756928189357306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.826923076923077,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T2-s5-GEO,2,s5-GEO,18,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.226,0.528,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,,monetary,-0.226,0.528,"b (currency per unit, as reported)",reported,-0.012754692492100598,0.029798573609863342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42803030303030304,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Newly built dummy,age,A_structural,binary,binary,-0.036,0.124,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.036,0.124,"b (currency per unit, as reported)",reported,-0.008651578771059359,0.029799882433648908,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.29032258064516125,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Property age [years]/10,age,A_structural,level,10 years,-0.72,0.103,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.72,0.103,"b (currency per unit, as reported)",reported,-0.20393954298266484,0.029174684621131222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.990291262135923,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,(Property age [years]/10)2,age,A_structural,squared,10 years,0.078,0.02,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,0.078,0.02,not harmonised,reported,0.11544677960339385,0.029601738359844577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.289,0.027,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.289,0.027,"b (currency per unit, as reported)",reported,0.30389500969538946,0.028391575300261304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.703703703703702,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.003,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.003,0.001,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.133,0.029,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.133,0.029,"b (currency per unit, as reported)",reported,0.13541464362525468,0.029526501241596885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.586206896551724,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,-0.014,0.029,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.014,0.029,"b (currency per unit, as reported)",reported,-0.014385199946588172,0.029797914175075508,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.48275862068965514,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.02,0.005,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.02,0.005,"b (currency per unit, as reported)",reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-OLS,3,s1-OLS,24,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.37,0.085,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.603,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.37,0.085,"b (currency per unit, as reported)",reported,-0.12864410758378908,0.02955337606654614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.352941176470588,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Newly built dummy,age,A_structural,binary,binary,0.052,0.099,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.052,0.099,"b (currency per unit, as reported)",reported,0.01565113203865646,0.02979734753513442,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5252525252525252,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Property age [years]/10,age,A_structural,level,10 years,-0.646,0.081,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.646,0.081,"b (currency per unit, as reported)",reported,-0.23123097406074958,0.02899335742866984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.9753086419753085,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,(Property age [years]/10)2,age,A_structural,squared,10 years,0.061,0.015,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,0.061,0.015,not harmonised,reported,0.12031043347201234,0.029584532820986647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.066666666666666,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.225,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.225,0.023,"b (currency per unit, as reported)",reported,0.2798804064623372,0.028609997105038906,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.782608695652174,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.003,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.003,0.001,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.143,0.025,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.143,0.025,"b (currency per unit, as reported)",reported,0.16803783485241094,0.029377243855316603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.719999999999999,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,0.026,0.032,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.026,0.032,"b (currency per unit, as reported)",reported,0.024206215851420076,0.029792265663286247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8125,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.015,0.007,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.015,0.007,"b (currency per unit, as reported)",reported,-0.06372946847761696,0.02974041862288792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.142857142857143,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s1-GEO,3,s1-GEO,24,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.33,0.15,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.987,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.33,0.15,"b (currency per unit, as reported)",reported,-0.06542174137572547,0.0297371551707843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Newly built dummy,age,A_structural,binary,binary,0.003,0.116,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.003,0.116,"b (currency per unit, as reported)",reported,0.0007707152302546693,0.029800988903180545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.02586206896551724,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Property age [years]/10,age,A_structural,level,10 years,-0.704,0.097,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.704,0.097,"b (currency per unit, as reported)",reported,-0.21139949670731972,0.02912748747245741,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.257731958762886,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,(Property age [years]/10)2,age,A_structural,squared,10 years,0.078,0.019,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,0.078,0.019,not harmonised,reported,0.12143553210537791,0.029580450128233078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.105263157894737,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.239,0.026,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.239,0.026,"b (currency per unit, as reported)",reported,0.2642058451302407,0.02874205846605129,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.192307692307692,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.145,0.028,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.145,0.028,"b (currency per unit, as reported)",reported,0.15252100960655776,0.02945233288954219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.178571428571428,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,-0.01,0.028,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.01,0.028,"b (currency per unit, as reported)",reported,-0.010642610713887127,0.02979930999888395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35714285714285715,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.026,0.005,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.026,0.005,"b (currency per unit, as reported)",reported,-0.15313736034812847,0.029449492374640097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.199999999999999,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.596,0.092,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.596,0.092,"b (currency per unit, as reported)",reported,-0.1895583787550094,0.029260689338021582,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.478260869565217,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.008,0.027,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.008,0.027,"b (currency per unit, as reported)",reported,0.00882958105665118,0.029799836066197734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2962962962962963,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Included in DID in 1960s or before,other,other,binary,binary,-0.614,0.126,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.614,0.126,"b (currency per unit, as reported)",reported,-0.14371325262793486,0.029491644676090868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.873015873015873,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Included in DID in 1970s,other,other,binary,binary,-0.694,0.126,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.694,0.126,"b (currency per unit, as reported)",reported,-0.16197449916426712,0.02940747391166809,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.507936507936508,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,24,Included in DID in 1980s,other,other,binary,binary,-0.614,0.114,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.614,0.114,"b (currency per unit, as reported)",reported,-0.15847870270844303,0.02942438454195848,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.385964912280701,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Included in DID in 1990s,other,other,binary,binary,-0.628,0.179,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.628,0.179,"b (currency per unit, as reported)",reported,-0.10398640870150012,0.02963943814899446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5083798882681565,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Included in DID in 2000s,other,other,binary,binary,-0.544,0.206,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.544,0.206,"b (currency per unit, as reported)",reported,-0.07845520557734094,0.029709140347301894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6407766990291264,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Included in DID in 2010s,other,other,binary,binary,-0.498,0.185,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.498,0.185,"b (currency per unit, as reported)",reported,-0.07996417536609086,0.0297055671540699,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.691891891891892,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.206,0.116,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.206,0.116,"b (currency per unit, as reported)",reported,0.052848504576497084,0.02975935209161972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7758620689655171,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Sediment disaster risk dummy,other,other,binary,binary,-0.232,0.056,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.232,0.056,"b (currency per unit, as reported)",reported,-0.12253095763828596,0.029576438050620744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.142857142857143,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.129,0.054,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.129,0.054,"b (currency per unit, as reported)",reported,-0.07101154957173401,0.029725764937004937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.388888888888889,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Hillside area dummy,other,other,binary,binary,-0.204,0.083,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.204,0.083,"b (currency per unit, as reported)",reported,-0.07305013273386265,0.029721377533875496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4578313253012047,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-OLS,3,s2-OLS,25,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.243,0.055,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.653,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.243,0.055,"b (currency per unit, as reported)",reported,0.13053957168700384,0.02954599359170869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.418181818181818,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Newly built dummy,age,A_structural,binary,binary,0.044,0.1,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.044,0.1,"b (currency per unit, as reported)",reported,0.013111311906618116,0.029798436151404813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43999999999999995,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Property age [years]/10,age,A_structural,level,10 years,-0.652,0.081,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.652,0.081,"b (currency per unit, as reported)",reported,-0.2332622821049978,0.028978903144946045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.049382716049383,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,(Property age [years]/10)2,age,A_structural,squared,10 years,0.062,0.016,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,0.062,0.016,not harmonised,reported,0.1147165060247919,0.029604259619301136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.875,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.215,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.215,0.023,"b (currency per unit, as reported)",reported,0.2683563499855554,0.02870788860310593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.347826086956522,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.003,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.003,0.001,not harmonised,reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.146,0.025,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.146,0.025,"b (currency per unit, as reported)",reported,0.17146049986694342,0.02935967463475059,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.839999999999999,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,0.024,0.032,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.024,0.032,"b (currency per unit, as reported)",reported,0.02234516768171576,0.029793556908954348,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.75,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.021,0.007,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.021,0.007,"b (currency per unit, as reported)",reported,-0.08904782657932872,0.029682608859776242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.476,0.15,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.476,0.15,"b (currency per unit, as reported)",reported,-0.09414844238615548,0.029668626802359918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1733333333333333,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.026,0.048,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.026,0.048,"b (currency per unit, as reported)",reported,0.016140104433403295,0.02979711587705224,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5416666666666666,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Included in DID in 1960s or before,other,other,binary,binary,-0.317,0.204,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.317,0.204,"b (currency per unit, as reported)",reported,-0.04625883941326633,0.029769095395288103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.553921568627451,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Included in DID in 1970s,other,other,binary,binary,-0.348,0.203,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.348,0.203,"b (currency per unit, as reported)",reported,-0.05102088776280884,0.02976218452830516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.714285714285714,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,24,Included in DID in 1980s,other,other,binary,binary,-0.269,0.191,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.269,0.191,"b (currency per unit, as reported)",reported,-0.04193412006935241,0.029774784138462115,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4083769633507854,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Included in DID in 1990s,other,other,binary,binary,-0.366,0.233,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.366,0.233,"b (currency per unit, as reported)",reported,-0.04676066124430543,0.029768399098150725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5708154506437768,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Included in DID in 2000s,other,other,binary,binary,-0.163,0.278,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.163,0.278,"b (currency per unit, as reported)",reported,-0.01747058007868173,0.02979644945934675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5863309352517986,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Included in DID in 2010s,other,other,binary,binary,-0.198,0.297,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.198,0.297,"b (currency per unit, as reported)",reported,-0.01986341207045473,0.02979511810568209,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6666666666666667,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,-0.114,0.159,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.114,0.159,"b (currency per unit, as reported)",reported,-0.021361877407269463,0.029794197436454777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7169811320754718,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Sediment disaster risk dummy,other,other,binary,binary,-0.14,0.054,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.14,0.054,"b (currency per unit, as reported)",reported,-0.07703227018499818,0.029712447071356434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.592592592592593,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.05,0.069,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.05,0.069,"b (currency per unit, as reported)",reported,-0.021589892371430996,0.029794051472574776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7246376811594203,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Hillside area dummy,other,other,binary,binary,-0.209,0.1,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.209,0.1,"b (currency per unit, as reported)",reported,-0.06216362611381739,0.029743361776946125,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.09,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s2-GEO,3,s2-GEO,25,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.221,0.084,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.989,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.221,0.084,"b (currency per unit, as reported)",reported,0.07816512011758849,0.029709819411210103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.630952380952381,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Newly built dummy,age,A_structural,binary,binary,0.086,0.093,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.086,0.093,"b (currency per unit, as reported)",reported,0.027547453588351017,0.02978968818275168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9247311827956989,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Property age [years]/10,age,A_structural,level,10 years,-0.563,0.077,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.563,0.077,"b (currency per unit, as reported)",reported,-0.21290012505333486,0.029117779092552018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.311688311688311,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,(Property age [years]/10)2,age,A_structural,squared,10 years,0.043,0.015,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,0.043,0.015,not harmonised,reported,0.08511948188528443,0.02969284251812248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8666666666666667,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.173,0.022,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.173,0.022,"b (currency per unit, as reported)",reported,0.22816290546771348,0.029014935955431774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.863636363636363,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.001,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.001,0.001,not harmonised,reported,-0.029787773436724965,0.029787773436724965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.242,0.045,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.242,0.045,"b (currency per unit, as reported)",reported,0.15824383823013827,0.029425507108910003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.377777777777778,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.012,0.003,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.012,0.003,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,0.022,0.022,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.022,0.022,"b (currency per unit, as reported)",reported,0.029787773436724965,0.029787773436724965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.025,0.007,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.025,0.007,"b (currency per unit, as reported)",reported,-0.1058343875078799,0.029633628502206372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5714285714285716,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.464,0.15,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.464,0.15,"b (currency per unit, as reported)",reported,-0.09179520850883248,0.02967517516449325,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0933333333333337,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.08,0.052,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.08,0.052,"b (currency per unit, as reported)",reported,0.045799578522158095,0.02976972603940276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5384615384615385,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Included in DID in 1960s or before,other,other,binary,binary,-0.073,0.231,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.073,0.231,"b (currency per unit, as reported)",reported,-0.009417213719427815,0.029799676290244187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.316017316017316,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Included in DID in 1970s,other,other,binary,binary,-0.164,0.224,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.164,0.224,"b (currency per unit, as reported)",reported,-0.02181339611721129,0.029793906891800788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7321428571428572,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,24,Included in DID in 1980s,other,other,binary,binary,-0.056,0.217,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.056,0.217,"b (currency per unit, as reported)",reported,-0.007690352645901528,0.029800116502868422,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.25806451612903225,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,25,Included in DID in 1990s,other,other,binary,binary,-0.116,0.219,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.116,0.219,"b (currency per unit, as reported)",reported,-0.01578303719833351,0.02979728574512964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5296803652968037,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,25,Included in DID in 2000s,other,other,binary,binary,-0.177,0.267,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.177,0.267,"b (currency per unit, as reported)",reported,-0.019751863526895095,0.029795183964299383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6629213483146067,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,25,Included in DID in 2010s,other,other,binary,binary,-0.044,0.271,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.044,0.271,"b (currency per unit, as reported)",reported,-0.0048384817431266275,0.029800648917893552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16236162361623613,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,25,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,-0.291,0.138,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.291,0.138,"b (currency per unit, as reported)",reported,-0.06271752001818323,0.02974232908078793,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1086956521739126,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,25,Sediment disaster risk dummy,other,other,binary,binary,-0.098,0.049,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.098,0.049,"b (currency per unit, as reported)",reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-OLS,3,s3-OLS,25,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.454,0.408,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,OLS,1136.0,0.861,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.454,0.408,"b (currency per unit, as reported)",reported,-0.033142696553784635,0.02978462597785051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1127450980392157,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,ols,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Newly built dummy,age,A_structural,binary,binary,0.089,0.115,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.089,0.115,"b (currency per unit, as reported)",reported,0.023057249385115174,0.02979307504818253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7739130434782608,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Property age [years]/10,age,A_structural,level,10 years,-0.593,0.094,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.593,0.094,"b (currency per unit, as reported)",reported,-0.18476313218333154,0.029287916400055927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.308510638297872,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,(Property age [years]/10)2,age,A_structural,squared,10 years,0.048,0.018,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,0.048,0.018,not harmonised,reported,0.07921957044344147,0.02970733891629054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.666666666666667,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.178,0.027,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.178,0.027,"b (currency per unit, as reported)",reported,0.1927805120157194,0.029241987777665304,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.592592592592593,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.002,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.002,0.001,not harmonised,reported,-0.059496411730872965,0.02974820586543648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.247,0.054,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.247,0.054,"b (currency per unit, as reported)",reported,0.13506294734621227,0.029527931808483645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.574074074074074,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.012,0.003,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,other,-0.012,0.003,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Date of transaction registered (yearly conversion),transaction_timing,G_market,level,years,0.029,0.031,,,,,,high,linear time trend in years from earliest transaction,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.029,0.031,"b (currency per unit, as reported)",reported,0.02786752548509328,0.029789423794410057,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.935483870967742,1126.0,years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.025,0.009,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.025,0.009,"b (currency per unit, as reported)",reported,-0.08249836696098885,0.029699412105955985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.777777777777778,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.495,0.193,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.495,0.193,"b (currency per unit, as reported)",reported,-0.07621032675966893,0.029714329423466877,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.564766839378238,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.108,0.074,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,0.108,0.074,"b (currency per unit, as reported)",reported,0.04345226878604093,0.029772850834879898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4594594594594594,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Included in DID in 1960s or before,other,other,binary,binary,-0.087,0.336,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.087,0.336,"b (currency per unit, as reported)",reported,-0.007716100064004427,0.029800110592017096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2589285714285714,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Included in DID in 1970s,other,other,binary,binary,-0.142,0.326,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.142,0.326,"b (currency per unit, as reported)",reported,-0.01297970920023179,0.029798487318842003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4355828220858895,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,24,Included in DID in 1980s,other,other,binary,binary,-0.046,0.317,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.046,0.317,"b (currency per unit, as reported)",reported,-0.00432439457083175,0.02980071910768837,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1451104100946372,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,25,Included in DID in 1990s,other,other,binary,binary,-0.115,0.333,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.115,0.333,"b (currency per unit, as reported)",reported,-0.010291090870767529,0.02979941965187467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.34534534534534533,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,25,Included in DID in 2000s,other,other,binary,binary,-0.053,0.386,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.053,0.386,"b (currency per unit, as reported)",reported,-0.004091812586988715,0.02980074827504989,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13730569948186527,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,25,Included in DID in 2010s,other,other,binary,binary,-0.029,0.394,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.029,0.394,"b (currency per unit, as reported)",reported,-0.002193469177236652,0.02980092606314624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.07360406091370558,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,25,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,-0.264,0.186,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.264,0.186,"b (currency per unit, as reported)",reported,-0.04226040243219516,0.02977437444086477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4193548387096775,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,25,Sediment disaster risk dummy,other,other,binary,binary,-0.13,0.063,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.13,0.063,"b (currency per unit, as reported)",reported,-0.06137818023044357,0.029744810419368807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0634920634920637,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T3-s3-GEO,3,s3-GEO,25,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.309,0.585,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,other,1136.0,0.997,se,in-sample 2016-2018 (2019 held out for out-of-sample prediction); n not printed,monetary,-0.309,0.585,"b (currency per unit, as reported)",reported,-0.01573909004161867,0.029797306389472242,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5282051282051282,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,other,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Newly built dummy,age,A_structural,binary,binary,-0.021,0.095,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.021,0.095,"b (currency per unit, as reported)",reported,-0.006587446043288104,0.029800351148208084,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2210526315789474,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Property age [years]/10,age,A_structural,level,10 years,-0.733,0.076,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.733,0.076,"b (currency per unit, as reported)",reported,-0.27623887577070966,0.028641411403238646,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.644736842105264,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,(Property age [years]/10)2,age,A_structural,squared,10 years,0.074,0.015,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,other,0.074,0.015,not harmonised,reported,0.1454547035108021,0.029484061522459887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.933333333333334,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.261,0.022,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.261,0.022,"b (currency per unit, as reported)",reported,0.33332898385964665,0.028096695957518108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.863636363636365,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.169,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.169,0.023,"b (currency per unit, as reported)",reported,0.21390434396212607,0.029111242077685794,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.347826086956522,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.024,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.024,0.006,"b (currency per unit, as reported)",reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.548,0.112,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.548,0.112,"b (currency per unit, as reported)",reported,-0.14428624650324523,0.029489159869276395,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.892857142857143,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.02,0.034,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.02,0.034,"b (currency per unit, as reported)",reported,0.01752730580773254,0.029796419873145318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5882352941176471,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Included in DID in 1960s or before,other,other,binary,binary,-0.004,0.149,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.004,0.149,"b (currency per unit, as reported)",reported,-0.0008000265293195757,0.029800988217154194,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.02684563758389262,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Included in DID in 1970s,other,other,binary,binary,-0.029,0.149,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.029,0.149,"b (currency per unit, as reported)",reported,-0.005800096630414802,0.02980049648040708,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.1946308724832215,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Included in DID in 1980s,other,other,binary,binary,0.056,0.14,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.056,0.14,"b (currency per unit, as reported)",reported,0.011919552271882992,0.029798880679707485,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.39999999999999997,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Included in DID in 1990s,other,other,binary,binary,-0.141,0.18,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.141,0.18,"b (currency per unit, as reported)",reported,-0.02333775684500173,0.029792881078725613,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7833333333333333,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Included in DID in 2000s,other,other,binary,binary,-0.028,0.211,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.028,0.211,"b (currency per unit, as reported)",reported,-0.003954603849883731,0.029800764725909548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.13270142180094788,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,31,Included in DID in 2010s,other,other,binary,binary,0.071,0.257,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.071,0.257,"b (currency per unit, as reported)",reported,0.00823268146144376,0.02979998782522601,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2762645914396887,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,32,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.238,0.114,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.238,0.114,"b (currency per unit, as reported)",reported,0.06209605208790891,0.029743487134544604,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.087719298245614,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,32,Sediment disaster risk dummy,other,other,binary,binary,-0.115,0.048,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.115,0.048,"b (currency per unit, as reported)",reported,-0.07121693302773785,0.029725328568099275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3958333333333335,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,32,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.057,0.061,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.057,0.061,"b (currency per unit, as reported)",reported,-0.02783604338192431,0.0297894499350418,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9344262295081968,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,32,Hillside area dummy,other,other,binary,binary,-0.072,0.089,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,-0.072,0.089,"b (currency per unit, as reported)",reported,-0.02410166907443268,0.029792340939229292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8089887640449438,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SLX,4,SLX,32,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.284,0.072,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_lag,1136.0,0.753,se,,monetary,0.284,0.072,"b (currency per unit, as reported)",reported,0.1167445809277529,0.029597217699993697,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.944444444444444,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Newly built dummy,age,A_structural,binary,binary,-0.014,0.091,,,,,,high,dummy for newly built house (mapped to age family),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.014,0.091,"b (currency per unit, as reported)",reported,-0.0045847206998435915,0.029800684548983342,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.15384615384615385,1126.0,binary,year_built_not_age,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Property age [years]/10,age,A_structural,level,10 years,-0.728,0.074,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.728,0.074,"b (currency per unit, as reported)",reported,-0.2813357704057045,0.02859731732145897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.837837837837839,1126.0,10 years,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,(Property age [years]/10)2,age,A_structural,squared,10 years,0.073,0.014,,,,,,high,square of age/10,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,other,0.073,0.014,not harmonised,reported,0.15354816162455726,0.029447592640326054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.2142857142857135,1126.0,10 years,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Floor space [m2] /100,living_area,A_structural,level,100 m2,0.254,0.021,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.254,0.021,"b (currency per unit, as reported)",reported,0.3390943459542383,0.028035359311177187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.095238095238095,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,(Floor space [m2] /100)2,living_area,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of floor space/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Land area [m2]/100,lot_size,A_structural,level,100 m2,0.168,0.023,,,,,,high,,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.168,0.023,"b (currency per unit, as reported)",reported,0.21269606050731563,0.02911910352183487,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.304347826086957,1126.0,100m2,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,(Land area [m2] /100)2,lot_size,A_structural,squared,100 m2,-0.004,0.001,,,,,,high,square of land area/100,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,other,-0.004,0.001,not harmonised,reported,-0.11836599123443642,0.029591497808609108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.0,1126.0,100m2,nonstandard_x_transform,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Walking time to nearest station [min],transit_distance,D_location,level,minutes,-0.023,0.006,,,,,,high,walking time (minutes) to nearest railway station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.023,0.006,"b (currency per unit, as reported)",reported,-0.11349897054862315,0.02960842709964082,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.833333333333333,1126.0,minutes,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Bus to closest station dummy,transit_distance,D_location,binary,binary,-0.539,0.113,,,,,,high,dummy = bus needed to reach closest station,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.539,0.113,"b (currency per unit, as reported)",reported,-0.14073339705982624,0.029504404207347616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.769911504424779,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Number of daily passengers of the closest station [ten thousand persons / day],other,other,level,"10,000 persons/day",0.011,0.036,,,,,,high,ridership of closest station (station importance),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.011,0.036,"b (currency per unit, as reported)",reported,0.009105482934419632,0.029799762330827883,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3055555555555556,1126.0,10000 persons/day,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Included in DID in 1960s or before,other,other,binary,binary,-0.036,0.152,,,,,,high,densely inhabited district (DID) inclusion decade; reference = not in DID,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.036,0.152,"b (currency per unit, as reported)",reported,-0.007057955245381921,0.029800255480501445,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.23684210526315788,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Included in DID in 1970s,other,other,binary,binary,-0.048,0.151,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.048,0.151,"b (currency per unit, as reported)",reported,-0.009472739812651385,0.02979966066063248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.31788079470198677,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Included in DID in 1980s,other,other,binary,binary,0.058,0.143,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.058,0.143,"b (currency per unit, as reported)",reported,0.012086235112510465,0.029798821053258553,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4055944055944056,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Included in DID in 1990s,other,other,binary,binary,-0.161,0.18,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.161,0.18,"b (currency per unit, as reported)",reported,-0.02664587252162428,0.02979041648380354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8944444444444445,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Included in DID in 2000s,other,other,binary,binary,-0.023,0.208,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.023,0.208,"b (currency per unit, as reported)",reported,-0.003295284744539482,0.029800835950617922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.11057692307692309,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,31,Included in DID in 2010s,other,other,binary,binary,0.032,0.254,,,,,,high,DID inclusion decade,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.032,0.254,"b (currency per unit, as reported)",reported,0.00375442994894439,0.029800787719746098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.12598425196850394,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,32,Tsunami risk dummy (over 3 m),flood_risk,E_environment,binary,binary,0.19,0.12,,,,,,high,tsunami inundation risk zone over 3 m,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.19,0.12,"b (currency per unit, as reported)",reported,0.04713247403449179,0.02976787833757376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5833333333333335,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,32,Sediment disaster risk dummy,other,other,binary,binary,-0.112,0.046,,,,,,high,landslide/sediment disaster risk zone,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.112,0.046,"b (currency per unit, as reported)",reported,-0.07236869772649016,0.029722857994808453,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4347826086956523,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,32,Ratio of residents over 20 years,demographics,F_neighbourhood,binary,binary,-0.066,0.061,,,,,,high,district-level; summary stats show 0/1 variable (mean 0.38),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.066,0.061,"b (currency per unit, as reported)",reported,-0.03222695436112045,0.029785518424671927,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0819672131147542,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,32,Hillside area dummy,other,other,binary,binary,-0.099,0.087,,,,,,high,hillside district dummy (topography),"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,-0.099,0.087,"b (currency per unit, as reported)",reported,-0.03389199809853883,0.02978387711689776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1379310344827587,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,False,False
e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90,e6c58cb070edd6cfaff8b1bababd256016f5ed90:T4-SDEM,4,SDEM,32,Promotion area dummy,public_services,F_neighbourhood,binary,binary,0.292,0.074,,,,,,high,city-designated promotion area with purchase subsidies,"transaction price (10,000 JPY / 1,000, i.e. in 10 million JPY)",level,spatial_error,1136.0,0.734,se,,monetary,0.292,0.074,"b (currency per unit, as reported)",reported,0.11678841516379512,0.02959706411685219,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.945945945945946,1126.0,binary,,2023.0,Japan,Asia,High income,2016.0,2019.0,single_family,transaction,journal,True,True,OLS,,2017.5,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,m2,living_area,A_structural,level,m2,1402.891,42.63756,,,**,,,medium,House size (square meters); Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,1402.891,42.63756,"b (currency per unit, as reported)",reported,0.6232972511585174,0.018943648468844942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.90270362562961,1704.0,m2,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,m2,living_area,A_structural,level,m2,1400.571,43.10532,,,**,,,medium,House size (square meters); Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,1400.571,43.10532,"b (currency per unit, as reported)",reported,0.618502972210548,0.019035642275962286,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),32.491836274501615,1704.0,m2,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,m2,living_area,A_structural,level,m2,1357.357,42.76402,,,**,,,medium,House size (square meters); Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,1357.357,42.76402,"b (currency per unit, as reported)",reported,0.6095556292139604,0.01920426911919148,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.740631493484475,1704.0,m2,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,lift,accessibility,B_interior,binary,binary,19696.78,2053.673,,,**,,,medium,Building has elevator; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,19696.78,2053.673,"b (currency per unit, as reported)",reported,0.22631446336589484,0.023596542324381315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.591001099006512,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,lift,accessibility,B_interior,binary,binary,19308.17,2083.496,,,**,,,medium,Building has elevator; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,19308.17,2083.496,"b (currency per unit, as reported)",reported,0.2190465255505224,0.02363675893667869,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.267198017178817,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,lift,accessibility,B_interior,binary,binary,18207.86,2035.105,,,**,,,medium,Building has elevator; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,18207.86,2035.105,"b (currency per unit, as reported)",reported,0.2118209728835228,0.0236753754158985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.946889718220927,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,bathr,bathrooms,A_structural,level,count,17679.49,2281.784,,,**,,,medium,Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,17679.49,2281.784,"b (currency per unit, as reported)",reported,0.1844768369322631,0.023809300770703624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.748099732490017,1704.0,count,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,bathr,bathrooms,A_structural,level,count,16844.87,2293.362,,,**,,,medium,Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,16844.87,2293.362,"b (currency per unit, as reported)",reported,0.1751829409107215,0.023850460094550694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.345054989138217,1704.0,count,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,bathr,bathrooms,A_structural,level,count,15889.03,2260.322,,,**,,,medium,Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,15889.03,2260.322,"b (currency per unit, as reported)",reported,0.16787451839496415,0.023881285841082945,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.029542693474647,1704.0,count,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,heating,heating,A_structural,binary,binary,5868.963,1855.498,,,**,,,medium,"Dummy: 0 if central, 1 if individual heating; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,5868.963,1855.498,"b (currency per unit, as reported)",reported,0.07640026826287236,0.0241542747775413,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.163012301818703,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,heating,heating,A_structural,binary,binary,5214.945,1889.629,,,**,,,medium,"Dummy: 0 if central, 1 if individual heating; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,5214.945,1889.629,"b (currency per unit, as reported)",reported,0.06670677964110168,0.024171120751309037,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7597718917311282,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,heating,heating,A_structural,binary,binary,5113.653,1827.406,,,**,,,medium,"Dummy: 0 if central, 1 if individual heating; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,5113.653,1827.406,"b (currency per unit, as reported)",reported,0.06763411651277623,0.024169608364147187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7983124713391554,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,a a,air_conditioning,A_structural,binary,binary,5675.952,1628.007,,,**,,,medium,"Printed as 'a a' (= a_a, air conditioning dummy per Table 1); Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,5675.952,1628.007,"b (currency per unit, as reported)",reported,0.08415969560639149,0.02413913534946641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.486442011612972,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,a a,air_conditioning,A_structural,binary,binary,5603.866,1601.317,,,**,,,medium,"Printed as 'a a' (= a_a, air conditioning dummy per Table 1); Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,5603.866,1601.317,"b (currency per unit, as reported)",reported,0.084473514577921,0.024138492059476928,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4995356946813154,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,a a,air_conditioning,A_structural,binary,binary,5568.183,1601.539,,,**,,,medium,"Printed as 'a a' (= a_a, air conditioning dummy per Table 1); Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,5568.183,1601.539,"b (currency per unit, as reported)",reported,0.08392787220095366,0.02413960901012828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.476770156705519,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,basem,basement,A_structural,binary,binary,8831.146,1909.752,,,**,,,medium,Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,8831.146,1909.752,"b (currency per unit, as reported)",reported,0.11132616852275837,0.024074494181012842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.6242370737142835,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,basem,basement,A_structural,binary,binary,8139.17,1907.114,,,**,,,medium,Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,8139.17,1907.114,"b (currency per unit, as reported)",reported,0.10283948642494604,0.024096636919222054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.26779416437612,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,basem,basement,A_structural,binary,binary,8132.938,1879.831,,,**,,,medium,Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,8132.938,1879.831,"b (currency per unit, as reported)",reported,0.1042369199636171,0.024093112906077276,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.3264197685855805,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,spool,pool,C_exterior,binary,binary,15517.48,2313.651,,,**,,,medium,Urbanization has swimming pool; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,15517.48,2313.651,"b (currency per unit, as reported)",reported,0.16037274894317272,0.023911522422785172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.706923386457163,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,spool,pool,C_exterior,binary,binary,15329.57,2406.694,,,**,,,medium,Urbanization has swimming pool; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,15329.57,2406.694,"b (currency per unit, as reported)",reported,0.1524982093995314,0.023941736498322905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.369555082615405,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,spool,pool,C_exterior,binary,binary,12687.42,2324.918,,,**,,,medium,Urbanization has swimming pool; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,12687.42,2324.918,"b (currency per unit, as reported)",reported,0.13105953577535237,0.02401612572104972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.457147305840464,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,parking,parking,A_structural,level,count,24322.4,2220.907,,,**,,,medium,Number of parking places included in price; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,24322.4,2220.907,"b (currency per unit, as reported)",reported,0.25643138154478945,0.023415051569437788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.951561681781362,1704.0,count,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,parking,parking,A_structural,level,count,23780.65,2252.612,,,**,,,medium,Number of parking places included in price; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,23780.65,2252.612,"b (currency per unit, as reported)",reported,0.24776807568553802,0.023469726038024663,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.556922363904658,1704.0,count,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,parking,parking,A_structural,level,count,22977.94,2194.465,,,**,,,medium,Number of parking places included in price; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,22977.94,2194.465,"b (currency per unit, as reported)",reported,0.24587083462959966,0.023481432239593474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.47086191850861,1704.0,count,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,zone B2,other,other,binary,binary,-31553.56,1899.969,,,**,,,medium,"Higher transit fare zone (Fuenlabrada, Mostoles) - submarket dummy; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,-31553.56,1899.969,"b (currency per unit, as reported)",reported,-0.37324202137041507,0.02247442982982352,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.607407805074715,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,False,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,zone B2,other,other,binary,binary,-31564.53,2246.84,,,**,,,medium,"Higher transit fare zone (Fuenlabrada, Mostoles) - submarket dummy; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,-31564.53,2246.84,"b (currency per unit, as reported)",reported,-0.3221775327845841,0.022933380213857612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.048410211675062,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,False,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,zone B2,other,other,binary,binary,-28817.85,1924.455,,,**,,,medium,"Higher transit fare zone (Fuenlabrada, Mostoles) - submarket dummy; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern)",House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,-28817.85,1924.455,"b (currency per unit, as reported)",reported,-0.34101508391535773,0.022772975198230603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.974551236583864,1704.0,binary,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,False,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,ms/pci,transit_distance,D_location,other,m / EUR per-capita income,-45862.74,18787.84,,,*,,,medium,Network distance to closest Metrosur station (m) divided by municipal per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,-45862.74,18787.84,not harmonised,reported,-0.05903238205896248,0.02418283227174517,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.441086362242812,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,ms/pci,transit_distance,D_location,other,m / EUR per-capita income,-45912.37,22044.94,,,*,,,medium,Network distance to closest Metrosur station (m) divided by municipal per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,-45912.37,22044.94,not harmonised,reported,-0.050388792446290735,0.024194305503090614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.082671579056237,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,ms/pci,transit_distance,D_location,other,m / EUR per-capita income,-42145.6,18492.35,,,*,,,medium,Network distance to closest Metrosur station (m) divided by municipal per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,-42145.6,18492.35,not harmonised,reported,-0.055127008862176155,0.024188241295235165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2790829721479424,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,cer/pci,transit_distance,D_location,other,m / EUR per-capita income,-76497.57,13989.91,,,**,,,medium,Network distance to Cercanias commuter rail station (m) divided by per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,-76497.57,13989.91,not harmonised,reported,-0.1313169373727435,0.024015300555564285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.468053046803018,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,cer/pci,transit_distance,D_location,other,m / EUR per-capita income,-74584.5,16442.75,,,**,,,medium,Network distance to Cercanias commuter rail station (m) divided by per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,-74584.5,16442.75,not harmonised,reported,-0.10922776208053708,0.02408013441063158,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.536011311976403,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,cer/pci,transit_distance,D_location,other,m / EUR per-capita income,-69047.84,13819.2,,,**,,,medium,Network distance to Cercanias commuter rail station (m) divided by per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,-69047.84,13819.2,not harmonised,reported,-0.1201639161928924,0.024049545802632185,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.996514993632047,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,hosp/pci,hospital_distance,D_location,other,m / EUR per-capita income,61411.55,18107.78,,,**,,,medium,Network distance to closest hospital (m) divided by per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,61411.55,18107.78,not harmonised,reported,0.0818821518708912,0.0241437317899432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3914455554463334,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,hosp/pci,hospital_distance,D_location,other,m / EUR per-capita income,60944.1,21288.32,,,**,,,medium,Network distance to closest hospital (m) divided by per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,60944.1,21288.32,not harmonised,reported,0.06918526205759964,0.024167031721955688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.86279518534107,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,hosp/pci,hospital_distance,D_location,other,m / EUR per-capita income,47469.47,17963.27,,,**,,,medium,Network distance to closest hospital (m) divided by per-capita income; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",other,47469.47,17963.27,not harmonised,reported,0.06388606005339453,0.024175592143231017,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6425851195244516,1704.0,m / eur per-capita income,nonstandard_x_transform,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c1,2,OLS,7,pop,density,F_neighbourhood,level,population within 100 m,-0.1244867,0.04346661,,,**,,,medium,Population around 100 m (Euclidean); Table 1 says 'x10^6'; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,OLS,1714.0,0.7591,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,-0.1244867,0.04346661,"b (currency per unit, as reported)",reported,-0.06921331476356549,0.024166984582571015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.863961555778102,1704.0,population within 100 m,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,ols,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c2,2,Spatial Error Model,7,pop,density,F_neighbourhood,level,population within 100 m,-0.1474505,0.05079898,,,**,,,medium,Population around 100 m (Euclidean); Table 1 says 'x10^6'; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_error,1714.0,0.7636,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,-0.1474505,0.05079898,"b (currency per unit, as reported)",reported,-0.07014317928561246,0.02416541118318515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9026271787346913,1704.0,population within 100 m,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674,e7545aa69fd7e3b0b4fddbe8806b08519280a674:T2-c3,2,Spatial Lag Model,7,pop,density,F_neighbourhood,level,population within 100 m,-0.07174224,0.04380104,,,,,,medium,Population around 100 m (Euclidean); Table 1 says 'x10^6'; Parentheses assumed to be standard errors (table note does not say; consistent with star pattern),House sale (asking) price (EUR),level,spatial_lag,1714.0,0.765,se,"Full sample of idealista.com listings, 5 municipalities of south Madrid",monetary,-0.07174224,0.04380104,"b (currency per unit, as reported)",reported,-0.03964734500343508,0.024206031821549758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.637911793875214,1704.0,population within 100 m,,2011.0,Spain,Europe,High income,,,condo_apartment,listing,journal,False,False,unknown,,2008.0,spatial,journal,False,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Number of Bedrooms,bedrooms,A_structural,level,count,0.19084,0.01905,,,***,,,medium,,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",semi_elasticity,19.084,1.905,100*b/1 (per unit count),reported,0.4180068176221759,0.04172620978674519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.01784776902887,474.0,count,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Number of Restrooms,bathrooms,A_structural,level,count,0.22406,0.01556,,,***,,,medium,Restrooms = bathrooms/washrooms,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),semi_elasticity,22.406000000000002,1.5559999999999998,100*b/1 (per unit count),reported,0.5516565529114305,0.03831016675578798,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.39974293059126,474.0,count,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Solar,other,other,binary,binary,0.19034,0.087,,,**,,,medium,House has a solar (electricity) system,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,20.509150280814502,10.524049068080132,100*(exp(b - se^2/2) - 1),reported,0.09998615006854258,0.04570135050942105,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1878160919540233,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Solar,other,other,binary,binary,0.15393,0.07881,,,*,,,medium,House has a solar (electricity) system,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,16.279256071709302,9.192471183195979,100*(exp(b - se^2/2) - 1),reported,0.08935360824600445,0.045747793580638026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9531785306433194,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Gas,other,other,binary,binary,0.2769,0.10152,,,***,,,medium,House has piped gas supply (utility access),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,31.225475514297084,13.390837848005683,100*(exp(b - se^2/2) - 1),reported,0.12430840530553575,0.04557525932328635,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7275413711583925,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Gas,other,other,binary,binary,0.25384,0.09281,,,***,,,medium,House has piped gas supply (utility access),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,28.342611182884745,11.962889299988344,100*(exp(b - se^2/2) - 1),reported,0.12464530809602095,0.045573318012888844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7350501023596596,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Water Supply,other,other,binary,binary,0.30605,0.08358,,,***,,,medium,House has water supply (utility access),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,35.331507273291066,11.350583633120948,100*(exp(b - se^2/2) - 1),reported,0.1658606908280972,0.04529533259079355,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6617611868868147,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Water Supply,other,other,binary,binary,0.21211,0.07702,,,***,,,medium,House has water supply (utility access),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,23.262243383722357,9.52185835768885,100*(exp(b - se^2/2) - 1),reported,0.12549356924118943,0.04556840650113813,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7539600103869124,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Commercial,other,other,binary,binary,-0.32337,0.0724,,,***,,,medium,Residence located in a commercial area (vs residential area),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-27.81882296749568,5.239631683203895,100*(exp(b - se^2/2) - 1),reported,-0.2009648397615977,0.044994447223736515,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.466436464088397,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Commercial,other,other,binary,binary,-0.20353,0.06739,,,***,,,medium,Residence located in a commercial area (vs residential area),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-18.600471554564947,5.497984364925433,100*(exp(b - se^2/2) - 1),reported,-0.1374057150977008,0.045495853881167676,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.0201810357619823,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Grocery Store within 1 km,commercial_distance,D_location,binary,binary (<=1 km),0.17134,0.07043,,,**,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,18.395414585635848,8.359296022895144,100*(exp(b - se^2/2) - 1),reported,0.11104969932612374,0.04564742805847378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.432770126366605,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Grocery Store within 1 km,commercial_distance,D_location,binary,binary (<=1 km),0.12619,0.06453,,,*,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,13.213806687764906,7.320913673394182,100*(exp(b - se^2/2) - 1),reported,0.08946007558597584,0.045747354604667737,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9555245622191226,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,School within 1 km,school_distance,D_location,binary,binary (<=1 km),-0.19937,0.07551,,,***,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-18.308553209555722,6.18613195224163,100*(exp(b - se^2/2) - 1),reported,-0.12039148733627232,0.0455974379734259,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.640312541385247,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,School within 1 km,school_distance,D_location,binary,binary (<=1 km),-0.22155,0.06892,,,***,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-20.062490131792565,5.5223932088800165,100*(exp(b - se^2/2) - 1),reported,-0.14606769369331193,0.045438887155689715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.214596633778294,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,True
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,College within 1 km,university_distance,D_location,binary,binary (<=1 km),0.17704,0.11147,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,18.62857643253588,13.305938021964486,100*(exp(b - se^2/2) - 1),reported,0.07275648437230786,0.045809790516161075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5882300170449448,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,College within 1 km,university_distance,D_location,binary,binary (<=1 km),0.10853,0.1022,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,10.883241130365494,11.391603908252925,100*(exp(b - se^2/2) - 1),reported,0.048718479652541724,0.04587697982576029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0619373776908023,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,University within 1 km,university_distance,D_location,binary,binary (<=1 km),-0.06739,0.13293,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-7.3392481954451805,12.426702452239805,100*(exp(b - se^2/2) - 1),reported,-0.023279067175615138,0.045919074041467875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5069585496125781,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,University within 1 km,university_distance,D_location,binary,binary (<=1 km),-0.04698,0.12162,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-5.292378843806422,11.603843004033422,100*(exp(b - se^2/2) - 1),reported,-0.017739872529485463,0.04592429325321461,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3862851504686729,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Hospital within 1 km,hospital_distance,D_location,binary,binary (<=1 km),0.08957,0.08688,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,8.958395726925827,9.502099395495787,100*(exp(b - se^2/2) - 1),reported,0.04730066122813825,0.045880109941952116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.030962246777164,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Hospital within 1 km,hospital_distance,D_location,binary,binary (<=1 km),0.04538,0.0795,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,4.312381038550117,8.319082136924674,100*(exp(b - se^2/2) - 1),reported,0.026209514363180454,0.045915742438802254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.570817610062893,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Bus stop within1 km,transit_distance,D_location,binary,binary (<=1 km),-0.06862,0.07352,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-6.883854454547134,6.864425738166677,100*(exp(b - se^2/2) - 1),reported,-0.04283091228873309,0.045889371487433064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9333514689880305,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Bus stop within1 km,transit_distance,D_location,binary,binary (<=1 km),-0.03853,0.06703,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-3.995633429951573,6.44964562459989,100*(exp(b - se^2/2) - 1),reported,-0.02639303313534961,0.04591552066084828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5748172460092495,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Police Station within 1 km,public_services,F_neighbourhood,binary,binary (<=1 km),0.02213,0.09772,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,1.7506880831796856,9.990664963872964,100*(exp(b - se^2/2) - 1),reported,0.010401244161875706,0.045929036579236054,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2264633647155137,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Police Station within 1 km,public_services,F_neighbourhood,binary,binary (<=1 km),0.14729,0.0887,,,*,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,15.41407814854372,10.2775797180626,100*(exp(b - se^2/2) - 1),reported,0.07605029839353732,0.04579850273275009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6605411499436302,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Railway Station within 1 km,transit_distance,D_location,binary,binary (<=1 km),-0.2292,0.27549,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-23.443975749944222,21.90612410655883,100*(exp(b - se^2/2) - 1),reported,-0.03818587413239564,0.04589802122484151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.831972122400087,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,True
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Railway Station within 1 km,transit_distance,D_location,binary,binary (<=1 km),-0.28402,0.25189,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-27.075409532209537,18.96105978851637,100*(exp(b - se^2/2) - 1),reported,-0.05172102993706297,0.0458700451758566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.127555679066259,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,True
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Airport within 1 km,airport_distance,D_location,binary,binary (<=1 km),-0.08662,0.3611,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-14.085417065675864,33.11379077329409,100*(exp(b - se^2/2) - 1),reported,-0.011017299635081504,0.045928733528376016,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2398781500969261,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Airport within 1 km,airport_distance,D_location,binary,binary (<=1 km),-0.17339,0.33029,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-20.382458120799672,27.771101438875046,100*(exp(b - se^2/2) - 1),reported,-0.024105338636273215,0.045918174624688154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5249629113809077,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Post office within 1 km,public_services,F_neighbourhood,binary,binary (<=1 km),0.04187,0.11913,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,3.53857075036601,12.422386910085812,100*(exp(b - se^2/2) - 1),reported,0.016141209176986495,0.045925537359789846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.35146478636783346,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Post office within 1 km,public_services,F_neighbourhood,binary,binary (<=1 km),0.02894,0.10902,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,2.326379699191339,11.22211358195275,100*(exp(b - se^2/2) - 1),reported,0.012191886153944103,0.04592810741198985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.26545587965510914,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Bank within 1 km,neighbourhood_amenities,F_neighbourhood,binary,binary (<=1 km),0.04841,0.06972,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,4.705300981234872,7.317817499118337,100*(exp(b - se^2/2) - 1),reported,0.03187629066762934,0.04590817982538975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6943488238668961,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Bank within 1 km,neighbourhood_amenities,F_neighbourhood,binary,binary (<=1 km),0.02741,0.06369,,,,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,2.5706652320746315,6.545988841110343,100*(exp(b - se^2/2) - 1),reported,0.019763496558696237,0.04592255001179728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.43036583451091226,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Trees,green_space,E_environment,binary,binary,0.09482,0.07637,,,,,,medium,Respondent agrees there are more than 6 trees in the neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,9.625936951968693,8.39658315559723,100*(exp(b - se^2/2) - 1),reported,0.056935472770513376,0.045857013873487715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2415870106062592,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Trees,green_space,E_environment,binary,binary,0.06448,0.06997,,,,,,medium,Respondent agrees there are more than 6 trees in the neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,6.399649798866558,7.463029908887014,100*(exp(b - se^2/2) - 1),reported,0.04228976627881883,0.0458904303121736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9215378019151064,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Availability of Park within 1 km,green_space,E_environment,binary,binary (<=1 km),0.34378,0.06762,,,***,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,40.704782427528244,9.53623453091232,100*(exp(b - se^2/2) - 1),reported,0.22739812361772652,0.04472820152141098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.083998816918071,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,True
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Availability of Park within 1 km,green_space,E_environment,binary,binary (<=1 km),0.2976,0.06208,,,***,,,medium,Dummy = 1 if amenity within 1 km of residence (vs. access but > 1 km),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,34.403063642468545,8.359835775969842,100*(exp(b - se^2/2) - 1),reported,0.2150361499590412,0.044857003324789244,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.79381443298969,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,True
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Open Air Waste Dump within 1 km,hazard_site,E_environment,binary,binary (<=1 km),0.02968,0.0753,,,,,,medium,Open-air dumping site (kachra) within 1 km,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,2.7208526870294403,7.756840054659914,100*(exp(b - se^2/2) - 1),reported,0.018101250913743407,0.04592399574814281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3941567065073041,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Open Air Waste Dump within 1 km,hazard_site,E_environment,binary,binary (<=1 km),0.03656,0.06888,,,,,,medium,Open-air dumping site (kachra) within 1 km,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,3.4778891816716895,7.144485262330184,100*(exp(b - se^2/2) - 1),reported,0.024372206717829435,0.04591787742680775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5307781649245065,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,16,Open Air Burning of Waste,hazard_site,E_environment,binary,binary,-0.10674,0.06896,,,,,,medium,Respondent often witnesses open-air burning of waste in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-10.337515804310883,6.19784427098262,100*(exp(b - se^2/2) - 1),reported,-0.07091628230378887,0.04581587809321042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5478538283062646,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,16,Open Air Burning of Waste,hazard_site,E_environment,binary,binary,-0.07552,0.06306,,,,,,medium,Respondent often witnesses open-air burning of waste in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-7.458063977315721,5.84730903339726,100*(exp(b - se^2/2) - 1),reported,-0.05492408059793317,0.045862189122161896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1975895972090072,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Choked Sewer pipes,public_services,F_neighbourhood,binary,binary,-0.0482,0.06954,,,,,,medium,Respondent often witnesses choking of sewer pipes in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-4.9358164121911186,6.626766869418911,100*(exp(b - se^2/2) - 1),reported,-0.031820221785184656,0.04590826188675812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6931262582686223,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Choked Sewer pipes,public_services,F_neighbourhood,binary,binary,-0.05673,0.06356,,,,,,medium,Respondent often witnesses choking of sewer pipes in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-5.705746509460297,6.005461160527606,100*(exp(b - se^2/2) - 1),reported,-0.0409614272039856,0.04589297220316103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8925424795468848,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Poor roads,public_services,F_neighbourhood,binary,binary,0.02837,0.07313,,,,,,medium,Poor road quality in neighbourhood (perceived),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,2.602898983580837,7.523440798353258,100*(exp(b - se^2/2) - 1),reported,0.017815813483862304,0.04592423123281108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3879392862026528,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Poor roads,public_services,F_neighbourhood,binary,binary,0.0243,0.06693,,,,,,medium,Poor road quality in neighbourhood (perceived),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,2.2305312670925392,6.857632078702955,100*(exp(b - se^2/2) - 1),reported,0.01667385032502369,0.04592513589521958,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.36306588973554454,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Streetlight,public_services,F_neighbourhood,binary,binary,-0.02707,0.08168,,,,,,medium,Access to streetlights in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-2.994820422099398,7.9498581112253905,100*(exp(b - se^2/2) - 1),reported,-0.015220644550831604,0.04592620047698285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.33141527913809987,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Streetlight,public_services,F_neighbourhood,binary,binary,-0.00476,0.07479,,,,,,medium,Access to streetlights in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-0.7528291433754886,7.443484553819781,100*(exp(b - se^2/2) - 1),reported,-0.002923293312057878,0.04593132495983376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.06364487230913224,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,No garbage Collection,public_services,F_neighbourhood,binary,binary,-0.17426,0.23352,,,,,,medium,No garbage collection in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-18.251750983620717,19.617511937886466,100*(exp(b - se^2/2) - 1),reported,-0.03425543596559503,0.04590456448230088,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7462315861596437,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,True
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,No garbage Collection,public_services,F_neighbourhood,binary,binary,-0.17237,0.2137,,,,,,medium,No garbage collection in neighbourhood,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-17.733305558047917,17.986438716961977,100*(exp(b - se^2/2) - 1),reported,-0.03702287512406565,0.04590003140925237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8065980346279831,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Crime,crime,F_neighbourhood,binary,binary,-0.11143,0.06979,,,,,,medium,Respondent hears about crime incidents in neighbourhood (perceived),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-10.762186855290457,6.243092421777712,100*(exp(b - se^2/2) - 1),reported,-0.07314001119217357,0.04580850202909265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.596647084109471,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Crime,crime,F_neighbourhood,binary,binary,-0.09445,0.06385,,,,,,medium,Respondent hears about crime incidents in neighbourhood (perceived),"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-9.19795796395767,5.8095405553531645,100*(exp(b - se^2/2) - 1),reported,-0.06778783399296014,0.0458258676596136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4792482380579484,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Respect Neighbourhood,other,other,binary,binary,0.12975,0.087,,,,,,medium,Social cohesion: people in neighbourhood respect each other,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,13.424303622325118,9.905330292783646,100*(exp(b - se^2/2) - 1),reported,0.06834116499883826,0.04582413375644645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4913793103448278,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Respect Neighbourhood,other,other,binary,binary,0.12689,0.07959,,,,,,medium,Social cohesion: people in neighbourhood respect each other,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,13.170202456811575,9.035790052948492,100*(exp(b - se^2/2) - 1),reported,0.07303287574966759,0.045808862644148816,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5942957657997237,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Trust Neighbourhood,other,other,binary,binary,0.02358,0.09925,,,,,,medium,Social cohesion: trust among neighbours,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,1.8829805049785264,10.161812547397155,100*(exp(b - se^2/2) - 1),reported,0.010911846740194058,0.045928786639705704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.23758186397984885,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Trust Neighbourhood,other,other,binary,binary,-0.0076,0.09089,,,,,,medium,Social cohesion: trust among neighbours,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-1.1661962022346595,9.020185426606057,100*(exp(b - se^2/2) - 1),reported,-0.003840653390620638,0.04593118245704075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.08361755968753438,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Friendly Neighbourhood,other,other,binary,binary,-0.14998,0.08965,,,*,,,medium,Social cohesion: neighbours friendly and helpful,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-14.272674558753506,7.716401335134075,100*(exp(b - se^2/2) - 1),reported,-0.07661529804660275,0.045796516001319745,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6729503625209148,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Friendly Neighbourhood,other,other,binary,binary,-0.10355,0.08213,,,,,,medium,Social cohesion: neighbours friendly and helpful,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-10.140483993995053,7.405094910776796,100*(exp(b - se^2/2) - 1),reported,-0.05781387662241197,0.04585469519071651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2608060392061367,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Hear Birds often,other,other,binary,binary,-0.2209,0.08997,,,**,,,medium,Natural soundscape: respondent often hears birds chirping at home,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-20.14416986863705,7.21376631313457,100*(exp(b - se^2/2) - 1),reported,-0.1120635993353996,0.04564220023633273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.455262865399578,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Hear Birds often,other,other,binary,binary,-0.16602,0.08251,,,**,,,medium,Natural soundscape: respondent often hears birds chirping at home,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-15.584905032317376,6.9888387001249574,100*(exp(b - se^2/2) - 1),reported,-0.09202753413635217,0.04573660909282265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0121197430614473,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,False,False,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Hear Traffic often,noise,E_environment,binary,binary,0.0363,0.06815,,,,,,medium,Perceived traffic noise: often hears traffic at home,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,3.4561627565970587,7.066929354662022,100*(exp(b - se^2/2) - 1),reported,0.024458040432276246,0.04591778114213847,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.532648569332355,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Hear Traffic often,noise,E_environment,binary,binary,0.02107,0.06227,,,,,,medium,Perceived traffic noise: often hears traffic at home,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,1.9315398026778619,6.359594871607259,100*(exp(b - se^2/2) - 1),reported,0.015539750983061063,0.04592597502207938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3383651838766661,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Hear Construction often,noise,E_environment,binary,binary,0.04938,0.06699,,,,,,medium,Perceived construction noise: often hears construction at home,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,4.826474151893989,7.038100099558813,100*(exp(b - se^2/2) - 1),reported,0.033837881130267006,0.04590521783954205,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7371249440214959,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Hear Construction often,noise,E_environment,binary,binary,0.0364,0.06132,,,,,,medium,Perceived construction noise: often hears construction at home,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,3.51226569229679,6.3593168687523205,100*(exp(b - se^2/2) - 1),reported,0.027255157763913167,0.04591445807920756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5936073059360731,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c1,3 (Appendix 1),1,17,Chowks,noise,E_environment,binary,binary,-0.12687,0.0691,,,*,,,medium,Noisy places/intersections (chowks) near residence,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.4625,se,"Model 1: number of bedrooms as structural size variable; full results (abridged duplicate in Table 2 p.11-12, which prints R-squared 0.4572)",pct_premium,-12.125225763128055,6.086660892132275,100*(exp(b - se^2/2) - 1),reported,-0.0840335799719065,0.04576905790225221,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8360347322720698,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f,e8f48e7e91bb043c222f2cfbe026c67e0e422a5f:T3-c2,3 (Appendix 1),2,17,Chowks,noise,E_environment,binary,binary,-0.11883,0.06324,,,*,,,medium,Noisy places/intersections (chowks) near residence,"Log (house value), self-reported approximate market value of the dwelling in PKR",log,OLS,484.0,0.5498,se,Model 2: number of restrooms replaces number of bedrooms; full results (abridged duplicate in Table 2 p.11-12),pct_premium,-11.38150906019918,5.615451077563088,100*(exp(b - se^2/2) - 1),reported,-0.08598715084089546,0.04576140216425338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.879032258064516,474.0,binary,,2026.0,Pakistan,Asia,Lower middle income,,,unknown,appraisal,journal,False,False,unknown,,2023.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,AREA,living_area,A_structural,level,m2,984.173,,40.962,,***,,,high,House size (m2) per Table IV,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,984.173,24.02648796445486,"b (currency per unit, as reported)",|b/t|,0.6344116835987579,0.015487810253375272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.962,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,AREA,living_area,A_structural,level,m2,0.008,,36.788,,***,,,high,House size (m2) per Table IV,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,semi_elasticity,0.8,0.021746221593998045,100*b/1 (X per m2->m2),|b/t|,0.5933272919223892,0.0161282834598888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),36.788,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,APP,dwelling_type,A_structural,binary,binary,26783.922,,11.888,,***,,,high,Apartment,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,26783.922,2253.0217025572006,"b (currency per unit, as reported)",|b/t|,0.23170696028032634,0.01949082774901803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.888,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,APP,dwelling_type,A_structural,binary,binary,0.232,,11.815,,***,,,high,Apartment,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,26.087662433870527,2.4763417442926148,100*(exp(b - se^2/2) - 1),|b/t|,0.2303598540957193,0.019497236910344416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.815,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,DET,dwelling_type,A_structural,binary,binary,44882.795,,20.154,,***,,,high,Detached,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,44882.795,2226.9919122754786,"b (currency per unit, as reported)",|b/t|,0.37443224036696626,0.018578557128459176,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.154,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,DET,dwelling_type,A_structural,binary,binary,0.349,,18.017,,***,,,high,Detached,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,41.738325017507314,2.746070752230411,100*(exp(b - se^2/2) - 1),|b/t|,0.339543993715126,0.018845756436428156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.017,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,SDT,dwelling_type,A_structural,binary,binary,12213.843,,9.105,,***,,,high,Semi-detached,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,12213.843,1341.443492586491,"b (currency per unit, as reported)",|b/t|,0.17946676262821623,0.01971079216125384,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.105,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,SDT,dwelling_type,A_structural,binary,binary,0.165,,14.084,,***,,,high,Semi-detached,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,17.931218498297554,1.3817087800864774,100*(exp(b - se^2/2) - 1),|b/t|,0.27158240009183343,0.01928304459612563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.084,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,PRE1919,age,A_structural,binary,binary,-12068.371,,-8.082,,***,,,high,Built before 1919 (age cohort dummy),"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-12068.371,1493.2406582529075,"b (currency per unit, as reported)",|b/t|,-0.15984952333928296,0.01977846119021071,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.082,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,PRE1919,age,A_structural,binary,binary,-0.082,,-6.332,,***,,,high,Built before 1919 (age cohort dummy),ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-7.88052890007247,1.1930559161892074,100*(exp(b - se^2/2) - 1),|b/t|,-0.12585971668813212,0.019876771428953274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.332,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,POST1980,age,A_structural,binary,binary,4313.548,,1.91,,*,,,high,Built after 1980,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,4313.548,2258.402094240838,"b (currency per unit, as reported)",|b/t|,0.03824095428271073,0.020021442032832844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.91,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,POST1980,age,A_structural,binary,binary,0.092,,4.663,,***,,,high,Built after 1980,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,9.615145488119081,2.163104517079887,100*(exp(b - se^2/2) - 1),|b/t|,0.09302321182538091,0.019949219778121575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.663,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,POSTWAR,age,A_structural,binary,binary,-5143.561,,-3.402,,***,,,high,Post-war cohort,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-5143.561,1511.9226925338035,"b (currency per unit, as reported)",|b/t|,-0.06800500541311398,0.019989713525312754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.402,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,POSTWAR,age,A_structural,binary,binary,0.0,,0.033,,,,,high,Post-war cohort,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.0,0.0,100*(exp(b - se^2/2) - 1),|b/t|,0.0006611910727255107,0.020036093112894265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.033,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,EARLYMODER,age,A_structural,binary,binary,-5168.802,,-2.731,,***,,,high,Early modern cohort,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-5168.802,1892.6407909190773,"b (currency per unit, as reported)",|b/t|,-0.05463684865720067,0.020006169409447334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.731,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,EARLYMODER,age,A_structural,binary,binary,0.008,,0.47,,,,,high,Early modern cohort,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.7886070608658313,1.715799294475359,100*(exp(b - se^2/2) - 1),|b/t|,0.009416548304542775,0.020035209158601647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.47,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,ELECHEAT,heating,A_structural,binary,binary,-1801.579,,-0.956,,,,,high,Electric heating,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-1801.579,1884.4968619246863,"b (currency per unit, as reported)",|b/t|,-0.01915099632050502,0.020032422929398556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.956,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,ELECHEAT,heating,A_structural,binary,binary,-0.024,,-1.453,,,,,high,Electric heating,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.3847460907523876,1.6125847924425207,100*(exp(b - se^2/2) - 1),|b/t|,-0.029100120587059394,0.020027612241610045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.453,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,GASHEAT,heating,A_structural,binary,binary,-235.123,,-0.224,,,,,high,Gas heating,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-235.123,1049.65625,"b (currency per unit, as reported)",|b/t|,-0.0044880406374430415,0.02003589570287072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.224,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,GASHEAT,heating,A_structural,binary,binary,-0.004,,-0.485,,,,,high,Gas heating,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-0.4025884302174543,0.8214498881187559,100*(exp(b - se^2/2) - 1),|b/t|,-0.009717048504511726,0.020035151555694283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.485,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,SOLIDHEAT,heating,A_structural,binary,binary,-20.006,,-0.012,,,,,high,Solid-fuel heating,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-20.006,1667.1666666666667,"b (currency per unit, as reported)",|b/t|,-0.00024043316296076516,0.020036096913397094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.012,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,SOLIDHEAT,heating,A_structural,binary,binary,0.002,,0.144,,,,,high,Solid-fuel heating,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.19053622829747763,1.3916694462972228,100*(exp(b - se^2/2) - 1),|b/t|,0.002885186030273357,0.020036014099120536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.144,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,SOC,other,other,binary,binary,-11022.089,,-6.185,,***,,,high,Social (public) build type,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-11022.089,1782.06774454325,"b (currency per unit, as reported)",|b/t|,-0.1229825409813483,0.019884000158665854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.185,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,SOC,other,other,binary,binary,-0.138,,-8.92,,***,,,high,Social (public) build type,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-12.900554956008015,1.3476638953017959,100*(exp(b - se^2/2) - 1),|b/t|,-0.17593426909299703,0.01972357276827321,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.92,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,NOGAR,garage,A_structural,binary,binary,-1338.239,,-1.198,,,,,high,No garage (sign reversed relative to garage presence),"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-1338.239,1117.0609348914859,"b (currency per unit, as reported)",|b/t|,-0.02399633297872712,0.02003032802898758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.198,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,NOGAR,garage,A_structural,binary,binary,-0.018,,-1.837,,*,,,high,No garage (sign reversed relative to garage presence),ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-1.788611626072989,0.9623788014398156,100*(exp(b - se^2/2) - 1),|b/t|,-0.03678140555733749,0.02002253976991698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.837,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Air Q Low,air_pollution,E_environment,binary,NO2 band (ug/m3),-5459.501,,-2.477,,**,,,high,NO2 band 24.47-29.66 ug/m3 = low air quality,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-5459.501,2204.0779168348813,"b (currency per unit, as reported)",|b/t|,-0.049568405591097646,0.020011467739643782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.477,2491.0,no2 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Air Q Low,air_pollution,E_environment,binary,NO2 band (ug/m3),-0.066,,-3.425,,***,,,high,NO2 band 24.47-29.66 ug/m3 = low air quality,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-6.404292898501329,1.803931008561952,100*(exp(b - se^2/2) - 1),|b/t|,-0.06846262105507887,0.019989086439439085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.425,2491.0,no2 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Air Q High,air_pollution,E_environment,binary,NO2 band (ug/m3),4677.732,,1.957,,**,,,high,NO2 band 12.98-18.54 ug/m3 = high air quality; stars as printed,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,4677.732,2390.256515074093,"b (currency per unit, as reported)",|b/t|,0.03918053482422296,0.020020712735934063,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.957,2491.0,no2 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Air Q High,air_pollution,E_environment,binary,NO2 band (ug/m3),0.05,,2.424,,**,,,high,NO2 band 12.98-18.54 ug/m3 = high air quality; stars as printed,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,5.104747500207174,2.1684634826238125,100*(exp(b - se^2/2) - 1),|b/t|,0.04851032089764824,0.020012508621142018,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.424,2491.0,no2 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Air Q Very High,air_pollution,E_environment,binary,NO2 band (ug/m3),29829.619,,4.988,,***,,,high,NO2 band 7.40-12.97 ug/m3 = very high air quality,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,29829.619,5980.2764635124295,"b (currency per unit, as reported)",|b/t|,0.09944466086347771,0.019936780445765377,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.988,2491.0,no2 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Air Q Very High,air_pollution,E_environment,binary,NO2 band (ug/m3),0.349,,6.704,,***,,,high,NO2 band 7.40-12.97 ug/m3 = very high air quality,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,41.57295166608226,7.38006514661923,100*(exp(b - se^2/2) - 1),|b/t|,0.13312640644143678,0.019857757524080668,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.704,2491.0,no2 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,True
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,PM8_9,air_pollution,E_environment,binary,PM2.5 band (ug/m3),6568.69,,2.602,,***,,,high,PM2.5 8.01-9.00 ug/m3,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,6568.69,2524.477325134512,"b (currency per unit, as reported)",|b/t|,0.05206322116847016,0.020008924353754867,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.602,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,PM8_9,air_pollution,E_environment,binary,PM2.5 band (ug/m3),0.061,,2.769,,***,,,high,PM2.5 8.01-9.00 ug/m3,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,6.26410309957941,2.341525235298624,100*(exp(b - se^2/2) - 1),|b/t|,0.05539476621323581,0.020005332688059155,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.769,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,PM10_11,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-2325.647,,-1.243,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-2325.647,1870.995172968624,"b (currency per unit, as reported)",|b/t|,-0.02489714912051088,0.0200298866617143,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.243,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,PM10_11,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-0.023,,-1.419,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.2865880329143384,1.5840054352914286,100*(exp(b - se^2/2) - 1),|b/t|,-0.028419738336025745,0.020028004465134423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.419,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,PM11_12,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-11669.558,,-4.199,,***,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-11669.558,2779.127887592284,"b (currency per unit, as reported)",|b/t|,-0.08383539796617245,0.01996556274498034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.199,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,PM11_12,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-0.098,,-4.061,,***,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-9.361505258186165,2.1879239735997107,100*(exp(b - se^2/2) - 1),|b/t|,-0.08109857731549099,0.019970100299308294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.061,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,PM12_13,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-11885.036,,-1.452,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-11885.036,8185.286501377411,"b (currency per unit, as reported)",|b/t|,-0.029080109917051167,0.020027623909814853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.452,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,PM12_13,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-0.101,,-1.982,,**,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-9.723986390343098,4.606318684231699,100*(exp(b - se^2/2) - 1),|b/t|,-0.03968026952789605,0.020020317622550986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.982,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,PM13_14,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-13055.665,,-1.386,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-13055.665,9419.67171717172,"b (currency per unit, as reported)",|b/t|,-0.027759329542108663,0.02002837629300769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.386,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,PM13_14,air_pollution,E_environment,binary,PM2.5 band (ug/m3),-0.118,,-2.434,,**,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-11.234768411544149,4.308386779314907,100*(exp(b - se^2/2) - 1),|b/t|,-0.048709972127899584,0.020012313939153488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.434,2491.0,pm2.5 band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,True
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,AQMA Within300,air_pollution,E_environment,binary,binary,-773.144,,-0.192,,,,,high,Within 300 m of Air Quality Management Area,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-773.144,4026.7916666666665,"b (currency per unit, as reported)",|b/t|,-0.003846902253754815,0.02003594923830633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.192,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,AQMA Within300,air_pollution,E_environment,binary,binary,0.013,,0.372,,,,,high,Within 300 m of Air Quality Management Area,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,1.2466446586055335,3.540350342924064,100*(exp(b - se^2/2) - 1),|b/t|,0.00745322124348369,0.020035540977106695,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.372,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,AQMA Inside,air_pollution,E_environment,binary,binary,2624.339,,1.527,,,,,high,Inside Air Quality Management Area,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,2624.339,1718.6240995415849,"b (currency per unit, as reported)",|b/t|,0.03058081146000196,0.020026726561887333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.527,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,AQMA Inside,air_pollution,E_environment,binary,binary,0.037,,2.489,,**,,,high,Inside Air Quality Management Area,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,3.757837228914962,1.542572992005296,100*(exp(b - se^2/2) - 1),|b/t|,0.04980794889550509,0.020011228965650902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.489,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road Noise V Low,noise,E_environment,binary,dB band,-128.152,,-0.096,,,,,high,Road noise LAeq band,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-128.152,1334.9166666666665,"b (currency per unit, as reported)",|b/t|,-0.001923461801151283,0.020036060428659196,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.096,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road Noise V Low,noise,E_environment,binary,dB band,-0.012,,-1.043,,,,,high,Road noise LAeq band,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-1.1993681148741664,1.1368035047308884,100*(exp(b - se^2/2) - 1),|b/t|,-0.020893088054022586,0.02003172392523738,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.043,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road Noise Low,noise,E_environment,binary,dB band,2914.37,,1.394,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,2914.37,2090.6527977044475,"b (currency per unit, as reported)",|b/t|,0.0279194320144541,0.020028286954414706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.394,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road Noise Low,noise,E_environment,binary,dB band,0.026,,1.456,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,2.6177322973832906,1.832751693702575,100*(exp(b - se^2/2) - 1),|b/t|,0.029160152387066273,0.020027577188919145,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.456,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road Noise Moderate,noise,E_environment,binary,dB band,-1833.483,,-0.621,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-1833.483,2952.468599033816,"b (currency per unit, as reported)",|b/t|,-0.012441453526216539,0.020034546741089433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.621,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road Noise Moderate,noise,E_environment,binary,dB band,-0.002,,-0.066,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-0.24561156228387615,3.0242484808101002,100*(exp(b - se^2/2) - 1),|b/t|,-0.0013223812782859612,0.020036079974029713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.066,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road Noise High,noise,E_environment,binary,dB band,3667.62,,0.9,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,3667.62,4075.133333333333,"b (currency per unit, as reported)",|b/t|,0.01802955664052375,0.020032840711693055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road Noise High,noise,E_environment,binary,dB band,0.002,,0.05,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.12007202880863765,4.008008005336001,100*(exp(b - se^2/2) - 1),|b/t|,0.0010018043719142535,0.02003608743828507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road Noise V High,noise,E_environment,binary,dB band,918.18,,0.187,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,918.18,4910.053475935829,"b (currency per unit, as reported)",|b/t|,0.003746723932681335,0.020035956859258477,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.187,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road Noise V High,noise,E_environment,binary,dB band,0.037,,0.863,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,3.673973926740204,4.448973553999052,100*(exp(b - se^2/2) - 1),|b/t|,0.017288567827116456,0.020033102928292534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.863,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road Noise Extreme,noise,E_environment,binary,dB band,1290.596,,0.204,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,1290.596,6326.450980392157,"b (currency per unit, as reported)",|b/t|,0.004087329746040774,0.020035930127650856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.204,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road Noise Extreme,noise,E_environment,binary,dB band,0.008,,0.151,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.6618357957040022,5.340567340419992,100*(exp(b - se^2/2) - 1),|b/t|,0.0030254368749579987,0.02003600579442383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.151,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_25,highway_distance,D_location,binary,m band,-4728.319,,-1.285,,,,,high,Within 25 m of main arterial road,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-4728.319,3679.625680933853,"b (currency per unit, as reported)",|b/t|,-0.025737856182945388,0.02002946006454894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.285,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_25,highway_distance,D_location,binary,m band,-0.023,,-0.711,,,,,high,Within 25 m of main arterial road,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.324870827594361,3.161327303345341,100*(exp(b - se^2/2) - 1),|b/t|,-0.014244220036760095,0.020034064749310965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.711,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_50,highway_distance,D_location,binary,m band,-2478.698,,-0.923,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-2478.698,2685.479956663055,"b (currency per unit, as reported)",|b/t|,-0.018490156413164528,0.02003267217027576,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.923,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_50,highway_distance,D_location,binary,m band,-0.021,,-0.886,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.1056052704021666,2.3209478844197124,100*(exp(b - se^2/2) - 1),|b/t|,-0.01774918592264631,0.02003294122194843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.886,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_75,highway_distance,D_location,binary,m band,-2109.971,,-0.913,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-2109.971,2311.0306681270536,"b (currency per unit, as reported)",|b/t|,-0.018289897071695097,0.020032745971188495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.913,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_75,highway_distance,D_location,binary,m band,-0.021,,-1.029,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.0984933184008625,1.9984060501417544,100*(exp(b - se^2/2) - 1),|b/t|,-0.020612763886107152,0.020031840511280034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.029,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_100,highway_distance,D_location,binary,m band,1617.957,,0.846,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,1617.957,1912.4787234042556,"b (currency per unit, as reported)",|b/t|,0.016948103882538874,0.02003321971931309,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.846,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_100,highway_distance,D_location,binary,m band,0.017,,1.025,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,1.700543664566534,1.6869727297101555,100*(exp(b - se^2/2) - 1),|b/t|,0.020532670370887,0.020031873532572686,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.025,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_125,highway_distance,D_location,binary,m band,-1048.081,,-0.575,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-1048.081,1822.7495652173914,"b (currency per unit, as reported)",|b/t|,-0.01151999157188143,0.02003476795109814,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.575,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_125,highway_distance,D_location,binary,m band,0.005,,0.289,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.48621189667577003,1.7387759876460225,100*(exp(b - se^2/2) - 1),|b/t|,0.005790335103776199,0.020035761604761938,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.289,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_150,highway_distance,D_location,binary,m band,2384.342,,1.316,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,2384.342,1811.8100303951367,"b (currency per unit, as reported)",|b/t|,0.02635834313532931,0.020029136121070903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.316,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_150,highway_distance,D_location,binary,m band,0.017,,1.074,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,1.701790893019317,1.6100065623397664,100*(exp(b - se^2/2) - 1),|b/t|,0.02151378822397252,0.02003146017129657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.074,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_175,highway_distance,D_location,binary,m band,970.0,,0.423,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,970.0,2293.144208037825,"b (currency per unit, as reported)",|b/t|,0.008474964865639638,0.020035377932954228,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.423,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_175,highway_distance,D_location,binary,m band,0.004,,0.204,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.3815024995922123,1.9686431581908668,100*(exp(b - se^2/2) - 1),|b/t|,0.004087329746040774,0.020035930127650856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.204,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_200,highway_distance,D_location,binary,m band,-2612.83,,-1.313,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-2612.83,1989.96953541508,"b (currency per unit, as reported)",|b/t|,-0.026298297330886428,0.020029167807225003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.313,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_200,highway_distance,D_location,binary,m band,-0.007,,-0.407,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-0.7122417595751052,1.7079044473053182,100*(exp(b - se^2/2) - 1),|b/t|,-0.00815442055357212,0.020035431335558036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.407,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_225,highway_distance,D_location,binary,m band,7514.066,,3.218,,***,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,7514.066,2335.011187072716,"b (currency per unit, as reported)",|b/t|,0.06434255878734632,0.01999458010793857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.218,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_225,highway_distance,D_location,binary,m band,0.055,,2.685,,***,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,5.631897503200212,2.1642358959833214,100*(exp(b - se^2/2) - 1),|b/t|,0.0537192432622232,0.020007166950548678,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.685,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),45,Road_250,highway_distance,D_location,binary,m band,4800.88,,2.338,,**,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,4800.88,2053.4131736526947,"b (currency per unit, as reported)",|b/t|,0.046793082762168904,0.020014150026590634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.338,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),45,Road_250,highway_distance,D_location,binary,m band,0.037,,2.082,,**,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,3.7529170776745113,1.8441230437565714,100*(exp(b - se^2/2) - 1),|b/t|,0.04167890688040453,0.020018687262442138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.082,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_25,transit_distance,D_location,binary,m band,915.277,,0.197,,,,,high,"Within 25 m of railway line (line, not station)","Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,915.277,4646.076142131979,"b (currency per unit, as reported)",|b/t|,0.003947080459007691,0.020035941416282694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.197,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_25,transit_distance,D_location,binary,m band,0.073,,1.795,,*,,,high,"Within 25 m of railway line (line, not station)",ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,7.484131377558989,4.3748372810458696,100*(exp(b - se^2/2) - 1),|b/t|,0.03594155791056064,0.02002315203930955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.795,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail Noise Low,noise,E_environment,binary,dB band,-27487.45,,-2.415,,***,,,high,Rail noise LAeq band,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-27487.45,11381.966873706004,"b (currency per unit, as reported)",|b/t|,-0.04833062981808718,0.020012683154487447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.415,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail Noise Low,noise,E_environment,binary,dB band,-0.441,,-4.16,,***,,,high,Rail noise LAeq band,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-36.021239140501805,6.82058133650658,100*(exp(b - se^2/2) - 1),|b/t|,-0.08306213822847534,0.019966860151075804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.16,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,True
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail Noise Mod,noise,E_environment,binary,dB band,-5678.95,,-0.46,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-5678.95,12345.543478260868,"b (currency per unit, as reported)",|b/t|,-0.009216213415538986,0.020035246555519533,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.46,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail Noise Mod,noise,E_environment,binary,dB band,0.004,,0.032,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-0.38052416489593455,12.550100133466774,100*(exp(b - se^2/2) - 1),|b/t|,0.0006411549879777422,0.020036093374304443,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.032,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail Noise High,noise,E_environment,binary,dB band,-24811.37,,-2.179,,**,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-24811.37,11386.585589720056,"b (currency per unit, as reported)",|b/t|,-0.043617107415120725,0.020017029561780964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.179,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail Noise High,noise,E_environment,binary,dB band,-0.365,,-3.445,,***,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-30.968880129349298,7.35505885545609,100*(exp(b - se^2/2) - 1),|b/t|,-0.06886051258751964,0.01998853776125389,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.445,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail Noise V high,noise,E_environment,binary,dB band,19069.76,,1.67,,*,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,19069.76,11419.017964071856,"b (currency per unit, as reported)",|b/t|,0.03344156761824591,0.02002489078936881,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.67,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail Noise V high,noise,E_environment,binary,dB band,0.151,,1.425,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,15.648556233638855,12.323683885638937,100*(exp(b - se^2/2) - 1),|b/t|,0.02853980868936829,0.020027935922363713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.425,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,True
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_50,transit_distance,D_location,binary,m band,-11206.596,,-3.087,,***,,,high,Distance band to railway line,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-11206.596,3630.2546161321666,"b (currency per unit, as reported)",|b/t|,-0.061733461920678905,0.019997882060472597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.087,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_50,transit_distance,D_location,binary,m band,-0.041,,-1.302,,,,,high,Distance band to railway line,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-4.064664548841501,3.0225034045975234,100*(exp(b - se^2/2) - 1),|b/t|,-0.026078126950821692,0.02002928337236689,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.302,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_75,transit_distance,D_location,binary,m band,-5504.543,,-1.718,,*,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-5504.543,3204.0413271245634,"b (currency per unit, as reported)",|b/t|,-0.034401640701158795,0.02002423789357322,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.718,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_75,transit_distance,D_location,binary,m band,-0.013,,-0.452,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-1.3324037912763265,2.8389587954123314,100*(exp(b - se^2/2) - 1),|b/t|,-0.009055944704156422,0.020035275894151376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.452,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_100,transit_distance,D_location,binary,m band,-17789.479,,-3.683,,***,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-17789.479,4830.15992397502,"b (currency per unit, as reported)",|b/t|,-0.07359284788859606,0.01998176700749282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.683,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_100,transit_distance,D_location,binary,m band,-0.103,,-2.459,,**,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-9.86640777015334,3.778734374485408,100*(exp(b - se^2/2) - 1),|b/t|,-0.04920907460819926,0.02001182375282605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.459,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_125,transit_distance,D_location,binary,m band,-12567.617,,-1.939,,**,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-12567.617,6481.494069107787,"b (currency per unit, as reported)",|b/t|,-0.038820707610885796,0.02002099412629489,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.939,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_125,transit_distance,D_location,binary,m band,-0.045,,-0.801,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-4.550997233384379,5.370772369848876,100*(exp(b - se^2/2) - 1),|b/t|,-0.016046847650195716,0.020033517665662564,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.801,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_150,transit_distance,D_location,binary,m band,3099.302,,0.584,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,3099.302,5307.02397260274,"b (currency per unit, as reported)",|b/t|,0.01170027998940355,0.020034726009252653,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.584,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_150,transit_distance,D_location,binary,m band,0.065,,1.4,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,6.600945347265275,4.954666898926609,100*(exp(b - se^2/2) - 1),|b/t|,0.02803950745936232,0.02002821961383023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_175,transit_distance,D_location,binary,m band,-373.185,,-0.075,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-373.185,4975.8,"b (currency per unit, as reported)",|b/t|,-0.0015027056152883088,0.020036074870510782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.075,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_175,transit_distance,D_location,binary,m band,0.011,,0.243,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,1.002534761583096,4.5768180851407045,100*(exp(b - se^2/2) - 1),|b/t|,0.004868713984743679,0.020035860019521314,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.243,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_200,transit_distance,D_location,binary,m band,-7930.388,,-1.569,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-7930.388,5054.421924792862,"b (currency per unit, as reported)",|b/t|,-0.03142111465078692,0.020026204366339655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.569,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_200,transit_distance,D_location,binary,m band,-0.103,,-2.33,,**,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-9.875405130293757,3.9879432733303086,100*(exp(b - se^2/2) - 1),|b/t|,-0.04663331835120124,0.020014299721545597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.33,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_225,transit_distance,D_location,binary,m band,-7541.898,,-1.668,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-7541.898,4521.521582733813,"b (currency per unit, as reported)",|b/t|,-0.033401562545539845,0.02002491759324931,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.668,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_225,transit_distance,D_location,binary,m band,-0.036,,-0.925,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-3.6089991709465585,3.7542757367991815,100*(exp(b - se^2/2) - 1),|b/t|,-0.018530208015191053,0.020032657313720056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.925,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Rail_250,transit_distance,D_location,binary,m band,-5447.877,,-1.121,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-5447.877,4859.836752899198,"b (currency per unit, as reported)",|b/t|,-0.02245480208817438,0.020031045573750562,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.121,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Rail_250,transit_distance,D_location,binary,m band,-0.027,,-0.635,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.751824229353439,4.138701341914503,100*(exp(b - se^2/2) - 1),|b/t|,-0.012721892285623393,0.0200344760403518,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.635,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport Noise Low,noise,E_environment,binary,dB band,-3105.489,,-1.517,,,,,high,Airport noise LAeq band,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-3105.489,2047.125247198418,"b (currency per unit, as reported)",|b/t|,-0.030380729647490327,0.02002684881179323,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.517,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport Noise Low,noise,E_environment,binary,dB band,-0.01,,-0.557,,,,,high,Airport noise LAeq band,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-1.0109710690844098,1.7774682832121507,100*(exp(b - se^2/2) - 1),|b/t|,-0.01115941138393931,0.02003484988139912,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.557,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport Noise Mod,noise,E_environment,binary,dB band,-4698.304,,-1.221,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-4698.304,3847.9148239148235,"b (currency per unit, as reported)",|b/t|,-0.024456757558819934,0.020030104470777996,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.221,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport Noise Mod,noise,E_environment,binary,dB band,-0.032,,-0.959,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-3.203244978626807,3.231721650316405,100*(exp(b - se^2/2) - 1),|b/t|,-0.019211071444158057,0.020032399837495368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.959,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport Noise High,noise,E_environment,binary,dB band,-6349.414,,-1.024,,,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-6349.414,6200.599609375,"b (currency per unit, as reported)",|b/t|,-0.02051264693025338,0.02003188176782557,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.024,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport Noise High,noise,E_environment,binary,dB band,-0.079,,-1.454,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-7.732298070692201,5.0205745551005405,100*(exp(b - se^2/2) - 1),|b/t|,-0.029120131222080636,0.02002760056539246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.454,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport400_1000,airport_distance,D_location,binary,m band,-16552.49,,-2.385,,**,,,high,400-1000 m from city airport,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-16552.49,6940.247379454928,"b (currency per unit, as reported)",|b/t|,-0.04773162575818956,0.020013260275970467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.385,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport400_1000,airport_distance,D_location,binary,m band,-0.056,,-0.931,,,,,high,400-1000 m from city airport,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-5.616983017028076,5.687453448964789,100*(exp(b - se^2/2) - 1),|b/t|,-0.018650362285270068,0.020032612551310492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.931,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport1001_1500,airport_distance,D_location,binary,m band,-10388.389,,-1.807,,*,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-10388.389,5748.97011621472,"b (currency per unit, as reported)",|b/t|,-0.0361815222297939,0.02002297854443492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.807,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport1001_1500,airport_distance,D_location,binary,m band,-0.026,,-0.527,,,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-2.6849968288986115,4.806966286494778,100*(exp(b - se^2/2) - 1),|b/t|,-0.010558434799311978,0.020034980643855744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.527,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport1501_2000,airport_distance,D_location,binary,m band,-9955.022,,-2.219,,**,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-9955.022,4486.26498422713,"b (currency per unit, as reported)",|b/t|,-0.04441622322236719,0.020016324120039293,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.219,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport1501_2000,airport_distance,D_location,binary,m band,-0.086,,-2.196,,**,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-8.310914293131777,3.593492890935017,100*(exp(b - se^2/2) - 1),|b/t|,-0.04395674195205095,0.020016731307855622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.196,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Airport2001_2500,airport_distance,D_location,binary,m band,-10345.695,,-3.125,,***,,,high,,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-10345.695,3310.6223999999997,"b (currency per unit, as reported)",|b/t|,-0.0624904318851203,0.019996938203238498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.125,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Airport2001_2500,airport_distance,D_location,binary,m band,-0.076,,-2.637,,***,,,high,,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-7.356863358369859,2.6711426506830884,100*(exp(b - se^2/2) - 1),|b/t|,-0.05276159690316847,0.0200081899519031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.637,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Industry Noise Base,noise,E_environment,binary,dB band,-12765.213,,-1.786,,*,,,high,Industrial noise base band,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-12765.213,7147.375699888018,"b (currency per unit, as reported)",|b/t|,-0.035761580588940564,0.020023281404781948,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.786,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Industry Noise Base,noise,E_environment,binary,dB band,-0.149,,-2.389,,**,,,high,Industrial noise base band,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-14.010497235584785,5.373536966092289,100*(exp(b - se^2/2) - 1),|b/t|,-0.0478114959557626,0.020013183740377815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.389,2491.0,db band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Industry Distance400,hazard_site,E_environment,binary,m band,-7160.737,,-3.119,,***,,,high,Within 400 m of industrially zoned area,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,-7160.737,2295.843860211606,"b (currency per unit, as reported)",|b/t|,-0.06237091744738403,0.019997087992107736,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.119,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Industry Distance400,hazard_site,E_environment,binary,m band,-0.084,,-4.216,,***,,,high,Within 400 m of industrially zoned area,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,-8.075121897358184,1.8318839068308934,100*(exp(b - se^2/2) - 1),|b/t|,-0.08417241259638966,0.019964993500092423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.216,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c1,V,1 (Linear),46,Industry Distance200,hazard_site,E_environment,binary,m band,3415.499,,0.851,,,,,high,Within 200 m of industrially zoned area,"Price (GBP, time adjusted)",level,OLS,2501.0,0.809,t,Full sample; t-statistics printed in adjacent column (not in parentheses),monetary,3415.499,4013.5123384253816,"b (currency per unit, as reported)",|b/t|,0.017048240954107913,0.020033185609997547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.851,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TV-c2,V,2 (Log-linear),46,Industry Distance200,hazard_site,E_environment,binary,m band,0.006,,0.166,,,,,high,Within 200 m of industrially zoned area,ln(Price),log,OLS,2501.0,0.815,t,Full sample; t-statistics printed in adjacent column,pct_premium,0.5361104292581187,3.636209768870114,100*(exp(b - se^2/2) - 1),|b/t|,0.0033259737874759107,0.020035986671541633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.166,2491.0,m band,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVII-c1,VII,1 (Linear),48,Mod. flight noise,noise,E_environment,binary,binary,-1215.8,,,,**,,,medium,"No SE or t printed; stars only (***99%, **95%, *90%)","Price (GBP, time adjusted)",level,OLS,2501.0,,none,Flight-path specification (only flight-path coefficients reported),monetary,-1215.8,,"b (currency per unit, as reported)",,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVII-c2,VII,2 (Log-linear),48,Mod. flight noise,noise,E_environment,binary,binary,-0.04,,,,**,,,medium,No SE or t printed; stars only,ln(Price),log,OLS,2501.0,,none,Flight-path specification,pct_premium,-3.9210560847676823,,100*(exp(b) - 1),,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVII-c1,VII,1 (Linear),48,High flight noise,noise,E_environment,binary,binary,-3854.2,,,,**,,,medium,"No SE or t printed; stars only (***99%, **95%, *90%)","Price (GBP, time adjusted)",level,OLS,2501.0,,none,Flight-path specification (only flight-path coefficients reported),monetary,-3854.2,,"b (currency per unit, as reported)",,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVII-c2,VII,2 (Log-linear),48,High flight noise,noise,E_environment,binary,binary,-0.075,,,,**,,,medium,No SE or t printed; stars only,ln(Price),log,OLS,2501.0,,none,Flight-path specification,pct_premium,-7.225651367144714,,100*(exp(b) - 1),,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVII-c1,VII,1 (Linear),48,Under flight path,noise,E_environment,binary,binary,-6560.8,,,,**,,,medium,"No SE or t printed; stars only (***99%, **95%, *90%)","Price (GBP, time adjusted)",level,OLS,2501.0,,none,Flight-path specification (only flight-path coefficients reported),monetary,-6560.8,,"b (currency per unit, as reported)",,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVII-c2,VII,2 (Log-linear),48,Under flight path,noise,E_environment,binary,binary,-0.056,,,,**,,,medium,No SE or t printed; stars only,ln(Price),log,OLS,2501.0,,none,Flight-path specification,pct_premium,-5.446086410960371,,100*(exp(b) - 1),,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,AREA,living_area,A_structural,level,m2,0.01009,,34.729,,***,,,high,,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,semi_elasticity,1.009,0.029053528751187767,100*b/1 (X per m2->m2),|b/t|,0.5711648560697951,0.0164463375297243,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.729,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,AREA,living_area,A_structural,level,m2,0.003986,,23.38,,***,,,high,,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,0.3986,0.017048759623609924,100*b/1 (X per m2->m2),|b/t|,0.4242066481671518,0.018143996927594174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.38,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,AREA,living_area,A_structural,level,m2,0.003905,,23.431,,***,,,high,,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,0.3905,0.016665955358286032,100*b/1 (X per m2->m2),|b/t|,0.4249650283672604,0.018136871169274055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.431,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,AREA,living_area,A_structural,level,m2,0.003931,,23.758,,***,,,high,,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,0.39309999999999995,0.016546005556023233,100*b/1 (X per m2->m2),|b/t|,0.42980633845819616,0.018091015172076612,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.758,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,AREA,living_area,A_structural,level,m2,0.004007,,23.735,,***,,,high,,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,0.4007,0.016882241415630928,100*b/1 (X per m2->m2),|b/t|,0.4294670204866153,0.018094249862507492,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.735,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,AREA,living_area,A_structural,level,m2,0.003974,,23.299,,***,,,high,,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,0.39740000000000003,0.017056526031160136,100*b/1 (X per m2->m2),|b/t|,0.42300032536892973,0.018155299599507693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.299,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,AREA,living_area,A_structural,level,m2,0.00398,,23.342,,***,,,high,,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,0.398,0.017050809699254564,100*b/1 (X per m2->m2),|b/t|,0.42364099959431706,0.01814930167056452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.342,2491.0,m2,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,APP,dwelling_type,A_structural,binary,binary,0.366772,,15.078,,***,,,high,Apartment,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,44.26419898856029,3.5102616568384084,100*(exp(b - se^2/2) - 1),|b/t|,0.28919538960728686,0.019179956864788888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.078,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,APP,dwelling_type,A_structural,binary,binary,0.051301,,3.835,,***,,,high,Apartment,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.254551176391997,1.4081217404006672,100*(exp(b - se^2/2) - 1),|b/t|,0.07661260078216471,0.019977210112689627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.835,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,APP,dwelling_type,A_structural,binary,binary,-0.00555,,-0.392,,,,,high,Apartment,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.5634294283669461,1.4079803109755946,100*(exp(b - se^2/2) - 1),|b/t|,-0.007853907976140361,0.02003547953097031,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.392,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,APP,dwelling_type,A_structural,binary,binary,-0.00617,,-0.447,,,,,high,Apartment,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.6245677389554727,1.37182288621665,100*(exp(b - se^2/2) - 1),|b/t|,-0.008955776404358825,0.02003529396948283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.447,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,APP,dwelling_type,A_structural,binary,binary,0.037728,,2.796,,***,,,high,Apartment,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,3.8354202296407403,1.4012372649165181,100*(exp(b - se^2/2) - 1),|b/t|,0.0559332284751822,0.020004731214299788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.796,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,APP,dwelling_type,A_structural,binary,binary,0.04626,,3.387,,***,,,high,Apartment,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,4.7249002228315184,1.43047704990812,100*(exp(b - se^2/2) - 1),|b/t|,0.06770653728517491,0.01999012024953496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.387,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,APP,dwelling_type,A_structural,binary,binary,0.05144,,3.784,,***,,,high,Apartment,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.268874481782193,1.4311657697310314,100*(exp(b - se^2/2) - 1),|b/t|,0.07559962504609086,0.019978759261651918,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.784,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,TER,dwelling_type,A_structural,binary,binary,-0.10434,,-6.728,,***,,,high,Terraced,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-9.91893997294988,1.3971742190741343,100*(exp(b - se^2/2) - 1),|b/t|,-0.13359450165567904,0.01985649548984528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.728,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,TER,dwelling_type,A_structural,binary,binary,-0.025474,,-3.101,,***,,,high,Terraced,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-2.518516731797482,0.8008149286818483,100*(exp(b - se^2/2) - 1),|b/t|,-0.06201235805545915,0.019997535651550838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.101,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,TER,dwelling_type,A_structural,binary,binary,-0.03527,,,,***,,,medium,"t printed as '(-4264)' (garbled, probably -4.264); t left null",ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-3.4655262011673127,,100*(exp(b) - 1),,,,,,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,TER,dwelling_type,A_structural,binary,binary,-0.0378,,-4.617,,***,,,high,Terraced,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-3.7126768103282304,0.7883436864496424,100*(exp(b - se^2/2) - 1),|b/t|,-0.09211337243496759,0.019950914540820357,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.617,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,TER,dwelling_type,A_structural,binary,binary,-0.03192,,-3.809,,***,,,high,Terraced,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-3.1449944051214374,0.8116881957811959,100*(exp(b - se^2/2) - 1),|b/t|,-0.07609621122275388,0.019978002421305825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.809,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,TER,dwelling_type,A_structural,binary,binary,-0.02886,,-3.441,,***,,,high,Terraced,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-2.8481698255471755,0.814850460631963,100*(exp(b - se^2/2) - 1),|b/t|,-0.06878093690376084,0.0199886477488407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.441,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,TER,dwelling_type,A_structural,binary,binary,-0.0255,,-3.11,,***,,,high,Terraced,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-2.52103888715548,0.7992916612098521,100*(exp(b - se^2/2) - 1),|b/t|,-0.06219164076143551,0.01999731214194068,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.11,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,DET,dwelling_type,A_structural,binary,binary,0.204127,,8.315,,***,,,high,Detached,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,22.608438810263245,3.0108521455306585,100*(exp(b - se^2/2) - 1),|b/t|,0.16433514854836176,0.01976369796131831,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.315,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,DET,dwelling_type,A_structural,binary,binary,0.03467,,2.65,,***,,,high,Detached,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,3.5189412591847047,1.3544561747260864,100*(exp(b - se^2/2) - 1),|b/t|,0.053020973943612555,0.02000791469570285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,DET,dwelling_type,A_structural,binary,binary,0.048884,,3.767,,***,,,high,Detached,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.001011764167362,1.3627028577186677,100*(exp(b - se^2/2) - 1),|b/t|,0.07526191429055386,0.01997927111509261,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.767,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,DET,dwelling_type,A_structural,binary,binary,0.058726,,4.497,,***,,,high,Detached,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,6.039420680115004,1.3848792588869505,100*(exp(b - se^2/2) - 1),|b/t|,0.0897387976898503,0.019955258547887548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.497,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,DET,dwelling_type,A_structural,binary,binary,0.03604,,2.777,,***,,,high,Detached,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,3.6610012096338407,1.3454292826968115,100*(exp(b - se^2/2) - 1),|b/t|,0.055554315647235605,0.020005155076426216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.777,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,DET,dwelling_type,A_structural,binary,binary,0.033414,,2.542,,**,,,high,Detached,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,3.3889193659157657,1.3591407628677483,100*(exp(b - se^2/2) - 1),|b/t|,0.05086582845292559,0.020010160681717386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.542,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,DET,dwelling_type,A_structural,binary,binary,0.0344,,2.629,,***,,,high,Detached,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,3.49099247488156,1.3542772614375174,100*(exp(b - se^2/2) - 1),|b/t|,0.05260197495167921,0.020008358673137775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.629,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,PRE1919,age,A_structural,binary,binary,-0.06371,,-2.222,,**,,,high,Built pre-1919,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-6.210854127969445,2.6902624446371326,100*(exp(b - se^2/2) - 1),|b/t|,-0.04447615349018914,0.020016270697654878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.222,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,PRE1919,age,A_structural,binary,binary,-0.065664,,-4.356,,***,,,high,Built pre-1919,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.366093434405662,1.4116334824544086,100*(exp(b - se^2/2) - 1),|b/t|,-0.08694671855886081,0.019960220054834898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.356,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,PRE1919,age,A_structural,binary,binary,-0.08027,,-5.421,,***,,,high,Built pre-1919,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.72340270361489,1.3665107031330141,100*(exp(b - se^2/2) - 1),|b/t|,-0.10798060864054194,0.019918946438026556,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.421,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,PRE1919,age,A_structural,binary,binary,-0.08606,,-5.841,,***,,,high,Built pre-1919,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-8.25604090192118,1.351881896787771,100*(exp(b - se^2/2) - 1),|b/t|,-0.11623754509553287,0.019900281646213467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.841,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,PRE1919,age,A_structural,binary,binary,-0.06855,,-4.579,,***,,,high,Built pre-1919,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.63578565208347,1.3978672459712835,100*(exp(b - se^2/2) - 1),|b/t|,-0.09136159182477824,0.01995230221113305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.579,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,PRE1919,age,A_structural,binary,binary,-0.06753,,-4.472,,***,,,high,Built pre-1919,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.540688399409933,1.411455038835327,100*(exp(b - se^2/2) - 1),|b/t|,-0.08924390057167697,0.01995614950171667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.472,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,PRE1919,age,A_structural,binary,binary,-0.0655,,-4.341,,***,,,high,Built pre-1919,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.350756424246063,1.4132051961012584,100*(exp(b - se^2/2) - 1),|b/t|,-0.0866495670767505,0.01996073878754907,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.341,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,INTERWAR,age,A_structural,binary,binary,-0.03624,,-1.355,,,,,high,Interwar cohort,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-3.5936059136850496,2.5793487222979996,100*(exp(b - se^2/2) - 1),|b/t|,-0.0271389123947264,0.020028717634484425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.355,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,INTERWAR,age,A_structural,binary,binary,-0.041892,,-2.978,,***,,,high,Interwar cohort,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-4.1121534409341525,1.3490030668597703,100*(exp(b - se^2/2) - 1),|b/t|,-0.05956156668040533,0.02000052608475666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.978,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,INTERWAR,age,A_structural,binary,binary,-0.05072,,-3.683,,***,,,high,Interwar cohort,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-4.9545345778921,1.3090315377821162,100*(exp(b - se^2/2) - 1),|b/t|,-0.07359284788859606,0.01998176700749282,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.683,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,INTERWAR,age,A_structural,binary,binary,-0.05496,,-4.013,,***,,,high,Interwar cohort,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-5.35657556951371,1.296309606929687,100*(exp(b - se^2/2) - 1),|b/t|,-0.08014620634467026,0.019971643743999568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.013,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,INTERWAR,age,A_structural,binary,binary,-0.03979,,-2.854,,***,,,high,Interwar cohort,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-3.9102165574895653,1.3397982090880138,100*(exp(b - se^2/2) - 1),|b/t|,-0.05708975957502108,0.020003419612831495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.854,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,INTERWAR,age,A_structural,binary,binary,-0.04325,,-3.071,,***,,,high,Interwar cohort,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-4.242302596464964,1.3487239172277172,100*(exp(b - se^2/2) - 1),|b/t|,-0.06141470577695433,0.019998276058923584,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.071,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,INTERWAR,age,A_structural,binary,binary,-0.0419,,-2.981,,***,,,high,Interwar cohort,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-4.1129050712806485,1.347892038619846,100*(exp(b - se^2/2) - 1),|b/t|,-0.05962135507763308,0.020000454571497173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.981,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,POSTWAR,age,A_structural,binary,binary,-0.03179,,-1.099,,,,,high,Post-war cohort,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-3.1695200080274932,2.802119251713384,100*(exp(b - se^2/2) - 1),|b/t|,-0.0220143347907407,0.020031241847807732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.099,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,POSTWAR,age,A_structural,binary,binary,-0.078489,,-5.157,,***,,,high,Post-war cohort,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.559484370495307,1.4070979325581108,100*(exp(b - se^2/2) - 1),|b/t|,-0.10277896216685788,0.019929990724618554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.157,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,POSTWAR,age,A_structural,binary,binary,-0.07516,,-5.055,,***,,,high,Post-war cohort,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.2507467141450395,1.379189805200218,100*(exp(b - se^2/2) - 1),|b/t|,-0.10076695081638393,0.019934114899383567,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.055,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,POSTWAR,age,A_structural,binary,binary,-0.07323,,-4.956,,***,,,high,Post-war cohort,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.07144050591737,1.3732650001897841,100*(exp(b - se^2/2) - 1),|b/t|,-0.09881293300647584,0.019938041365309894,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.956,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,POSTWAR,age,A_structural,binary,binary,-0.07922,,-5.257,,***,,,high,Post-war cohort,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.6268232020831555,1.3921692990011374,100*(exp(b - se^2/2) - 1),|b/t|,-0.10475029856173669,0.01992586999462368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.257,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,POSTWAR,age,A_structural,binary,binary,-0.07649,,-5.004,,***,,,high,Post-war cohort,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.374604030200449,1.4160160468592926,100*(exp(b - se^2/2) - 1),|b/t|,-0.09976047975326321,0.019936147033026222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.004,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,POSTWAR,age,A_structural,binary,binary,-0.0785,,-5.154,,***,,,high,Post-war cohort,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-7.560516682906604,1.408098791319886,100*(exp(b - se^2/2) - 1),|b/t|,-0.10271980324175013,0.01993011316293173,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.154,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,EARLYMODER,age,A_structural,binary,binary,-0.00802,,-0.249,,,,,high,Early modern cohort,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-0.8502353337069057,3.19515535609048,100*(exp(b - se^2/2) - 1),|b/t|,-0.004988926188827759,0.020035848147902647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.249,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,EARLYMODER,age,A_structural,binary,binary,-0.068598,,-4.054,,***,,,high,Early modern cohort,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.643170752464911,1.579923200792304,100*(exp(b - se^2/2) - 1),|b/t|,-0.08095970365371424,0.01997032650560292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.054,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,EARLYMODER,age,A_structural,binary,binary,-0.06558,,-3.967,,***,,,high,Early modern cohort,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.36038391962932,1.5482039805849044,100*(exp(b - se^2/2) - 1),|b/t|,-0.07923331145445693,0.01997310598801536,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.967,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,EARLYMODER,age,A_structural,binary,binary,-0.06552,,-3.99,,***,,,high,Early modern cohort,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.354595141682062,1.5379634645757656,100*(exp(b - se^2/2) - 1),|b/t|,-0.07968978396309202,0.019972376933105766,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.99,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,EARLYMODER,age,A_structural,binary,binary,-0.0695,,-4.149,,***,,,high,Early modern cohort,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.727073622668589,1.5626362803139708,100*(exp(b - se^2/2) - 1),|b/t|,-0.0828440121380886,0.019967223942658135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.149,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,EARLYMODER,age,A_structural,binary,binary,-0.06687,,-3.934,,***,,,high,Early modern cohort,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.4818326567649525,1.5898483321619556,100*(exp(b - se^2/2) - 1),|b/t|,-0.07857828564089689,0.01997414479941456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.934,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,EARLYMODER,age,A_structural,binary,binary,-0.0685,,-4.051,,***,,,high,Early modern cohort,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-6.634002919321691,1.5789891882767544,100*(exp(b - se^2/2) - 1),|b/t|,-0.0809001849262062,0.019970423334042506,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.051,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,ELECHEAT,heating,A_structural,binary,binary,0.00069,,0.022,,,,,high,,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,0.019818079224376284,3.138528474055841,100*(exp(b - se^2/2) - 1),|b/t|,0.0004407941020124436,0.020036095546020167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.022,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,ELECHEAT,heating,A_structural,binary,binary,-0.012883,,-0.794,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-1.2930308347778308,1.6017749170381868,100*(exp(b - se^2/2) - 1),|b/t|,-0.01590664866527546,0.02003356255072476,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.794,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,ELECHEAT,heating,A_structural,binary,binary,-0.01024,,-0.646,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-1.0312095300700874,1.5689903162653684,100*(exp(b - se^2/2) - 1),|b/t|,-0.01294223492245693,0.020034419384608253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.646,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,ELECHEAT,heating,A_structural,binary,binary,-0.00894,,-0.567,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.9023345285839923,1.5626865249095054,100*(exp(b - se^2/2) - 1),|b/t|,-0.011359734257027021,0.02003480468611468,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.567,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,ELECHEAT,heating,A_structural,binary,binary,-0.01371,,-0.854,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-1.374354640794384,1.583526759439776,100*(exp(b - se^2/2) - 1),|b/t|,-0.017108322951003514,0.020033165048013484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.854,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,ELECHEAT,heating,A_structural,binary,binary,-0.01285,,-0.792,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-1.289772340621409,1.6017593285837668,100*(exp(b - se^2/2) - 1),|b/t|,-0.015866591639754582,0.020033575302720433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.792,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,ELECHEAT,heating,A_structural,binary,binary,-0.0129,,-0.778,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-1.2952844798781338,1.6368455969106597,100*(exp(b - se^2/2) - 1),|b/t|,-0.015586190332801453,0.020033663666839913,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.778,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,GASHEAT,heating,A_structural,binary,binary,-0.01906,,-0.811,,,,,high,,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-1.9150423883874002,2.3058146240802526,100*(exp(b - se^2/2) - 1),|b/t|,-0.016247130270474197,0.02003345286125055,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.811,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,GASHEAT,heating,A_structural,binary,binary,-0.002525,,-0.204,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.2598219416263481,1.2346237340470938,100*(exp(b - se^2/2) - 1),|b/t|,-0.004087329746040774,0.020035930127650856,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.204,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,GASHEAT,heating,A_structural,binary,binary,-0.00191,,-0.157,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.19820341446837864,1.2142390966359573,100*(exp(b - se^2/2) - 1),|b/t|,-0.0031456517429019537,0.020035998362432826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.157,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,GASHEAT,heating,A_structural,binary,binary,-0.00047,,-0.039,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.05424695097222543,1.2045619279573543,100*(exp(b - se^2/2) - 1),|b/t|,-0.0007814075636453681,0.02003609137552226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.039,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,GASHEAT,heating,A_structural,binary,binary,-0.00419,,-0.342,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.42559667551447244,1.2200235756473168,100*(exp(b - se^2/2) - 1),|b/t|,-0.0068521844734144785,0.020035627115247014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.342,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,GASHEAT,heating,A_structural,binary,binary,-0.0023,,-0.185,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.23744591894652167,1.240387069642521,100*(exp(b - se^2/2) - 1),|b/t|,-0.0037066525724977453,0.020035959851339165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.185,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,GASHEAT,heating,A_structural,binary,binary,-0.0026,,-0.215,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.2669551000216819,1.2061622234365958,100*(exp(b - se^2/2) - 1),|b/t|,-0.00430772099230941,0.02003591159213679,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.215,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,OILHEAT,heating,A_structural,binary,binary,-0.01857,,-0.858,,,,,high,,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,-1.8628521147239896,2.124514831806657,100*(exp(b - se^2/2) - 1),|b/t|,-0.01718843199179585,0.020033137519575583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.858,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,OILHEAT,heating,A_structural,binary,binary,-0.002954,,-0.259,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.3014488912803204,1.1371763551319878,100*(exp(b - se^2/2) - 1),|b/t|,-0.0051892793790844485,0.020035827718472773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.259,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,OILHEAT,heating,A_structural,binary,binary,-0.00241,,-0.217,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.24686194506852166,1.1079257572080865,100*(exp(b - se^2/2) - 1),|b/t|,-0.004347792061494826,0.02003590811748768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.217,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,OILHEAT,heating,A_structural,binary,binary,-0.00062,,-0.056,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.06810562411506682,1.1064566413203152,100*(exp(b - se^2/2) - 1),|b/t|,-0.001122020753307425,0.02003608488048973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.056,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,OILHEAT,heating,A_structural,binary,binary,-0.0038,,-0.337,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.38561196884461246,1.1233197036441314,100*(exp(b - se^2/2) - 1),|b/t|,-0.006752010938803448,0.020035640767962756,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.337,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,OILHEAT,heating,A_structural,binary,binary,-0.00291,,-0.256,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.2970186717420442,1.1334157066963815,100*(exp(b - se^2/2) - 1),|b/t|,-0.005129173486527357,0.02003583393174749,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.256,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,OILHEAT,heating,A_structural,binary,binary,-0.0029,,-0.258,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,-0.29587864234141836,1.1207760398141566,100*(exp(b - se^2/2) - 1),|b/t|,-0.005169244087782548,0.020035829797606774,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.258,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,PRIV,other,other,binary,binary,0.186685,,8.525,,***,,,high,Private build type (vs social),ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,20.495862121260554,2.639315460738125,100*(exp(b - se^2/2) - 1),|b/t|,0.16836927838769009,0.019750061980960715,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.525,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,PRIV,other,other,binary,binary,0.056389,,4.849,,***,,,high,Private build type (vs social),ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.793763252009887,1.230358404735953,100*(exp(b - se^2/2) - 1),|b/t|,0.09669972937673581,0.01994220032516721,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.849,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,PRIV,other,other,binary,binary,0.057544,,5.054,,***,,,high,Private build type (vs social),ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.916322013339692,1.2060237250271781,100*(exp(b - se^2/2) - 1),|b/t|,0.10074721905260732,0.019934154937199706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.054,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,PRIV,other,other,binary,binary,0.055683,,4.92,,***,,,high,Private build type (vs social),ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.719476822466718,1.1965761501006968,100*(exp(b - se^2/2) - 1),|b/t|,0.09810209637374151,0.01993945048246779,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.92,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,PRIV,other,other,binary,binary,0.05453,,4.721,,***,,,high,Private build type (vs social),ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.597371381742122,1.2197858058793434,100*(exp(b - se^2/2) - 1),|b/t|,0.09417006828629046,0.01994705958192977,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.721,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,PRIV,other,other,binary,binary,0.052528,,4.445,,***,,,high,Private build type (vs social),ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.385848398991366,1.2454655537815151,100*(exp(b - se^2/2) - 1),|b/t|,0.08870933744108263,0.019957106285957843,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.445,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,PRIV,other,other,binary,binary,0.0564,,4.836,,***,,,high,Private build type (vs social),ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,5.794885672163264,1.233920048366564,100*(exp(b - se^2/2) - 1),|b/t|,0.09644289531474783,0.019942699610162904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.836,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,False,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c1,VIII,1 (Base No Lag),49,GAR,garage,A_structural,binary,binary,0.003206,,0.219,,,,,high,,ln(Price),log,OLS,2501.0,0.546,t,Base model without spatial lag,pct_premium,0.31036522820337975,1.4686278218971405,100*(exp(b - se^2/2) - 1),|b/t|,0.004387863109736231,0.02003590461066772,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.219,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,ols,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c2,VIII,2 (Base + Lag),49,GAR,garage,A_structural,binary,binary,0.010816,,1.409,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8744,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,1.0844921120916062,0.7759844430545575,100*(exp(b - se^2/2) - 1),|b/t|,0.02821961834928235,0.02002811806194631,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.409,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,GAR,garage,A_structural,binary,binary,0.012548,,1.673,,*,,,high,,ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,1.2598574467585166,0.7595005561638323,100*(exp(b - se^2/2) - 1),|b/t|,0.0335015749258589,0.02002485052352594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.673,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,GAR,garage,A_structural,binary,binary,0.012156,,1.632,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,1.2202105284014886,0.7539626300416469,100*(exp(b - se^2/2) - 1),|b/t|,0.03268144396864691,0.020025394588631684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.632,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,GAR,garage,A_structural,binary,binary,0.009612,,1.264,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,0.9629151121072876,0.7677876581525704,100*(exp(b - se^2/2) - 1),|b/t|,0.02531750936632713,0.020029675131587914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.264,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,GAR,garage,A_structural,binary,binary,0.011042,,1.439,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,1.107342102929998,0.7758583322200402,100*(exp(b - se^2/2) - 1),|b/t|,0.028819968034038936,0.020027774867295997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.439,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,GAR,garage,A_structural,binary,binary,0.0105,,1.378,,,,,high,,ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",pct_premium,1.0525982238419607,0.7700167520807532,100*(exp(b - se^2/2) - 1),|b/t|,0.027599224933440383,0.020028465118606953,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.378,2491.0,binary,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c3,VIII,3 (NO2),49,NO2,air_pollution,E_environment,level,ug/m3,-0.19054,,-6.17,,***,,,medium,"Entered continuously in the SLM (not banded as in OLS); unit assumed ug/m3; spatially lagged NO2 interaction term (0.017422, t 6.439) not extracted",ln(Price),log,spatial_lag,2501.0,0.8804,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,-19.054,3.0881685575364664,100*b/1 (per ug/m3),|b/t|,-0.12268877592330678,0.01988472867476609,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.17,2491.0,ug/m3,implausible_magnitude,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c4,VIII,4 (PM2.5),49,PM2.5,air_pollution,E_environment,level,ug/m3,-0.70096,,-6.478,,***,,,medium,"Continuous PM2.5; spatial-lag interaction term (0.064866, t 6.812) not extracted",ln(Price),log,spatial_lag,2501.0,0.8819,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,-70.096,10.820623649274468,100*b/1 (per ug/m3),|b/t|,-0.12871417976778066,0.019869431887585776,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.478,2491.0,ug/m3,implausible_magnitude,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c5,VIII,5 Rail,49,Rail Noise,noise,E_environment,level,db,-0.9084,,-4.973,,***,,,medium,"Scale of noise variable in SLM not stated (dB level assumed); spatial-lag interaction (0.081707, t 5.131) not extracted",ln(Price),log,spatial_lag,2501.0,0.8771,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,-90.84,18.266639855218177,100*b/1 (per dB),|b/t|,-0.09914855335528412,0.019937372482462117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.973,2491.0,db,implausible_magnitude,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,False,True,False
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c6,VIII,6 Airport,49,Airport Noise,noise,E_environment,level,db,0.04471,,0.326,,,,,medium,"Scale not stated; spatial-lag interaction (-0.0033, t -0.2737) not extracted",ln(Price),log,spatial_lag,2501.0,0.8746,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,4.471,13.714723926380367,100*b/1 (per dB),|b/t|,0.006531628451381448,0.02003567009626211,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.326,2491.0,db,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,True
eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad,eb83365b4894fa9c413cfdff9bdf069881dad3ad:TVIII-c7,VIII,7 Road,49,Road Noise,noise,E_environment,level,db,0.02389,,0.234,,,,,medium,"Scale not stated; spatial-lag interaction (-0.002, t 0.233) not extracted",ln(Price),log,spatial_lag,2501.0,0.8745,t,"Spatial lag model, W = 1/d (inverse distance); column 1 is the non-spatial base",semi_elasticity,2.3890000000000002,10.209401709401709,100*b/1 (per dB),|b/t|,0.004688395284474154,0.02003587728407758,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.234,2491.0,db,,2018.0,United Kingdom,Europe,High income,2013.0,2013.0,mixed,transaction,journal,False,True,unknown,,2013.0,spatial,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-BICR,2,Panel A: Birmingham Civil Rights National Monument,47,"Birmingham Civil Rights National Monument x AFTER, distance band 1-3km",green_space,E_environment,other,binary,4.128,,,0.812,,,,medium,DID treatment effect (delta22: distance band 1-3km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,275643.0,,p,"Model 2 (DID): Birmingham Civil Rights National Monument; Alabama, 1998-2021; designation 2017 (treatment year 2017); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,4.128,17.355717273051287,not harmonised,|b/z(p)| (two-sided normal),0.0004530347035084463,0.0019047340670962237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2378466954177493,275633.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-BICR,2,Panel A: Birmingham Civil Rights National Monument,47,"Birmingham Civil Rights National Monument x AFTER, distance band 3-5km",green_space,E_environment,other,binary,20.697,,,0.21,,,,medium,DID treatment effect (delta22: distance band 3-5km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,275643.0,,p,"Model 2 (DID): Birmingham Civil Rights National Monument; Alabama, 1998-2021; designation 2017 (treatment year 2017); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,20.697,16.51050624469484,not harmonised,|b/z(p)| (two-sided normal),0.002387702234696398,0.001904728832991579,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2535654384704504,275633.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-BICR,2,Panel A: Birmingham Civil Rights National Monument,47,"Birmingham Civil Rights National Monument x AFTER, distance band 5-7km",green_space,E_environment,other,binary,8.635,,,0.626,,,,medium,DID treatment effect (delta22: distance band 5-7km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,275643.0,,p,"Model 2 (DID): Birmingham Civil Rights National Monument; Alabama, 1998-2021; designation 2017 (treatment year 2017); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,8.635,17.717742757277748,not harmonised,|b/z(p)| (two-sided normal),0.0009282995862307888,0.0019047334418673407,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.48736456546944074,275633.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-BICR,2,Panel A: Birmingham Civil Rights National Monument,47,"Birmingham Civil Rights National Monument x AFTER, distance band 7-10km",green_space,E_environment,other,binary,4.157,,,0.672,,,,medium,DID treatment effect (delta22: distance band 7-10km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,275643.0,,p,"Model 2 (DID): Birmingham Civil Rights National Monument; Alabama, 1998-2021; designation 2017 (treatment year 2017); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,4.157,9.818029370324082,not harmonised,|b/z(p)| (two-sided normal),0.0008064732194092732,0.0019047336431417036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4234047223941827,275633.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-CUVA,2,Panel A: Cuyahoga Valley National Park,47,"Cuyahoga Valley National Park x AFTER, distance band <1km",green_space,E_environment,other,binary,0.224,,,0.973,,,,medium,DID treatment effect (delta22: distance band <1km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,492635.0,,p,"Model 2 (DID): Cuyahoga Valley National Park; Ohio, 1993-2021; redesignated 2000 (treatment year 2001); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,0.224,6.618223120685274,not harmonised,|b/z(p)| (two-sided normal),4.822235206140355e-05,0.001424760202440206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.03384594262165133,492625.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-CUVA,2,Panel A: Cuyahoga Valley National Park,47,"Cuyahoga Valley National Park x AFTER, distance band 1-2km",green_space,E_environment,other,binary,2.712,,,0.636,,,,medium,DID treatment effect (delta22: distance band 1-2km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,492635.0,,p,"Model 2 (DID): Cuyahoga Valley National Park; Ohio, 1993-2021; redesignated 2000 (treatment year 2001); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,2.712,5.729995204450674,not harmonised,|b/z(p)| (two-sided normal),0.0006743371778009441,0.0014247598801556823,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4732988254324369,492625.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-CUVA,2,Panel A: Cuyahoga Valley National Park,47,"Cuyahoga Valley National Park x AFTER, distance band 2-3km",green_space,E_environment,other,binary,-0.565,,,0.899,,,,medium,DID treatment effect (delta22: distance band 2-3km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,492635.0,,p,"Model 2 (DID): Cuyahoga Valley National Park; Ohio, 1993-2021; redesignated 2000 (treatment year 2001); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-0.565,4.45145836108506,not harmonised,|b/z(p)| (two-sided normal),-0.00018083725306508935,0.0014247601808004373,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.12692469617131924,492625.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-CUVA,2,Panel A: Cuyahoga Valley National Park,47,"Cuyahoga Valley National Park x AFTER, distance band 3-5km",green_space,E_environment,other,binary,-1.55,,,0.546,,,,medium,DID treatment effect (delta22: distance band 3-5km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,492635.0,,p,"Model 2 (DID): Cuyahoga Valley National Park; Ohio, 1993-2021; redesignated 2000 (treatment year 2001); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-1.55,2.5672246924007904,not harmonised,|b/z(p)| (two-sided normal),-0.0008602197952563498,0.0014247596769510023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6037648377986297,492625.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-PAGR,2,Panel A: Paterson Great Falls National Historical Park,47,"Paterson Great Falls National Historical Park x AFTER, distance band <1km",green_space,E_environment,other,binary,-12.558,,,0.041,,,,medium,DID treatment effect (delta22: distance band <1km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,779069.0,,p,"Model 2 (DID): Paterson Great Falls National Historical Park; New Jersey, 1994-2021; established Nov 2011 (treatment year 2012); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-12.558,6.145248640480068,not harmonised,|b/z(p)| (two-sided normal),-0.0023152328763579493,0.0011329576115490878,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0435300074398564,779059.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-PAGR,2,Panel A: Paterson Great Falls National Historical Park,47,"Paterson Great Falls National Historical Park x AFTER, distance band 1-2km",green_space,E_environment,other,binary,-11.245,,,0.034,,,,medium,DID treatment effect (delta22: distance band 1-2km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,779069.0,,p,"Model 2 (DID): Paterson Great Falls National Historical Park; New Jersey, 1994-2021; established Nov 2011 (treatment year 2012); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-11.245,5.304065921170641,not harmonised,|b/z(p)| (two-sided normal),-0.0024019508666436743,0.0011329573798213425,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.120071689742151,779059.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-PAGR,2,Panel A: Paterson Great Falls National Historical Park,47,"Paterson Great Falls National Historical Park x AFTER, distance band 2-3km",green_space,E_environment,other,binary,-10.233,,,0.043,,,,medium,DID treatment effect (delta22: distance band 2-3km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,779069.0,,p,"Model 2 (DID): Paterson Great Falls National Historical Park; New Jersey, 1994-2021; established Nov 2011 (treatment year 2012); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-10.233,5.056554568509241,not harmonised,|b/z(p)| (two-sided normal),-0.0022927777564191237,0.0011329576701649064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0237099909349667,779059.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-PAGR,2,Panel A: Paterson Great Falls National Historical Park,47,"Paterson Great Falls National Historical Park x AFTER, distance band 3-5km",green_space,E_environment,other,binary,-6.813,,,0.143,,,,medium,DID treatment effect (delta22: distance band 3-5km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,779069.0,,p,"Model 2 (DID): Paterson Great Falls National Historical Park; New Jersey, 1994-2021; established Nov 2011 (treatment year 2012); all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-6.813,4.651432061510307,not harmonised,|b/z(p)| (two-sided normal),-0.0016594567361323633,0.0011329590880867935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4647102032030621,779059.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-RIRA,2,Panel A: River Raisin National Battlefield Park,47,"River Raisin National Battlefield Park x AFTER, distance band 1-3km",green_space,E_environment,other,binary,-10.155,,,0.686,,,,medium,DID treatment effect (delta22: distance band 1-3km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,575846.0,,p,"Model 2 (DID): River Raisin National Battlefield Park; Michigan, 1994-2021; all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-10.155,25.118152381677614,not harmonised,|b/z(p)| (two-sided normal),-0.0005327736889498571,0.0013178031220079786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4042892902985788,575836.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-RIRA,2,Panel A: River Raisin National Battlefield Park,47,"River Raisin National Battlefield Park x AFTER, distance band 3-5km",green_space,E_environment,other,binary,8.317,,,0.763,,,,medium,DID treatment effect (delta22: distance band 3-5km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,575846.0,,p,"Model 2 (DID): River Raisin National Battlefield Park; Michigan, 1994-2021; all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,8.317,27.581416761060645,not harmonised,|b/z(p)| (two-sided normal),0.00039737513670364775,0.001317803204990592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3015436107597603,575836.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-RIRA,2,Panel A: River Raisin National Battlefield Park,47,"River Raisin National Battlefield Park x AFTER, distance band 5-10km",green_space,E_environment,other,binary,6.455,,,0.816,,,,medium,DID treatment effect (delta22: distance band 5-10km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,575846.0,,p,"Model 2 (DID): River Raisin National Battlefield Park; Michigan, 1994-2021; all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,6.455,27.74044323539387,not harmonised,|b/z(p)| (two-sided normal),0.0003066432604452016,0.0013178032470791965,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2326927491830449,575836.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-TUSK,2,Panel A: Tule Springs Fossil Beds National Monument,47,"Tule Springs Fossil Beds National Monument x AFTER, distance band 1-3km",green_space,E_environment,other,binary,-9.92,,,0.099,,,,medium,DID treatment effect (delta22: distance band 1-3km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,1480403.0,,p,"Model 2 (DID): Tule Springs Fossil Beds National Monument; Nevada (Las Vegas), 1994-2018; all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-9.92,6.013137746966413,not harmonised,|b/z(p)| (two-sided normal),-0.001355881105775253,0.000821885066283844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.649721063683361,1480393.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-TUSK,2,Panel A: Tule Springs Fossil Beds National Monument,47,"Tule Springs Fossil Beds National Monument x AFTER, distance band 3-5km",green_space,E_environment,other,binary,-8.61,,,0.144,,,,medium,DID treatment effect (delta22: distance band 3-5km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,1480403.0,,p,"Model 2 (DID): Tule Springs Fossil Beds National Monument; Nevada (Las Vegas), 1994-2018; all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-8.61,5.8929968554815195,not harmonised,|b/z(p)| (two-sided normal),-0.0012008205666742512,0.0008218852291996398,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4610562691869062,1480393.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2A-TUSK,2,Panel A: Tule Springs Fossil Beds National Monument,47,"Tule Springs Fossil Beds National Monument x AFTER, distance band 5-10km",green_space,E_environment,other,binary,-11.25,,,0.003,,,,medium,DID treatment effect (delta22: distance band 5-10km to park x AFTER designation) expressed as percent difference in house prices vs houses outside the farthest band; interaction term; Panel A values are in percent (e.g. -10.155 = -10.2%),log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,1480403.0,,p,"Model 2 (DID): Tule Springs Fossil Beds National Monument; Nevada (Las Vegas), 1994-2018; all hedonic coefficients allowed to differ before/after designation; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",other,-11.25,3.790765991813236,not harmonised,|b/z(p)| (two-sided normal),-0.002439134467861697,0.0008218833769092973,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9677379253417833,1480393.0,binary,nonstandard_x_transform,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-BOST-NHS,2,Panel B: National Historic Sites,47,"National Historic Sites, distance band <=1 km",green_space,E_environment,binary,binary,0.039,,,0.252,,,,medium,"Model 1 distance-band dummy (<=1 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,966983.0,,p,"Model 1 (constant coefficients): Boston National Historic Sites; Boston MA, 1987-2021; block group FE; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,3.9168039443294944,3.5400148134705303,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0011649024103461757,0.001016933446745225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1455050613926967,966973.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-BOST-NHS,2,Panel B: National Historic Sites,47,"National Historic Sites, distance band 1-2 km",green_space,E_environment,binary,binary,-0.007,,,0.724,,,,medium,"Model 1 distance-band dummy (1-2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,966983.0,,p,"Model 1 (constant coefficients): Boston National Historic Sites; Boston MA, 1987-2021; block group FE; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-0.7170650977595727,1.9685124100821962,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0003590976968413132,0.0010169340711666452,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.35311797197368927,966973.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-BOST-NHS,2,Panel B: National Historic Sites,47,"National Historic Sites, distance band 2-3 km",green_space,E_environment,binary,binary,-0.01,,,0.516,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,966983.0,,p,"Model 1 (constant coefficients): Boston National Historic Sites; Boston MA, 1987-2021; block group FE; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-1.0067496895998729,1.5242707733537961,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0006605225731385868,0.0010169339148949066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6495235958443252,966973.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-BOST-NHP,2,Panel B: National Historical Parks,47,"National Historical Parks, distance band <=1 km",green_space,E_environment,binary,binary,-0.003,,,0.028,,,,medium,"Model 1 distance-band dummy (<=1 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,966983.0,,p,"Model 1 (constant coefficients): Boston National Historical Parks; Boston MA, 1987-2021; same regression as NHS row; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-0.29964337541844577,0.13612306153203488,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.002234489946223038,0.0010169315979826254,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.197286376641052,966973.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-BOST-NHP,2,Panel B: National Historical Parks,47,"National Historical Parks, distance band 1-2 km",green_space,E_environment,binary,binary,-0.009,,,0.553,,,,medium,"Model 1 distance-band dummy (1-2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,966983.0,,p,"Model 1 (constant coefficients): Boston National Historical Parks; Boston MA, 1987-2021; same regression as NHS row; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-0.907365029576479,1.5034221842018967,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0006033171236290851,0.0010169339516563295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5932707061716577,966973.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-BOST-NHP,2,Panel B: National Historical Parks,47,"National Historical Parks, distance band 2-3 km",green_space,E_environment,binary,binary,0.007,,,0.622,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,966983.0,,p,"Model 1 (constant coefficients): Boston National Historical Parks; Boston MA, 1987-2021; same regression as NHS row; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,0.6923058904715784,1.4298006469074684,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0005013665825533918,0.0010169340089214768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.49301781448446486,966973.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGE,2,Panel B: Saguaro East,47,"Saguaro East, distance band <=2 km",green_space,E_environment,binary,binary,0.113,,,0.003,,,,medium,"Model 1 distance-band dummy (<=2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro East; Tucson AZ, 1994-2019; four parks (Saguaro East/West, Coronado NF, Tucson Mountain Park) in one regression; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,11.882061015995182,4.263126044346371,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.00557824969868737,0.0018796301557001344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGE,2,Panel B: Saguaro East,47,"Saguaro East, distance band 2-3 km",green_space,E_environment,binary,binary,0.059,,,0.102,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro East; Tucson AZ, 1994-2019; four parks (Saguaro East/West, Coronado NF, Tucson Mountain Park) in one regression; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,6.008500690660812,3.8273261572775237,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0030736684697708653,0.0018796505214822964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.6352340153886502,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGE,2,Panel B: Saguaro East,47,"Saguaro East, distance band 3-5 km",green_space,E_environment,binary,binary,0.022,,,0.48,,,,medium,"Model 1 distance-band dummy (3-5 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro East; Tucson AZ, 1994-2019; four parks (Saguaro East/West, Coronado NF, Tucson Mountain Park) in one regression; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,2.174801138519711,3.1840976433553467,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0013276070818474827,0.0018796577439972617,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7063025628400874,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGW,2,Panel B: Saguaro West,47,"Saguaro West, distance band <=1 km",green_space,E_environment,binary,binary,-0.11,,,0.063,,,,medium,"Model 1 distance-band dummy (<=1 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro West; Tucson AZ, 1994-2019; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-10.573245243529318,5.300247724649277,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.003494625430798274,0.0018796479228682013,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8591914944718684,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGW,2,Panel B: Saguaro West,47,"Saguaro West, distance band 1-2 km",green_space,E_environment,binary,binary,-0.062,,,0.197,,,,medium,"Model 1 distance-band dummy (1-2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro West; Tucson AZ, 1994-2019; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-6.12017859889562,4.516755681306303,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0024250277694299175,0.001879653873565226,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2901459165086897,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGW,2,Panel B: Saguaro West,47,"Saguaro West, distance band 2-3 km",green_space,E_environment,binary,binary,-0.026,,,0.567,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro West; Tucson AZ, 1994-2019; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-2.6669266140188497,4.425118666764574,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0010760579972777806,0.0018796583122560719,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5724753218505098,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-SAGW,2,Panel B: Saguaro West,47,"Saguaro West, distance band 3-5 km",green_space,E_environment,binary,binary,-0.006,,,0.87,,,,medium,"Model 1 distance-band dummy (3-5 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Saguaro West; Tucson AZ, 1994-2019; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-0.6649831856289534,3.64423985678774,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0003076221975620631,0.0018796593115485434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.16365848623314128,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-CORO,2,Panel B: Coronado,47,"Coronado, distance band <=1 km",green_space,E_environment,binary,binary,0.141,,,0.006,,,,medium,"Model 1 distance-band dummy (<=1 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Coronado; Tucson AZ, 1994-2019 (Coronado National Forest, not a national park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,14.99097145897641,5.908434936848398,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.005164824223353847,0.0018796343299768818,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7477813854449926,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-CORO,2,Panel B: Coronado,47,"Coronado, distance band 1-2 km",green_space,E_environment,binary,binary,0.099,,,0.008,,,,medium,"Model 1 distance-band dummy (1-2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Coronado; Tucson AZ, 1994-2019 (Coronado National Forest, not a national park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,10.329732077437438,4.1214059799876965,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0049849260075862595,0.0018796360460546727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6520698079021963,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-CORO,2,Panel B: Coronado,47,"Coronado, distance band 2-3 km",green_space,E_environment,binary,binary,0.083,,,0.003,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Coronado; Tucson AZ, 1994-2019 (Coronado National Forest, not a national park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,8.611695757573612,3.0387780989494795,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.00557824969868737,0.0018796301557001344,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-CORO,2,Panel B: Coronado,47,"Coronado, distance band 3-5 km",green_space,E_environment,binary,binary,0.051,,,0.01,,,,medium,"Model 1 distance-band dummy (3-5 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Coronado; Tucson AZ, 1994-2019 (Coronado National Forest, not a national park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,5.211664865121501,2.083541307783888,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.00484162501639356,0.0018796373694960735,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.575829303548901,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-TUCS,2,Panel B: Tucson,47,"Tucson, distance band <=1 km",green_space,E_environment,binary,binary,-0.021,,,0.615,,,,medium,"Model 1 distance-band dummy (<=1 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Tucson; Tucson AZ, 1994-2019 (Tucson Mountain Park, county park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-2.163423558131794,4.088603563625489,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0009453727390209976,0.001879658560532137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5029491838950579,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-TUCS,2,Panel B: Tucson,47,"Tucson, distance band 1-2 km",green_space,E_environment,binary,binary,-0.033,,,0.426,,,,medium,"Model 1 distance-band dummy (1-2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Tucson; Tucson AZ, 1994-2019 (Tucson Mountain Park, county park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-3.329242567465396,4.010874595747744,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0014963108093853264,0.0018796572962567995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7960551172626629,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-TUCS,2,Panel B: Tucson,47,"Tucson, distance band 2-3 km",green_space,E_environment,binary,binary,-0.043,,,0.167,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Tucson; Tucson AZ, 1994-2019 (Tucson Mountain Park, county park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-4.255223720056655,2.980675595195537,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.002597507301659213,0.0018796530594027542,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3819078412718098,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-TUCS,2,Panel B: Tucson,47,"Tucson, distance band 3-5 km",green_space,E_environment,binary,binary,-0.021,,,0.372,,,,medium,"Model 1 distance-band dummy (3-5 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,283046.0,,p,"Model 1 (constant coefficients): Tucson; Tucson AZ, 1994-2019 (Tucson Mountain Park, county park); hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-2.1051920357105347,2.3034424386030783,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0016780322226868183,0.0018796567541190152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.892733324320856,283036.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-GRSM-TN,2,Panel B: Tennessee,47,"Tennessee, distance band <=1 km",green_space,E_environment,binary,binary,-5.535,,,0.249,,,,medium,"Model 1 distance-band dummy (<=1 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,358668.0,,p,"Model 1 (constant coefficients): Great Smoky Mountains NP - Tennessee; Tennessee side, 1994-2021; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-99.99999610840828,1.8947442448184617,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.001924890395408848,0.001669778766978078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.152781693884193,358658.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,True
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-GRSM-TN,2,Panel B: Tennessee,47,"Tennessee, distance band 1-2 km",green_space,E_environment,binary,binary,-4.067,,,0.398,,,,medium,"Model 1 distance-band dummy (1-2 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,358668.0,,p,"Model 1 (constant coefficients): Great Smoky Mountains NP - Tennessee; Tennessee side, 1994-2021; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-99.99998393646804,8.242136045405674,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.001411295777188309,0.0016697801975216952,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8451985352820499,358658.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-GRSM-TN,2,Panel B: Tennessee,47,"Tennessee, distance band 2-3 km",green_space,E_environment,binary,binary,-6.154,,,0.078,,,,medium,"Model 1 distance-band dummy (2-3 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,358668.0,,p,"Model 1 (constant coefficients): Great Smoky Mountains NP - Tennessee; Tennessee side, 1994-2021; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-99.99952166277536,0.741997135235128,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0029428280031141697,0.0016697746300526597,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7624102978623897,358658.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-GRSM-TN,2,Panel B: Tennessee,47,"Tennessee, distance band 3-5 km",green_space,E_environment,binary,binary,-5.5,,,0.024,,,,medium,"Model 1 distance-band dummy (3-5 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,358668.0,,p,"Model 1 (constant coefficients): Great Smoky Mountains NP - Tennessee; Tennessee side, 1994-2021; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-99.9790085624328,0.9958332233947331,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0037688867011957793,0.0016697700011652032,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2571292444862254,358658.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-GRSM-NC,2,Panel B: North Carolina,47,"North Carolina, distance band <=5 km",green_space,E_environment,binary,binary,-24.04,,,0.0,,,,medium,"Model 1 distance-band dummy (<=5 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,49043.0,,p,"Model 1 (constant coefficients): Great Smoky Mountains NP - North Carolina; North Carolina side, 2005-2021; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-99.99999999637289,,100*(exp(b) - 1),,,,,,49033.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,False,False,False
ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7,ebd764414d12429dec7ee666a2f902c6faad3bb7:T2B-GRSM-NC,2,Panel B: North Carolina,47,"North Carolina, distance band 5-10 km",green_space,E_environment,binary,binary,-12.846,,,0.02,,,,medium,"Model 1 distance-band dummy (5-10 km to park) vs houses outside the farthest band; values for Boston and Tucson sites appear to be log-point coefficients (delta2), whereas Great Smoky Mountains values are in percent; table note says 'percent difference in house prices ... (the estimate of delta2 in Model 1)' - scale ambiguous, recorded as printed",log real sale price of single-family home (CPI less shelter deflated),log,FE_panel,49043.0,,p,"Model 1 (constant coefficients): Great Smoky Mountains NP - North Carolina; North Carolina side, 2005-2021; hedonic controls (house characteristics X, distances to lake/river/highway, PROT_5000 share of protected open space within 5 km) and block group + year-month fixed effects included but coefficients not reported; p-value printed next to each estimate",pct_premium,-99.99999999993693,0.0014559496119161716,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.010505251633626502,0.004515769868664997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3263478740408408,49033.0,binary,,2023.0,United States,North America,High income,1987.0,2021.0,single_family,transaction,journal,True,True,unknown,,2004.0,panel,journal,True,True,True
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,LR,storeys,A_structural,binary,binary,0.0486,,,,b,,,high,"low-rise building (11-12 storeys) dummy vs high-rise (25-26 storeys); building height, not unit floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,0.0486,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,LR,storeys,A_structural,binary,binary,0.0391,,,,b,,,high,"low-rise building (11-12 storeys) dummy vs high-rise (25-26 storeys); building height, not unit floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.0391,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,LR,storeys,A_structural,binary,binary,0.1092,,,,a,,,high,"low-rise building (11-12 storeys) dummy vs high-rise (25-26 storeys); building height, not unit floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,0.1092,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,LR,storeys,A_structural,binary,binary,0.106,,,,b,,,high,"low-rise building (11-12 storeys) dummy vs high-rise (25-26 storeys); building height, not unit floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.106,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,FL,floor_level,A_structural,level,count,0.1181,,,,b,,,high,"floor level 1-10; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,0.1181,,"b (currency per unit, as reported)",,,,,,797.0,count,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,FL,floor_level,A_structural,level,count,0.128,,,,b,,,high,"floor level 1-10; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,0.128,,"b (currency per unit, as reported)",,,,,,797.0,count,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,FL2,floor_level,A_structural,squared,count,-0.0082,,,,b,,,high,"floor level squared; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,other,-0.0082,,not harmonised,,,,,,797.0,count,nonstandard_x_transform,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,FL2,floor_level,A_structural,squared,count,-0.0084,,,,b,,,high,"floor level squared; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,other,-0.0084,,not harmonised,,,,,,797.0,count,nonstandard_x_transform,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(2),floor_level,A_structural,dummy,binary,0.246,,,,b,,,high,"dummy for floor 2 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.246,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(2),floor_level,A_structural,dummy,binary,0.2888,,,,b,,,high,"dummy for floor 2 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.2888,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(3),floor_level,A_structural,dummy,binary,0.2966,,,,b,,,high,"dummy for floor 3 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.2966,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(3),floor_level,A_structural,dummy,binary,0.3455,,,,b,,,high,"dummy for floor 3 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.3455,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(4),floor_level,A_structural,dummy,binary,0.3214,,,,b,,,high,"dummy for floor 4 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.3214,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(4),floor_level,A_structural,dummy,binary,0.4023,,,,b,,,high,"dummy for floor 4 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.4023,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(5),floor_level,A_structural,dummy,binary,0.3345,,,,b,,,high,"dummy for floor 5 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.3345,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(5),floor_level,A_structural,dummy,binary,0.3566,,,,b,,,high,"dummy for floor 5 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.3566,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(6),floor_level,A_structural,dummy,binary,0.349,,,,b,,,high,"dummy for floor 6 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.349,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(6),floor_level,A_structural,dummy,binary,0.4168,,,,b,,,high,"dummy for floor 6 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.4168,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(7),floor_level,A_structural,dummy,binary,0.3739,,,,b,,,high,"dummy for floor 7 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.3739,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(7),floor_level,A_structural,dummy,binary,0.3993,,,,b,,,high,"dummy for floor 7 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.3993,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(8),floor_level,A_structural,dummy,binary,0.3691,,,,b,,,high,"dummy for floor 8 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.3691,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(8),floor_level,A_structural,dummy,binary,0.4365,,,,b,,,high,"dummy for floor 8 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.4365,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(9),floor_level,A_structural,dummy,binary,0.3662,,,,b,,,high,"dummy for floor 9 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.3662,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(9),floor_level,A_structural,dummy,binary,0.4075,,,,b,,,high,"dummy for floor 9 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.4075,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,FL(10),floor_level,A_structural,dummy,binary,0.3611,,,,b,,,high,"dummy for floor 10 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.3611,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,FL(10),floor_level,A_structural,dummy,binary,0.468,,,,b,,,high,"dummy for floor 10 vs 1st floor; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.468,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,AGE,age,A_structural,level,months,0.0022,,,,,,,high,"building age in months; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,0.0022,,"b (currency per unit, as reported)",,,,,,797.0,months,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,AGE,age,A_structural,level,months,0.0017,,,,,,,high,"building age in months; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.0017,,"b (currency per unit, as reported)",,,,,,797.0,months,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,AGE,age,A_structural,level,months,0.0023,,,,,,,high,"building age in months; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,0.0023,,"b (currency per unit, as reported)",,,,,,797.0,months,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,AGE,age,A_structural,level,months,-0.0017,,,,,,,high,"building age in months; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,-0.0017,,"b (currency per unit, as reported)",,,,,,797.0,months,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,SIZE,living_area,A_structural,level,sqft,0.0034,,,,b,,,high,"gross floor area in square feet; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,0.0034,,"b (currency per unit, as reported)",,,,,,797.0,sqft,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,SIZE,living_area,A_structural,level,sqft,0.0033,,,,b,,,high,"gross floor area in square feet; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.0033,,"b (currency per unit, as reported)",,,,,,797.0,sqft,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,SIZE,living_area,A_structural,level,sqft,0.0034,,,,b,,,high,"gross floor area in square feet; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,0.0034,,"b (currency per unit, as reported)",,,,,,797.0,sqft,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,SIZE,living_area,A_structural,level,sqft,0.0033,,,,b,,,high,"gross floor area in square feet; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.0033,,"b (currency per unit, as reported)",,,,,,797.0,sqft,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,RDV,view,E_environment,binary,binary,-0.0935,,,,b,,,high,"road view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,-0.0935,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,RDV,view,E_environment,binary,binary,-0.0997,,,,b,,,high,"road view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,-0.0997,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,RDV,view,E_environment,binary,binary,-0.0966,,,,b,,,high,"road view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,-0.0966,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,RDV,view,E_environment,binary,binary,-0.1059,,,,b,,,high,"road view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,-0.1059,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,GDV,view,E_environment,binary,binary,0.042,,,,b,,,high,"podium garden view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,0.042,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,GDV,view,E_environment,binary,binary,0.047,,,,b,,,high,"podium garden view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.047,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,GDV,view,E_environment,binary,binary,0.0415,,,,b,,,high,"podium garden view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,0.0415,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,GDV,view,E_environment,binary,binary,0.0436,,,,b,,,high,"podium garden view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.0436,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,DPV,view,E_environment,binary,binary,-0.0817,,,,b,,,high,"MTR depot view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,-0.0817,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,DPV,view,E_environment,binary,binary,-0.0839,,,,b,,,high,"MTR depot view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,-0.0839,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,DPV,view,E_environment,binary,binary,-0.0816,,,,b,,,high,"MTR depot view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,-0.0816,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,DPV,view,E_environment,binary,binary,-0.081,,,,b,,,high,"MTR depot view dummy vs building view; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,-0.081,,"b (currency per unit, as reported)",,,,,,797.0,binary,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,MTRD,transit_distance,D_location,level,m,-2.68e-05,,,,,,,medium,"distance to MTR station in metres; printed as -2.68 x 10^-5 etc., insignificant; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,-2.68e-05,,"b (currency per unit, as reported)",,,,,,797.0,m,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,MTRD,transit_distance,D_location,level,m,-9.96e-05,,,,,,,medium,"distance to MTR station in metres; printed as -2.68 x 10^-5 etc., insignificant; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,-9.96e-05,,"b (currency per unit, as reported)",,,,,,797.0,m,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,MTRD,transit_distance,D_location,level,m,-2.13e-05,,,,,,,medium,"distance to MTR station in metres; printed as -2.68 x 10^-5 etc., insignificant; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,-2.13e-05,,"b (currency per unit, as reported)",,,,,,797.0,m,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,MTRD,transit_distance,D_location,level,m,-8.35e-05,,,,,,,medium,"distance to MTR station in metres; printed as -2.68 x 10^-5 etc., insignificant; not significant at 5% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,-8.35e-05,,"b (currency per unit, as reported)",,,,,,797.0,m,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c1,4,Eq. 4a,9,INDEX,other,other,level,index,0.0198,,,,b,,,high,"monthly residential price index (RVD, 1999 = 100), time control; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.852,none,quadratic floor-level specification,monetary,0.0198,,"b (currency per unit, as reported)",,,,,,797.0,index,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c2,4,Eq. 4b,9,INDEX,other,other,level,index,0.0199,,,,b,,,high,"monthly residential price index (RVD, 1999 = 100), time control; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8671,none,floor-level step dummies (base floor 1),monetary,0.0199,,"b (currency per unit, as reported)",,,,,,797.0,index,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c3,4,Eq. 5a,9,INDEX,other,other,level,index,0.0198,,,,b,,,high,"monthly residential price index (RVD, 1999 = 100), time control; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8532,none,quadratic floor specification with LR x FL and LR x FL2 interactions,monetary,0.0198,,"b (currency per unit, as reported)",,,,,,797.0,index,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a,ebefda640bee5302366b339dc1624f32b129581a:T4-c4,4,Eq. 5b,9,INDEX,other,other,level,index,0.0199,,,,b,,,high,"monthly residential price index (RVD, 1999 = 100), time control; significance letters: a = 5%, b = 1% (White SEs, not printed)",transaction price (HK$ million),level,OLS,807.0,0.8698,none,floor dummies with LR x FL(j) interactions,monetary,0.0199,,"b (currency per unit, as reported)",,,,,,797.0,index,,2011.0,Hong Kong,Asia,High income,2000.0,2006.0,condo_apartment,transaction,journal,False,False,robust,,2003.0,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,age,age,A_structural,level,years,-0.0068,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.6799999999999999,0.01,100*b/1 (per year),reported,-0.3175352087712794,0.0046696354231070495,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-68.0,41236.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,age,age,A_structural,level,years,-0.0074,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.74,0.01,100*b/1 (per year),reported,-0.34238717494183607,0.004626853715430218,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-74.0,41236.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,age,age,A_structural,level,years,-0.0076,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.76,0.01,100*b/1 (per year),reported,-0.35051711504104477,0.0046120673031716415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-76.0,41236.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,age,age,A_structural,level,years,-0.0049,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.49,0.01,100*b/1 (per year),reported,-0.2345679310153749,0.004787100632966835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-48.99999999999999,41236.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,age,age,A_structural,level,years,-0.007,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.7000000000000001,0.01,100*b/1 (per year),reported,-0.3469174567047161,0.00495596366721023,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-70.0,35814.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,age,age,A_structural,level,years,-0.0073,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.73,0.01,100*b/1 (per year),reported,-0.3598942286560665,0.004930057926795431,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-73.0,35814.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,age,age,A_structural,level,years,-0.0076,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.76,0.01,100*b/1 (per year),reported,-0.37266545097522125,0.004903492775989754,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-76.0,35814.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,age,age,A_structural,level,years,-0.005,0.0001,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.5,0.01,100*b/1 (per year),reported,-0.25544138300475255,0.005108827660095051,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-50.0,35814.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,age,age,A_structural,level,years,-0.0056,0.0003,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.5599999999999999,0.03,100*b/1 (per year),reported,-0.24594544067140883,0.0131756486073969,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.666666666666668,5412.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,age,age,A_structural,level,years,-0.0074,0.0002,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.74,0.02,100*b/1 (per year),reported,-0.4493190705875161,0.012143758664527462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.0,5412.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,age,age,A_structural,level,years,-0.0074,0.0002,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.74,0.02,100*b/1 (per year),reported,-0.4493190705875161,0.012143758664527462,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-37.0,5412.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,age,age,A_structural,level,years,-0.0046,0.0002,,,,,,high,Age of the building in years,ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,-0.45999999999999996,0.02,100*b/1 (per year),reported,-0.2983994785177526,0.012973890370337072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-23.0,5412.0,years,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,41236.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,41236.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,41236.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,41236.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,35814.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,35814.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,35814.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,35814.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,age2,age,A_structural,squared,years squared,0.0,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0,,not harmonised,,,,,,5412.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,5412.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,5412.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,age2,age,A_structural,squared,years squared,0.0001,0.0,,,,,,high,"Age squared; printed to 4 decimals (0.0001 / 0.0000), SE printed 0.0000",ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,other,0.0001,,not harmonised,,,,,,5412.0,years sqd,nonstandard_x_transform,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0153,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.53,0.01,100*b/1 (X per m2->m2),reported,0.6017608934988574,0.00393307773528665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),153.0,41236.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0167,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.67,0.01,100*b/1 (X per m2->m2),reported,0.6351830480426849,0.003803491305644819,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),167.0,41236.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0181,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.81,0.01,100*b/1 (X per m2->m2),reported,0.6653827206191764,0.0036761476277302565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),181.0,41236.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0172,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.72,0.01,100*b/1 (X per m2->m2),reported,0.6463243017852776,0.0037576994289841713,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),172.0,41236.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.015,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.5,0.01,100*b/1 (X per m2->m2),reported,0.6211619473468838,0.004141079648979225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),150.0,35814.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0165,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.6500000000000001,0.01,100*b/1 (X per m2->m2),reported,0.6571723561131936,0.003982862764322385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),165.0,35814.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0179,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.79,0.01,100*b/1 (X per m2->m2),reported,0.687166696961311,0.0038389200947559275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),178.99999999999997,35814.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0169,0.0001,,,,,,high,,ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.69,0.01,100*b/1 (X per m2->m2),reported,0.6660825920390622,0.003941317112657172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),168.99999999999997,35814.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0167,0.0003,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.67,0.03,100*b/1 (X per m2->m2),reported,0.6034073430838687,0.010839652869770093,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),55.66666666666667,5412.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.018,0.0003,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.7999999999999998,0.03,100*b/1 (X per m2->m2),reported,0.6320343161814018,0.010533905269690029,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.0,5412.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.02,0.0003,,,,,,high,,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,2.0,0.03,100*b/1 (X per m2->m2),reported,0.6715039965266848,0.010072559947900272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),66.66666666666667,5412.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,area,living_area,A_structural,level,m2 (unit not stated; 'area of dwelling'),0.0188,0.0003,,,,,,high,,ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,1.8800000000000001,0.03,100*b/1 (X per m2->m2),reported,0.6484609427337763,0.010347781001070897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),62.66666666666667,5412.0,m2,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0303,0.0015,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0303,0.0015,b (log-log),reported,0.09898626600916596,0.004900310198473563,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.2,41236.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.031,0.0014,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.031,0.0014,b (log-log),reported,0.10839985173743932,0.004895477175239195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),22.142857142857142,41236.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0244,0.0016,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0244,0.0016,b (log-log),reported,0.07488767599420604,0.004910667278308593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.25,41236.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0297,0.0014,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0297,0.0014,b (log-log),reported,0.10390419029866543,0.004897840620139111,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.214285714285715,41236.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0308,0.0016,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0308,0.0016,b (log-log),reported,0.10119733387777068,0.005257004357286789,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.25,35814.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0322,0.0015,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0322,0.0015,b (log-log),reported,0.11270988142668895,0.00525046031490787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.466666666666665,35814.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0247,0.0018,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0247,0.0018,b (log-log),reported,0.07232015173183706,0.005270294458190554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.722222222222223,35814.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0306,0.0015,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0306,0.0015,b (log-log),reported,0.10717538232539056,0.00525369521202895,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),20.4,35814.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0183,0.0046,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0183,0.0046,b (log-log),reported,0.05399832359647695,0.013573349100753768,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.9782608695652177,5412.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0219,0.0044,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0219,0.0044,b (log-log),reported,0.0675026509070573,0.013562176437947587,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.977272727272727,5412.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0233,0.0046,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0233,0.0046,b (log-log),reported,0.06868979298438814,0.013561075009793361,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.065217391304349,5412.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,lnairbnb,other,other,log,count (monthly number of active Airbnb apartments in the district),0.0228,0.004,,,,,,high,Short-term rental (Airbnb) supply in district; elasticity of rent w.r.t. Airbnb listings,ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,0.0228,0.004,b (log-log),reported,0.07724960251196249,0.013552561844203946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.7,5412.0,count,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,False,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,lncc,cbd_distance,D_location,log,m,-0.08,0.0024,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.08,0.0024,b (log-log),reported,-0.1619820351151483,0.004859461053454448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-33.333333333333336,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,lncc,cbd_distance,D_location,log,m,-0.096,0.0021,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.096,0.0021,b (log-log),reported,-0.21962342673642132,0.004804262459859216,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-45.714285714285715,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,lncc,cbd_distance,D_location,log,m,-0.1305,0.0025,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1305,0.0025,b (log-log),reported,-0.24896454967928502,0.0047694358176108235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-52.2,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,lncc,cbd_distance,D_location,log,m,-0.1125,0.0021,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1125,0.0021,b (log-log),reported,-0.25508498378091227,0.004761586363910362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-53.57142857142858,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,lncc,cbd_distance,D_location,log,m,-0.0799,0.0026,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0799,0.0026,b (log-log),reported,-0.1602858736668837,0.0052158106574955905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-30.73076923076923,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,lncc,cbd_distance,D_location,log,m,-0.0944,0.0023,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0944,0.0023,b (log-log),reported,-0.2119516597902561,0.005164076456754121,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-41.04347826086956,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,lncc,cbd_distance,D_location,log,m,-0.1302,0.0027,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1302,0.0027,b (log-log),reported,-0.24692234354174475,0.0051205094282850295,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-48.22222222222222,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,lncc,cbd_distance,D_location,log,m,-0.1111,0.0023,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1111,0.0023,b (log-log),reported,-0.24731716726520364,0.005119977360125727,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-48.30434782608696,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,lncc,cbd_distance,D_location,log,m,-0.0894,0.0065,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0894,0.0065,b (log-log),reported,-0.18377433150935965,0.013361668398331519,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.753846153846153,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,lncc,cbd_distance,D_location,log,m,-0.1088,0.006,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1088,0.006,b (log-log),reported,-0.23932648591671735,0.013198151796877795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-18.133333333333333,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,lncc,cbd_distance,D_location,log,m,-0.1359,0.0063,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1359,0.0063,b (log-log),reported,-0.2813772607441109,0.01304397897489256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-21.57142857142857,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,lncc,cbd_distance,D_location,log,m,-0.1232,0.0055,,,,,,high,Log distance to the city centre in metres,ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.1232,0.0055,b (log-log),reported,-0.29128369200228493,0.013003736250102003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.400000000000002,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0179,0.0014,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0179,0.0014,b (log-log),reported,-0.0628387563267251,0.004914763064660065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.785714285714285,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0168,0.0012,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0168,0.0012,b (log-log),reported,-0.0687796698252508,0.004912833558946486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.0,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0179,0.0012,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0179,0.0012,b (log-log),reported,-0.07325966967066262,0.004911262771217606,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.916666666666668,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0123,0.0012,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0123,0.0012,b (log-log),reported,-0.05041189793539793,0.004918233944916871,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.250000000000002,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0191,0.0015,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0191,0.0015,b (log-log),reported,-0.06713281206058405,0.005272210371250056,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.733333333333333,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0181,0.0012,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0181,0.0012,b (log-log),reported,-0.07945035796268024,0.0052674270472495175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-15.083333333333336,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0191,0.0013,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0191,0.0013,b (log-log),reported,-0.07740316229841025,0.0052682780621954636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.692307692307692,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0137,0.0012,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0137,0.0012,b (log-log),reported,-0.060217685980920505,0.005274541837744861,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.416666666666668,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0066,0.0043,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0066,0.0043,b (log-log),reported,-0.02085941292855464,0.013590223574664386,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5348837209302326,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.006,0.0038,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.006,0.0038,b (log-log),reported,-0.02145797586481132,0.013590051381047168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5789473684210527,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0082,0.0036,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0082,0.0036,b (log-log),reported,-0.03094741557507248,0.013586670252470845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.277777777777778,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,lnsubway,transit_distance,D_location,log,m (unit not stated; presumably metres),-0.0024,0.0035,,,,,,high,Log distance to the nearest subway station,ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0024,0.0035,b (log-log),reported,-0.009320633646447165,0.013592590734402117,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.6857142857142856,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,lnuga,green_space,E_environment,log,m,-0.0043,0.0012,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0043,0.0012,b (log-log),reported,-0.017643361725734486,0.004923728853693345,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5833333333333335,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,lnuga,green_space,E_environment,log,m,-0.0069,0.0011,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0069,0.0011,b (log-log),reported,-0.030875289505175586,0.004922147602274368,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.2727272727272725,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,lnuga,green_space,E_environment,log,m,-0.013,0.0012,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.013,0.0012,b (log-log),reported,-0.05327294420411994,0.004917502541918762,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.833333333333334,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,lnuga,green_space,E_environment,log,m,-0.0093,0.0011,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0093,0.0011,b (log-log),reported,-0.041598331952356175,0.004920232811569011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.454545454545453,41236.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,lnuga,green_space,E_environment,log,m,-0.0037,0.0013,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0037,0.0013,b (log-log),reported,-0.01503774954072118,0.00528353362241555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8461538461538463,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,lnuga,green_space,E_environment,log,m,-0.007,0.0011,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.007,0.0011,b (log-log),reported,-0.03360729395070938,0.005281146192254332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.363636363636363,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,lnuga,green_space,E_environment,log,m,-0.0139,0.0013,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0139,0.0013,b (log-log),reported,-0.05640959218116617,0.00527571725435367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-10.692307692307692,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,lnuga,green_space,E_environment,log,m,-0.0091,0.0011,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0091,0.0011,b (log-log),reported,-0.04367246802221729,0.005279089541147144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.272727272727273,35814.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,lnuga,green_space,E_environment,log,m,-0.01,0.0035,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.01,0.0035,b (log-log),reported,-0.03880840299107001,0.013582941046874504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.857142857142857,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,lnuga,green_space,E_environment,log,m,-0.0066,0.0033,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0066,0.0033,b (log-log),reported,-0.0271763212599322,0.013588160629966099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,lnuga,green_space,E_environment,log,m,-0.0065,0.0033,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0065,0.0033,b (log-log),reported,-0.02676485605306519,0.013588311534633099,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9696969696969697,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,lnuga,green_space,E_environment,log,m,-0.0115,0.003,,,,,,high,Log distance to the nearest urban green area in metres,ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,elasticity,-0.0115,0.003,b (log-log),reported,-0.05203659863228437,0.013574764860595922,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.833333333333333,5412.0,m,,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,True,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c1,B1,1,10,quality,condition,A_structural,level,ordinal 1-5,0.0456,0.0015,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.5600000000000005,0.15,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.14805478856362875,0.004870223308014103,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),30.400000000000002,41236.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c2,B1,2,10,quality,condition,A_structural,level,ordinal 1-5,0.0462,0.0013,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.62,0.13,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.17238892376041554,0.004850770582003034,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),35.53846153846154,41236.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c3,B1,3,10,quality,condition,A_structural,level,ordinal 1-5,0.0417,0.0016,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,41246.0,,se,January 2017-December 2020; quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.17,0.16,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.12730047427329957,0.004884430667560655,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),26.0625,41236.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c4,B1,4,10,quality,condition,A_structural,level,ordinal 1-5,0.052,0.0013,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,OLS,41246.0,0.8184,se,January 2017-December 2020; OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,5.2,0.13,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.1932660347854726,0.004831650869636815,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),40.0,41236.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c5,B1,5,10,quality,condition,A_structural,level,ordinal 1-5,0.0458,0.0016,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.58,0.16,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.14955706609912475,0.005224700999096062,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.625,35814.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c6,B1,6,10,quality,condition,A_structural,level,ordinal 1-5,0.0473,0.0014,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.73,0.13999999999999999,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.17574934441287568,0.005201883344144312,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),33.785714285714285,35814.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c7,B1,7,10,quality,condition,A_structural,level,ordinal 1-5,0.0427,0.0018,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,35824.0,,se,January 2017-March 2020 (before lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.2700000000000005,0.18,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.12437796849861182,0.00524309937464874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),23.722222222222225,35814.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c8,B1,8,10,quality,condition,A_structural,level,ordinal 1-5,0.0521,0.0014,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,OLS,35824.0,0.817,se,January 2017-March 2020 (before lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,5.21,0.13999999999999999,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.19294992803431907,0.005184834918388611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),37.214285714285715,35814.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c9,B1,9,10,quality,condition,A_structural,level,ordinal 1-5,0.0469,0.0041,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.25; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.6899999999999995,0.41000000000000003,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.15364639431102284,0.013431774342754664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.439024390243901,5412.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c10,B1,10,10,quality,condition,A_structural,level,ordinal 1-5,0.0418,0.0038,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.50 (median); apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,4.18,0.38,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.1478809902466911,0.013443726386062827,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.0,5412.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c11,B1,11,10,quality,condition,A_structural,level,ordinal 1-5,0.034,0.0038,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,quantile,5422.0,,se,April 2020-December 2020 (after lockdown); quantile 0.75; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,3.4000000000000004,0.38,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.12073352081001253,0.013493746443471987,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.947368421052632,5412.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,quantile,journal,False,True,False
ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501,ede799d5a89d38a16239a4734a2cb00510a8c501:TB1-c12,B1,12,10,quality,condition,A_structural,level,ordinal 1-5,0.0504,0.0035,,,,,,high,Quality of the apartment (values 1-5),ln asking rent (long-term rental offers),log,OLS,5422.0,0.8156,se,April 2020-December 2020 (after lockdown); OLS; apartments offered for long-term rent in the 7 Warsaw districts with Airbnb activity; district ('area') effects and year-quarter time dummies q2-q16 included,semi_elasticity,5.04,0.35000000000000003,100*b (unit as reported: ordinal 1-5; no harmonised unit defined for this characteristic),reported,0.1920963284450464,0.013340022808683778,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.4,5412.0,ordinal 1-5,unit_not_harmonisable,2021.0,Poland,Europe,High income,2017.0,2020.0,condo_apartment,rent,journal,True,True,unknown,,2018.5,ols,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Days BWNs_year,other,other,level,days,-0.0664,0.0173,,,***,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-6.64,1.73,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.08604482382042462,0.02241830500140581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.8381502890173413,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Days BWNs_year,other,other,level,days,-0.0271,0.0121,,,**,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-2.71,1.21,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.05033262231994065,0.022473237272003024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.239669421487603,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Days BWNs_year,other,other,level,days,-0.0155,0.0095,,,,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-1.55,0.95,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.036688677142973324,0.02248660857149978,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.631578947368421,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Days BWNs_year,other,other,level,days,-0.0075,0.0048,,,,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.75,0.48,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.03513728604433628,0.022487863068375222,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5625,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Days BWNs_year,other,other,level,days,-0.0064,0.0033,,,**,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.64,0.33,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.04359827794375525,0.022480362064748804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9393939393939394,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Days BWNs_year,other,other,level,days,-0.0049,0.0025,,,**,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.49,0.25,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.04406061507585758,0.02247990565094775,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.96,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Days BWNs_year,other,other,level,days,-0.0048,0.0034,,,,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.48,0.33999999999999997,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.03175117091664508,0.022490412732623605,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4117647058823528,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Days BWNs_year,other,other,level,days,-0.0015,0.0033,,,,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.15,0.33,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,-0.010227536871615327,0.02250058111755372,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.45454545454545453,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Days BWNs_year,other,other,level,days,0.0009,0.003,,,,,,high,Water-supply unreliability: number of days the house was under a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.09,0.3,100*b (unit as reported: days; no harmonised unit defined for this characteristic),reported,0.006750373601327533,0.022501245337758446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.3,1975.0,days,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,SQFT,living_area,A_structural,level,sqft,0.0003,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.32291731250129163,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Story height,storeys,A_structural,level,count,-0.0575,0.0306,,,*,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-5.75,3.06,100*b/1 (per unit count),reported,-0.042244968725283855,0.022481670312933667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8790849673202616,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,True
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Story height,storeys,A_structural,level,count,-0.0303,0.0283,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-3.0300000000000002,2.83,100*b/1 (per unit count),reported,-0.02408499952647783,0.022495230580835728,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0706713780918728,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Story height,storeys,A_structural,level,count,-0.046,0.0244,,,*,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-4.6,2.44,100*b/1 (per unit count),reported,-0.042383228417925196,0.022481538552116844,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8852459016393441,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Story height,storeys,A_structural,level,count,-0.0413,0.0192,,,**,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-4.130000000000001,1.92,100*b/1 (per unit count),reported,-0.04834562074971869,0.022475445965970915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.151041666666667,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Story height,storeys,A_structural,level,count,-0.0323,0.0173,,,*,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-3.2300000000000004,1.73,100*b/1 (per unit count),reported,-0.0419749262029845,0.022481926418316773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8670520231213874,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Story height,storeys,A_structural,level,count,-0.0266,0.0198,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-2.6599999999999997,1.9800000000000002,100*b/1 (per unit count),reported,-0.03021583158358071,0.022491483659958577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3434343434343432,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Story height,storeys,A_structural,level,count,-0.0092,0.0243,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.9199999999999999,2.4299999999999997,100*b/1 (per unit count),reported,-0.008518874976330472,0.02250094151356853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3786008230452675,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Story height,storeys,A_structural,level,count,0.0052,0.0225,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.52,2.25,100*b/1 (per unit count),reported,0.005200335978559193,0.022501453753381122,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2311111111111111,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Story height,storeys,A_structural,level,count,-0.0063,0.0222,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.63,2.22,100*b/1 (per unit count),reported,-0.006385503844929527,0.022501299263085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.28378378378378377,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Construction material,roof_materials,A_structural,binary,binary,-0.0672,0.0286,,,**,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-6.537414046045431,2.674123397657616,100*(exp(b - se^2/2) - 1),reported,-0.0527975207782204,0.022470373426444992,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.3496503496503496,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Construction material,roof_materials,A_structural,binary,binary,-0.0668,0.0231,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-6.486727229185007,2.1607330184798705,100*(exp(b - se^2/2) - 1),reported,-0.06493269805578154,0.02245427133366098,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.891774891774892,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Construction material,roof_materials,A_structural,binary,binary,-0.0636,0.0185,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-6.178029000501139,1.736003511674754,100*(exp(b - se^2/2) - 1),reported,-0.07712696903147646,0.02243473155789803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4378378378378383,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Construction material,roof_materials,A_structural,binary,binary,-0.0525,0.0154,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.125818741883026,1.461235654425934,100*(exp(b - se^2/2) - 1),reported,-0.07648582810656243,0.022435842911258313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.4090909090909087,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Construction material,roof_materials,A_structural,binary,binary,-0.0297,0.0141,,,**,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-2.935978040381859,1.3687387623450147,100*(exp(b - se^2/2) - 1),reported,-0.047344170434756604,0.022476525357914747,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1063829787234045,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Construction material,roof_materials,A_structural,binary,binary,-0.0298,0.016,,,*,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-2.948459185762109,1.5530234273046761,100*(exp(b - se^2/2) - 1),reported,-0.04187276760558521,0.02248202287548199,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8625,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Construction material,roof_materials,A_structural,binary,binary,-0.0285,0.0168,,,*,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-2.823485102842227,1.6327958541655092,100*(exp(b - se^2/2) - 1),reported,-0.03814484396003178,0.022485381702755575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6964285714285716,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Construction material,roof_materials,A_structural,binary,binary,-0.0256,0.0174,,,,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-2.542264108602277,1.6960213287871213,100*(exp(b - se^2/2) - 1),reported,-0.03308790742036723,0.022489437074780842,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4712643678160922,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Construction material,roof_materials,A_structural,binary,binary,-0.0311,0.0159,,,*,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.0743896195688625,1.5413120222818362,100*(exp(b - se^2/2) - 1),reported,-0.04397030504358065,0.022479995183052486,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9559748427672954,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Physical condition,condition,A_structural,level,index,0.2683,0.0281,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,26.83,2.81,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.21005442749345618,0.021999736908558026,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.548042704626335,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Physical condition,condition,A_structural,level,index,0.2637,0.0217,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,26.369999999999997,2.17,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.26375995417635223,0.02170493365804643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.152073732718893,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Physical condition,condition,A_structural,level,index,0.2266,0.0187,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,22.66,1.87,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2630645042782757,0.021709206663741203,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.117647058823529,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Physical condition,condition,A_structural,level,index,0.1998,0.0146,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,19.98,1.46,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2942977410144653,0.02150524033439036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.684931506849315,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Physical condition,condition,A_structural,level,index,0.1758,0.0156,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,17.580000000000002,1.5599999999999998,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.24579801708010252,0.021811428136800905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.26923076923077,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Physical condition,condition,A_structural,level,index,0.1634,0.0158,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,16.34,1.58,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.22665201415075956,0.0219161678309792,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.341772151898732,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Physical condition,condition,A_structural,level,index,0.1472,0.0181,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,14.719999999999999,1.81,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1800084660261916,0.02213419317305753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.132596685082872,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Physical condition,condition,A_structural,level,index,0.1373,0.0167,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,13.73,1.67,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.18191270862095868,0.022126309060233138,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.221556886227546,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Physical condition,condition,A_structural,level,index,0.1473,0.0159,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,14.729999999999999,1.59,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.20407279792928718,0.022028224623731614,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.264150943396224,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Quality grade factor,condition,A_structural,level,index,0.4243,0.1245,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,42.43,12.45,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.07646221237406696,0.02243588366856313,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4080321285140562,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Quality grade factor,condition,A_structural,level,index,0.3806,0.0792,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,38.06,7.920000000000001,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.10750674518987938,0.022371345819859292,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.805555555555555,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Quality grade factor,condition,A_structural,level,index,0.3937,0.0677,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,39.37,6.77,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.12974971231544186,0.02231154565343006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.8153618906942395,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Quality grade factor,condition,A_structural,level,index,0.3875,0.0544,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,38.75,5.4399999999999995,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.15826359113564142,0.02221816608459069,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.123161764705883,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Quality grade factor,condition,A_structural,level,index,0.3909,0.0488,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,39.09,4.88,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.17738617396904613,0.022144909925017782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.010245901639344,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Quality grade factor,condition,A_structural,level,index,0.3472,0.0507,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,34.72,5.07,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.15229732004360205,0.022239268796689585,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.848126232741618,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Quality grade factor,condition,A_structural,level,index,0.364,0.0668,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,36.4,6.68,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.12170292484046075,0.02233449280039225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.449101796407185,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Quality grade factor,condition,A_structural,level,index,0.4398,0.0532,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,43.980000000000004,5.319999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.18288288752699472,0.022122259246103046,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.266917293233083,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Quality grade factor,condition,A_structural,level,index,0.4374,0.0657,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,43.74,6.569999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14815303025138898,0.02225343870031151,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.657534246575343,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Age,age,A_structural,level,years,-0.0025,0.0007,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.25,0.06999999999999999,100*b/1 (per year),reported,-0.08010516701400018,0.02242944676392005,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5714285714285716,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Age,age,A_structural,level,years,-0.0025,0.0005,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.25,0.05,100*b/1 (per year),reported,-0.11180339887498948,0.022360679774997897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Age,age,A_structural,level,years,-0.0026,0.0005,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.26,0.05,100*b/1 (per year),reported,-0.1162162796335999,0.02234928454492306,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.199999999999999,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Age,age,A_structural,level,years,-0.0024,0.0003,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.24,0.03,100*b/1 (per year),reported,-0.17716640556940677,0.022145800696175846,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Age,age,A_structural,level,years,-0.0025,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.25,0.04,100*b/1 (per year),reported,-0.1392655009388382,0.02228248015021411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Age,age,A_structural,level,years,-0.0026,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.26,0.04,100*b/1 (per year),reported,-0.14472164640665922,0.022264868677947577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.499999999999999,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Age,age,A_structural,level,years,-0.0025,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.25,0.04,100*b/1 (per year),reported,-0.1392655009388382,0.02228248015021411,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.25,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Age,age,A_structural,level,years,-0.0021,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.21,0.04,100*b/1 (per year),reported,-0.11731843575418992,0.0223463687150838,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.249999999999999,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Age,age,A_structural,level,years,-0.0016,0.0003,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.16,0.03,100*b/1 (per year),reported,-0.11915439776336491,0.022341449580630914,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.333333333333334,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Bedrooms,bedrooms,A_structural,level,count,0.0235,0.0264,,,,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.35,2.64,100*b/1 (per unit count),reported,0.02002595719101095,0.02249724552522081,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8901515151515151,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Bedrooms,bedrooms,A_structural,level,count,0.0198,0.0212,,,,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,1.9800000000000002,2.12,100*b/1 (per unit count),reported,0.02101115344825426,0.022496790560757086,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9339622641509435,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Bedrooms,bedrooms,A_structural,level,count,0.0411,0.0164,,,**,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.109999999999999,1.6400000000000001,100*b/1 (per unit count),reported,0.05630215116775526,0.02246606518616025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5060975609756095,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Bedrooms,bedrooms,A_structural,level,count,0.0487,0.015,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.87,1.5,100*b/1 (per unit count),reported,0.07286153047869028,0.02244194983943232,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2466666666666666,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Bedrooms,bedrooms,A_structural,level,count,0.0518,0.0126,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,5.18,1.26,100*b/1 (per unit count),reported,0.09211393054213171,0.02240609121295096,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.111111111111111,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Bedrooms,bedrooms,A_structural,level,count,0.0463,0.0158,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.63,1.58,100*b/1 (per unit count),reported,0.06579581391472535,0.02245299913288684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9303797468354427,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Bedrooms,bedrooms,A_structural,level,count,0.0319,0.0152,,,**,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,3.19,1.52,100*b/1 (per unit count),reported,0.04717151462455008,0.022476709162795025,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0986842105263155,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Bedrooms,bedrooms,A_structural,level,count,0.0448,0.014,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.4799999999999995,1.4000000000000001,100*b/1 (per unit count),reported,0.07181968066622665,0.022443650208195828,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.1999999999999997,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Bedrooms,bedrooms,A_structural,level,count,0.0474,0.0133,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.74,1.3299999999999998,100*b/1 (per unit count),reported,0.07993760331533249,0.022429749453458273,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.56390977443609,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,Total bathrooms,bathrooms,A_structural,level,count,0.0059,0.032,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.59,3.2,100*b/1 (per unit count),reported,0.004148725930420199,0.022501564368380744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18437499999999998,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,Total bathrooms,bathrooms,A_structural,level,count,0.0258,0.0192,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.58,1.92,100*b/1 (per unit count),reported,0.030222924683891112,0.022491478834523616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3437500000000002,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,Total bathrooms,bathrooms,A_structural,level,count,0.0372,0.0153,,,**,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,3.7199999999999998,1.53,100*b/1 (per unit count),reported,0.05462846084960648,0.0224681572849188,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4313725490196076,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,Total bathrooms,bathrooms,A_structural,level,count,0.0572,0.0134,,,***,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,5.72,1.34,100*b/1 (per unit count),reported,0.09561223197653544,0.02239866972876879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.268656716417911,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,Total bathrooms,bathrooms,A_structural,level,count,0.034,0.0123,,,***,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,3.4000000000000004,1.23,100*b/1 (per unit count),reported,0.06208000870237194,0.022458356089387493,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7642276422764227,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,Total bathrooms,bathrooms,A_structural,level,count,0.028,0.0142,,,**,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.8000000000000003,1.4200000000000002,100*b/1 (per unit count),reported,0.04432605353074288,0.022479641433448175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.971830985915493,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,Total bathrooms,bathrooms,A_structural,level,count,0.0279,0.0145,,,*,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.79,1.4500000000000002,100*b/1 (per unit count),reported,0.043255961597428755,0.022480696887552575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9241379310344828,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,Total bathrooms,bathrooms,A_structural,level,count,0.0152,0.0149,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,1.52,1.49,100*b/1 (per unit count),reported,0.022948768223110223,0.022495832008180415,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0201342281879195,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,Total bathrooms,bathrooms,A_structural,level,count,0.0152,0.0178,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,1.52,1.78,100*b/1 (per unit count),reported,0.01921143812110656,0.02249760516813795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8539325842696629,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,21,AC,air_conditioning,A_structural,binary,binary,0.2074,0.054,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,22.868193837620332,6.644563181394435,100*(exp(b - se^2/2) - 1),reported,0.08610246696537205,0.02241819294180371,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.840740740740741,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,21,AC,air_conditioning,A_structural,binary,binary,0.2328,0.0335,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,26.142101478696244,4.228132244729097,100*(exp(b - se^2/2) - 1),reported,0.1544930236731349,0.022231599196950252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.949253731343283,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,True
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,21,AC,air_conditioning,A_structural,binary,binary,0.2202,0.0309,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,24.57311106125033,3.8511472499471857,100*(exp(b - se^2/2) - 1),reported,0.15832969772038955,0.02221792760926447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.12621359223301,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,21,AC,air_conditioning,A_structural,binary,binary,0.2217,0.025,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,24.780686205221002,3.120492156577545,100*(exp(b - se^2/2) - 1),reported,0.195687631359129,0.022066715308877875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.868,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,21,AC,air_conditioning,A_structural,binary,binary,0.2169,0.0244,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,24.18501449601811,3.031016492411316,100*(exp(b - se^2/2) - 1),reported,0.19614053023201608,0.022064679288433347,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.889344262295081,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,21,AC,air_conditioning,A_structural,binary,binary,0.2099,0.0259,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,23.314102728288756,3.1949066686460346,100*(exp(b - se^2/2) - 1),reported,0.1794012009089977,0.02213668939277294,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.104247104247104,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,21,AC,air_conditioning,A_structural,binary,binary,0.1711,0.0233,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,18.628734855174066,2.7647999113881885,100*(exp(b - se^2/2) - 1),reported,0.16302759357810512,0.022200718470893333,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.343347639484978,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,21,AC,air_conditioning,A_structural,binary,binary,0.1615,0.0271,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,17.484097146226183,3.184988361609197,100*(exp(b - se^2/2) - 1),reported,0.132907541387981,0.022302132331976997,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.959409594095941,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,21,AC,air_conditioning,A_structural,binary,binary,0.0909,0.0371,,,**,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.440604889011507,4.063041685024519,100*(exp(b - se^2/2) - 1),reported,0.0550487408204113,0.02246763789259911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4501347708894876,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Fire place,fireplace,B_interior,binary,binary,0.1527,0.0362,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,16.421236869473233,4.217211070658037,100*(exp(b - se^2/2) - 1),reported,0.0944929308514448,0.022401074635378534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.218232044198895,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Fire place,fireplace,B_interior,binary,binary,0.1059,0.022,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,11.144169843892083,2.4457635397311694,100*(exp(b - se^2/2) - 1),reported,0.10768542914567668,0.022370910681821406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.8136363636363635,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Fire place,fireplace,B_interior,binary,binary,0.113,0.0191,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,11.94277251106881,2.1384969919317993,100*(exp(b - se^2/2) - 1),reported,0.13196138196281026,0.02230497695123607,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.916230366492147,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Fire place,fireplace,B_interior,binary,binary,0.1052,0.0138,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,11.082699671083308,1.533087229076988,100*(exp(b - se^2/2) - 1),reported,0.16906585089205012,0.022177839755801253,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.6231884057971016,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Fire place,fireplace,B_interior,binary,binary,0.0905,0.0158,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.458487303060625,1.7296599820738017,100*(exp(b - se^2/2) - 1),reported,0.12782928970044333,0.022317157759856408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.727848101265822,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Fire place,fireplace,B_interior,binary,binary,0.0867,0.015,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.044677763657782,1.635854189699706,100*(exp(b - se^2/2) - 1),reported,0.12897389742876822,0.022313823084561975,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.78,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Fire place,fireplace,B_interior,binary,binary,0.0799,0.0163,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,8.303485906158258,1.7655813533466467,100*(exp(b - se^2/2) - 1),reported,0.10963512899487267,0.02236611517667615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.901840490797547,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Fire place,fireplace,B_interior,binary,binary,0.0667,0.0176,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,6.880918765778832,1.881395538254554,100*(exp(b - se^2/2) - 1),reported,0.08496816044991524,0.022420384166694285,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.7897727272727266,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Fire place,fireplace,B_interior,binary,binary,0.0553,0.0153,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,5.673393174727548,1.6169921653457382,100*(exp(b - se^2/2) - 1),reported,0.0810622303428218,0.022427705682552857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6143790849673207,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Basement garage,garage,A_structural,binary,binary,0.0573,0.0263,,,**,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,5.860727743237781,2.785100186079013,100*(exp(b - se^2/2) - 1),reported,0.048965935082826664,0.022474766015328825,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1787072243346004,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Basement garage,garage,A_structural,binary,binary,0.0169,0.0203,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,1.6834077714927398,2.064598534143488,100*(exp(b - se^2/2) - 1),reported,0.018729704593707056,0.022497810843328594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8325123152709359,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Basement garage,garage,A_structural,binary,binary,0.0298,0.0168,,,*,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,3.0103085249393535,1.730817418939464,100*(exp(b - se^2/2) - 1),reported,0.03988207696993092,0.022483855472981186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.773809523809524,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Basement garage,garage,A_structural,binary,binary,0.0159,0.014,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,1.5927511843224273,1.4224379086652394,100*(exp(b - se^2/2) - 1),reported,0.025547227115240777,0.022494413812161693,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1357142857142857,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Basement garage,garage,A_structural,binary,binary,0.0113,0.0144,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,1.125923337094492,1.4563642840757791,100*(exp(b - se^2/2) - 1),reported,0.017654877446975897,0.022498250905880788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.7847222222222222,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Basement garage,garage,A_structural,binary,binary,0.0142,0.0144,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,1.4196141606379165,1.4605938704355859,100*(exp(b - se^2/2) - 1),reported,0.02218377304924891,0.02249622055698481,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9861111111111112,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Basement garage,garage,A_structural,binary,binary,0.0085,0.0185,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,0.8363656576123146,1.865792021008273,100*(exp(b - se^2/2) - 1),reported,0.010338093083900622,0.022500555535548406,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.45945945945945954,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Basement garage,garage,A_structural,binary,binary,0.0032,0.0172,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,0.3056742338233054,1.7255128158010131,100*(exp(b - se^2/2) - 1),reported,0.004186336900709637,0.022501560841314296,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.18604651162790697,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Basement garage,garage,A_structural,binary,binary,-0.0058,0.0179,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-0.5942478214090441,1.779648049675891,100*(exp(b - se^2/2) - 1),reported,-0.007290878642561196,0.022501159948594036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3240223463687151,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Lot size,lot_size,A_structural,level,acres,-0.0009,0.0075,,,,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-2.223948433204488e-05,0.000185329036100374,100*b/4046.86 (X per acre->m2),reported,-0.002700201118469234,0.02250167598724362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.12,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Lot size,lot_size,A_structural,level,acres,0.0088,0.0065,,,,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0002174527356911055,0.00016061849795365746,100*b/4046.86 (X per acre->m2),reported,0.030449792354873283,0.02249132389848595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.353846153846154,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Lot size,lot_size,A_structural,level,acres,0.0112,0.0044,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.00027675802724322514,0.00010872636784555275,100*b/4046.86 (X per acre->m2),reported,0.057183478747808276,0.02246493807949611,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.5454545454545454,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Lot size,lot_size,A_structural,level,acres,0.0105,0.005,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0002594606505405236,0.00012355269073358266,100*b/4046.86 (X per acre->m2),reported,0.047201023372093365,0.022476677796234936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.1,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Lot size,lot_size,A_structural,level,acres,0.0117,0.0065,,,*,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.00028911329631658343,0.00016061849795365746,100*b/4046.86 (X per acre->m2),reported,0.040469982405454144,0.022483323558585636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Lot size,lot_size,A_structural,level,acres,0.0108,0.0073,,,,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.00026687381198453857,0.00018038692847103068,100*b/4046.86 (X per acre->m2),reported,0.033271840612988574,0.022489299673594126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4794520547945207,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Lot size,lot_size,A_structural,level,acres,0.0244,0.0102,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0006029371307798833,0.00025204748909650866,100*b/4046.86 (X per acre->m2),reported,0.0537499230041436,0.02246923010828954,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.392156862745098,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Lot size,lot_size,A_structural,level,acres,0.0212,0.01,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0005238634087103906,0.0002471053814671653,100*b/4046.86 (X per acre->m2),reported,0.047649541075213894,0.02247619862038391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.12,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Lot size,lot_size,A_structural,level,acres,0.0381,0.007,,,***,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0009414715033898999,0.00017297376702701575,100*b/4046.86 (X per acre->m2),reported,0.12156551596490041,0.022334871699588,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.442857142857143,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Sidewalk,other,other,binary,binary,-0.0753,0.073,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-7.500273663646828,6.770495996696638,100*(exp(b - se^2/2) - 1),reported,-0.02320446780194452,0.022495699197104247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0315068493150685,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Sidewalk,other,other,binary,binary,-0.0562,0.0529,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.597176658001713,5.000901748485797,100*(exp(b - se^2/2) - 1),reported,-0.023898631657394268,0.022495331221995672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0623818525519848,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Sidewalk,other,other,binary,binary,-0.0216,0.0462,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-2.2412248699110737,4.521278035496768,100*(exp(b - se^2/2) - 1),reported,-0.010519720322024096,0.022500512910995984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4675324675324676,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Sidewalk,other,other,binary,binary,-0.035,0.0325,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.49044094651898,3.138217602837091,100*(exp(b - se^2/2) - 1),reported,-0.024225550638076066,0.02249515416392777,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.076923076923077,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Sidewalk,other,other,binary,binary,-0.0415,0.0291,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-4.105677198385493,2.7917065658436844,100*(exp(b - se^2/2) - 1),reported,-0.03207362591673346,0.022490181064504667,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4261168384879725,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Sidewalk,other,other,binary,binary,-0.0376,0.0298,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.7329436799718674,2.8700323472226863,100*(exp(b - se^2/2) - 1),reported,-0.028380043982042732,0.02249269443257642,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.261744966442953,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Sidewalk,other,other,binary,binary,-0.0559,0.0315,,,*,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.483534162495863,2.9787461377752913,100*(exp(b - se^2/2) - 1),reported,-0.03989989289781485,0.022483839468357206,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7746031746031745,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Sidewalk,other,other,binary,binary,-0.0885,0.0298,,,***,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-8.510321193685911,2.727603269993273,100*(exp(b - se^2/2) - 1),reported,-0.06667697781265138,0.022451682924486002,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.969798657718121,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Sidewalk,other,other,binary,binary,-0.0933,0.041,,,**,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-8.9845123503144,3.7347727513072027,100*(exp(b - se^2/2) - 1),reported,-0.05113822240491697,0.022472316383725575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2756097560975608,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Fence,other,other,binary,binary,0.1351,0.0308,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,14.410844151584268,3.5255258307580832,100*(exp(b - se^2/2) - 1),reported,0.09822361206317637,0.022392947827874408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.386363636363636,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Fence,other,other,binary,binary,0.1127,0.0249,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,11.894916013991331,2.78704727343028,100*(exp(b - se^2/2) - 1),reported,0.10132118666665912,0.022385958722269853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.526104417670683,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Fence,other,other,binary,binary,0.0967,0.016,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,10.138887828829946,1.762447784140293,100*(exp(b - se^2/2) - 1),reported,0.1347545901637051,0.022296519572071172,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.043749999999999,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Fence,other,other,binary,binary,0.0944,0.0155,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.886724835517757,1.7034488494536608,100*(exp(b - se^2/2) - 1),reported,0.13577392590682388,0.022293388258006044,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.090322580645161,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Fence,other,other,binary,binary,0.0903,0.013,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.441010367203774,1.4228533608030154,100*(exp(b - se^2/2) - 1),reported,0.1544257520732404,0.0222318358466459,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.946153846153847,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Fence,other,other,binary,binary,0.0912,0.0163,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.53425605182876,1.785645571975163,100*(exp(b - se^2/2) - 1),reported,0.12491331967822289,0.022325516565296414,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.595092024539878,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Fence,other,other,binary,binary,0.0764,0.0161,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,7.925435968941974,1.737824735279895,100*(exp(b - se^2/2) - 1),reported,0.10617495919583152,0.02237456600854565,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.745341614906832,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Fence,other,other,binary,binary,0.0659,0.0156,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,6.798993891280314,1.6662670437429623,100*(exp(b - se^2/2) - 1),reported,0.09462895253201031,0.02240078390742581,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.2243589743589745,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Fence,other,other,binary,binary,0.0434,0.0161,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,4.422020860408438,1.6814124411909006,100*(exp(b - se^2/2) - 1),reported,0.06054563358518886,0.022460476975150703,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6956521739130435,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Crime,crime,F_neighbourhood,level,index,-0.0004,0.001,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.04,0.1,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.009000338644112347,0.02250084661028087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.4,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Crime,crime,F_neighbourhood,level,index,-0.0004,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.04,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.017998490439918875,0.022498113049898592,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Crime,crime,F_neighbourhood,level,index,-0.0007,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06999999999999999,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.031486841249961726,0.022490600892829803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Crime,crime,F_neighbourhood,level,index,-0.0005,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.022496063533292376,0.02249606353329238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Crime,crime,F_neighbourhood,level,index,-0.0002,0.0004,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.02,0.04,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.011250166995905777,0.022500333991811554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Crime,crime,F_neighbourhood,level,index,-0.0005,0.0004,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.04,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.02811607785577666,0.022492862284621332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Crime,crime,F_neighbourhood,level,index,-0.0008,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.08,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.03597950202026603,0.02248718876266627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Crime,crime,F_neighbourhood,level,index,-0.0005,0.0004,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.04,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.02811607785577666,0.022492862284621332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Crime,crime,F_neighbourhood,level,index,-0.0006,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.026992271194846446,0.022493559329038706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,School quality,school_quality,F_neighbourhood,level,share,0.0014,0.0014,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.13999999999999999,0.13999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.022496063533292376,0.02249606353329238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,School quality,school_quality,F_neighbourhood,level,share,0.0001,0.0011,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.01,0.11,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.0020456100853578187,0.022501710938936003,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09090909090909091,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,School quality,school_quality,F_neighbourhood,level,share,-0.0006,0.0008,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06,0.08,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.016873915753919864,0.02249855433855982,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7499999999999999,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,School quality,school_quality,F_neighbourhood,level,share,-0.0014,0.0007,,,**,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.13999999999999999,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.04495801196469979,0.0224790059823499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,School quality,school_quality,F_neighbourhood,level,share,-0.001,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.1,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.03212877315609996,0.022490141209269968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4285714285714286,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,School quality,school_quality,F_neighbourhood,level,share,-0.001,0.0008,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.1,0.08,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02811607785577666,0.022492862284621332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.25,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,School quality,school_quality,F_neighbourhood,level,share,-0.0009,0.001,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.09,0.1,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.020247430637529387,0.022497145152810434,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8999999999999999,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,School quality,school_quality,F_neighbourhood,level,share,-0.0005,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.016070608663330627,0.022498852128662875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7142857142857143,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,School quality,school_quality,F_neighbourhood,level,share,-0.0007,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06999999999999999,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.022496063533292376,0.02249606353329238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0046,0.0116,,,,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.2858307484291736,0.7207905829953073,100*b/1.60934 (X per miles->km),reported,-0.008922755722028166,0.022500862255549287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.39655172413793105,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.007,0.0085,,,,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.4349598345661338,0.5281655134017339,100*b/1.60934 (X per miles->km),reported,-0.018527678682394196,0.02249789554290724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8235294117647058,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0156,0.0072,,,**,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.9693390598902408,0.4473872584108804,100*b/1.60934 (X per miles->km),reported,-0.048695969839064907,0.02247506300264534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1666666666666665,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0204,0.0061,,,***,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-1.2675972321641613,0.3790364272647737,100*b/1.60934 (X per miles->km),reported,-0.07503961255423391,0.022438315518667984,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3442622950819674,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0139,0.0058,,,**,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.8637059572098941,0.36039529149765365,100*b/1.60934 (X per miles->km),reported,-0.05384838608001197,0.022469110738422258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.396551724137931,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0103,0.0062,,,*,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.640012328004454,0.385250139187147,100*b/1.60934 (X per miles->km),reported,-0.03735586121477098,0.022486052381706804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6612903225806452,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0154,0.0064,,,**,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.9569116360454942,0.3976775630318937,100*b/1.60934 (X per miles->km),reported,-0.054065662017658174,0.022468846552793006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.40625,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.0128,0.0064,,,**,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.7953551260637874,0.3976775630318937,100*b/1.60934 (X per miles->km),reported,-0.04495801196469979,0.0224790059823499,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Distance to highway exit,highway_distance,D_location,level,miles,-0.014,0.0067,,,**,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.8699196691322676,0.41631869879901373,100*b/1.60934 (X per miles->km),reported,-0.04696671170923583,0.022476926317991437,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.08955223880597,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.1,A1,0.1,22,Distance to FSU,university_distance,D_location,level,miles,-0.0093,0.0106,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.5778752087807205,0.658653463771574,100*b/1.60934 (X per miles->km),reported,-0.0197382623227127,0.0224973742602962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8773584905660377,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.2,A1,0.2,22,Distance to FSU,university_distance,D_location,level,miles,-0.0115,0.0078,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.714576871072934,0.4846695299451204,100*b/1.60934 (X per miles->km),reported,-0.033157426944858195,0.022489385232164684,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4743589743589745,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.3,A1,0.3,22,Distance to FSU,university_distance,D_location,level,miles,-0.0072,0.006,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.4473872584108804,0.37282271534240036,100*b/1.60934 (X per miles->km),reported,-0.026992271194846446,0.022493559329038706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.4,A1,0.4,22,Distance to FSU,university_distance,D_location,level,miles,0.0005,0.0054,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0310685596118667,0.33554044380816034,100*b/1.60934 (X per miles->km),reported,0.0020834915906581624,0.022501709179108157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.09259259259259259,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.5,A1,0.5,22,Distance to FSU,university_distance,D_location,level,miles,-0.0016,0.0052,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.09941939075797343,0.32311301996341363,100*b/1.60934 (X per miles->km),reported,-0.006923451910869664,0.02250121871032641,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.3076923076923077,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.6,A1,0.6,22,Distance to FSU,university_distance,D_location,level,miles,-0.0044,0.0052,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.27340332458442695,0.32311301996341363,100*b/1.60934 (X per miles->km),reported,-0.019036498852701242,0.022497680462283287,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8461538461538463,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.7,A1,0.7,22,Distance to FSU,university_distance,D_location,level,miles,-0.0024,0.0056,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.14912908613696013,0.34796786765290694,100*b/1.60934 (X per miles->km),reported,-0.009643162186485746,0.02250071176846674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.42857142857142855,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.8,A1,0.8,22,Distance to FSU,university_distance,D_location,level,miles,0.0015,0.0061,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.09320567883560009,0.3790364272647737,100*b/1.60934 (X per miles->km),reported,0.005533134482884204,0.0225014135637291,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2459016393442623,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA1-q0.9,A1,0.9,22,Distance to FSU,university_distance,D_location,level,miles,0.0055,0.0067,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (1) with Days BWNs_year; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.34175415573053364,0.41631869879901373,100*b/1.60934 (X per miles->km),reported,0.018468441958305798,0.022497920203754337,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8208955223880596,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,True
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,BWN,other,other,binary,binary,-0.5392,0.0653,,,***,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-41.80274777534241,3.80839158290809,100*(exp(b - se^2/2) - 1),reported,-0.18267668170060758,0.022123121875092123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.257274119448699,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,BWN,other,other,binary,binary,-0.2971,0.0483,,,***,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-25.78964163859583,3.5885437073941135,100*(exp(b - se^2/2) - 1),reported,-0.13710436152428057,0.022289265101389275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.151138716356106,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,BWN,other,other,binary,binary,-0.175,0.0265,,,***,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-16.083768183545644,2.2245611050383993,100*(exp(b - se^2/2) - 1),reported,-0.14698261959791842,0.022257368110541933,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.60377358490566,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,BWN,other,other,binary,binary,-0.1017,0.0209,,,***,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-9.689676262870318,1.8878980474552718,100*(exp(b - se^2/2) - 1),reported,-0.10884368209468714,0.022368072328210042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.866028708133972,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,BWN,other,other,binary,binary,-0.0676,0.0171,,,***,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-6.5502385694602205,1.5982245718048376,100*(exp(b - se^2/2) - 1),reported,-0.0886044512135468,0.02241325615017235,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.9532163742690054,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,BWN,other,other,binary,binary,-0.0509,0.0185,,,***,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-4.978891563445309,1.758191350829409,100*(exp(b - se^2/2) - 1),reported,-0.061791935110313355,0.022458758340683636,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.7513513513513517,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,BWN,other,other,binary,binary,-0.0449,,,,**,,,medium,"Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest); printed SE '(0.0449)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-4.390691363857002,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,BWN,other,other,binary,binary,-0.0074,0.0205,,,,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-0.7581241314669751,2.0348859908042236,100*(exp(b - se^2/2) - 1),reported,-0.008122317885090298,0.022501015762750154,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.36097560975609755,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,BWN,other,other,binary,binary,0.0077,0.0172,,,,,,high,Water-supply unreliability: 1 if house affected by a boil water notice in the year before sale (variable of interest),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,0.7580668879349162,1.7332951205251121,100*(exp(b - se^2/2) - 1),reported,0.01007295037705546,0.022500616426669336,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.4476744186046512,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,SQFT,living_area,A_structural,level,sqft,0.0003,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.32291731250129163,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,SQFT,living_area,A_structural,level,sqft,0.0003,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.32291731250129163,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; printed SE '(0.0002)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,SQFT,living_area,A_structural,level,sqft,0.0002,,,,***,,,medium,"Building area in square feet; SE printed as (0.0000), rounded to zero in source; set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.21527820833419445,,100*b/0.092903 (X per sqft->m2),,,,,,1975.0,sqft,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Story height,storeys,A_structural,level,count,-0.0357,0.0372,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-3.5700000000000003,3.7199999999999998,100*b/1 (per unit count),reported,-0.021589395876154757,0.02249651334994277,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9596774193548389,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Story height,storeys,A_structural,level,count,-0.0317,0.0285,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-3.17,2.85,100*b/1 (per unit count),reported,-0.025020435846255445,0.02249471361571862,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1122807017543859,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Story height,storeys,A_structural,level,count,-0.0466,0.0205,,,**,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-4.66,2.0500000000000003,100*b/1 (per unit count),reported,-0.05108355498337805,0.022472379338181327,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.2731707317073173,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Story height,storeys,A_structural,level,count,-0.0374,0.0197,,,*,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-3.74,1.97,100*b/1 (per unit count),reported,-0.042680147438484334,0.022481254132035857,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8984771573604065,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Story height,storeys,A_structural,level,count,-0.0238,0.0217,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-2.3800000000000003,2.17,100*b/1 (per unit count),reported,-0.02467183520816971,0.022494908572154734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0967741935483872,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Story height,storeys,A_structural,level,count,-0.0326,0.0194,,,*,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-3.26,1.94,100*b/1 (per unit count),reported,-0.037785230196385466,0.022485689135272338,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6804123711340204,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Story height,storeys,A_structural,level,count,-0.0065,,,,,,,medium,"Story height (number of storeys, 1-3); printed SE '(0.0065)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.65,,100*b/1 (per unit count),,,,,,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Story height,storeys,A_structural,level,count,0.0089,0.0211,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.89,2.11,100*b/1 (per unit count),reported,0.009490835383976682,0.022500744562012134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.42180094786729855,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Story height,storeys,A_structural,level,count,-0.0044,0.0208,,,,,,high,"Story height (number of storeys, 1-3)","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.44,2.08,100*b/1 (per unit count),reported,-0.004759933349409432,0.022501503106299132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.21153846153846156,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Construction material,roof_materials,A_structural,binary,binary,-0.078,0.0262,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-7.5352985272781225,2.423406797544073,100*(exp(b - se^2/2) - 1),reported,-0.06684015670301559,0.02245143725152575,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.9770992366412212,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Construction material,roof_materials,A_structural,binary,binary,-0.0762,0.0235,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-7.362496877132929,2.1775825253549232,100*(exp(b - se^2/2) - 1),reported,-0.0727697052279855,0.022442100693670065,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2425531914893617,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Construction material,roof_materials,A_structural,binary,binary,-0.0519,0.0202,,,**,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.07698772100722,1.9178360850422804,100*(exp(b - se^2/2) - 1),reported,-0.057717544304130126,0.0224642465307019,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.5693069306930694,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Construction material,roof_materials,A_structural,binary,binary,-0.0567,0.0159,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.524193959240442,1.5023552092563248,100*(exp(b - se^2/2) - 1),reported,-0.07998502769182117,0.022429663850087416,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5660377358490565,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Construction material,roof_materials,A_structural,binary,binary,-0.0407,0.0152,,,***,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.9993784440433666,1.4593780252627888,100*(exp(b - se^2/2) - 1),reported,-0.06014235136182725,0.022461025570019024,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6776315789473686,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Construction material,roof_materials,A_structural,binary,binary,-0.0311,0.0146,,,**,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.0724680502955737,1.4152928003342646,100*(exp(b - se^2/2) - 1),reported,-0.047876860977024184,0.022475954027799134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1301369863013697,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Construction material,roof_materials,A_structural,binary,binary,-0.0275,,,,*,,,medium,"1 = Alum/Vinyl siding construction material, 0 otherwise; printed SE '(0.0275)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-2.7125317446546005,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Construction material,roof_materials,A_structural,binary,binary,-0.0197,0.017,,,,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-1.964889394820668,1.666837720937568,100*(exp(b - se^2/2) - 1),reported,-0.026066706315576504,0.02249411204897465,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1588235294117646,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Construction material,roof_materials,A_structural,binary,binary,-0.0308,0.0151,,,**,,,high,"1 = Alum/Vinyl siding construction material, 0 otherwise","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.0441053287260766,1.4642009262479827,100*(exp(b - se^2/2) - 1),reported,-0.04584935808745915,0.02247809438703354,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.0397350993377485,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Physical condition,condition,A_structural,level,index,0.2086,0.0238,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,20.86,2.3800000000000003,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1934940925284111,0.022076507201228116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.76470588235294,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Physical condition,condition,A_structural,level,index,0.2265,0.0227,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,22.650000000000002,2.27,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.21906820701889476,0.021955180129487467,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.977973568281937,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Physical condition,condition,A_structural,level,index,0.2058,0.0173,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,20.580000000000002,1.73,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.25857630411027244,0.021736492036480627,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.895953757225435,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Physical condition,condition,A_structural,level,index,0.2014,0.0173,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,20.14,1.73,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.2534065743179191,0.021767297595332677,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.641618497109826,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Physical condition,condition,A_structural,level,index,0.1733,0.0176,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,17.330000000000002,1.76,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.21631951398023844,0.021968975453272917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.846590909090908,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Physical condition,condition,A_structural,level,index,0.1622,0.0153,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,16.220000000000002,1.53,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.23203732896764423,0.02188761487795904,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.601307189542485,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Physical condition,condition,A_structural,level,index,0.1478,,,,***,,,medium,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly; printed SE '(0.1478)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,14.78,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Physical condition,condition,A_structural,level,index,0.1407,0.0166,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,14.069999999999999,1.66,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.18734580015640537,0.02210334244915657,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.475903614457831,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Physical condition,condition,A_structural,level,index,0.1474,0.0181,,,***,,,high,"Assessor physical condition ordinal 1 (unsound) to 6 (excellent), entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,14.74,1.81,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1802451020810437,0.022133218098147157,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.143646408839778,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Quality grade factor,condition,A_structural,level,index,0.5079,0.0963,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,50.79,9.629999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.11785047461789767,0.02234495118272011,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.274143302180685,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Quality grade factor,condition,A_structural,level,index,0.4762,0.0734,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,47.620000000000005,7.340000000000001,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14445434595900802,0.022265747571170072,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.487738419618529,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Quality grade factor,condition,A_structural,level,index,0.4752,0.0509,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,47.52,5.09,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.20558786133234103,0.022021090365774744,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.335952848722986,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Quality grade factor,condition,A_structural,level,index,0.4117,0.0578,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,41.17,5.779999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.15825656420117515,0.022218191427806466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.122837370242215,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Quality grade factor,condition,A_structural,level,index,0.4025,0.0609,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,40.25,6.09,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1471006847835894,0.022256973175951787,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.609195402298851,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Quality grade factor,condition,A_structural,level,index,0.3566,0.0585,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,35.66,5.8500000000000005,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.1358921737413542,0.022293023454484643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.0957264957264945,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Quality grade factor,condition,A_structural,level,index,0.3477,,,,***,,,medium,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly; printed SE '(0.3477)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,34.77,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Quality grade factor,condition,A_structural,level,index,0.4225,0.0632,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,42.25,6.32,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14875349513736408,0.022251410396879077,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.68512658227848,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Quality grade factor,condition,A_structural,level,index,0.4505,0.0692,,,***,,,high,"Assessor quality grade factor 0.5 (E=poor) to 2.5 (X=excellent), material and workmanship quality, entered linearly","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,45.050000000000004,6.92,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,0.14494214113742623,0.022264142434428178,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.5101156069364166,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Age,age,A_structural,level,years,-0.0021,0.0006,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.21,0.06,100*b/1 (per year),reported,-0.07851303965519903,0.02243229704434258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.5,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Age,age,A_structural,level,years,-0.0025,0.0005,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.25,0.05,100*b/1 (per year),reported,-0.11180339887498948,0.022360679774997897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Age,age,A_structural,level,years,-0.0024,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.24,0.04,100*b/1 (per year),reported,-0.13379664241538733,0.02229944040256456,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.999999999999999,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Age,age,A_structural,level,years,-0.0025,0.0005,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.25,0.05,100*b/1 (per year),reported,-0.11180339887498948,0.022360679774997897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.0,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Age,age,A_structural,level,years,-0.0026,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.26,0.04,100*b/1 (per year),reported,-0.14472164640665922,0.022264868677947577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.499999999999999,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Age,age,A_structural,level,years,-0.0026,0.0003,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.26,0.03,100*b/1 (per year),reported,-0.19140945801850193,0.022085706694442528,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.666666666666668,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Age,age,A_structural,level,years,-0.0023,,,,***,,,medium,"Age of the house in years; printed SE '(0.0023)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.22999999999999998,,100*b/1 (per year),,,,,,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Age,age,A_structural,level,years,-0.0023,0.0004,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.22999999999999998,0.04,100*b/1 (per year),reported,-0.12831553284287514,0.02231574484223916,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.75,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Age,age,A_structural,level,years,-0.0017,0.0003,,,***,,,high,Age of the house in years,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.16999999999999998,0.03,100*b/1 (per year),reported,-0.12648585450796923,0.02232103314846516,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.666666666666667,1975.0,years,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Bedrooms,bedrooms,A_structural,level,count,0.0165,0.0161,,,,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,1.6500000000000001,1.6099999999999999,100*b/1 (per unit count),reported,0.023054678473045803,0.022495777176729535,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0248447204968945,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Bedrooms,bedrooms,A_structural,level,count,0.024,0.0192,,,,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.4,1.92,100*b/1 (per unit count),reported,0.028116077855776668,0.022492862284621332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2500000000000002,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Bedrooms,bedrooms,A_structural,level,count,0.0406,0.0166,,,**,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.06,1.66,100*b/1 (per unit count),reported,0.05495126528084473,0.022467758710887256,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4457831325301203,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Bedrooms,bedrooms,A_structural,level,count,0.0463,0.0165,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.63,1.6500000000000001,100*b/1 (per unit count),reported,0.063015805254518,0.02245703642979583,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.806060606060606,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Bedrooms,bedrooms,A_structural,level,count,0.0447,0.0154,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.47,1.54,100*b/1 (per unit count),reported,0.06517467931677591,0.022453916364168887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.902597402597402,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Bedrooms,bedrooms,A_structural,level,count,0.0481,0.013,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.81,1.3,100*b/1 (per unit count),reported,0.08296944387137543,0.02242417401929066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.6999999999999997,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Bedrooms,bedrooms,A_structural,level,count,0.0366,,,,**,,,medium,"printed SE '(0.0366)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,3.66,,100*b/1 (per unit count),,,,,,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Bedrooms,bedrooms,A_structural,level,count,0.0433,0.0128,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.33,1.28,100*b/1 (per unit count),reported,0.07589965938154761,0.022436850810249643,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3828124999999996,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Bedrooms,bedrooms,A_structural,level,count,0.047,0.0141,,,***,,,high,,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.7,1.41,100*b/1 (per unit count),reported,0.07479575920067659,0.022438727760202972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3333333333333335,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Total bathrooms,bathrooms,A_structural,level,count,-0.0178,0.0211,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-1.78,2.11,100*b/1 (per unit count),reported,-0.018979106599404525,0.022497705013900873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8436018957345971,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Total bathrooms,bathrooms,A_structural,level,count,0.021,0.0223,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.1,2.23,100*b/1 (per unit count),reported,0.021185240618682374,0.022496707895076993,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9417040358744395,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Total bathrooms,bathrooms,A_structural,level,count,0.0406,0.0148,,,***,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.06,1.48,100*b/1 (per unit count),reported,0.06161052930550961,0.02245901068279661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.743243243243243,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Total bathrooms,bathrooms,A_structural,level,count,0.0454,0.0168,,,***,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,4.54,1.68,100*b/1 (per unit count),reported,0.06069620907643688,0.02246027120009118,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.7023809523809526,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Total bathrooms,bathrooms,A_structural,level,count,0.0335,0.0118,,,***,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,3.35,1.18,100*b/1 (per unit count),reported,0.06375215822026115,0.02245598408952482,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.838983050847458,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Total bathrooms,bathrooms,A_structural,level,count,0.0273,0.0141,,,*,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,2.73,1.41,100*b/1 (per unit count),reported,0.0435259448120545,0.022480433034797374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9361702127659577,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Total bathrooms,bathrooms,A_structural,level,count,0.037,,,,***,,,medium,"Total bathrooms (full + half); printed SE '(0.0370)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,3.6999999999999997,,100*b/1 (per unit count),,,,,,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Total bathrooms,bathrooms,A_structural,level,count,0.0148,0.0172,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,1.48,1.72,100*b/1 (per unit count),reported,0.019358349580987096,0.02249754140493095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8604651162790699,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Total bathrooms,bathrooms,A_structural,level,count,0.0143,0.0153,,,,,,high,Total bathrooms (full + half),"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,1.43,1.53,100*b/1 (per unit count),reported,0.02102640534918535,0.022496783345631873,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.934640522875817,1975.0,count,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,AC,air_conditioning,A_structural,binary,binary,0.1847,0.0347,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,20.21335723232214,4.173915624710704,100*(exp(b - se^2/2) - 1),reported,0.11892166291716157,0.02234207744031135,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.322766570605187,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,AC,air_conditioning,A_structural,binary,binary,0.2161,0.0372,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,24.03679630596787,4.61736256305276,100*(exp(b - se^2/2) - 1),reported,0.12961322094622857,0.022311947335491446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.809139784946237,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,AC,air_conditioning,A_structural,binary,binary,0.2305,0.0272,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,25.876373926749487,3.4251041510170164,100*(exp(b - se^2/2) - 1),reported,0.18731084589696312,0.022103492444240334,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.474264705882353,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,AC,air_conditioning,A_structural,binary,binary,0.2244,0.0271,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,25.111204897522278,3.391758894904287,100*(exp(b - se^2/2) - 1),reported,0.18317207105719407,0.022121047797014078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.280442804428045,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,AC,air_conditioning,A_structural,binary,binary,0.2281,0.0268,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,25.575989076795413,3.3666453198580024,100*(exp(b - se^2/2) - 1),reported,0.18809829305967732,0.02210010633055394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.511194029850746,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,AC,air_conditioning,A_structural,binary,binary,0.2081,0.0295,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,23.080062836571557,3.632442076215176,100*(exp(b - se^2/2) - 1),reported,0.15677003134583536,0.022223526788573488,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.054237288135594,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,AC,air_conditioning,A_structural,binary,binary,0.1684,,,,***,,,medium,"1 = central heating with A/C; printed SE '(0.1684)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,18.3409879939618,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,AC,air_conditioning,A_structural,binary,binary,0.1468,0.0287,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,15.764544672252834,3.3238110452059906,100*(exp(b - se^2/2) - 1),reported,0.11434124626767166,0.022354180980123817,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.114982578397213,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,AC,air_conditioning,A_structural,binary,binary,0.0936,0.0334,,,***,,,high,1 = central heating with A/C,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.750807108068704,3.667722169038845,100*(exp(b - se^2/2) - 1),reported,0.06293381754258681,0.02245715284105128,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8023952095808387,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Fire place,fireplace,B_interior,binary,binary,0.1368,0.026,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,14.621132091799737,2.981156895146893,100*(exp(b - se^2/2) - 1),reported,0.11757271807582378,0.022345692031954807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.261538461538462,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Fire place,fireplace,B_interior,binary,binary,0.1306,0.0234,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,13.919995252318685,2.666457811800267,100*(exp(b - se^2/2) - 1),reported,0.12460791950380472,0.022326380676791963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.581196581196581,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Fire place,fireplace,B_interior,binary,binary,0.1319,0.0204,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,14.075682076384478,2.327628196846946,100*(exp(b - se^2/2) - 1),reported,0.1439735332471638,0.022267324323291447,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.465686274509803,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Fire place,fireplace,B_interior,binary,binary,0.1094,0.0159,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,11.546749492580233,1.7738175221652854,100*(exp(b - se^2/2) - 1),reported,0.15300054211518946,0.022236824676704865,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.880503144654088,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Fire place,fireplace,B_interior,binary,binary,0.0972,0.0149,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,10.195843804851812,1.6421003439242732,100*(exp(b - se^2/2) - 1),reported,0.1452336345768627,0.022263180609004674,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.523489932885906,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Fire place,fireplace,B_interior,binary,binary,0.0896,0.0159,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.359845646386301,1.7390413554072615,100*(exp(b - se^2/2) - 1),reported,0.12579507317700409,0.022323009637437108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.635220125786163,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Fire place,fireplace,B_interior,binary,binary,0.0836,,,,***,,,medium,"1 = wood-burning fireplace; printed SE '(0.0836)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,8.71939292500139,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Fire place,fireplace,B_interior,binary,binary,0.077,0.0165,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,7.98950656710955,1.7820694260477907,100*(exp(b - se^2/2) - 1),reported,0.1044340002510476,0.022378714339510202,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.666666666666666,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Fire place,fireplace,B_interior,binary,binary,0.0604,0.0171,,,***,,,high,1 = wood-burning fireplace,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,6.210606955203923,1.8164669360690116,100*(exp(b - se^2/2) - 1),reported,0.07923003759339474,0.022431020576937917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5321637426900585,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,23,Basement garage,garage,A_structural,binary,binary,0.0209,0.0253,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,2.0793182499322826,2.583433434381911,100*(exp(b - se^2/2) - 1),reported,0.018585198213287357,0.022497871521347853,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8260869565217391,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,23,Basement garage,garage,A_structural,binary,binary,0.0326,0.0214,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,3.290066102475908,2.210913611635065,100*(exp(b - se^2/2) - 1),reported,0.034258258095237214,0.02248854979257903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5233644859813082,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,23,Basement garage,garage,A_structural,binary,binary,0.0232,0.0184,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,2.3297974778154984,1.883187032512153,100*(exp(b - se^2/2) - 1),reported,0.028360369692245015,0.022492706997297773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.2608695652173911,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,23,Basement garage,garage,A_structural,binary,binary,0.017,0.0179,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,1.6982383611024554,1.8206901269621807,100*(exp(b - se^2/2) - 1),reported,0.021365506524925512,0.022496621576245095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.9497206703910616,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,23,Basement garage,garage,A_structural,binary,binary,0.0153,0.0151,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,1.5301888129742647,1.5332806427718377,100*(exp(b - se^2/2) - 1),reported,0.02279387084216148,0.02249591174618552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0132450331125826,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,23,Basement garage,garage,A_structural,binary,binary,0.0081,0.0153,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,0.8014903743807977,1.542443327442323,100*(exp(b - se^2/2) - 1),reported,0.01191185023244971,0.022500161550182786,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5294117647058824,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,23,Basement garage,garage,A_structural,binary,binary,-0.0006,,,,,,,medium,"1 = basement garage; printed SE '(0.0006)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-0.059982003599456934,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,23,Basement garage,garage,A_structural,binary,binary,0.0019,0.0165,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,0.17654315425554845,1.6531379801371213,100*(exp(b - se^2/2) - 1),reported,0.0025911028312705104,0.022501682482086015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.11515151515151514,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,23,Basement garage,garage,A_structural,binary,binary,-0.0104,0.0181,,,,,,high,1 = basement garage,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-1.050820397033847,1.791273546346463,100*(exp(b - se^2/2) - 1),reported,-0.01292810641674237,0.022499877513753548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.574585635359116,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,Lot size,lot_size,A_structural,level,acres,0.0022,0.0049,,,,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,5.436318392277638e-05,0.000121081636918911,100*b/4046.86 (X per acre->m2),reported,0.010102314586555252,0.022500609760963967,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.44897959183673475,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,Lot size,lot_size,A_structural,level,acres,0.0088,0.0066,,,,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0002174527356911055,0.00016308955176832913,100*b/4046.86 (X per acre->m2),reported,0.029988849969241192,0.022491637476930892,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3333333333333335,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,Lot size,lot_size,A_structural,level,acres,0.0122,0.005,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0003014685653899417,0.00012355269073358266,100*b/4046.86 (X per acre->m2),reported,0.05482172222242831,0.022467918943618163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.44,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,Lot size,lot_size,A_structural,level,acres,0.0111,0.0047,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0002742869734285535,0.00011613952928956771,100*b/4046.86 (X per acre->m2),reported,0.053067568030608865,0.022470051328275824,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3617021276595747,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,Lot size,lot_size,A_structural,level,acres,0.01,0.0057,,,*,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0002471053814671653,0.00014085006743628427,100*b/4046.86 (X per acre->m2),reported,0.039446043761315525,0.02248424494394985,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7543859649122806,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,Lot size,lot_size,A_structural,level,acres,0.0112,0.0078,,,,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.00027675802724322514,0.00019274219754438895,100*b/4046.86 (X per acre->m2),reported,0.032293364708325875,0.02249002185044123,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.435897435897436,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,Lot size,lot_size,A_structural,level,acres,0.022,,,,***,,,medium,"Lot size in acres; printed SE '(0.0220)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0005436318392277637,,100*b/4046.86 (X per acre->m2),,,,,,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,Lot size,lot_size,A_structural,level,acres,0.0218,0.0099,,,**,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.0005386897315984205,0.0002446343276524937,100*b/4046.86 (X per acre->m2),reported,0.04948861235081379,0.022474186342800757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.202020202020202,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,Lot size,lot_size,A_structural,level,acres,0.0372,0.0074,,,***,,,high,Lot size in acres,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.000919232019057855,0.00018285798228570235,100*b/4046.86 (X per acre->m2),reported,0.11240012670044684,0.022359164988798568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.027027027027026,1975.0,acre,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,Sidewalk,other,other,binary,binary,-0.0141,0.0515,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-1.5307751795351376,5.077894538109608,100*(exp(b - se^2/2) - 1),reported,-0.006160558588760219,0.02250133101568449,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2737864077669903,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,Sidewalk,other,other,binary,binary,-0.0541,0.0345,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.322624839525014,3.2683139196971203,100*(exp(b - se^2/2) - 1),reported,-0.03526341981715859,0.02248776309966675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5681159420289854,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,Sidewalk,other,other,binary,binary,-0.0527,0.0443,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.226584664017809,4.202584041411632,100*(exp(b - se^2/2) - 1),reported,-0.026758871733983405,0.02249370052780769,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.1896162528216703,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,Sidewalk,other,other,binary,binary,-0.0385,0.0334,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.8304859204222663,3.2138538938309904,100*(exp(b - se^2/2) - 1),reported,-0.025928934667034467,0.022494192672180547,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.152694610778443,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,Sidewalk,other,other,binary,binary,-0.0523,0.04,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-5.1714820777208015,3.796176443593456,100*(exp(b - se^2/2) - 1),reported,-0.029408323467856833,0.022492025596831233,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.3074999999999999,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,Sidewalk,other,other,binary,binary,-0.0374,0.0312,,,,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-3.7177996809684366,3.0054671149736265,100*(exp(b - se^2/2) - 1),reported,-0.026963454275428764,0.022493576828699926,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.198717948717949,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,Sidewalk,other,other,binary,binary,-0.0647,,,,**,,,medium,"1 = sidewalk present; printed SE '(0.0647)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-6.265137421133038,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,Sidewalk,other,other,binary,binary,-0.0876,0.0357,,,**,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-8.445635719602308,3.2705742980104158,100*(exp(b - se^2/2) - 1),reported,-0.055130425651010125,0.022467536481062353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4537815126050417,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,Sidewalk,other,other,binary,binary,-0.0889,0.0445,,,**,,,high,1 = sidewalk present,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,-8.59683910228638,4.071469929042545,100*(exp(b - se^2/2) - 1),reported,-0.04490759927414944,0.022479057004495498,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9977528089887644,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,Fence,other,other,binary,binary,0.0783,0.0277,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,8.103223194722297,2.9956083122209014,100*(exp(b - se^2/2) - 1),reported,0.0634777750957776,0.022456377652018387,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.826714801444043,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,Fence,other,other,binary,binary,0.104,0.0226,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,10.931712133181136,2.507697028109216,100*(exp(b - se^2/2) - 1),reported,0.10299720845257156,0.022382085682962665,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.601769911504425,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,Fence,other,other,binary,binary,0.1003,0.0176,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,10.533131211718882,1.9456844335961743,100*(exp(b - se^2/2) - 1),reported,0.12719292978416682,0.022318998646075137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.698863636363636,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,Fence,other,other,binary,binary,0.0936,0.0182,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.793855424946486,1.9985791460032032,100*(exp(b - se^2/2) - 1),reported,0.11495614696188355,0.022352584131477362,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.142857142857142,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,Fence,other,other,binary,binary,0.0993,0.0156,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,10.426319421602503,1.7228602078532544,100*(exp(b - se^2/2) - 1),reported,0.14178532941287217,0.02227443241531526,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.365384615384616,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,Fence,other,other,binary,binary,0.0938,0.017,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,9.818135726589272,1.867178095094125,100*(exp(b - se^2/2) - 1),reported,0.12321074992262651,0.022330306489175385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5176470588235285,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,Fence,other,other,binary,binary,0.0729,,,,***,,,medium,"1 = fence around property; printed SE '(0.0729)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,7.562296923948542,,100*(exp(b) - 1),,,,,,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,Fence,other,other,binary,binary,0.066,0.0144,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,6.811596916123341,1.5382464727190304,100*(exp(b - se^2/2) - 1),reported,0.1025889109415296,0.022383035114515545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.583333333333334,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,Fence,other,other,binary,binary,0.0514,0.0155,,,***,,,high,1 = fence around property,"ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",pct_premium,5.261745337104107,1.6317530552882407,100*(exp(b - se^2/2) - 1),reported,0.07441185965283352,0.022439374019823335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3161290322580648,1975.0,binary,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,Crime,crime,F_neighbourhood,level,index,-0.0001,0.0008,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.01,0.08,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.0028127086261162345,0.022501669008929876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.125,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,Crime,crime,F_neighbourhood,level,index,-0.0009,0.0007,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.09,0.06999999999999999,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.02891873188100322,0.022492347018558064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2857142857142856,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,Crime,crime,F_neighbourhood,level,index,-0.0007,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06999999999999999,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.031486841249961726,0.022490600892829803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,Crime,crime,F_neighbourhood,level,index,-0.0004,0.0004,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.04,0.04,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.022496063533292376,0.02249606353329238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,Crime,crime,F_neighbourhood,level,index,-0.0001,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.01,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.004500306031215538,0.022501530156077688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,Crime,crime,F_neighbourhood,level,index,-0.0004,0.0004,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.04,0.04,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.022496063533292376,0.02249606353329238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.0,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,Crime,crime,F_neighbourhood,level,index,-0.0007,,,,*,,,medium,"Total crime index (US average = 100), block group 2010; printed SE '(0.0007)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06999999999999999,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,Crime,crime,F_neighbourhood,level,index,-0.0006,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.026992271194846446,0.022493559329038706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,Crime,crime,F_neighbourhood,level,index,-0.0007,0.0005,,,,,,high,"Total crime index (US average = 100), block group 2010","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.06999999999999999,0.05,100*b (unit as reported: index; no harmonised unit defined for this characteristic),reported,-0.031486841249961726,0.022490600892829803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4,1975.0,index,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,School quality,school_quality,F_neighbourhood,level,share,0.0018,0.0012,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.18,0.12,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.033733427253988944,0.0224889515026593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.5,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,School quality,school_quality,F_neighbourhood,level,share,2e-05,0.0011,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.002,0.11,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,0.00040912283882420773,0.022501756135331424,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.01818181818181818,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,School quality,school_quality,F_neighbourhood,level,share,-0.0008,0.0009,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.08,0.09,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.01999756294551498,0.022497258313704346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.888888888888889,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,School quality,school_quality,F_neighbourhood,level,share,-0.0012,0.0008,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.12,0.08,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.033733427253988944,0.0224889515026593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.4999999999999998,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,School quality,school_quality,F_neighbourhood,level,share,-0.0005,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.016070608663330627,0.022498852128662875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7142857142857143,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,School quality,school_quality,F_neighbourhood,level,share,-0.0009,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.09,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.02891873188100322,0.022492347018558064,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.2857142857142856,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,School quality,school_quality,F_neighbourhood,level,share,-0.0008,,,,,,,medium,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points; printed SE '(0.0008)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.08,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,School quality,school_quality,F_neighbourhood,level,share,-0.0005,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.016070608663330627,0.022498852128662875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7142857142857143,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,School quality,school_quality,F_neighbourhood,level,share,-0.0005,0.0007,,,,,,high,"Percentage of elementary students at or above proficiency in math (attendance zone), in percentage points","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.05,0.06999999999999999,100*b (unit as reported: share; no harmonised unit defined for this characteristic),reported,-0.016070608663330627,0.022498852128662875,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7142857142857143,1975.0,share,unit_not_harmonisable,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0158,0.0104,,,,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.9817664837349876,0.6462260399268273,100*b/1.60934 (X per miles->km),reported,-0.03416540545910642,0.022488621314854858,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.5192307692307694,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0151,0.0089,,,*,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.9382705002783742,0.5530203610912272,100*b/1.60934 (X per miles->km),reported,-0.038149348907668516,0.022485377832996672,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.6966292134831462,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.02,0.0067,,,***,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-1.242742384474668,0.41631869879901373,100*b/1.60934 (X per miles->km),reported,-0.06701841250816758,0.022451168190236136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.985074626865672,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.022,0.0065,,,***,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-1.3670166229221345,0.4038912749542671,100*b/1.60934 (X per miles->km),reported,-0.07593987722070189,0.02243678190611647,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.3846153846153846,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0123,0.0071,,,*,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.7642865664519207,0.44117354648850715,100*b/1.60934 (X per miles->km),reported,-0.038952334266932676,0.02248468075570911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.732394366197183,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0124,0.0058,,,**,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.770500278374294,0.36039529149765365,100*b/1.60934 (X per miles->km),reported,-0.04805163590074266,0.02247576517937963,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.137931034482759,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0146,,,,***,,,medium,"Distance to nearest I-79 highway interchange, miles; printed SE '(0.0146)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.9072019406665075,,100*b/1.60934 (X per miles->km),,,,,,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0163,0.0061,,,***,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-1.0128350433468543,0.3790364272647737,100*b/1.60934 (X per miles->km),reported,-0.0600192515364106,0.02246119229276716,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.6721311475409832,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,Distance to highway exit,highway_distance,D_location,level,miles,-0.0141,0.0076,,,*,,,high,"Distance to nearest I-79 highway interchange, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.8761333810546408,0.4722421061003738,100*b/1.60934 (X per miles->km),reported,-0.04171035236281598,0.022482175741659675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.8552631578947367,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.1,A2,0.1,24,Distance to FSU,university_distance,D_location,level,miles,-0.0024,0.0094,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.1; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.14912908613696013,0.5840889207030939,100*b/1.60934 (X per miles->km),reported,-0.005745034895847397,0.022501386675402305,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.2553191489361702,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.2,A2,0.2,24,Distance to FSU,university_distance,D_location,level,miles,-0.0072,0.0079,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.2; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.4473872584108804,0.4908832418674938,100*b/1.60934 (X per miles->km),reported,-0.02050362015432846,0.02249702766933262,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.911392405063291,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.3,A2,0.3,24,Distance to FSU,university_distance,D_location,level,miles,-0.0032,0.0064,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.3; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.19883878151594686,0.3976775630318937,100*b/1.60934 (X per miles->km),reported,-0.011250166995905777,0.022500333991811554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.5,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.4,A2,0.4,24,Distance to FSU,university_distance,D_location,level,miles,0.0014,0.0061,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.4; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.08699196691322673,0.3790364272647737,100*b/1.60934 (X per miles->km),reported,0.005164269039824748,0.0225014579592364,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.22950819672131145,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.5,A2,0.5,24,Distance to FSU,university_distance,D_location,level,miles,-0.0033,0.0061,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.5; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.20505249343832022,0.3790364272647737,100*b/1.60934 (X per miles->km),reported,-0.012172180379383278,0.022500091004314545,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.540983606557377,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.6,A2,0.6,24,Distance to FSU,university_distance,D_location,level,miles,-0.0006,0.0048,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.6; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.03728227153424003,0.29825817227392026,100*b/1.60934 (X per miles->km),reported,-0.0028127086261162345,0.022501669008929876,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.125,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.7,A2,0.7,24,Distance to FSU,university_distance,D_location,level,miles,-0.0017,,,,,,,medium,"Distance to Fairmont State University, miles; printed SE '(0.0017)' equals |beta| for every row of the 0.7 column of Table A2 (typesetting error in source); SE not usable, set to null","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.7; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,-0.10563310268034676,,100*b/1.60934 (X per miles->km),,,,,,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,False,False,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.8,A2,0.8,24,Distance to FSU,university_distance,D_location,level,miles,0.0012,0.0056,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.8; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.07456454306848007,0.34796786765290694,100*b/1.60934 (X per miles->km),reported,0.004821749237640972,0.02250149644232454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.21428571428571427,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee,eeedb48b06f3660101615bf3de6b27e1951880ee:TA2-q0.9,A2,0.9,24,Distance to FSU,university_distance,D_location,level,miles,0.0043,0.0065,,,,,,high,"Distance to Fairmont State University, miles","ln(sale price), USD",log,quantile,1985.0,,se,"Spatial quantile regression at quantile 0.9; model (2) with BWN dummy; year fixed effects included; no goodness-of-fit reported (qregspiv, McSpatial)",semi_elasticity,0.2671896126620536,0.4038912749542671,100*b/1.60934 (X per miles->km),reported,0.01488412941251288,0.022499265391007845,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.6615384615384615,1975.0,miles,,2021.0,United States,North America,High income,2012.0,2017.0,single_family,transaction,journal,True,False,bootstrap,,2014.5,quantile,journal,True,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T5-P1,5,logP1,9,M2,living_area,A_structural,level,m2,0.0,,,,**,,,medium,"Floor area in m2; dependent variable is log price per m2; Coefficient printed as 0,00** (rounded to 2 decimals); no SE printed",log asked price per m2 (P1),per_m2,OLS,4670.0,0.346,none,Whole Croatia; 3 region dummies (base Zagreb) + quarterly dummies; price type: listing,monetary,0.0,,"b (currency per unit, as reported)",,,,,,4660.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T5-P1,5,logP1,9,ROOMS,bedrooms,A_structural,level,count,0.03,,,,***,,,medium,Number of rooms (not bedrooms); Rounded to 2 decimals; no SE printed,log asked price per m2 (P1),per_m2,OLS,4670.0,0.346,none,Whole Croatia; 3 region dummies (base Zagreb) + quarterly dummies; price type: listing,monetary,0.03,,"b (currency per unit, as reported)",,,,,,4660.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T5-P1,5,logP1,9,NEWBUILT,age,A_structural,dummy,binary,0.07,,,,***,,,medium,1 if dwelling sold in the construction year or within two years after construction; Rounded to 2 decimals; no SE printed,log asked price per m2 (P1),per_m2,OLS,4670.0,0.346,none,Whole Croatia; 3 region dummies (base Zagreb) + quarterly dummies; price type: listing,monetary,0.07,,"b (currency per unit, as reported)",,,,,,4660.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T5-P2,5,logP2,9,M2,living_area,A_structural,level,m2,0.0,,,,,,,medium,"Floor area in m2; dependent variable is log price per m2; Coefficient printed as 0,00 (rounded); no SE printed",log realized price per m2 (P2),per_m2,OLS,3616.0,0.29,none,Whole Croatia; 3 region dummies + quarterly dummies; price type: transaction,monetary,0.0,,"b (currency per unit, as reported)",,,,,,3606.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T5-P2,5,logP2,9,ROOMS,bedrooms,A_structural,level,count,0.01,,,,***,,,medium,Number of rooms (not bedrooms); Rounded to 2 decimals; no SE printed,log realized price per m2 (P2),per_m2,OLS,3616.0,0.29,none,Whole Croatia; 3 region dummies + quarterly dummies; price type: transaction,monetary,0.01,,"b (currency per unit, as reported)",,,,,,3606.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T5-P2,5,logP2,9,NEWBUILT,age,A_structural,dummy,binary,0.07,,,,***,,,medium,1 if dwelling sold in the construction year or within two years after construction; Rounded to 2 decimals; no SE printed,log realized price per m2 (P2),per_m2,OLS,3616.0,0.29,none,Whole Croatia; 3 region dummies + quarterly dummies; price type: transaction,monetary,0.07,,"b (currency per unit, as reported)",,,,,,3606.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T9,9,LOG(P1),19,M2,living_area,A_structural,level,m2,-2.91e-07,3.7e-07,-0.785351,0.4323,,,,high,Floor area in m2; dependent variable is log price per m2; Coefficient of order 1e-6/1e-7: likely unit inconsistency of M2 in this subsample; recorded as printed,log asked price per m2 (P1),per_m2,OLS,3219.0,0.436415,se,Zagreb; 13 district dummies (base Donji grad) + year dummies; price type: listing,monetary,-2.91e-07,3.7e-07,"b (currency per unit, as reported)",reported,-0.013862361190111151,0.017651166408537266,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.785351,3209.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T9,9,LOG(P1),19,ROOMS,bedrooms,A_structural,level,count,0.007505,0.003754,1.99928,0.0457,,,,high,Number of rooms (not bedrooms),log asked price per m2 (P1),per_m2,OLS,3219.0,0.436415,se,Zagreb; 13 district dummies (base Donji grad) + year dummies; price type: listing,monetary,0.007505,0.003754,"b (currency per unit, as reported)",reported,0.035271055259601614,0.017641878706135015,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.99928,3209.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T9,9,LOG(P1),19,NEWBUILT,age,A_structural,dummy,binary,0.099279,0.007989,12.4275,0.0,,,,high,1 if dwelling sold in the construction year or within two years after construction,log asked price per m2 (P1),per_m2,OLS,3219.0,0.436415,se,Zagreb; 13 district dummies (base Donji grad) + year dummies; price type: listing,monetary,0.099279,0.007989,"b (currency per unit, as reported)",reported,0.2142849901778989,0.01724280749771868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.4275,3209.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T10,10,LOG(P2),19,M2,living_area,A_structural,level,m2,-8.16e-07,3.1e-07,-2.628739,0.0086,,,,high,Floor area in m2; dependent variable is log price per m2; Coefficient of order 1e-6/1e-7: likely unit inconsistency of M2 in this subsample; recorded as printed,log realized price per m2 (P2),per_m2,OLS,2165.0,0.385131,se,Zagreb; 13 district dummies + year dummies; price type: transaction,monetary,-8.16e-07,3.1e-07,"b (currency per unit, as reported)",reported,-0.056536463165438935,0.021507065998350897,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.628739,2155.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T10,10,LOG(P2),19,ROOMS,bedrooms,A_structural,level,count,0.001888,0.004493,0.420138,0.6744,,,,high,Number of rooms (not bedrooms),log realized price per m2 (P2),per_m2,OLS,2165.0,0.385131,se,Zagreb; 13 district dummies + year dummies; price type: transaction,monetary,0.001888,0.004493,"b (currency per unit, as reported)",reported,0.009050040874059088,0.02154063872836803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.420138,2155.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T10,10,LOG(P2),19,NEWBUILT,age,A_structural,dummy,binary,0.105327,0.010633,9.90529,0.0,,,,high,1 if dwelling sold in the construction year or within two years after construction,log realized price per m2 (P2),per_m2,OLS,2165.0,0.385131,se,Zagreb; 13 district dummies + year dummies; price type: transaction,monetary,0.105327,0.010633,"b (currency per unit, as reported)",reported,0.20867745801022625,0.021067273952627964,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.90529,2155.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T11,11,LOG(P1),20,M2,living_area,A_structural,level,m2,-0.001484,0.000729,-2.034951,0.0422,,,,high,Floor area in m2; dependent variable is log price per m2,log asked price per m2 (P1),per_m2,OLS,760.0,0.262737,se,Middle and South Adriatic; 3 county dummies + year dummies; price type: listing,monetary,-0.001484,0.000729,"b (currency per unit, as reported)",reported,-0.07410161488758091,0.03641444677910225,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.034951,750.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T11,11,LOG(P1),20,ROOMS,bedrooms,A_structural,level,count,0.026734,0.017047,1.568189,0.1173,,,,high,Number of rooms (not bedrooms),log asked price per m2 (P1),per_m2,OLS,760.0,0.262737,se,Middle and South Adriatic; 3 county dummies + year dummies; price type: listing,monetary,0.026734,0.017047,"b (currency per unit, as reported)",reported,0.0571685161730251,0.036455118721675187,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.568189,750.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T11,11,LOG(P1),20,NEWBUILT,age,A_structural,dummy,binary,0.097784,0.026151,3.739135,0.0002,,,,high,1 if dwelling sold in the construction year or within two years after construction,log asked price per m2 (P1),per_m2,OLS,760.0,0.262737,se,Middle and South Adriatic; 3 county dummies + year dummies; price type: listing,monetary,0.097784,0.026151,"b (currency per unit, as reported)",reported,0.13527882717247197,0.03617917704829378,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.739135,750.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T12,12,LOG(P2),21,M2,living_area,A_structural,level,m2,-0.001967,0.000884,-2.225607,0.0263,,,,high,Floor area in m2; dependent variable is log price per m2,log realized price per m2 (P2),per_m2,OLS,760.0,0.146218,se,Middle and South Adriatic; 3 county dummies + year dummies; White SE not stated in table header; price type: transaction,monetary,-0.001967,0.000884,"b (currency per unit, as reported)",reported,-0.08100063566072033,0.03639485122967367,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.225607,750.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T12,12,LOG(P2),21,ROOMS,bedrooms,A_structural,level,count,0.010474,0.020836,0.502682,0.6153,,,,high,Number of rooms (not bedrooms),log realized price per m2 (P2),per_m2,OLS,760.0,0.146218,se,Middle and South Adriatic; 3 county dummies + year dummies; White SE not stated in table header; price type: transaction,monetary,0.010474,0.020836,"b (currency per unit, as reported)",reported,0.018352260025297374,0.03650868745110701,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.502682,750.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T12,12,LOG(P2),21,NEWBUILT,age,A_structural,dummy,binary,0.074622,0.049487,1.507921,0.132,,,,high,1 if dwelling sold in the construction year or within two years after construction,log realized price per m2 (P2),per_m2,OLS,760.0,0.146218,se,Middle and South Adriatic; 3 county dummies + year dummies; White SE not stated in table header; price type: transaction,monetary,0.074622,0.049487,"b (currency per unit, as reported)",reported,0.05497821226459107,0.03645961046009112,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.507921,750.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T13,13,LOG(P1),21,M2,living_area,A_structural,level,m2,-0.000919,0.000506,-1.815385,0.0703,,,,high,Floor area in m2; dependent variable is log price per m2,log asked price per m2 (P1),per_m2,OLS,371.0,0.380502,se,Istria and North Adriatic; 2 county dummies + year dummies; price type: listing,monetary,-0.000919,0.000506,"b (currency per unit, as reported)",reported,-0.09511341306235473,0.05239297067143042,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.815385,361.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T13,13,LOG(P1),21,ROOMS,bedrooms,A_structural,level,count,-0.034939,0.023296,-1.499773,0.1346,,,,high,Number of rooms (not bedrooms),log asked price per m2 (P1),per_m2,OLS,371.0,0.380502,se,Istria and North Adriatic; 2 county dummies + year dummies; price type: listing,monetary,-0.034939,0.023296,"b (currency per unit, as reported)",reported,-0.07869064886733247,0.05246837279197083,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.499773,361.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T13,13,LOG(P1),21,NEWBUILT,age,A_structural,dummy,binary,0.175797,0.042835,4.104006,0.0001,,,,high,1 if dwelling sold in the construction year or within two years after construction,log asked price per m2 (P1),per_m2,OLS,371.0,0.380502,se,Istria and North Adriatic; 2 county dummies + year dummies; price type: listing,monetary,0.175797,0.042835,"b (currency per unit, as reported)",reported,0.21113117957451757,0.05144514398237175,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.104006,361.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T14,14,LOG(P2),22,M2,living_area,A_structural,level,m2,-0.001171,0.000619,-1.89003,0.0596,,,,high,Floor area in m2; dependent variable is log price per m2,log realized price per m2 (P2),per_m2,OLS,371.0,0.356532,se,Istria and North Adriatic; 2 county dummies + year dummies; price type: transaction,monetary,-0.001171,0.000619,"b (currency per unit, as reported)",reported,-0.09898671575789145,0.05237309236249765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.89003,361.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T14,14,LOG(P2),22,ROOMS,bedrooms,A_structural,level,count,-0.03408,0.025415,-1.340913,0.1808,,,,high,Number of rooms (not bedrooms),log realized price per m2 (P2),per_m2,OLS,371.0,0.356532,se,Istria and North Adriatic; 2 county dummies + year dummies; price type: transaction,monetary,-0.03408,0.025415,"b (currency per unit, as reported)",reported,-0.07039926591343425,0.05250099440711981,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.340913,361.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T14,14,LOG(P2),22,NEWBUILT,age,A_structural,dummy,binary,0.156879,0.042463,3.694459,0.0003,,,,high,1 if dwelling sold in the construction year or within two years after construction,log realized price per m2 (P2),per_m2,OLS,371.0,0.356532,se,Istria and North Adriatic; 2 county dummies + year dummies; price type: transaction,monetary,0.156879,0.042463,"b (currency per unit, as reported)",reported,0.19087038370362205,0.051663960461767765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.694459,361.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T15,15,LOG(P1),23,M2,living_area,A_structural,level,m2,-3.24e-06,8.75e-07,-3.697922,0.0003,,,,high,Floor area in m2; dependent variable is log price per m2; Coefficient of order 1e-6/1e-7: likely unit inconsistency of M2 in this subsample; recorded as printed,log asked price per m2 (P1),per_m2,OLS,320.0,0.331336,se,Other region; 6 county dummies + year dummies; price type: listing,monetary,-3.24e-06,8.75e-07,"b (currency per unit, as reported)",reported,-0.20554334418070058,0.055583472063688896,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.697922,310.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T15,15,LOG(P1),23,ROOMS,bedrooms,A_structural,level,count,-0.016824,0.011742,-1.432834,0.1529,,,,high,Number of rooms (not bedrooms),log asked price per m2 (P1),per_m2,OLS,320.0,0.331336,se,Other region; 6 county dummies + year dummies; price type: listing,monetary,-0.016824,0.011742,"b (currency per unit, as reported)",reported,-0.08111136089842289,0.056609042567682566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.432834,310.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T15,15,LOG(P1),23,NEWBUILT,age,A_structural,dummy,binary,0.192023,0.024329,7.892637,0.0,,,,high,1 if dwelling sold in the construction year or within two years after construction,log asked price per m2 (P1),per_m2,OLS,320.0,0.331336,se,Other region; 6 county dummies + year dummies; price type: listing,monetary,0.192023,0.024329,"b (currency per unit, as reported)",reported,0.40905271031569634,0.051827128286236444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.892637,310.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T16,16,LOG(P2),23,M2,living_area,A_structural,level,m2,-5.21e-06,9.54e-07,-5.461383,0.0,,,,high,Floor area in m2; dependent variable is log price per m2; Coefficient of order 1e-6/1e-7: likely unit inconsistency of M2 in this subsample; recorded as printed,log realized price per m2 (P2),per_m2,OLS,320.0,0.327567,se,Other region; 6 county dummies + year dummies; price type: transaction,monetary,-5.21e-06,9.54e-07,"b (currency per unit, as reported)",reported,-0.2962605892723174,0.0542464407408009,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.461383,310.0,m2,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T16,16,LOG(P2),23,ROOMS,bedrooms,A_structural,level,count,-0.018994,0.013385,-1.419035,0.1569,,,,high,Number of rooms (not bedrooms),log realized price per m2 (P2),per_m2,OLS,320.0,0.327567,se,Other region; 6 county dummies + year dummies; price type: transaction,monetary,-0.018994,0.013385,"b (currency per unit, as reported)",reported,-0.08033527841233602,0.05661261238259524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.419035,310.0,count,,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,True,False
f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c,f2f2e8d4e0c68be7054271b21b3018d65c7e131c:T16,16,LOG(P2),23,NEWBUILT,age,A_structural,dummy,binary,0.216455,0.051607,4.194316,0.0,,,,high,1 if dwelling sold in the construction year or within two years after construction,log realized price per m2 (P2),per_m2,OLS,320.0,0.327567,se,Other region; 6 county dummies + year dummies; price type: transaction,monetary,0.216455,0.051607,"b (currency per unit, as reported)",reported,0.2317364306196125,0.05525011244255618,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.194316,310.0,binary,year_built_not_age,2020.0,Croatia,Europe,High income,2010.0,2019.0,condo_apartment,listing,conference,True,True,robust,,2014.5,ols,other,False,False,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SQFT2,living_area,A_structural,dummy,binary,0.154,,28.7,0.0,,,,high,"2nd square-footage quintile (1312-1580 sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,16.647409387167844,0.6259219392469677,100*(exp(b - se^2/2) - 1),|b/t|,0.15206551349896075,0.00529844994769898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.7,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SQFT3,living_area,A_structural,dummy,binary,0.284,,47.61,0.0,,,,high,"3rd square-footage quintile (1581-1930 sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,32.840929627064575,0.7924279612135973,100*(exp(b - se^2/2) - 1),|b/t|,0.24729977692328722,0.005194282229012544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.61,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SQFT4,living_area,A_structural,dummy,binary,0.449,,63.84,0.0,,,,high,"4th square-footage quintile (1931-2541 sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,56.67059073712544,1.1019241088385865,100*(exp(b - se^2/2) - 1),|b/t|,0.32379593147260854,0.005071991407778955,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),63.84,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SQFT5,living_area,A_structural,dummy,binary,0.666,,74.79,0.0,,,,high,"5th square-footage quintile (2542+ sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,94.63588118667975,1.7332883615841368,100*(exp(b - se^2/2) - 1),|b/t|,0.3721377105328211,0.004975768291654246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),74.79,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,ONEBEDROOM,bedrooms,A_structural,dummy,binary,-0.149,,-8.06,0.0,,,,high,"One bedroom; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-13.85780910888046,1.5927270238206548,100*(exp(b - se^2/2) - 1),|b/t|,-0.043167720818191105,0.005355796627567134,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.06,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,True
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,TWOBEDROOMS,bedrooms,A_structural,dummy,binary,0.02,,2.67,0.008,,,,high,"Two bedrooms; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,2.0172718864181594,0.7641957603196673,100*(exp(b - se^2/2) - 1),|b/t|,0.014311853339242828,0.005360244696345629,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.67,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,FOURBEDROOMS,bedrooms,A_structural,dummy,binary,-0.025,,-5.72,0.0,,,,high,"Four bedrooms; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-2.469940330774689,0.42627181469769787,100*(exp(b - se^2/2) - 1),|b/t|,-0.03064933437566218,0.005358275240500381,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.72,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,FIVEBEDROOMS,bedrooms,A_structural,dummy,binary,-0.064,,-6.67,0.0,,,,high,"Five bedrooms; base three bedrooms; printed as 'FIV EBEDROOMS'; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-6.203817955491475,0.9000347821584211,100*(exp(b - se^2/2) - 1),|b/t|,-0.03573365837355882,0.005357370070998324,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.67,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SIXPLUSBEDROOMS,bedrooms,A_structural,dummy,binary,-0.105,,-3.26,0.001,,,,high,"Six or more bedrooms; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-10.014235159017716,2.8998182475938004,100*(exp(b - se^2/2) - 1),|b/t|,-0.017473519465634607,0.005359975296206935,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.26,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,TWOFULLBATH,bathrooms,A_structural,dummy,binary,0.16,,19.75,0.0,,,,high,"Two full bathrooms; base one full bath; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,17.347236256638254,0.9506923511832388,100*(exp(b - se^2/2) - 1),|b/t|,0.10528720424589905,0.005330997683336661,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.75,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,THREEFULLBATH,bathrooms,A_structural,dummy,binary,0.298,,29.34,0.0,,,,high,"Three full bathrooms; base one; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,34.70923029104655,1.368282933838129,100*(exp(b - se^2/2) - 1),|b/t|,0.15537552902280397,0.005295689469079889,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.34,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,FOURFULLBATH,bathrooms,A_structural,dummy,binary,0.516,,34.15,0.0,,,,high,"Four full bathrooms; base one; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,67.51217460187662,2.531366021485081,100*(exp(b - se^2/2) - 1),|b/t|,0.18007831074686476,0.0052731569764821315,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.15,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,FIVEPLUSFULLBATH,bathrooms,A_structural,dummy,binary,0.739,,24.97,0.0,,,,high,"Five or more full bathrooms; base one; printed as 'FIV EPLUSFULLBATH'; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,109.29238356369298,6.1968290871294025,100*(exp(b - se^2/2) - 1),|b/t|,0.1326756405667321,0.005313401704714942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.97,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,ONEHALFBATH,bathrooms,A_structural,dummy,binary,0.089,,21.89,0.0,,,,high,"One half bathroom; base zero half baths; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,9.30716217267602,0.44442292559636076,100*(exp(b - se^2/2) - 1),|b/t|,0.1165480563353456,0.005324260225461197,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.89,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,TWOHALFBATH,bathrooms,A_structural,dummy,binary,0.18,,9.47,0.0,,,,high,"Two half bathrooms; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,19.700111674566777,2.2755979446877066,100*(exp(b - se^2/2) - 1),|b/t|,0.050701423471159565,0.005353898993786649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.47,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,THREEHALFBATH,bathrooms,A_structural,dummy,binary,0.425,,3.65,0.0,,,,high,"Three half bathrooms; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,51.92564759286535,17.81029940703934,100*(exp(b - se^2/2) - 1),|b/t|,0.01956315252991156,0.005359767816414126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.65,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,FOURPLUSHALFBATH,bathrooms,A_structural,dummy,binary,0.164,,2.83,0.005,,,,high,"Four or more half bathrooms; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,17.623759782283965,6.827814405484328,100*(exp(b - se^2/2) - 1),|b/t|,0.015169300718867318,0.00536017693246195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.83,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,FIREPLACE,fireplace,B_interior,dummy,binary,0.099,,25.42,0.0,,,,high,"Presence of a fireplace; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,10.405792652578306,0.429986481732216,100*(exp(b - se^2/2) - 1),|b/t|,0.13502345858275874,0.0053117017538457404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.42,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,ONEGARAGE,garage,A_structural,dummy,binary,0.073,,13.16,0.0,,,,high,"One garage space; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,7.571398668208307,0.5967198267080042,100*(exp(b - se^2/2) - 1),|b/t|,0.07037313858437586,0.005347502931943454,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.16,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,TWOGARAGE,garage,A_structural,dummy,binary,0.163,,31.66,0.0,,,,high,"Two garage spaces; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,17.70210901943472,0.6059917258977262,100*(exp(b - se^2/2) - 1),|b/t|,0.16732980536438824,0.005285211792937089,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.66,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,THREEPLUSGARAGE,garage,A_structural,dummy,binary,0.316,,31.74,0.0,,,,high,"Three or more garage spaces; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,37.15622794673272,1.3655802166913344,100*(exp(b - se^2/2) - 1),|b/t|,0.16774074009811157,0.005284837432202634,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.74,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,True
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,6-10YEARS,age,A_structural,dummy,binary,-0.02,,-4.53,0.0,,,,high,"House 6-10 years old at sale; base 2-5 years; printed as '6 -10Y EARS'; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-1.9810879820999716,0.43275879616192287,100*(exp(b - se^2/2) - 1),|b/t|,-0.024277238202337962,0.005359213731200433,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.53,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,11-25YEARS,age,A_structural,dummy,binary,-0.048,,-9.67,0.0,,,,high,"House 11-25 years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-4.687795515622028,0.4731170814862485,100*(exp(b - se^2/2) - 1),|b/t|,-0.051769363107411585,0.005353605285151147,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.67,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,26-50YEARS,age,A_structural,dummy,binary,-0.108,,-16.06,0.0,,,,high,"House 26-50 years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-10.239269990909373,0.6036349963542154,100*(exp(b - se^2/2) - 1),|b/t|,-0.08577703466314729,0.0053410357822632195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.06,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,51-100YEARS,age,A_structural,dummy,binary,-0.059,,-5.49,0.0,,,,high,"House 51-100 years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-5.734766774780587,1.0131092783291236,100*(exp(b - se^2/2) - 1),|b/t|,-0.029418019937671163,0.005358473576989283,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.49,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,101+YEARS,age,A_structural,dummy,binary,0.047,,2.45,0.014,,,,high,"House 101 or more years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,4.792916538822145,2.0106830378262783,100*(exp(b - se^2/2) - 1),|b/t|,0.013132812022774264,0.005360331437867047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,POOL,pool,C_exterior,dummy,binary,0.089,,12.44,0.0,,,,high,"Swimming pool; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,9.305268224014762,0.7820271576611204,100*(exp(b - se^2/2) - 1),|b/t|,0.06654047478014333,0.005348912763677116,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.44,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,WATERFRONT,waterfront,C_exterior,dummy,binary,0.198,,34.6,0.0,,,,high,"Marketed as waterfront property; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,21.894243499807132,0.6975565143834833,100*(exp(b - se^2/2) - 1),|b/t|,0.18237280479717524,0.005270890312057087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.6,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,CONDO,dwelling_type,A_structural,dummy,binary,-0.092,,-13.38,0.0,,,,high,"Condominium/townhouse vs single-family; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-8.791641166873942,0.6271575019293596,100*(exp(b - se^2/2) - 1),|b/t|,-0.07154361683566667,0.005347056564698555,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.38,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SLR1,flood_risk,E_environment,dummy,binary,-0.025,,-1.86,0.062,,,,high,"Inundated with 0-1 ft sea level rise; base NORISK (>6 ft); robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-2.4778182139371108,1.3109004193929201,100*(exp(b - se^2/2) - 1),|b/t|,-0.009970580733994719,0.005360527276341247,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.86,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SLR2,flood_risk,E_environment,dummy,binary,-0.077,,-3.45,0.001,,,,high,"Inundated with 1.01-2 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-7.434072473268428,2.0664788037014534,100*(exp(b - se^2/2) - 1),|b/t|,-0.01849157612926335,0.0053598771389169126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.45,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SLR3,flood_risk,E_environment,dummy,binary,-0.022,,-1.42,0.156,,,,high,"Inundated with 2.01-3 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,-2.1877162256627725,1.515583462755396,100*(exp(b - se^2/2) - 1),|b/t|,-0.007612106573798522,0.00536063843225248,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.42,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SLR4,flood_risk,E_environment,dummy,binary,0.0,,0.04,0.965,,,,high,"Inundated with 3.01-4 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,0.0,0.0,100*(exp(b - se^2/2) - 1),|b/t|,0.0002144317449847155,0.005360793624617888,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.04,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,False,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SLR5,flood_risk,E_environment,dummy,binary,0.006,,0.69,0.487,,,,high,"Inundated with 4.01-5 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,0.5980002064690959,0.874798292220926,100*(exp(b - se^2/2) - 1),|b/t|,0.00369892238138974,0.005360757074477885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.69,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M1,3,Model 1,7,SLR6,flood_risk,E_environment,dummy,binary,0.005,,0.63,0.528,,,,high,"Inundated with 5.01-6 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; six SLR inundation-band dummies (SLR1-SLR6); t and p in separate columns,pct_premium,0.49808694139785725,0.7976289848090483,100*(exp(b - se^2/2) - 1),|b/t|,0.0033772808003146343,0.005360763175102594,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.63,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,SQFT2,living_area,A_structural,dummy,binary,0.154,,28.75,0.0,,,,high,"2nd square-footage quintile (1312-1580 sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,16.647415223057884,0.624833379352625,100*(exp(b - se^2/2) - 1),|b/t|,0.1523242943029288,0.005298236323580132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),28.75,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,SQFT3,living_area,A_structural,dummy,binary,0.285,,47.7,0.0,,,,high,"3rd square-footage quintile (1581-1930 sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,32.97382929148001,0.7945119035945911,100*(exp(b - se^2/2) - 1),|b/t|,0.24773859609159066,0.0051936812597817746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),47.7,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,SQFT4,living_area,A_structural,dummy,binary,0.449,,63.93,0.0,,,,high,"4th square-footage quintile (1931-2541 sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,56.670601639633624,1.1003728313507801,100*(exp(b - se^2/2) - 1),|b/t|,0.32420446106508277,0.005071241374395162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),63.93,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,SQFT5,living_area,A_structural,dummy,binary,0.666,,74.9,0.0,,,,high,"5th square-footage quintile (2542+ sqft); base 0-1311 sqft; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,94.63590383709484,1.730742811253372,100*(exp(b - se^2/2) - 1),|b/t|,0.3726091025292622,0.004974754372887346,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),74.9,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,True
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,ONEBEDROOM,bedrooms,A_structural,dummy,binary,-0.149,,-8.08,0.0,,,,high,"One bedroom; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-13.857736331007475,1.5887846301973365,100*(exp(b - se^2/2) - 1),|b/t|,-0.04327463640315941,0.0053557718320741835,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.08,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,TWOBEDROOMS,bedrooms,A_structural,dummy,binary,0.02,,2.65,0.008,,,,high,"Two bedrooms; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,2.017228522255343,0.7699632754918911,100*(exp(b - se^2/2) - 1),|b/t|,0.014204670157941798,0.005360252889789358,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.65,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,FOURBEDROOMS,bedrooms,A_structural,dummy,binary,-0.025,,-5.71,0.0,,,,high,"Four bedrooms; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-2.469943596430879,0.4270183502751019,100*(exp(b - se^2/2) - 1),|b/t|,-0.030595801826118103,0.005358284032595114,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.71,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,FIVEBEDROOMS,bedrooms,A_structural,dummy,binary,-0.064,,-6.68,0.0,,,,high,"Five bedrooms; base three bedrooms; printed as 'FIV EBEDROOMS'; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-6.2038050375945115,0.8986874247001001,100*(exp(b - se^2/2) - 1),|b/t|,-0.035787163512592715,0.005357359807274359,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.68,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,SIXPLUSBEDROOMS,bedrooms,A_structural,dummy,binary,-0.105,,-3.25,0.001,,,,high,"Six or more bedrooms; base three bedrooms; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-10.014522833133622,2.9087407652787043,100*(exp(b - se^2/2) - 1),|b/t|,-0.01741993600275667,0.005359980308540514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.25,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,TWOFULLBATH,bathrooms,A_structural,dummy,binary,0.159,,19.73,0.0,,,,high,"Two full bathrooms; base one full bath; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,17.229987904945745,0.9447629676747799,100*(exp(b - se^2/2) - 1),|b/t|,0.10518176444171382,0.005331057498312915,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.73,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,THREEFULLBATH,bathrooms,A_structural,dummy,binary,0.298,,29.32,0.0,,,,high,"Three full bathrooms; base one; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,34.70922080852637,1.3692162782677597,100*(exp(b - se^2/2) - 1),|b/t|,0.15527216960618115,0.0052957765895696165,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),29.32,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,FOURFULLBATH,bathrooms,A_structural,dummy,binary,0.516,,34.14,0.0,,,,high,"Four full bathrooms; base one; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,67.51216339810023,2.532107487806547,100*(exp(b - se^2/2) - 1),|b/t|,0.18002728844020763,0.005273207042771167,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.14,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,FIVEPLUSFULLBATH,bathrooms,A_structural,dummy,binary,0.739,,24.97,0.0,,,,high,"Five or more full bathrooms; base one; printed as 'FIV EPLUSFULLBATH'; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,109.29238356369298,6.1968290871294025,100*(exp(b - se^2/2) - 1),|b/t|,0.1326756405667321,0.005313401704714942,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),24.97,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,ONEHALFBATH,bathrooms,A_structural,dummy,binary,0.089,,21.88,0.0,,,,high,"One half bathroom; base zero half baths; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,9.307161346658944,0.4446260439352988,100*(exp(b - se^2/2) - 1),|b/t|,0.11649553646250638,0.005324293256970127,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),21.88,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,TWOHALFBATH,bathrooms,A_structural,dummy,binary,0.18,,9.44,0.0,,,,high,"Two half bathrooms; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,19.699974023951363,2.2828297178170107,100*(exp(b - se^2/2) - 1),|b/t|,0.050541217433949355,0.005353942524782771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.44,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,THREEHALFBATH,bathrooms,A_structural,dummy,binary,0.425,,3.65,0.0,,,,high,"Three half bathrooms; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,51.92564759286535,17.81029940703934,100*(exp(b - se^2/2) - 1),|b/t|,0.01956315252991156,0.005359767816414126,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.65,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,FOURPLUSHALFBATH,bathrooms,A_structural,dummy,binary,0.163,,2.83,0.005,,,,high,"Four or more half bathrooms; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,17.508593703389018,6.7793986013858705,100*(exp(b - se^2/2) - 1),|b/t|,0.015169300718867318,0.00536017693246195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.83,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,FIREPLACE,fireplace,B_interior,dummy,binary,0.099,,25.42,0.0,,,,high,"Presence of a fireplace; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,10.405792652578306,0.429986481732216,100*(exp(b - se^2/2) - 1),|b/t|,0.13502345858275874,0.0053117017538457404,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),25.42,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,ONEGARAGE,garage,A_structural,dummy,binary,0.073,,13.19,0.0,,,,high,"One garage space; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,7.571406188125529,0.5953626170945667,100*(exp(b - se^2/2) - 1),|b/t|,0.07053276648130044,0.0053474424928961675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.19,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,TWOGARAGE,garage,A_structural,dummy,binary,0.163,,31.7,0.0,,,,high,"Two garage spaces; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,17.702112953705786,0.6052270675685177,100*(exp(b - se^2/2) - 1),|b/t|,0.16753528364684456,0.005285024720720648,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.7,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,THREEPLUSGARAGE,garage,A_structural,dummy,binary,0.316,,31.77,0.0,,,,high,"Three or more garage spaces; base zero; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,37.156240778155514,1.364290716958859,100*(exp(b - se^2/2) - 1),|b/t|,0.1678948180956017,0.005284696823909403,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),31.77,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,True
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,6-10YEARS,age,A_structural,dummy,binary,-0.02,,-4.52,0.0,,,,high,"House 6-10 years old at sale; base 2-5 years; printed as '6 -10Y EARS'; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-1.9810922138028708,0.4337162271268829,100*(exp(b - se^2/2) - 1),|b/t|,-0.024223677546937734,0.005359220696225163,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.52,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,11-25YEARS,age,A_structural,dummy,binary,-0.048,,-9.7,0.0,,,,high,"House 11-25 years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-4.687788263660886,0.47165383278062095,100*(exp(b - se^2/2) - 1),|b/t|,-0.05192953882559311,0.005353560703669393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-9.7,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,26-50YEARS,age,A_structural,dummy,binary,-0.108,,-16.06,0.0,,,,high,"House 26-50 years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-10.239269990909373,0.6036349963542154,100*(exp(b - se^2/2) - 1),|b/t|,-0.08577703466314729,0.0053410357822632195,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-16.06,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,51-100YEARS,age,A_structural,dummy,binary,-0.059,,-5.5,0.0,,,,high,"House 51-100 years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-5.734746998107065,1.011267261459434,100*(exp(b - se^2/2) - 1),|b/t|,-0.029471558173474667,0.00535846512244994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.5,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,101+YEARS,age,A_structural,dummy,binary,0.047,,2.45,0.014,,,,high,"House 101 or more years old; base 2-5 years; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,4.792916538822145,2.0106830378262783,100*(exp(b - se^2/2) - 1),|b/t|,0.013132812022774264,0.005360331437867047,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.45,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,POOL,pool,C_exterior,dummy,binary,0.089,,12.47,0.0,,,,high,"Swimming pool; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,9.305281667517917,0.7801457771695539,100*(exp(b - se^2/2) - 1),|b/t|,0.06670022911135165,0.005348855582305666,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.47,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,WATERFRONT,waterfront,C_exterior,dummy,binary,0.198,,34.55,0.0,,,,high,"Marketed as waterfront property; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,21.894237718880483,0.698566002826875,100*(exp(b - se^2/2) - 1),|b/t|,0.18211800736904946,0.005271143483908812,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),34.55,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,CONDO,dwelling_type,A_structural,dummy,binary,-0.091,,-13.49,0.0,,,,high,"Condominium/townhouse vs single-family; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-8.700306228871534,0.6158977886718545,100*(exp(b - se^2/2) - 1),|b/t|,-0.07212874544970381,0.005346830648606658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.49,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,HIRISK,flood_risk,E_environment,dummy,binary,-0.031,,-3.17,0.002,,,,high,"Inundated with 0-3 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,-3.0570782433120613,0.9480675951216657,100*(exp(b - se^2/2) - 1),|b/t|,-0.016991262935096563,0.005360019853342765,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.17,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545,f326d0249d4fa4d2ec6af5feb9cf6966a73ca545:T3-M2,3,Model 2,7,LOWRISK,flood_risk,E_environment,dummy,binary,0.004,,0.69,0.493,,,,high,"Inundated with 3.01-6 ft SLR; base NORISK; robust SE (not printed), t and p printed","ln(real sales price, 2012 USD)",log,OLS,34807.0,0.788,t,Full sample; HIRISK and LOWRISK dummies; t and p in separate columns,pct_premium,0.3991140279098637,0.5820336293781694,100*(exp(b - se^2/2) - 1),|b/t|,0.00369892238138974,0.005360757074477885,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.69,34797.0,binary,,2020.0,United States,North America,High income,2007.0,2016.0,mixed,transaction,journal,True,True,robust,,2011.5,ols,journal,True,True,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,ln (Distance to Transit Station),transit_distance,D_location,log,miles,-0.044,,,,***,,,high,"Log road-network distance (miles) to nearest NJ Transit station. Standardized Beta printed = -0.062. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),elasticity,-0.044,,b (log-log),,,,,,1770.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,ln (Distance to New York Penn Station),cbd_distance,D_location,log,miles,-0.377,,,,***,,,high,"Log road-network distance (miles) to New York Penn Station (regional employment centre). Standardized Beta printed = -0.479. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),elasticity,-0.377,,b (log-log),,,,,,1770.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,Beds,bedrooms,A_structural,level,count,0.248,,,,***,,,high,"Number of bedrooms; quadratic. Standardized Beta printed = 0.531. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,24.8,,100*b/1 (per unit count),,,,,,1770.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,Beds²,bedrooms,A_structural,squared,count,-0.018,,,,***,,,high,"Bedrooms squared. Standardized Beta printed = -0.348. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),other,-0.018,,not harmonised,,,,,,1770.0,count,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,Age,age,A_structural,level,years,-0.008,,,,***,,,high,"Age of house at sale date; quadratic. Standardized Beta printed = -0.412. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,-0.8,,100*b/1 (per year),,,,,,1770.0,years,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,Age²,age,A_structural,squared,years,0.0,,,,***,,,low,"Age squared. Printed as .000 (rounded to zero). Standardized Beta printed = 0.256. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),other,0.0,,not harmonised,,,,,,1770.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,Parking Spots,parking,A_structural,level,count,0.051,,,,***,,,high,"Number of parking spots. Standardized Beta printed = 0.145. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,5.1,,100*b/1 (per unit count),,,,,,1770.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,14,Housing Size in 100 sqft,living_area,A_structural,level,100 sqft,0.014,,,,***,,,high,"Housing size in units of 100 square feet. Standardized Beta printed = 0.184. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,0.1506947458339361,,100*b/9.2903 (X per 100sqft->m2),,,,,,1770.0,100sqft,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,Walk Score,walkability,D_location,level,index,-0.001,,,,,,,high,"Walk Score (0-100); quadratic. Standardized Beta printed = -0.055. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,-0.1,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1770.0,index,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,Walk Score²,walkability,D_location,squared,index,0.0,,,,,,,low,"Walk Score squared. Printed as .000 (rounded to zero). Standardized Beta printed = 0.003. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),other,0.0,,not harmonised,,,,,,1770.0,index,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,Transit Village,other,other,binary,binary,-0.005,,,,,,,high,"Located in a designated NJ Transit Village. Standardized Beta printed = -0.002. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),pct_premium,-0.498752080731768,,100*(exp(b) - 1),,,,,,1770.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,Northeast Corridor Line,other,other,binary,binary,0.213,,,,***,,,high,"Nearest station is on the Northeast Corridor commuter-rail line (direct access to NY Penn). Standardized Beta printed = 0.093. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),pct_premium,23.738465124360065,,100*(exp(b) - 1),,,,,,1770.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,Light Rail Transit,other,other,binary,binary,-0.098,,,,***,,,high,"Nearest transit station is a light rail station. Standardized Beta printed = -0.047. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),pct_premium,-9.335109624607906,,100*(exp(b) - 1),,,,,,1770.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,"Population Density in 1,000",density,F_neighbourhood,level,count,0.0,,,,,,,low,"Block-group population density divided by 1,000. Printed as .000 (rounded to zero). Standardized Beta printed = -0.01. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,0.0,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1770.0,count,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,Proportion of the White,demographics,F_neighbourhood,level,share,0.005,,,,***,,,high,"Percent non-Hispanic white in block group. Standardized Beta printed = 0.204. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,0.5,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,1770.0,share,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c1,4,1,15,"Per Capita Income in $1,000s",income,F_neighbourhood,level,currency,0.01,,,,***,,,high,"Block-group per capita income in $1,000s (USD). Standardized Beta printed = 0.347. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1780.0,0.699,none,All residential properties (Model 1),semi_elasticity,1.0,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,1770.0,currency,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,ln (Distance to Transit Station),transit_distance,D_location,log,miles,-0.041,,,,**,,,high,"Log road-network distance (miles) to nearest NJ Transit station. Standardized Beta printed = -0.057. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),elasticity,-0.041,,b (log-log),,,,,,1491.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,ln (Distance to New York Penn Station),cbd_distance,D_location,log,miles,-0.381,,,,***,,,high,"Log road-network distance (miles) to New York Penn Station (regional employment centre). Standardized Beta printed = -0.439. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),elasticity,-0.381,,b (log-log),,,,,,1491.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,Beds,bedrooms,A_structural,level,count,0.167,,,,***,,,high,"Number of bedrooms; quadratic. Standardized Beta printed = 0.337. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,16.7,,100*b/1 (per unit count),,,,,,1491.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,Beds²,bedrooms,A_structural,squared,count,-0.011,,,,***,,,high,"Bedrooms squared. Standardized Beta printed = -0.211. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),other,-0.011,,not harmonised,,,,,,1491.0,count,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,Age,age,A_structural,level,years,-0.008,,,,***,,,high,"Age of house at sale date; quadratic. Standardized Beta printed = -0.409. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,-0.8,,100*b/1 (per year),,,,,,1491.0,years,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,Age²,age,A_structural,squared,years,0.0,,,,***,,,low,"Age squared. Printed as .000 (rounded to zero). Standardized Beta printed = 0.254. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),other,0.0,,not harmonised,,,,,,1491.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,Parking Spots,parking,A_structural,level,count,0.046,,,,***,,,high,"Number of parking spots. Standardized Beta printed = 0.137. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,4.6,,100*b/1 (per unit count),,,,,,1491.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,14,Housing Size in 100 sqft,living_area,A_structural,level,100 sqft,0.013,,,,***,,,high,"Housing size in units of 100 square feet. Standardized Beta printed = 0.179. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,0.1399308354172264,,100*b/9.2903 (X per 100sqft->m2),,,,,,1491.0,100sqft,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,Walk Score,walkability,D_location,level,index,-0.001,,,,,,,high,"Walk Score (0-100); quadratic. Standardized Beta printed = -0.031. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,-0.1,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,1491.0,index,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,Walk Score²,walkability,D_location,squared,index,0.0,,,,,,,low,"Walk Score squared. Printed as .000 (rounded to zero). Standardized Beta printed = -0.018. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),other,0.0,,not harmonised,,,,,,1491.0,index,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,Transit Village,other,other,binary,binary,0.012,,,,,,,high,"Located in a designated NJ Transit Village. Standardized Beta printed = 0.006. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),pct_premium,1.2072288866077807,,100*(exp(b) - 1),,,,,,1491.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,Northeast Corridor Line,other,other,binary,binary,0.194,,,,***,,,high,"Nearest station is on the Northeast Corridor commuter-rail line (direct access to NY Penn). Standardized Beta printed = 0.087. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),pct_premium,21.409628295623317,,100*(exp(b) - 1),,,,,,1491.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,Light Rail Transit,other,other,binary,binary,-0.106,,,,**,,,high,"Nearest transit station is a light rail station. Standardized Beta printed = -0.042. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),pct_premium,-10.057535192407595,,100*(exp(b) - 1),,,,,,1491.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,"Population Density in 1,000",density,F_neighbourhood,level,count,0.0,,,,,,,low,"Block-group population density divided by 1,000. Printed as .000 (rounded to zero). Standardized Beta printed = -0.004. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,0.0,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,1491.0,count,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,Proportion of the White,demographics,F_neighbourhood,level,share,0.005,,,,***,,,high,"Percent non-Hispanic white in block group. Standardized Beta printed = 0.223. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,0.5,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,1491.0,share,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c2,4,2,15,"Per Capita Income in $1,000s",income,F_neighbourhood,level,currency,0.01,,,,***,,,high,"Block-group per capita income in $1,000s (USD). Standardized Beta printed = 0.357. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,1501.0,0.697,none,Single-family housing (Model 2),semi_elasticity,1.0,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,1491.0,currency,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,ln (Distance to Transit Station),transit_distance,D_location,log,miles,-0.028,,,,,,,high,"Log road-network distance (miles) to nearest NJ Transit station. Standardized Beta printed = -0.037. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),elasticity,-0.028,,b (log-log),,,,,,269.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,ln (Distance to New York Penn Station),cbd_distance,D_location,log,miles,-0.416,,,,***,,,high,"Log road-network distance (miles) to New York Penn Station (regional employment centre). Standardized Beta printed = -0.594. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),elasticity,-0.416,,b (log-log),,,,,,269.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,Beds,bedrooms,A_structural,level,count,0.331,,,,***,,,high,"Number of bedrooms; quadratic. Standardized Beta printed = 0.465. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,33.1,,100*b/1 (per unit count),,,,,,269.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,Beds²,bedrooms,A_structural,squared,count,-0.039,,,,**,,,high,"Bedrooms squared. Standardized Beta printed = -0.329. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),other,-0.039,,not harmonised,,,,,,269.0,count,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,Age,age,A_structural,level,years,-0.011,,,,***,,,high,"Age of house at sale date; quadratic. Standardized Beta printed = -0.538. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,-1.0999999999999999,,100*b/1 (per year),,,,,,269.0,years,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,Age²,age,A_structural,squared,years,0.0,,,,**,,,low,"Age squared. Printed as .000 (rounded to zero). Standardized Beta printed = 0.379. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),other,0.0,,not harmonised,,,,,,269.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,Parking Spots,parking,A_structural,level,count,0.144,,,,***,,,high,"Number of parking spots. Standardized Beta printed = 0.187. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,14.399999999999999,,100*b/1 (per unit count),,,,,,269.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,14,Housing Size in 100 sqft,living_area,A_structural,level,100 sqft,0.024,,,,***,,,high,"Housing size in units of 100 square feet. Standardized Beta printed = 0.211. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,0.2583338500010333,,100*b/9.2903 (X per 100sqft->m2),,,,,,269.0,100sqft,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,Walk Score,walkability,D_location,level,index,-0.007,,,,**,,,high,"Walk Score (0-100); quadratic. Standardized Beta printed = -0.312. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,-0.7000000000000001,,100*b (unit as reported: index; no harmonised unit defined for this characteristic),,,,,,269.0,index,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,Walk Score²,walkability,D_location,squared,index,0.0,,,,**,,,low,"Walk Score squared. Printed as .000 (rounded to zero). Standardized Beta printed = 0.347. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),other,0.0,,not harmonised,,,,,,269.0,index,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,Transit Village,other,other,binary,binary,0.02,,,,,,,high,"Located in a designated NJ Transit Village. Standardized Beta printed = 0.01. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),pct_premium,2.0201340026755776,,100*(exp(b) - 1),,,,,,269.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,Northeast Corridor Line,other,other,binary,binary,0.237,,,,**,,,high,"Nearest station is on the Northeast Corridor commuter-rail line (direct access to NY Penn). Standardized Beta printed = 0.088. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),pct_premium,26.744111777528357,,100*(exp(b) - 1),,,,,,269.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,Light Rail Transit,other,other,binary,binary,-0.099,,,,,,,high,"Nearest transit station is a light rail station. Standardized Beta printed = -0.065. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),pct_premium,-9.42572919764515,,100*(exp(b) - 1),,,,,,269.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,"Population Density in 1,000",density,F_neighbourhood,level,count,0.0,,,,,,,low,"Block-group population density divided by 1,000. Printed as .000 (rounded to zero). Standardized Beta printed = 0.0. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,0.0,,100*b (unit as reported: count; no harmonised unit defined for this characteristic),,,,,,269.0,count,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,Proportion of the White,demographics,F_neighbourhood,level,share,0.002,,,,*,,,high,"Percent non-Hispanic white in block group. Standardized Beta printed = 0.063. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,0.2,,100*b (unit as reported: share; no harmonised unit defined for this characteristic),,,,,,269.0,share,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T4-c3,4,3,15,"Per Capita Income in $1,000s",income,F_neighbourhood,level,currency,0.009,,,,***,,,high,"Block-group per capita income in $1,000s (USD). Standardized Beta printed = 0.345. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","ln(Housing Price), July 2018 sale price",log,OLS,279.0,0.771,none,Apartments and condos (Model 3),semi_elasticity,0.8999999999999999,,100*b (unit as reported: currency; no harmonised unit defined for this characteristic),,,,,,269.0,currency,unit_not_harmonisable,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,ln (Distance to Transit Station),transit_distance,D_location,log,miles,-34.0,,,,***,,,high,"Log road-network distance (miles) to nearest NJ Transit station. Standardized Beta printed = -0.104. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),other,-34.0,,not harmonised,,,,,,1770.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,ln (Distance to New York Penn Station),cbd_distance,D_location,log,miles,-99.463,,,,***,,,high,"Log road-network distance (miles) to New York Penn Station (regional employment centre). Standardized Beta printed = -0.277. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),other,-99.463,,not harmonised,,,,,,1770.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Beds,bedrooms,A_structural,level,count,88.008,,,,***,,,high,"Number of bedrooms; quadratic. Standardized Beta printed = 0.413. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,88.008,,"b (currency per unit, as reported)",,,,,,1770.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Beds²,bedrooms,A_structural,squared,count,-6.354,,,,***,,,high,"Bedrooms squared. Standardized Beta printed = -0.268. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),other,-6.354,,not harmonised,,,,,,1770.0,count,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Age,age,A_structural,level,years,-3.903,,,,***,,,high,"Age of house at sale date; quadratic. Standardized Beta printed = -0.446. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,-3.903,,"b (currency per unit, as reported)",,,,,,1770.0,years,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Age²,age,A_structural,squared,years,0.021,,,,***,,,high,"Age squared. Standardized Beta printed = 0.321. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),other,0.021,,not harmonised,,,,,,1770.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Parking Spots,parking,A_structural,level,count,19.685,,,,***,,,high,"Number of parking spots. Standardized Beta printed = 0.123. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,19.685,,"b (currency per unit, as reported)",,,,,,1770.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Housing Size in 100 sqft,living_area,A_structural,level,100 sqft,8.633,,,,***,,,high,"Housing size in units of 100 square feet. Standardized Beta printed = 0.258. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,8.633,,"b (currency per unit, as reported)",,,,,,1770.0,100sqft,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Walk Score,walkability,D_location,level,index,-1.374,,,,**,,,high,"Walk Score (0-100); quadratic. Standardized Beta printed = -0.135. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,-1.374,,"b (currency per unit, as reported)",,,,,,1770.0,index,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Walk Score²,walkability,D_location,squared,index,0.015,,,,**,,,high,"Walk Score squared. Standardized Beta printed = 0.148. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),other,0.015,,not harmonised,,,,,,1770.0,index,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Transit Village,other,other,binary,binary,-47.069,,,,**,,,high,"Located in a designated NJ Transit Village. Standardized Beta printed = -0.052. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,-47.069,,"b (currency per unit, as reported)",,,,,,1770.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Northeast Corridor Line,other,other,binary,binary,78.728,,,,***,,,high,"Nearest station is on the Northeast Corridor commuter-rail line (direct access to NY Penn). Standardized Beta printed = 0.075. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,78.728,,"b (currency per unit, as reported)",,,,,,1770.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,21,Light Rail Transit,other,other,binary,binary,-10.033,,,,,,,high,"Nearest transit station is a light rail station. Standardized Beta printed = -0.011. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,-10.033,,"b (currency per unit, as reported)",,,,,,1770.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,22,"Population Density in 1,000",density,F_neighbourhood,level,count,1.167,,,,**,,,high,"Block-group population density divided by 1,000. Standardized Beta printed = 0.057. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,1.167,,"b (currency per unit, as reported)",,,,,,1770.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,22,Proportion of the White,demographics,F_neighbourhood,level,share,1.193,,,,***,,,high,"Percent non-Hispanic white in block group. Standardized Beta printed = 0.107. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,1.193,,"b (currency per unit, as reported)",,,,,,1770.0,share,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c1,7,1,22,"Per Capita Income in $1,000s",income,F_neighbourhood,level,currency,5.63,,,,***,,,high,"Block-group per capita income in $1,000s (USD). Standardized Beta printed = 0.427. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1780.0,0.641,none,All residential properties (Model 1),monetary,5.63,,"b (currency per unit, as reported)",,,,,,1770.0,currency,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,ln (Distance to Transit Station),transit_distance,D_location,log,miles,-33.876,,,,***,,,high,"Log road-network distance (miles) to nearest NJ Transit station. Standardized Beta printed = -0.103. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),other,-33.876,,not harmonised,,,,,,1491.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,ln (Distance to New York Penn Station),cbd_distance,D_location,log,miles,-97.568,,,,***,,,high,"Log road-network distance (miles) to New York Penn Station (regional employment centre). Standardized Beta printed = -0.245. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),other,-97.568,,not harmonised,,,,,,1491.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Beds,bedrooms,A_structural,level,count,54.563,,,,***,,,high,"Number of bedrooms; quadratic. Standardized Beta printed = 0.24. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,54.563,,"b (currency per unit, as reported)",,,,,,1491.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Beds²,bedrooms,A_structural,squared,count,-3.232,,,,**,,,high,"Bedrooms squared. Standardized Beta printed = -0.14. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),other,-3.232,,not harmonised,,,,,,1491.0,count,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Age,age,A_structural,level,years,-3.707,,,,***,,,high,"Age of house at sale date; quadratic. Standardized Beta printed = -0.412. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,-3.707,,"b (currency per unit, as reported)",,,,,,1491.0,years,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Age²,age,A_structural,squared,years,0.019,,,,***,,,high,"Age squared. Standardized Beta printed = 0.29. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),other,0.019,,not harmonised,,,,,,1491.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Parking Spots,parking,A_structural,level,count,19.854,,,,***,,,high,"Number of parking spots. Standardized Beta printed = 0.129. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,19.854,,"b (currency per unit, as reported)",,,,,,1491.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Housing Size in 100 sqft,living_area,A_structural,level,100 sqft,8.123,,,,***,,,high,"Housing size in units of 100 square feet. Standardized Beta printed = 0.243. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,8.123,,"b (currency per unit, as reported)",,,,,,1491.0,100sqft,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Walk Score,walkability,D_location,level,index,-1.327,,,,*,,,high,"Walk Score (0-100); quadratic. Standardized Beta printed = -0.127. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,-1.327,,"b (currency per unit, as reported)",,,,,,1491.0,index,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Walk Score²,walkability,D_location,squared,index,0.014,,,,*,,,high,"Walk Score squared. Standardized Beta printed = 0.13. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),other,0.014,,not harmonised,,,,,,1491.0,index,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Transit Village,other,other,binary,binary,-36.421,,,,**,,,high,"Located in a designated NJ Transit Village. Standardized Beta printed = -0.04. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,-36.421,,"b (currency per unit, as reported)",,,,,,1491.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Northeast Corridor Line,other,other,binary,binary,83.4,,,,***,,,high,"Nearest station is on the Northeast Corridor commuter-rail line (direct access to NY Penn). Standardized Beta printed = 0.082. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,83.4,,"b (currency per unit, as reported)",,,,,,1491.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,21,Light Rail Transit,other,other,binary,binary,-2.271,,,,,,,high,"Nearest transit station is a light rail station. Standardized Beta printed = -0.002. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,-2.271,,"b (currency per unit, as reported)",,,,,,1491.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,22,"Population Density in 1,000",density,F_neighbourhood,level,count,1.966,,,,**,,,high,"Block-group population density divided by 1,000. Standardized Beta printed = 0.069. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,1.966,,"b (currency per unit, as reported)",,,,,,1491.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,22,Proportion of the White,demographics,F_neighbourhood,level,share,1.478,,,,***,,,high,"Percent non-Hispanic white in block group. Standardized Beta printed = 0.137. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,1.478,,"b (currency per unit, as reported)",,,,,,1491.0,share,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c2,7,2,22,"Per Capita Income in $1,000s",income,F_neighbourhood,level,currency,5.984,,,,***,,,high,"Block-group per capita income in $1,000s (USD). Standardized Beta printed = 0.444. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,1501.0,0.638,none,Single-family housing (Model 2),monetary,5.984,,"b (currency per unit, as reported)",,,,,,1491.0,currency,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,ln (Distance to Transit Station),transit_distance,D_location,log,miles,-11.717,,,,,,,high,"Log road-network distance (miles) to nearest NJ Transit station. Standardized Beta printed = -0.034. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),other,-11.717,,not harmonised,,,,,,269.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,ln (Distance to New York Penn Station),cbd_distance,D_location,log,miles,-125.284,,,,***,,,high,"Log road-network distance (miles) to New York Penn Station (regional employment centre). Standardized Beta printed = -0.4. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),other,-125.284,,not harmonised,,,,,,269.0,miles,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Beds,bedrooms,A_structural,level,count,104.778,,,,**,,,high,"Number of bedrooms; quadratic. Standardized Beta printed = 0.329. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,104.778,,"b (currency per unit, as reported)",,,,,,269.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Beds²,bedrooms,A_structural,squared,count,-9.423,,,,,,,high,"Bedrooms squared. Standardized Beta printed = -0.178. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),other,-9.423,,not harmonised,,,,,,269.0,count,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Age,age,A_structural,level,years,-7.071,,,,***,,,high,"Age of house at sale date; quadratic. Standardized Beta printed = -0.74. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,-7.071,,"b (currency per unit, as reported)",,,,,,269.0,years,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Age²,age,A_structural,squared,years,0.047,,,,***,,,high,"Age squared. Standardized Beta printed = 0.608. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),other,0.047,,not harmonised,,,,,,269.0,years,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Parking Spots,parking,A_structural,level,count,15.695,,,,,,,high,"Number of parking spots. Standardized Beta printed = 0.046. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,15.695,,"b (currency per unit, as reported)",,,,,,269.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Housing Size in 100 sqft,living_area,A_structural,level,100 sqft,15.393,,,,***,,,high,"Housing size in units of 100 square feet. Standardized Beta printed = 0.295. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,15.393,,"b (currency per unit, as reported)",,,,,,269.0,100sqft,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Walk Score,walkability,D_location,level,index,-2.622,,,,,,,high,"Walk Score (0-100); quadratic. Standardized Beta printed = -0.258. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,-2.622,,"b (currency per unit, as reported)",,,,,,269.0,index,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Walk Score²,walkability,D_location,squared,index,0.035,,,,**,,,high,"Walk Score squared. Standardized Beta printed = 0.384. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),other,0.035,,not harmonised,,,,,,269.0,index,nonstandard_x_transform,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Transit Village,other,other,binary,binary,-63.014,,,,*,,,high,"Located in a designated NJ Transit Village. Standardized Beta printed = -0.071. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,-63.014,,"b (currency per unit, as reported)",,,,,,269.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Northeast Corridor Line,other,other,binary,binary,1.938,,,,,,,high,"Nearest station is on the Northeast Corridor commuter-rail line (direct access to NY Penn). Standardized Beta printed = 0.002. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,1.938,,"b (currency per unit, as reported)",,,,,,269.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,21,Light Rail Transit,other,other,binary,binary,-68.5,,,,**,,,high,"Nearest transit station is a light rail station. Standardized Beta printed = -0.101. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,-68.5,,"b (currency per unit, as reported)",,,,,,269.0,binary,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,22,"Population Density in 1,000",density,F_neighbourhood,level,count,0.87,,,,,,,high,"Block-group population density divided by 1,000. Standardized Beta printed = 0.067. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,0.87,,"b (currency per unit, as reported)",,,,,,269.0,count,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,22,Proportion of the White,demographics,F_neighbourhood,level,share,-1.155,,,,**,,,high,"Percent non-Hispanic white in block group. Standardized Beta printed = -0.088. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,-1.155,,"b (currency per unit, as reported)",,,,,,269.0,share,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05,f34b3a1e2f48af6ea9bd6c13d722fba48077ca05:T7-c3,7,3,22,"Per Capita Income in $1,000s",income,F_neighbourhood,level,currency,5.403,,,,***,,,high,"Block-group per capita income in $1,000s (USD). Standardized Beta printed = 0.439. Stars only (* p<0.10, ** p<0.05, *** p<0.001).","Housing Price in $1,000",level,OLS,279.0,0.73,none,Apartments and condos (Model 3),monetary,5.403,,"b (currency per unit, as reported)",,,,,,269.0,currency,,2020.0,United States,North America,High income,2018.0,2018.0,mixed,transaction,journal,False,False,unknown,,2018.0,ols,journal,False,False,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c1,5,1,21,Age,age,A_structural,level,years,243.99,47.25,,,***,,,high,"WTP in EUR per additional year of building age (marginal WTP, Eq. 9); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,68070.0,0.775,se,"Ask data (IS24 listings), district dummies, year dummies, no spatial smooth; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,243.99,47.25,not harmonised,reported,0.01978969875505166,0.0038323835656223237,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.163809523809524,68060.0,years,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c2,5,2,21,Age,age,A_structural,level,years,-78.96,43.88,,,,,,high,"WTP in EUR per additional year of building age (marginal WTP, Eq. 9); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,59502.0,0.816,se,"Ask data, thin-plate spline f4(LAT,LON) instead of district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,-78.96,43.88,not harmonised,reported,-0.007377333515633785,0.004099764370136911,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.7994530537830444,59492.0,years,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c3,5,3,21,Age,age,A_structural,level,years,-1173.73,103.73,,,***,,,high,"WTP in EUR per additional year of building age (marginal WTP, Eq. 9); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12524.0,0.665,se,"Sale data (GAA transactions), district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-1173.73,103.73,not harmonised,reported,-0.10063645693360912,0.00889388503124507,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-11.315241492335872,12514.0,years,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c4,5,4,21,Age,age,A_structural,level,years,-1461.39,106.05,,,***,,,high,"WTP in EUR per additional year of building age (marginal WTP, Eq. 9); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.731,se,"Sale data, spatial smooth f4(LAT,LON), year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-1461.39,106.05,not harmonised,reported,-0.12376034217280936,0.008981027848436374,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-13.780198019801981,12208.0,years,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Age,age,A_structural,level,years,-1562.89,110.14,,,***,,,high,"WTP in EUR per additional year of building age (marginal WTP, Eq. 9); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-1562.89,110.14,not harmonised,reported,-0.12738218709931828,0.008976878786810917,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-14.19003086980207,12208.0,years,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c1,5,1,21,Floor area,living_area,A_structural,level,m2,1419.07,10.39,,,***,,,high,"WTP in EUR per additional m2 of floor area (exterior floor area; IS24 interior area converted with factor 1.25); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,68070.0,0.775,se,"Ask data (IS24 listings), district dummies, year dummies, no spatial smooth; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,1419.07,10.39,not harmonised,reported,0.463813269765685,0.003395900042186409,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),136.58036573628488,68060.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c2,5,2,21,Floor area,living_area,A_structural,level,m2,1293.92,7.41,,,***,,,high,"WTP in EUR per additional m2 of floor area (exterior floor area; IS24 interior area converted with factor 1.25); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,59502.0,0.816,se,"Ask data, thin-plate spline f4(LAT,LON) instead of district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,1293.92,7.41,not harmonised,reported,0.5821137221609443,0.003333639391316771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),174.61808367071527,59492.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c3,5,3,21,Floor area,living_area,A_structural,level,m2,1047.99,17.46,,,***,,,high,"WTP in EUR per additional m2 of floor area (exterior floor area; IS24 interior area converted with factor 1.25); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12524.0,0.665,se,"Sale data (GAA transactions), district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,1047.99,17.46,not harmonised,reported,0.4727974172427481,0.007877024499335281,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),60.02233676975945,12514.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c4,5,4,21,Floor area,living_area,A_structural,level,m2,906.73,14.82,,,***,,,high,"WTP in EUR per additional m2 of floor area (exterior floor area; IS24 interior area converted with factor 1.25); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.731,se,"Sale data, spatial smooth f4(LAT,LON), year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,906.73,14.82,not harmonised,reported,0.4844301462211853,0.00791774262128524,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),61.182860998650476,12208.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Floor area,living_area,A_structural,level,m2,830.24,16.87,,,***,,,high,"WTP in EUR per additional m2 of floor area (exterior floor area; IS24 interior area converted with factor 1.25); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,830.24,16.87,not harmonised,reported,0.4068798279114154,0.008267564435422986,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),49.2139893301719,12208.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c1,5,1,21,Plot area,lot_size,A_structural,level,m2,1465.18,7.95,,,***,,,high,"WTP in EUR per additional m2 of plot area; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,68070.0,0.775,se,"Ask data (IS24 listings), district dummies, year dummies, no spatial smooth; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,1465.18,7.95,not harmonised,reported,0.5769894516628974,0.003130718506067537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),184.2993710691824,68060.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c2,5,2,21,Plot area,lot_size,A_structural,level,m2,1380.25,7.46,,,***,,,high,"WTP in EUR per additional m2 of plot area; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,59502.0,0.816,se,"Ask data, thin-plate spline f4(LAT,LON) instead of district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,1380.25,7.46,not harmonised,reported,0.6043555309487413,0.0032664316325865675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),185.0201072386059,59492.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c3,5,3,21,Plot area,lot_size,A_structural,level,m2,1152.82,18.14,,,***,,,high,"WTP in EUR per additional m2 of plot area; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12524.0,0.665,se,"Sale data (GAA transactions), district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,1152.82,18.14,not harmonised,reported,0.4939567932913166,0.007772571806790725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),63.55126791620727,12514.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c4,5,4,21,Plot area,lot_size,A_structural,level,m2,1011.7,14.6,,,***,,,high,"WTP in EUR per additional m2 of plot area; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.731,se,"Sale data, spatial smooth f4(LAT,LON), year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,1011.7,14.6,not harmonised,reported,0.531312675327152,0.007667455826605137,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),69.29452054794521,12208.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Plot area,lot_size,A_structural,level,m2,940.84,16.79,,,***,,,high,"WTP in EUR per additional m2 of plot area; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,940.84,16.79,not harmonised,reported,0.45231306250342584,0.008071868032218571,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),56.0357355568791,12208.0,m2,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c1,5,1,21,Detached,dwelling_type,A_structural,binary,binary,6881.76,1424.74,,,***,,,high,"WTP in EUR for a detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,68070.0,0.775,se,"Ask data (IS24 listings), district dummies, year dummies, no spatial smooth; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,6881.76,1424.74,not harmonised,reported,0.018511580857473793,0.0038324774056167628,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.830186560354872,68060.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c2,5,2,21,Detached,dwelling_type,A_structural,binary,binary,17771.6,1263.12,,,***,,,high,"WTP in EUR for a detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,59502.0,0.816,se,"Ask data, thin-plate spline f4(LAT,LON) instead of district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,17771.6,1263.12,not harmonised,reported,0.05758790725677904,0.004093071947049379,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.069605421495979,59492.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c3,5,3,21,Detached,dwelling_type,A_structural,binary,binary,-7918.0,2468.32,,,**,,,high,"WTP in EUR for a detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12524.0,0.665,se,"Sale data (GAA transactions), district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-7918.0,2468.32,not harmonised,reported,-0.028664044701916053,0.008935594192805435,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.207849873598237,12514.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c4,5,4,21,Detached,dwelling_type,A_structural,binary,binary,4130.49,2198.43,,,,,,high,"WTP in EUR for a detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.731,se,"Sale data, spatial smooth f4(LAT,LON), year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,4130.49,2198.43,not harmonised,reported,0.017002151918771042,0.009049299439723574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8788362604222104,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Detached,dwelling_type,A_structural,binary,binary,6546.97,2999.16,,,**,,,high,"WTP in EUR for a detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,6546.97,2999.16,not harmonised,reported,0.019753029476499876,0.009048841813043189,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.182934555008736,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c1,5,1,21,Semi-detached,dwelling_type,A_structural,binary,binary,-1042.73,1205.12,,,,,,high,"WTP in EUR for a semi-detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,68070.0,0.775,se,"Ask data (IS24 listings), district dummies, year dummies, no spatial smooth; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,-1042.73,1205.12,not harmonised,reported,-0.003316600895540045,0.0038331131464839595,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.8652499336165694,68060.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c2,5,2,21,Semi-detached,dwelling_type,A_structural,binary,binary,5074.97,1011.21,,,***,,,high,"WTP in EUR for a semi-detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log ask price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,59502.0,0.816,se,"Ask data, thin-plate spline f4(LAT,LON) instead of district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type listing",other,5074.97,1011.21,not harmonised,reported,0.02057173511174393,0.004099008321693839,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.018710258007733,59492.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c3,5,3,21,Semi-detached,dwelling_type,A_structural,binary,binary,-8160.82,2630.25,,,**,,,high,"WTP in EUR for a semi-detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12524.0,0.665,se,"Sale data (GAA transactions), district dummies, year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-8160.82,2630.25,not harmonised,reported,-0.02772501017500674,0.008935830959733394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.1026784526185724,12514.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c4,5,4,21,Semi-detached,dwelling_type,A_structural,binary,binary,3741.82,2315.45,,,,,,high,"WTP in EUR for a semi-detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.731,se,"Sale data, spatial smooth f4(LAT,LON), year dummies; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,3741.82,2315.45,not harmonised,reported,0.01462442425069761,0.00904963978258649,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.616022803342763,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Semi-detached,dwelling_type,A_structural,binary,binary,3705.94,2158.45,,,,,,high,"WTP in EUR for a semi-detached house vs terraced (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,3705.94,2158.45,not harmonised,reported,0.015537520040362846,0.009049515138162297,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.7169450299983786,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Listed,other,other,binary,binary,12599.23,3702.38,,,**,,,high,"WTP in EUR for a listed (heritage-protected) building; printed name 'Listed'; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,12599.23,3702.38,not harmonised,reported,0.030784695701486543,0.00904631804255258,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.403008335178993,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,False,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Prefabricated,roof_materials,A_structural,binary,binary,-6544.52,1686.19,,,***,,,high,"WTP in EUR for a prefabricated construction; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-6544.52,1686.19,not harmonised,reported,-0.03510599168262394,0.009045028835624868,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.881247071800924,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Converted attic,other,other,binary,binary,3426.77,1141.96,,,**,,,high,"WTP in EUR for a converted attic (attic fitted out as living space); printed name 'Converted attic'; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,3426.77,1141.96,not harmonised,reported,0.027148866014099975,0.009047271638733152,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.000779361799012,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,False,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Swimming pool,pool,C_exterior,binary,binary,14744.26,5509.92,,,**,,,high,"WTP in EUR for a swimming pool; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,14744.26,5509.92,not harmonised,reported,0.02421185670759652,0.00904795449282095,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.675948108139501,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Flat roof,roof_materials,A_structural,binary,binary,-5158.22,1426.43,,,***,,,high,"WTP in EUR for a flat roof; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-5158.22,1426.43,not harmonised,reported,-0.03271106331561254,0.009045764245280193,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.6161746457940454,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,No basement,basement,A_structural,binary,binary,-21888.12,1724.46,,,***,,,high,"WTP in EUR for having NO basement (reverse-coded: dummy = 1 when basement absent); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-21888.12,1724.46,not harmonised,reported,-0.11412641812803105,0.00899147313725731,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-12.692738596430186,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Backland develop.,other,other,binary,binary,396.67,1363.94,,,,,,high,"WTP in EUR for backland (rear-plot) development; printed name 'Backland develop.'; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,396.67,1363.94,not harmonised,reported,0.0026321481232328063,0.009050576325918658,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.2908265759490887,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,False,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Waterfront,waterfront,C_exterior,binary,binary,59947.35,6417.56,,,***,,,high,"WTP in EUR for a waterfront location; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,59947.35,6417.56,not harmonised,reported,0.08424250009444623,0.009018435325433307,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.341143674542971,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Poor condition,condition,A_structural,binary,binary,-53313.91,2341.98,,,***,,,high,"WTP in EUR for poor building condition vs average (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-53313.91,2341.98,not harmonised,reported,-0.20179371155344308,0.00886441899654204,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-22.764459986848735,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Good condition,condition,A_structural,binary,binary,29031.33,1657.96,,,***,,,high,"WTP in EUR for good building condition vs average (base); effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,29031.33,1657.96,not harmonised,reported,0.15652518674910904,0.008939049593062144,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.510271659147385,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Poor amenities,luxury_finishes,B_interior,binary,binary,-6263.71,1720.23,,,***,,,high,"WTP in EUR for poor fit-out/equipment quality (Ausstattung) vs average; fitting grade, not neighbourhood amenities; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,-6263.71,1720.23,not harmonised,reported,-0.03293723680390928,0.009045697017772036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.641204955151346,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Good amenities,luxury_finishes,B_interior,binary,binary,16298.74,2243.77,,,***,,,high,"WTP in EUR for good fit-out/equipment quality vs average; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,16298.74,2243.77,not harmonised,reported,0.06560197266086787,0.009031111496795181,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.263997646817633,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8,f8260edb97a0dbb7c5a255fca59c10da9cde8ca8:T5-c5,5,5,21,Excel. amenities,luxury_finishes,B_interior,binary,binary,37654.98,9613.84,,,***,,,high,"WTP in EUR for excellent fit-out/equipment quality vs average; effect-size family: monetary marginal price (EUR), derived from a log-price semiparametric model, star legend *** 0.001, ** 0.01, * 0.05","log sale price (EUR) in the semiparametric additive model Eq. (6); Table 5 reports average marginal WILLINGNESS TO PAY in EUR (Eq. 9 continuous / Eq. 10 discrete), not raw coefficients",other,other,12218.0,0.761,se,"Sale data, spatial smooth, year dummies, buyer/seller-type dummies and extended characteristics only available in the GAA data; penalised least squares (cubic regression splines); SE from pairs bootstrap (200 replications); r2 = adjusted R2; price type transaction",other,37654.98,9613.84,not harmonised,reported,0.03542668934676549,0.00904492641104863,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.916747106255149,12208.0,binary,,2021.0,Germany,Europe,High income,2007.0,2015.0,single_family,transaction,journal,True,True,robust,,2011.0,other,journal,False,True,False
fb704039a7cb48f523fc8649b0477ed96bedc7f7,fb704039a7cb48f523fc8649b0477ed96bedc7f7,fb704039a7cb48f523fc8649b0477ed96bedc7f7:T3-c1,3,M1 (Total population row),13,Floodplain (= 1),flood_risk,E_environment,dummy,binary,-0.056,,,,,,,medium,coefficient on 100-year floodplain dummy from the full-sample regression ('Total population' row of Table 3); no SE or significance printed; for M1 and M2 the text states the floodplain coefficient 'was controlled for the interaction terms' with the post-Fran and post-Floyd period dummies (exact adjustment not specified); other hedonic coefficients of the models are not printed,ln sales price (USD),log,OLS,4779.0,,none,"full sample (N = 4779), Pitt County NC single-family sales 1992-2002; full traditional hedonic: age, bedrooms, lot size, structure sqft each as X + X^2, amenity dummies, log distances (creek, airport, major road, business centre, railroad, Tar River, park), sale-period dummies, floodplain and floodplain x period interactions; Monte Carlo sub-sample rows of Table 3 not extracted (resampling averages)",pct_premium,-5.446086410960371,,100*(exp(b) - 1),,,,,,4769.0,binary,,2016.0,United States,North America,High income,1992.0,2002.0,single_family,transaction,journal,True,False,unknown,,1997.0,ols,journal,False,False,False
fb704039a7cb48f523fc8649b0477ed96bedc7f7,fb704039a7cb48f523fc8649b0477ed96bedc7f7,fb704039a7cb48f523fc8649b0477ed96bedc7f7:T3-c2,3,M2 (Total population row),13,Floodplain (= 1),flood_risk,E_environment,dummy,binary,-0.056,,,,,,,medium,coefficient on 100-year floodplain dummy from the full-sample regression ('Total population' row of Table 3); no SE or significance printed; for M1 and M2 the text states the floodplain coefficient 'was controlled for the interaction terms' with the post-Fran and post-Floyd period dummies (exact adjustment not specified); other hedonic coefficients of the models are not printed,ln sales price (USD),log,OLS,4779.0,,none,"full sample (N = 4779), Pitt County NC single-family sales 1992-2002; as M1 but X + sqrt(X) (age, bedrooms, lot size) and X + ln(X) (structure sqft); Monte Carlo sub-sample rows of Table 3 not extracted (resampling averages)",pct_premium,-5.446086410960371,,100*(exp(b) - 1),,,,,,4769.0,binary,,2016.0,United States,North America,High income,1992.0,2002.0,single_family,transaction,journal,True,False,unknown,,1997.0,ols,journal,False,False,False
fb704039a7cb48f523fc8649b0477ed96bedc7f7,fb704039a7cb48f523fc8649b0477ed96bedc7f7,fb704039a7cb48f523fc8649b0477ed96bedc7f7:T3-c3,3,M3 & M4 (Total population row),13,Floodplain (= 1),flood_risk,E_environment,dummy,binary,-0.054,,,,,,,medium,coefficient on 100-year floodplain dummy from the full-sample regression ('Total population' row of Table 3); no SE or significance printed; for M1 and M2 the text states the floodplain coefficient 'was controlled for the interaction terms' with the post-Fran and post-Floyd period dummies (exact adjustment not specified); other hedonic coefficients of the models are not printed,ln sales price (USD),log,other,4779.0,,none,"full sample (N = 4779), Pitt County NC single-family sales 1992-2002; reduced model: age, bedrooms, sqrt(lot size), ln(structure sqft), floodplain dummy; M4 additionally kriges residuals for spatial variation (floodplain coefficient identical for M3 and M4); Monte Carlo sub-sample rows of Table 3 not extracted (resampling averages)",pct_premium,-5.256789349820168,,100*(exp(b) - 1),,,,,,4769.0,binary,,2016.0,United States,North America,High income,1992.0,2002.0,single_family,transaction,journal,True,False,unknown,,1997.0,other,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T3-c1,3,1,14,Airbnb_Phase1_Ring1,other,other,level,count,0.0034,0.0002,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in the sale month and 3 prior months; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 1 (Eq. 1), Airbnb measure (1) Active; adj. R2",semi_elasticity,0.33999999999999997,0.02,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.053119553450016566,0.0031246796147068566,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T3-c2,3,2,14,Airbnb_Phase1_Ring1,other,other,level,count,0.0035,0.0002,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in the sale month and 3 prior months; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 1 (Eq. 1), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,0.35000000000000003,0.02,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.05467728900860546,0.003124416514777455,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),17.5,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T3-c3,3,3,14,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0169,0.0018,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in the sale month and 3 prior months; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 1 (Eq. 1), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,1.69,0.18,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.029366077454610718,0.0031277478945739234,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.388888888888888,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c1,4,1,16,Airbnb_Phase1_Ring1,other,other,level,count,0.0033,0.0006,,,***,,,high,"Short-term rental density: count of active Airbnb listings within 0-300 m, months S to S-3; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (1) Active; adj. R2",semi_elasticity,0.33,0.06,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01720748758583277,0.0031286341065150484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.500000000000001,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c1,4,1,16,Airbnb_Phase2_Ring1,other,other,level,count,-0.0006,0.0008,,,,,,high,"Short-term rental density: count of active Airbnb listings within 0-300 m, months S-4 to S-7; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (1) Active; adj. R2",semi_elasticity,-0.06,0.08,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.0023468165873505612,0.0031290887831340822,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.7499999999999999,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c1,4,1,16,Airbnb_Phase3_Ring1,other,other,level,count,0.0008,0.0007,,,,,,high,"Short-term rental density: count of active Airbnb listings within 0-300 m, months S-8 to S-11; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (1) Active; adj. R2",semi_elasticity,0.08,0.06999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.003576088447795613,0.0031290773918211615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.142857142857143,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c2,4,2,16,Airbnb_Phase1_Ring1,other,other,level,count,0.0019,0.0005,,,***,,,high,"Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m, months S to S-3; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,0.19,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.01188972963098289,0.0031288762186797085,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.8,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c2,4,2,16,Airbnb_Phase2_Ring1,other,other,level,count,0.0011,0.0006,,,*,,,high,"Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m, months S-4 to S-7; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,0.11,0.06,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.005736584173390197,0.0031290459127582887,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.8333333333333337,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c2,4,2,16,Airbnb_Phase3_Ring1,other,other,level,count,0.0012,0.0005,,,**,,,high,"Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m, months S-8 to S-11; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,0.12,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.0075096220005807,0.0031290091669086252,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c3,4,3,16,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0101,0.003,,,***,,,high,"Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m, months S to S-3; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,1.01,0.3,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.010534043403895825,0.003128923783335394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.3666666666666663,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c3,4,3,16,Airbnb_Phase2_Ring1,other,other,level,hundreds of reservations,0.0067,0.004,,,*,,,high,"Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m, months S-4 to S-7; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,0.67,0.4,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.005241166156519426,0.0031290544218026423,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.675,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T4-c3,4,3,16,Airbnb_Phase3_Ring1,other,other,level,hundreds of reservations,0.0055,0.0038,,,,,,high,"Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m, months S-8 to S-11; bootstrap SE",ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 2 time effect (Eq. 2), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,0.5499999999999999,0.38,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.00452891031609991,0.003129065309305393,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.4473684210526314,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c1,5,1,17,Airbnb_Phase1_Ring1,other,other,level,count,0.0036,0.0002,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (1) Active; adj. R2",semi_elasticity,0.36,0.02,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.05623462544630699,0.003124145858128166,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),18.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c1,5,1,17,Airbnb_Phase1_Ring2,other,other,level,count,0.0008,0.0003,,,***,,,high,Short-term rental density: count of active Airbnb listings within 300-500 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (1) Active; adj. R2",semi_elasticity,0.08,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.008343969256918854,0.0031289884713445694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.666666666666667,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c1,5,1,17,Airbnb_Phase1_Ring3,other,other,level,count,-0.0007,0.0001,,,***,,,high,Short-term rental density: count of active Airbnb listings within 500-1000 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (1) Active; adj. R2",semi_elasticity,-0.06999999999999999,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.021898429311044153,0.003128347044434879,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c2,5,2,17,Airbnb_Phase1_Ring1,other,other,level,count,0.0038,0.0003,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,0.38,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.03960413784584653,0.0031266424615141995,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),12.666666666666668,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c2,5,2,17,Airbnb_Phase1_Ring2,other,other,level,count,0.0008,0.0003,,,**,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 300-500 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,0.08,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.008343969256918854,0.0031289884713445694,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.666666666666667,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c2,5,2,17,Airbnb_Phase1_Ring3,other,other,level,count,-0.0008,0.0001,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 500-1000 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (2) Positive Occupancy; adj. R2",semi_elasticity,-0.08,0.01,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,-0.02502493961323176,0.00312811745165397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-8.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c3,5,3,17,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0183,0.0022,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,1.83,0.22,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.026019588746968265,0.003128037991438807,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.318181818181818,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c3,5,3,17,Airbnb_Phase1_Ring2,other,other,level,hundreds of reservations,0.0112,0.0027,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 300-500 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,1.1199999999999999,0.27,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.012978866295637074,0.003128833839126795,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.148148148148148,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T5-c3,5,3,17,Airbnb_Phase1_Ring3,other,other,level,hundreds of reservations,-0.0069,0.0009,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 500-1000 m in months S to S-3; bootstrap SE,ln(inflation-adjusted house sale price),log,OLS,102142.0,0.789,se,"Model 3 distance effect (Eq. 3), Airbnb measure (3) Sum of reservations; adj. R2",semi_elasticity,-0.69,0.09,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,-0.023982846566348828,0.003128197378219412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-7.666666666666667,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,ols,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q10,6,"row Measure (1), quantile 0.1",18,Airbnb_Phase1_Ring1,other,other,level,count,0.003,0.0007,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.1; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.1, Airbnb measure (1) Active",semi_elasticity,0.3,0.06999999999999999,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.013409211731065728,0.0031288160705820033,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.285714285714286,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q20,6,"row Measure (1), quantile 0.2",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0018,0.0005,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.2; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.2, Airbnb measure (1) Active",semi_elasticity,0.18,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.011264035989863991,0.003128898886073331,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.5999999999999996,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q30,6,"row Measure (1), quantile 0.3",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0017,0.0004,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.3; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.3, Airbnb measure (1) Active",semi_elasticity,0.16999999999999998,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.013297488141809052,0.0031288207392491884,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.25,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q40,6,"row Measure (1), quantile 0.4",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0023,0.0003,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.4; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.4, Airbnb measure (1) Active",semi_elasticity,0.22999999999999998,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.023982846566348828,0.003128197378219412,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.666666666666667,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q50,6,"row Measure (1), quantile 0.5",18,Airbnb_Phase1_Ring1,other,other,level,count,0.003,0.0003,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.5; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.5, Airbnb measure (1) Active",semi_elasticity,0.3,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.03127566635158991,0.0031275666351589905,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.000000000000002,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q60,6,"row Measure (1), quantile 0.6",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0041,0.0003,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.6; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.6, Airbnb measure (1) Active",semi_elasticity,0.41000000000000003,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.042725281237126746,0.0031262400905214685,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.66666666666667,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q70,6,"row Measure (1), quantile 0.7",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0059,0.0003,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.7; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.7, Airbnb measure (1) Active",semi_elasticity,0.59,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.06142272034590674,0.003123189170130851,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),19.666666666666668,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q80,6,"row Measure (1), quantile 0.8",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0053,0.0004,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.8; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.8, Airbnb measure (1) Active",semi_elasticity,0.53,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.041424951579689645,0.003126411439976577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),13.25,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m1-q90,6,"row Measure (1), quantile 0.9",18,Airbnb_Phase1_Ring1,other,other,level,count,0.004,0.0005,,,***,,,high,Short-term rental density: count of active Airbnb listings within 0-300 m in months S to S-3; UQR at quantile 0.9; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.9, Airbnb measure (1) Active",semi_elasticity,0.4,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.02502493961323176,0.00312811745165397,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q10,6,"row Measure (2), quantile 0.1",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0031,0.0009,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.1; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.1, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.31,0.09,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.010777376194907698,0.003128915669489332,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.444444444444444,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q20,6,"row Measure (2), quantile 0.2",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0017,0.0005,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.2; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.2, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.16999999999999998,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.010638329117429934,0.0031289203286558633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.4,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q30,6,"row Measure (2), quantile 0.3",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0015,0.0005,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.3; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.3, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.15,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.009386878617269208,0.0031289595390897353,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q40,6,"row Measure (2), quantile 0.4",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0022,0.0004,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.4; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.4, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.22,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.017207487585832768,0.0031286341065150484,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.5,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q50,6,"row Measure (2), quantile 0.5",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0029,0.0003,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.5; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.5, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.29,0.03,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.030234113526111227,0.003127666916494265,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.666666666666666,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q60,6,"row Measure (2), quantile 0.6",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0041,0.0004,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.6; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.6, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.41000000000000003,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.03205676429921188,0.00312748919992311,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),10.25,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q70,6,"row Measure (2), quantile 0.7",18,Airbnb_Phase1_Ring1,other,other,level,count,0.006,0.0004,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.7; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.7, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.6,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.046884845019658165,0.0031256563346438782,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),15.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q80,6,"row Measure (2), quantile 0.8",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0058,0.0004,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.8; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.8, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.58,0.04,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.04532528273550546,0.0031258815679658936,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),14.499999999999998,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m2-q90,6,"row Measure (2), quantile 0.9",18,Airbnb_Phase1_Ring1,other,other,level,count,0.0045,0.0005,,,***,,,high,Short-term rental density: count of Airbnb listings with at least one reservation within 0-300 m in months S to S-3; UQR at quantile 0.9; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.9, Airbnb measure (2) Positive Occupancy",semi_elasticity,0.44999999999999996,0.05,100*b (unit as reported: count; no harmonised unit defined for this characteristic),reported,0.028150715768416833,0.0031278573076018706,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),9.0,102132.0,count,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q10,6,"row Measure (3), quantile 0.1",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0123,0.0063,,,*,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.1; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.1, Airbnb measure (3) Sum of reservations",semi_elasticity,1.23,0.63,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.006109076160821352,0.0031290390092011803,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9523809523809523,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q20,6,"row Measure (3), quantile 0.2",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.002,0.0039,,,,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.2; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.2, Airbnb measure (3) Sum of reservations",semi_elasticity,0.2,0.38999999999999996,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.001604663267356391,0.0031290933713449616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5128205128205129,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q30,6,"row Measure (3), quantile 0.3",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0002,0.0034,,,,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.3; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.3, Airbnb measure (3) Sum of reservations",semi_elasticity,0.02,0.33999999999999997,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.00018406454982156624,0.0031290973469666255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.05882352941176471,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q40,6,"row Measure (3), quantile 0.4",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0077,0.0028,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.4; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.4, Airbnb measure (3) Sum of reservations",semi_elasticity,0.77,0.27999999999999997,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.008604699282612491,0.003128981557313633,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.75,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q50,6,"row Measure (3), quantile 0.5",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0139,0.0025,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.5; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.5, Airbnb measure (3) Sum of reservations",semi_elasticity,1.39,0.25,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.01739514913678518,0.0031286239454649603,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.56,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q60,6,"row Measure (3), quantile 0.6",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0228,0.0026,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.6; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.6, Airbnb measure (3) Sum of reservations",semi_elasticity,2.2800000000000002,0.26,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.027429452757916816,0.003127920051341391,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),8.76923076923077,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q70,6,"row Measure (3), quantile 0.7",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0356,0.003,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.7; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.7, Airbnb measure (3) Sum of reservations",semi_elasticity,3.56,0.3,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.03710638381484385,0.003126942456307066,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.866666666666667,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q80,6,"row Measure (3), quantile 0.8",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0361,0.0032,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.8; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.8, Airbnb measure (3) Sum of reservations",semi_elasticity,3.61,0.32,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.03527815684533454,0.0031271496372595724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),11.28125,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c,fb8eee688ea40496609ebe82ad7180db4acffe2c:T6-m3-q90,6,"row Measure (3), quantile 0.9",18,Airbnb_Phase1_Ring1,other,other,level,hundreds of reservations,0.0312,0.004,,,***,,,high,Short-term rental density: number of Airbnb reservations in hundreds within 0-300 m in months S to S-3; UQR at quantile 0.9; bootstrap SE,ln(inflation-adjusted house sale price),log,quantile,102142.0,,se,"Unconditional quantile regression (RIF) at tau=0.9, Airbnb measure (3) Sum of reservations",semi_elasticity,3.1199999999999997,0.4,100*b (unit as reported: hundreds of reservations; no harmonised unit defined for this characteristic),reported,0.024399693356975893,0.0031281658149969094,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.8,102132.0,hundreds of reservations,unit_not_harmonisable,2024.0,United States,North America,High income,2015.0,2019.0,mixed,transaction,journal,True,True,robust,,2017.0,quantile,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,NRM,bedrooms,A_structural,level,count,-0.009,,,0.331,,,,high,Number of rooms,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,semi_elasticity,-0.8999999999999999,0.9258291400497722,100*b/1 (per unit count),|b/z(p)| (two-sided normal),-0.02129966457111355,0.021910944592469624,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.9721016125627848,2082.0,count,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,NRM,bedrooms,A_structural,level,count,-0.021,,,0.001,***,,,high,Number of rooms,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,semi_elasticity,-2.1,0.638195696726001,100*b/1 (per unit count),|b/z(p)| (two-sided normal),-0.07192811842790625,0.02185915031156568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,2082.0,count,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,NBT,bathrooms,A_structural,level,count,0.07,,,0.0,***,,,high,Number of bathrooms,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,semi_elasticity,7.000000000000001,,100*b/1 (per unit count),,,,,,2082.0,count,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,NBT,bathrooms,A_structural,level,count,0.034,,,0.001,***,,,high,Number of bathrooms,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,semi_elasticity,3.4000000000000004,1.0332692232706682,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.07192811842790625,0.02185915031156568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,2082.0,count,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,LTR = 1,terrace_balcony,C_exterior,dummy,binary,0.081,,,0.0,***,,,high,Large terrace present,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,8.437089656676044,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,LTR = 1,terrace_balcony,C_exterior,dummy,binary,0.085,,,0.0,***,,,high,Large terrace present,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,8.871706669839874,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,NVW,view,E_environment,level,count,0.017,,,0.17,,,,high,Number of views (exposures),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,semi_elasticity,1.7000000000000002,1.2388830207667634,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.03005951440675393,0.021906012947659616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.3722038089987258,2082.0,count,unit_not_harmonisable,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,NVW,view,E_environment,level,count,0.031,,,0.001,***,,,high,Number of views (exposures),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,semi_elasticity,3.1,0.942098409452668,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.07192811842790625,0.02185915031156568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,2082.0,count,unit_not_harmonisable,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,CSS = 1,other,other,dummy,binary,0.128,,,0.0,***,,,high,Custodian (concierge) service,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,13.655300269706029,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,CSS = 1,other,other,dummy,binary,0.047,,,0.001,***,,,high,Custodian (concierge) service,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,4.801509754497801,1.4970774725908094,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.07192811842790625,0.02185915031156568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,PNT = 1,dwelling_type,A_structural,dummy,binary,0.092,,,0.014,*,,,high,Penthouse,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,9.559667365310043,4.104792511599759,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.05377525721592513,0.02188420558832698,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.457263390205437,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,PNT = 1,dwelling_type,A_structural,dummy,binary,0.09,,,0.0,***,,,high,Penthouse,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,9.41742837052104,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,CBX = 1,garage,A_structural,dummy,binary,0.019,,,0.293,,,,high,Car box,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,1.9015298696204663,1.8414919201190625,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.023039853641387942,0.021910098877600183,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.0515631978704927,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,CBX = 1,garage,A_structural,dummy,binary,0.057,,,0.0,***,,,high,Car box,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,5.865581039550016,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,LFT = 1,accessibility,B_interior,dummy,binary,0.069,,,0.0,***,,,high,Lift,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,7.143620914834625,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,LFT = 1,accessibility,B_interior,dummy,binary,0.111,,,0.0,***,,,high,Lift,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,11.73949068544582,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,CTP = 1,age,A_structural,dummy,binary,0.016,,,0.392,,,,high,"Construction period <=1945 (ref. CTP=3, 1976-now)",LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,1.5951194015172643,1.899314861522387,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.01875663628593222,0.021912061042128075,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.8559959854926817,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,CTP = 1,age,A_structural,dummy,binary,-0.034,,,0.013,*,,,high,"Construction period <=1945 (ref. CTP=3, 1976-now)",LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,-3.351905172594749,1.323127363196706,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.05435361328265819,0.02188351930686972,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4837692932505324,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,CTP = 2,age,A_structural,dummy,binary,-0.1,,,0.0,***,,,high,Construction period 1946-1975 (ref. CTP=3 in Table 3; sole CTP dummy in Tables 4),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,-9.51625819640405,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,CTP = 2,age,A_structural,dummy,binary,-0.07,,,0.0,***,,,high,Construction period 1946-1975 (ref. CTP=3 in Table 3; sole CTP dummy in Tables 4),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,-6.760618009405173,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,MTL = 1,condition,A_structural,dummy,binary,-0.053,,,0.031,*,,,high,Maintenance: to renovate (ref. MTL=2 partially to renovate),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,-5.190621348054025,2.3302015067545874,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.04722148823099137,0.021891468068247898,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.1570727044790092,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,MTL = 1,condition,A_structural,dummy,binary,-0.057,,,0.001,***,,,high,Maintenance: to renovate (ref. MTL=2 partially to renovate),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,-5.55476409619483,1.6362687906042457,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.07192811842790625,0.02185915031156568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.290526731491894,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,MTL = 3,condition,A_structural,dummy,binary,0.099,,,0.0,***,,,high,Maintenance: good (ref. MTL=2),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,10.406629955888192,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,MTL = 3,condition,A_structural,dummy,binary,0.096,,,0.0,***,,,high,Maintenance: good (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,10.075906399397883,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,MTL = 4,condition,A_structural,dummy,binary,0.199,,,0.0,***,,,high,Maintenance: renovated (ref. MTL=2),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,22.018196589987248,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,MTL = 4,condition,A_structural,dummy,binary,0.181,,,0.0,***,,,high,Maintenance: renovated (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,19.841517929319963,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,BLC = 2,condition,A_structural,dummy,binary,0.194,,,0.0,***,,,high,Building category: economic (ref. BLC=1 tenement),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,21.409628295623317,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,BLC = 2,condition,A_structural,dummy,binary,0.1,,,0.001,***,,,high,Building category: economic (ref. BLC=1 tenement),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,10.466068532128347,3.358644400298105,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.07192811842790625,0.02185915031156568,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,BLC = 3,condition,A_structural,dummy,binary,0.388,,,0.0,***,,,high,Building category: medium (ref. BLC=1),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,47.40297842881416,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,BLC = 3,condition,A_structural,dummy,binary,0.159,,,0.0,***,,,high,Building category: medium (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,17.233794668071756,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,BLC = 4,condition,A_structural,dummy,binary,0.606,,,0.0,***,,,high,Building category: noble (ref. BLC=1),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,83.30843770260485,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,BLC = 4,condition,A_structural,dummy,binary,0.205,,,0.0,***,,,high,Building category: noble (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,22.75250649631777,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,BLC = 5,condition,A_structural,dummy,binary,0.831,,,0.0,***,,,high,Building category: prestigious (ref. BLC=1),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,129.56132060461326,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,BLC = 5,condition,A_structural,dummy,binary,0.251,,,0.0,***,,,high,Building category: prestigious (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,28.53100843311953,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,BLT = 1,dwelling_type,A_structural,dummy,binary,0.088,,,0.005,**,,,high,Building type: detached house/villa (ref. condominium),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,9.145164480809797,3.42336297561461,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.06140263669432942,0.021874562887983343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,BLT = 1,dwelling_type,A_structural,dummy,binary,0.096,,,0.0,***,,,high,Building type: detached house/villa (ref. condominium),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,10.075906399397883,,100*(exp(b) - 1),,,,,,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,ARC = 1,air_conditioning,A_structural,dummy,binary,0.054,,,0.017,*,,,high,Air-conditioning system,LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,5.521448300980647,2.3880665274879047,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.052235477860772775,0.021885996808857444,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3867077344922505,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,ARC = 1,air_conditioning,A_structural,dummy,binary,0.045,,,0.004,**,,,high,Air-conditioning system,LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,4.590001561401391,1.6354624222989391,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.06295243983936984,0.02187244691090704,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.878161739095483,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,EPC = 3,energy_efficiency,A_structural,dummy,binary,0.025,,,0.585,,,,high,"High EPC labels A-A4/B (ref. EPC=2 C-D, except NZ where ref. is EPC=1)",LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,2.4241274569337,4.693841646943766,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.011967335671084108,0.021914347092495432,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.5460959261333559,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,EPC = 3,energy_efficiency,A_structural,dummy,binary,0.062,,,0.05,*,,,high,"High EPC labels A-A4/B (ref. EPC=2 C-D, except NZ where ref. is EPC=1)",LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,6.343014479170561,3.3656570168363724,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.04291483462977536,0.021895726129807537,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9599639845400543,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c1,3,1 (OLS),15,EPC = 1,energy_efficiency,A_structural,dummy,binary,-0.033,,,0.018,*,,,high,Low EPC labels E/F/G (ref. EPC=2 C-D),LogLP: log listing price per m2,log,OLS,2092.0,0.537,p,complete sample,pct_premium,-3.2555576415076337,1.3497010402418894,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.051775153583376664,0.021886522171694036,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.365618126864293,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,ols,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T3-c2,3,2 (SEM),15,EPC = 1,energy_efficiency,A_structural,dummy,binary,-0.027,,,0.005,**,,,high,Low EPC labels E/F/G (ref. EPC=2 C-D),LogLP: log listing price per m2,log,spatial_error,2092.0,0.754,p,complete sample; lambda=0.797,pct_premium,-2.668378477582145,0.9362464327126057,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.06140263669432942,0.021874562887983343,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.8070337683438042,2082.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,NRM,bedrooms,A_structural,level,count,-0.033,,,0.0,***,,,high,Number of rooms,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,semi_elasticity,-3.3000000000000003,,100*b/1 (per unit count),,,,,,876.0,count,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,NBT,bathrooms,A_structural,level,count,0.043,,,0.001,***,,,high,Number of bathrooms,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,semi_elasticity,4.3,1.3067816647246682,100*b/1 (per unit count),|b/z(p)| (two-sided normal),0.11049581540212391,0.0335799780456505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,876.0,count,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,LTR = 1,terrace_balcony,C_exterior,dummy,binary,0.093,,,0.0,***,,,high,Large terrace present,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,9.7461735268082,,100*(exp(b) - 1),,,,,,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,NVW,view,E_environment,level,count,0.029,,,0.019,**,,,high,Number of views (exposures); stars printed '**' although p=0.019 (legend '**' = p<=0.01) — recorded as printed,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,semi_elasticity,2.9000000000000004,1.236393821311442,100*b (unit as reported: count; no harmonised unit defined for this characteristic),|b/z(p)| (two-sided normal),0.07900046365342991,0.03368127073856548,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.3455309708066743,876.0,count,unit_not_harmonisable,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,ALL = 2,floor_level,A_structural,dummy,binary,0.06,,,0.001,***,,,high,Allocation level: intermediate floor (ref. lower),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,6.166003901887729,1.936170041804704,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11049581540212391,0.0335799780456505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,ALL = 3,floor_level,A_structural,dummy,binary,0.054,,,0.001,***,,,high,Allocation level: higher floor (ref. lower),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,5.534248407246745,1.7321290227152415,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.11049581540212391,0.0335799780456505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,PNT = 1,dwelling_type,A_structural,dummy,binary,0.096,,,0.003,**,,,high,Penthouse,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,10.0183305912539,3.560721087973158,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.09977027213550016,0.033618289298240504,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,LFT = 1,accessibility,B_interior,dummy,binary,0.091,,,0.0,***,,,high,Lift,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,9.52690052584655,,100*(exp(b) - 1),,,,,,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,MTL = 3,condition,A_structural,dummy,binary,0.101,,,0.0,***,,,high,Maintenance: good (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,10.627664176342355,,100*(exp(b) - 1),,,,,,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,MTL = 4,condition,A_structural,dummy,binary,0.154,,,0.0,***,,,high,Maintenance: renovated (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,16.64908867784396,,100*(exp(b) - 1),,,,,,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,BLT = 1,dwelling_type,A_structural,dummy,binary,0.072,,,0.036,*,,,high,Building type: detached house/villa (ref. condominium),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,7.402204419025726,3.6899314538238337,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0706714655794949,0.03370238979021729,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.0969274291643423,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,BLC = 4,condition,A_structural,dummy,binary,0.06,,,0.0,***,,,high,Building category: noble (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,6.183654654535964,,100*(exp(b) - 1),,,,,,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,BLC = 5,condition,A_structural,dummy,binary,0.138,,,0.0,***,,,high,Building category: prestigious (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,14.797555027417864,,100*(exp(b) - 1),,,,,,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,CTP = 2,age,A_structural,dummy,binary,-0.069,,,0.001,**,,,high,Construction period 1946-1975 (ref. CTP=3 in Table 3; sole CTP dummy in Tables 4),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,-6.687849437273852,1.9571195185579844,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.11049581540212391,0.0335799780456505,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.2905267314918945,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,EPC = 3,energy_efficiency,A_structural,dummy,binary,0.039,,,0.544,,,,high,"High EPC labels A-A4/B (ref. EPC=2 C-D, except NZ where ref. is EPC=1)",LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,3.7624964645435854,6.68304141873796,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.02049673281056913,0.03377977097134937,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.606775363514265,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,EPC = 1,energy_efficiency,A_structural,dummy,binary,-0.037,,,0.049,*,,,high,Low EPC labels E/F/G (ref. EPC=2 C-D),LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,-3.6494062832989282,1.8112449405339477,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.06636591196818671,0.03371238078824572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.9685916691865943,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-CZ,4,Central Zone (SEM),17,ARC = 1,air_conditioning,A_structural,dummy,binary,0.083,,,0.014,*,,,high,Air-conditioning system,LogLP: log listing price per m2,log,spatial_error,886.0,0.583,p,Central Zone LISA high-high cluster; stepwise selection; lambda=0.627,pct_premium,8.592216013314701,3.6700571240742783,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08273857315292865,0.033671023416830946,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.4572633902054366,876.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,CSS = 1,other,other,dummy,binary,0.082,,,0.025,*,,,high,Custodian (concierge) service,LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,8.472966247931279,3.971056843547791,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08757725475580977,0.03907252082689781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.241402727604945,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,CBX = 1,garage,A_structural,dummy,binary,0.063,,,0.025,*,,,high,Car box,LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,6.4606222939519675,2.9935133943228727,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08757725475580977,0.03907252082689781,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.241402727604945,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,LTR = 1,terrace_balcony,C_exterior,dummy,binary,0.105,,,0.001,***,,,high,Large terrace present,LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,11.014527242119598,3.5442536591846046,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.1280033535074993,0.038900566369039574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.290526731491895,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,LFT = 1,accessibility,B_interior,dummy,binary,0.144,,,0.0,***,,,high,Lift,LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,15.488410852491352,,100*(exp(b) - 1),,,,,,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,MTL = 1,condition,A_structural,dummy,binary,-0.093,,,0.002,***,,,high,Maintenance: to renovate (ref. MTL=2 partially to renovate),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,-8.92190395729826,2.742220879589085,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.12032819968845838,0.038938237571426136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.090232306167813,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,MTL = 3,condition,A_structural,dummy,binary,0.089,,,0.001,***,,,high,Maintenance: good (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,9.268090312125965,2.956492572510895,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.1280033535074993,0.038900566369039574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,MTL = 4,condition,A_structural,dummy,binary,0.21,,,0.0,***,,,high,Maintenance: renovated (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,23.367805995674317,,100*(exp(b) - 1),,,,,,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,BLT = 1,dwelling_type,A_structural,dummy,binary,0.217,,,0.0,***,,,high,Building type: detached house/villa (ref. condominium),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,24.234410213776393,,100*(exp(b) - 1),,,,,,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,BLC = 4,condition,A_structural,dummy,binary,0.266,,,0.0,***,,,high,Building category: noble (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,30.473505868692797,,100*(exp(b) - 1),,,,,,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,BLC = 5,condition,A_structural,dummy,binary,0.281,,,0.001,***,,,high,Building category: prestigious (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,31.963304935214243,11.310391711574646,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.1280033535074993,0.038900566369039574,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,BLC = 2,condition,A_structural,dummy,binary,0.125,,,0.009,**,,,high,Building category: economic (ref. BLC=1 tenement),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,13.18516803337455,5.42268838909646,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.10191968100734164,0.039018977209781903,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.6120541412292777,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,BLC = 3,condition,A_structural,dummy,binary,0.18,,,0.0,***,,,high,Building category: medium (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,19.721736312181015,,100*(exp(b) - 1),,,,,,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,CTP = 2,age,A_structural,dummy,binary,-0.045,,,0.015,*,,,high,Construction period 1946-1975 (ref. CTP=3 in Table 3; sole CTP dummy in Tables 4),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,-4.416610641757835,1.7686341497910059,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),-0.0949744939863757,0.03904592651839688,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.4323790585844467,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,EPC = 3,energy_efficiency,A_structural,dummy,binary,0.079,,,0.054,.,,,high,"High EPC labels A-A4/B (ref. EPC=2 C-D, except NZ where ref. is EPC=1)",LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,8.129511837792736,4.43702089890968,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.07536182728611503,0.0391116861345734,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.9268365732639108,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,EPC = 2,energy_efficiency,A_structural,dummy,binary,0.031,,,0.034,*,,,high,Medium EPC labels C-D (ref. EPC=1 E-G in NZ model),LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,3.1375240274801097,1.5082532715849442,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.08287002663032582,0.03908831339585724,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.120071689742151,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T4-NZ,4,Northern Zone (SEM),17,ARC = 1,air_conditioning,A_structural,dummy,binary,0.024,,,0.245,,,,high,Air-conditioning system,LogLP: log listing price per m2,log,spatial_error,660.0,0.603,p,Northern Zone LISA low-low cluster; stepwise selection; lambda=0.65,pct_premium,2.4072083251617027,2.1145185944906837,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.0455527983507734,0.039182510669987874,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.1625798748436225,650.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,LTR = 1,terrace_balcony,C_exterior,dummy,binary,0.149,,,0.001,***,,,high,Large terrace present,LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,15.94836685290053,5.255701883137238,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.21472909057873477,0.06525675312820771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,CSS = 1,other,other,dummy,binary,0.146,,,0.003,**,,,high,Custodian (concierge) service,LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,15.579670104146292,5.692909808940048,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.1945033530547922,0.06553926187144438,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.9677379253417833,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,CBX = 1,garage,A_structural,dummy,binary,0.114,,,0.004,**,,,high,Car box,LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,11.987332788953765,4.439143933479786,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.18884509995302962,0.06561309511826732,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.878161739095483,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,LFT = 1,accessibility,B_interior,dummy,binary,0.09,,,0.016,*,,,high,Lift,LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,9.341089306092808,4.0879675381119736,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.15890729916228175,0.06596632224750278,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.408915545815461,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,MTL = 3,condition,A_structural,dummy,binary,0.116,,,0.0,***,,,high,Maintenance: good (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,12.29958721332547,,100*(exp(b) - 1),,,,,,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,MTL = 4,condition,A_structural,dummy,binary,0.189,,,0.0,***,,,high,Maintenance: renovated (ref. MTL=2),LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,20.80409524829019,,100*(exp(b) - 1),,,,,,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,BLC = 3,condition,A_structural,dummy,binary,0.128,,,0.0,***,,,high,Building category: medium (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,13.655300269706029,,100*(exp(b) - 1),,,,,,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,BLC = 4,condition,A_structural,dummy,binary,0.214,,,0.0,***,,,high,Building category: noble (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,23.862265479345226,,100*(exp(b) - 1),,,,,,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,False,False,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,BLC = 5,condition,A_structural,dummy,binary,0.387,,,0.001,***,,,high,Building category: prestigious (ref. BLC=1),LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,46.24072843630556,17.318788407603222,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.21472909057873477,0.06525675312820771,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.2905267314918945,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215,fb9d007b500b1eff5cd9c2ed633d123752bd4215:T5-LEE,5,SEM,18,ARC = 1,air_conditioning,A_structural,dummy,binary,0.143,,,0.005,**,,,high,Air-conditioning system,LogLP: log listing price per m2,log,spatial_error,234.0,0.718,p,"Low energy-efficient sub-sample (EPC E-G, built 1946-1990); lambda=0.739",pct_premium,15.223367221458407,5.877498286583933,100*(exp(b - se^2/2) - 1),|b/z(p)| (two-sided normal),0.18433869858773683,0.06567028179945968,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.8070337683438042,224.0,binary,,2021.0,Italy,Europe,High income,2015.0,2018.0,mixed,listing,journal,False,False,unknown,,2016.5,spatial,journal,True,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,AREA,living_area,A_structural,level,m2,0.4507,0.4957,0.909,0.364,,,,high,"Finished interior area in m2 (2012 survey); dependent variable is a value PER m2, so the coefficient measures the unit-value size effect","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.4507,0.4957,"b (currency per unit, as reported)",reported,0.05293961591745963,0.058239401449350525,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.909,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,Q_POS,other,other,level,index,7.8002,9.0757,0.859,0.3908,,,,high,"Composite position-quality index (panoramic views, distance from other buildings/daylighting, apartment floor level); printed name Q_POS; no single taxonomy leaf","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,7.8002,9.0757,"b (currency per unit, as reported)",reported,0.050035147616268094,0.05824813459402572,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.859,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,Q_TYP,condition,A_structural,level,index,13.5892,11.9822,1.134,0.2577,,,,high,"Composite typological-quality index (building and apartment maintenance level, construction quality, equipment/mechanical systems, building age)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,13.5892,11.9822,"b (currency per unit, as reported)",reported,0.06599205590365391,0.05819405282509162,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.134,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,DISCOAST,water_proximity,E_environment,level,m,-0.0371,0.0297,-1.248,0.213,,,,high,Distance from the coastline in metres,"CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0371,0.0297,"b (currency per unit, as reported)",reported,-0.07259280705027585,0.05816731334156718,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.248,294.0,m,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,DENSITY,density,F_neighbourhood,level,residents per km2,-0.0021,0.0017,-1.27,0.2052,,,,high,Population density in the census tract (2001 census),"CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0021,0.0017,"b (currency per unit, as reported)",reported,-0.0738655659994585,0.05816186299169961,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.27,294.0,residents per km2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,FOR_2012,demographics,F_neighbourhood,level,count,0.1165,0.1406,0.829,0.4077,,,,high,Number of permanent foreign residents in the neighbourhood (2012),"CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.1165,0.1406,"b (currency per unit, as reported)",reported,0.048291852357095405,0.05825313915210544,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.829,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,RES_2012,other,other,level,count,-0.0037,0.0086,-0.433,0.6651,,,,high,Number of residents in the neighbourhood (2012); printed name RES_2012 (neighbourhood population size),"CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0037,0.0086,"b (currency per unit, as reported)",reported,-0.025245024508021744,0.05830259701621651,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.433,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,PL_ZONE,other,other,binary,binary,-200.8341,147.2615,-1.364,0.1737,,,,high,Dummy = 1 if located in a residential completion ('B') area of the Masterplan; printed name PL_ZONE (planning/zoning),"CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-200.8341,147.2615,"b (currency per unit, as reported)",reported,-0.07929957866411848,0.058137521014749616,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.364,294.0,binary,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,A_ZONE,other,other,level,m2,-0.0035,0.0045,-0.784,0.4337,,,,high,"Area of Masterplan 'A' (historic centre) zone within a 150 m buffer, m2; printed name A_ZONE (zoning)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0035,0.0045,"b (currency per unit, as reported)",reported,-0.045676086725536656,0.05826031470093962,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.784,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,B_ZONE,other,other,level,m2,-0.005,0.003,-1.687,0.0927,,,,high,"Area of Masterplan 'B' (residential completion) zone within a 150 m buffer, m2; printed name B_ZONE (zoning)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.005,0.003,"b (currency per unit, as reported)",reported,-0.09791506232187114,0.05804093795013108,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.687,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,C_ZONE,other,other,level,m2,-0.0018,0.0089,-0.206,0.837,,,,high,"Area of Masterplan 'C' (residential expansion) zone within a 150 m buffer, m2; printed name C_ZONE (zoning)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0018,0.0089,"b (currency per unit, as reported)",reported,-0.0120132970073292,0.05831697576373399,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.206,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,EZ_ZONE,other,other,level,m2,0.0076,0.0057,1.325,0.1863,,,,high,"Area of Masterplan 'EZ' zone within a 150 m buffer, m2; printed name EZ_ZONE (zoning)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0076,0.0057,"b (currency per unit, as reported)",reported,0.07704587143215794,0.05814782749596826,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.325,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,MIXUSE,other,other,level,share,-2.2312,1.9821,-1.126,0.2612,,,,high,Percent area of the 'IS' (services) zone within a 150 m buffer; printed name MIXUSE (zoning / mixed use),"CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-2.2312,1.9821,"b (currency per unit, as reported)",reported,-0.065528509627241,0.058195834482452045,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.126,294.0,share,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,PARKS,green_space,E_environment,level,m2,8.8e-05,0.0001,0.588,0.5572,,,,high,"Area of 'S3' and recreational 'G' (park) zones within an 800 m buffer, printed unit m2 (Table 3 mean 24.17 suggests a rescaled unit, e.g. hectares; unit as printed)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,8.8e-05,0.0001,"b (currency per unit, as reported)",reported,0.0342727099668147,0.05828692171226991,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.588,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T4-c1,4,1,40,LC_URB,other,other,level,m2,0.0016,0.0022,0.713,0.4763,,,,high,"Artificial surfaces / urban fabric (CORINE 2008, code 1.1) in the neighbourhood, m2; printed name LC_URB (land cover)","CAD_VAL: Cadastral assessed value (EUR/m2), 2013 cadastral register",per_m2,spatial_lag,304.0,0.09,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (cadastral); OLS with spatially lagged dependent variable as regressor (CAD_LAG 0.3395 (0.1458)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0016,0.0022,"b (currency per unit, as reported)",reported,0.04154709945171923,0.05827082672050383,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.713,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,AREA,living_area,A_structural,level,m2,-2.8768,0.4852,-5.929,0.0,,,,high,"Finished interior area in m2 (2012 survey); dependent variable is a value PER m2, so the coefficient measures the unit-value size effect",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-2.8768,0.4852,"b (currency per unit, as reported)",reported,-0.3268003510851909,0.05511896628186725,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.929,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,Q_POS,other,other,level,index,71.349,8.9995,7.928,0.0,,,,high,"Composite position-quality index (panoramic views, distance from other buildings/daylighting, apartment floor level); printed name Q_POS; no single taxonomy leaf",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,71.349,8.9995,"b (currency per unit, as reported)",reported,0.41968056066951737,0.05293649857082712,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),7.928,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,Q_TYP,condition,A_structural,level,index,43.0423,11.8714,3.626,0.0003,,,,high,"Composite typological-quality index (building and apartment maintenance level, construction quality, equipment/mechanical systems, building age)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,43.0423,11.8714,"b (currency per unit, as reported)",reported,0.20689692763318449,0.05705927403011156,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.626,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,DISCOAST,water_proximity,E_environment,level,m,-0.0822,0.0305,-2.696,0.0074,,,,high,Distance from the coastline in metres,EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0822,0.0305,"b (currency per unit, as reported)",reported,-0.15532561643009837,0.057613359209977136,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.696,294.0,m,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,DENSITY,density,F_neighbourhood,level,residents per km2,-0.0048,0.0017,-2.903,0.004,,,,high,Population density in the census tract (2001 census),EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0048,0.0017,"b (currency per unit, as reported)",reported,-0.16693079491330234,0.05750285735904318,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.903,294.0,residents per km2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,FOR_2012,demographics,F_neighbourhood,level,count,0.3796,0.1425,2.663,0.0082,,,,high,Number of permanent foreign residents in the neighbourhood (2012),EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.3796,0.1425,"b (currency per unit, as reported)",reported,0.15346942608638195,0.05763027641246038,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.663,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,RES_2012,other,other,level,count,-0.0164,0.0086,-1.912,0.0569,,,,high,Number of residents in the neighbourhood (2012); printed name RES_2012 (neighbourhood population size),EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0164,0.0086,"b (currency per unit, as reported)",reported,-0.11082321732296534,0.05796193374632078,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.912,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,PL_ZONE,other,other,binary,binary,-237.8951,146.9027,-1.619,0.1065,,,,high,Dummy = 1 if located in a residential completion ('B') area of the Masterplan; printed name PL_ZONE (planning/zoning),EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-237.8951,146.9027,"b (currency per unit, as reported)",reported,-0.09400388088939328,0.05806292828251593,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.619,294.0,binary,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,A_ZONE,other,other,level,m2,-0.006,0.0045,-1.348,0.1787,,,,high,"Area of Masterplan 'A' (historic centre) zone within a 150 m buffer, m2; printed name A_ZONE (zoning)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.006,0.0045,"b (currency per unit, as reported)",reported,-0.07837512593226872,0.05814178481622309,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.348,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,B_ZONE,other,other,level,m2,-0.0059,0.003,-1.99,0.0475,,,,high,"Area of Masterplan 'B' (residential completion) zone within a 150 m buffer, m2; printed name B_ZONE (zoning)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0059,0.003,"b (currency per unit, as reported)",reported,-0.11528532301152136,0.05793232312136753,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.99,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,C_ZONE,other,other,level,m2,-0.0179,0.0088,-2.039,0.0423,,,,high,"Area of Masterplan 'C' (residential expansion) zone within a 150 m buffer, m2; printed name C_ZONE (zoning)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0179,0.0088,"b (currency per unit, as reported)",reported,-0.1180848932560568,0.05791314039041529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.039,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,EZ_ZONE,other,other,level,m2,-0.0088,0.0056,-1.557,0.1205,,,,high,"Area of Masterplan 'EZ' zone within a 150 m buffer, m2; printed name EZ_ZONE (zoning)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0088,0.0056,"b (currency per unit, as reported)",reported,-0.09043400163171612,0.05808221042499429,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.557,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,MIXUSE,other,other,level,share,-5.7069,1.9711,-2.895,0.0041,,,,high,Percent area of the 'IS' (services) zone within a 150 m buffer; printed name MIXUSE (zoning / mixed use),EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-5.7069,1.9711,"b (currency per unit, as reported)",reported,-0.16648353952917322,0.057507267540301645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.895,294.0,share,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,PARKS,green_space,E_environment,level,m2,0.0002,0.0001,1.524,0.1287,,,,high,"Area of 'S3' and recreational 'G' (park) zones within an 800 m buffer, printed unit m2 (Table 3 mean 24.17 suggests a rescaled unit, e.g. hectares; unit as printed)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0002,0.0001,"b (currency per unit, as reported)",reported,0.08853247323176376,0.058092174036590394,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.524,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T5-c1,5,1,41,LC_URB,other,other,level,m2,-0.0002,0.0022,-0.081,0.9357,,,,high,"Artificial surfaces / urban fabric (CORINE 2008, code 1.1) in the neighbourhood, m2; printed name LC_URB (land cover)",EST_VAL: Market value (EUR/m2) estimated for each apartment by the authors' appraisal regression within market areas (2013 direct survey),per_m2,spatial_lag,304.0,0.5208,se,"Full sample of 304 apartments, Cagliari; price type: appraisal; OLS with spatially lagged dependent variable as regressor (EST_LAG 0.4775 (0.0630)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0002,0.0022,"b (currency per unit, as reported)",reported,-0.004723963222051599,0.05832053360557529,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.081,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,AREA,living_area,A_structural,level,m2,-0.4112,0.219,-1.878,0.0614,,,,medium,"Finished interior area in m2 (2012 survey); dependent variable is a value PER m2, so the coefficient measures the unit-value size effect ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.4112,0.219,"b (currency per unit, as reported)",reported,-0.10887608123763935,0.05797448415209763,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.878,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,Q_POS,other,other,level,index,21.5585,4.0668,5.301,0.0,,,,medium,"Composite position-quality index (panoramic views, distance from other buildings/daylighting, apartment floor level); printed name Q_POS; no single taxonomy leaf ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,21.5585,4.0668,"b (currency per unit, as reported)",reported,0.29536704399630975,0.05571911790158645,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),5.301,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,Q_TYP,condition,A_structural,level,index,1.995,5.3698,0.372,0.7105,,,,medium,"Composite typological-quality index (building and apartment maintenance level, construction quality, equipment/mechanical systems, building age) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,1.995,5.3698,"b (currency per unit, as reported)",reported,0.021690376415800298,0.058307463483334133,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.372,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,DISCOAST,water_proximity,E_environment,level,m,-0.0838,0.014,-5.986,0.0,,,,medium,"Distance from the coastline in metres ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0838,0.014,"b (currency per unit, as reported)",reported,-0.32960226643499585,0.05506218951470027,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-5.986,294.0,m,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,DENSITY,density,F_neighbourhood,level,residents per km2,-0.0009,0.0008,-1.168,0.2436,,,,medium,"Population density in the census tract (2001 census) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0009,0.0008,"b (currency per unit, as reported)",reported,-0.06796164739635764,0.058186341948936335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.168,294.0,residents per km2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,FOR_2012,demographics,F_neighbourhood,level,count,0.1709,0.0633,2.698,0.0074,,,,medium,"Number of permanent foreign residents in the neighbourhood (2012) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.1709,0.0633,"b (currency per unit, as reported)",reported,0.15543806016382308,0.057612327710831385,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.698,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,RES_2012,other,other,level,count,-0.019,0.0039,-4.934,0.0,,,,medium,"Number of residents in the neighbourhood (2012); printed name RES_2012 (neighbourhood population size) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.019,0.0039,"b (currency per unit, as reported)",reported,-0.27653529400213356,0.05604687758454268,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.934,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,PL_ZONE,other,other,binary,binary,-171.1874,66.21,-2.586,0.0102,,,,medium,"Dummy = 1 if located in a residential completion ('B') area of the Masterplan; printed name PL_ZONE (planning/zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-171.1874,66.21,"b (currency per unit, as reported)",reported,-0.14913202277789198,0.05766899566043773,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.586,294.0,binary,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,A_ZONE,other,other,level,m2,-0.0034,0.002,-1.679,0.0943,,,,medium,"Area of Masterplan 'A' (historic centre) zone within a 150 m buffer, m2; printed name A_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0034,0.002,"b (currency per unit, as reported)",reported,-0.09745515517800167,0.05804357068374132,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.679,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,B_ZONE,other,other,level,m2,-0.0025,0.0013,-1.861,0.0637,,,,medium,"Area of Masterplan 'B' (residential completion) zone within a 150 m buffer, m2; printed name B_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0025,0.0013,"b (currency per unit, as reported)",reported,-0.10790204160211325,0.05798067791623496,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.861,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,C_ZONE,other,other,level,m2,-0.0077,0.004,-1.936,0.0538,,,,medium,"Area of Masterplan 'C' (residential expansion) zone within a 150 m buffer, m2; printed name C_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0077,0.004,"b (currency per unit, as reported)",reported,-0.11219689972723054,0.05795294407398272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.936,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,EZ_ZONE,other,other,level,m2,-0.0066,0.0026,-2.603,0.0097,,,,medium,"Area of Masterplan 'EZ' zone within a 150 m buffer, m2; printed name EZ_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0066,0.0026,"b (currency per unit, as reported)",reported,-0.1500903812265176,0.0576605383121466,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.603,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,MIXUSE,other,other,level,share,-3.7663,0.8908,-4.228,0.0,,,,medium,"Percent area of the 'IS' (services) zone within a 150 m buffer; printed name MIXUSE (zoning / mixed use) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-3.7663,0.8908,"b (currency per unit, as reported)",reported,-0.23941095113920913,0.05662510670274577,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.228,294.0,share,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,PARKS,green_space,E_environment,level,m2,0.0003,6.7e-05,4.638,0.0,,,,medium,"Area of 'S3' and recreational 'G' (park) zones within an 800 m buffer, printed unit m2 (Table 3 mean 24.17 suggests a rescaled unit, e.g. hectares; unit as printed) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0003,6.7e-05,"b (currency per unit, as reported)",reported,0.2611099739621663,0.05629796765031615,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.638,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T6-c1,6,1,42,LC_URB,other,other,level,m2,-0.0006,0.001,-0.637,0.5245,,,,medium,"Artificial surfaces / urban fabric (CORINE 2008, code 1.1) in the neighbourhood, m2; printed name LC_URB (land cover) ; dependent variable is an OMI zone-average value, not an apartment-specific price",OMI_VAL: Market value (EUR/m2) from the OMI (Osservatorio del Mercato Immobiliare) average zone value range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.6725,se,"Full sample of 304 apartments, Cagliari; price type: appraisal (zone average); OLS with spatially lagged dependent variable as regressor (OMI_LAG 0.3096 (0.0353)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0006,0.001,"b (currency per unit, as reported)",reported,-0.037124983933191316,0.058280979486956534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.637,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,AREA,living_area,A_structural,level,m2,-0.0017,0.0007,-2.492,0.0133,,,,medium,"Finished interior area in m2 (2012 survey); dependent variable is a value PER m2, so the coefficient measures the unit-value size effect ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0017,0.0007,"b (currency per unit, as reported)",reported,-0.14382534168268377,0.05771482411022623,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.492,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,Q_POS,other,other,level,index,0.0852,0.0129,6.618,0.0,,,,medium,"Composite position-quality index (panoramic views, distance from other buildings/daylighting, apartment floor level); printed name Q_POS; no single taxonomy leaf ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0852,0.0129,"b (currency per unit, as reported)",reported,0.36007939871862,0.05440909621012692,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.618,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,Q_TYP,condition,A_structural,level,index,0.0138,0.017,0.814,0.4163,,,,medium,"Composite typological-quality index (building and apartment maintenance level, construction quality, equipment/mechanical systems, building age) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0138,0.017,"b (currency per unit, as reported)",reported,0.047420038204794,0.05825557519016463,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.814,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,DISCOAST,water_proximity,E_environment,level,m,-0.0002,4.31e-05,-4.187,0.0,,,,medium,"Distance from the coastline in metres ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0002,4.31e-05,"b (currency per unit, as reported)",reported,-0.23722057153597848,0.05665645367470229,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.187,294.0,m,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,DENSITY,density,F_neighbourhood,level,residents per km2,-2.91e-07,2.38e-06,-0.122,0.9029,,,,medium,"Population density in the census tract (2001 census) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-2.91e-07,2.38e-06,"b (currency per unit, as reported)",reported,-0.007115004391647252,0.058319708128256174,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.122,294.0,residents per km2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,FOR_2012,demographics,F_neighbourhood,level,count,0.0005,0.0002,2.68,0.0078,,,,medium,"Number of permanent foreign residents in the neighbourhood (2012) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0005,0.0002,"b (currency per unit, as reported)",reported,0.1544258495376813,0.05762158564838854,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.68,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,RES_2012,other,other,level,count,-8.04e-05,1.22e-05,-6.591,0.0,,,,medium,"Number of residents in the neighbourhood (2012); printed name RES_2012 (neighbourhood population size) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-8.04e-05,1.22e-05,"b (currency per unit, as reported)",reported,-0.3587998114116225,0.05443784120947087,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-6.591,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,PL_ZONE,other,other,binary,binary,-0.5894,0.2092,-2.817,0.0052,,,,medium,"Dummy = 1 if located in a residential completion ('B') area of the Masterplan; printed name PL_ZONE (planning/zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.5894,0.2092,"b (currency per unit, as reported)",reported,-0.16211745070402683,0.05754968076110289,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.817,294.0,binary,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,A_ZONE,other,other,level,m2,-4.33e-06,6.36e-06,-0.681,0.4965,,,,medium,"Area of Masterplan 'A' (historic centre) zone within a 150 m buffer, m2; printed name A_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-4.33e-06,6.36e-06,"b (currency per unit, as reported)",reported,-0.03968543860749022,0.05827524024594746,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.681,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,B_ZONE,other,other,level,m2,-1.01e-05,4.24e-06,-2.385,0.0177,,,,medium,"Area of Masterplan 'B' (residential completion) zone within a 150 m buffer, m2; printed name B_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-1.01e-05,4.24e-06,"b (currency per unit, as reported)",reported,-0.13776964636348926,0.05776505088615902,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.385,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,C_ZONE,other,other,level,m2,-3.22e-05,1.26e-05,-2.558,0.0111,,,,medium,"Area of Masterplan 'C' (residential expansion) zone within a 150 m buffer, m2; printed name C_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-3.22e-05,1.26e-05,"b (currency per unit, as reported)",reported,-0.14755263466002827,0.05768281261142622,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.558,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,EZ_ZONE,other,other,level,m2,-2.52e-05,8.06e-06,-3.131,0.0019,,,,medium,"Area of Masterplan 'EZ' zone within a 150 m buffer, m2; printed name EZ_ZONE (zoning) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-2.52e-05,8.06e-06,"b (currency per unit, as reported)",reported,-0.1796333310996791,0.057372510731293236,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.131,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,MIXUSE,other,other,level,share,-0.0089,0.0028,-3.149,0.0018,,,,medium,"Percent area of the 'IS' (services) zone within a 150 m buffer; printed name MIXUSE (zoning / mixed use) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0089,0.0028,"b (currency per unit, as reported)",reported,-0.18063243428670162,0.057361840040235514,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.149,294.0,share,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,PARKS,green_space,E_environment,level,m2,7.25e-07,2.09e-07,3.479,0.0006,,,,medium,"Area of 'S3' and recreational 'G' (park) zones within an 800 m buffer, printed unit m2 (Table 3 mean 24.17 suggests a rescaled unit, e.g. hectares; unit as printed) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,7.25e-07,2.09e-07,"b (currency per unit, as reported)",reported,0.1988475859442284,0.057156535195236675,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.479,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T7-c1,7,1,43,LC_URB,other,other,level,m2,-4.73e-06,3.1e-06,-1.523,0.1289,,,,medium,"Artificial surfaces / urban fabric (CORINE 2008, code 1.1) in the neighbourhood, m2; printed name LC_URB (land cover) ; dependent variable is an OMI zone-average value, not an apartment-specific price",RENT_VAL: Rent value (EUR/m2 per month) from the OMI average zone rent range - a zone-level average assigned to each apartment,per_m2,spatial_lag,304.0,0.581,se,"Full sample of 304 apartments, Cagliari; price type: rent (zone average); OLS with spatially lagged dependent variable as regressor (RENT_LAG 0.2300 (0.0347)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-4.73e-06,3.1e-06,"b (currency per unit, as reported)",reported,-0.0884748359395687,0.05809247271146994,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.523,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,AREA,living_area,A_structural,level,m2,0.388,0.2667,1.454,0.1469,,,,high,"Finished interior area in m2 (2012 survey); dependent variable is a value PER m2, so the coefficient measures the unit-value size effect","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.388,0.2667,"b (currency per unit, as reported)",reported,0.08449574723917617,0.05811261845885569,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.454,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,Q_POS,other,other,level,index,-2.3448,4.9547,-0.473,0.6364,,,,high,"Composite position-quality index (panoramic views, distance from other buildings/daylighting, apartment floor level); printed name Q_POS; no single taxonomy leaf","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-2.3448,4.9547,"b (currency per unit, as reported)",reported,-0.027575429977205832,0.05829900629430408,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.473,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,Q_TYP,condition,A_structural,level,index,-2.0876,6.5567,-0.318,0.7504,,,,high,"Composite typological-quality index (building and apartment maintenance level, construction quality, equipment/mechanical systems, building age)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-2.0876,6.5567,"b (currency per unit, as reported)",reported,-0.018542947889495343,0.058311156885205474,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.318,294.0,index,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,DISCOAST,water_proximity,E_environment,level,m,-0.0849,0.0179,-4.738,0.0,,,,high,Distance from the coastline in metres,"SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0849,0.0179,"b (currency per unit, as reported)",reported,-0.2663443183538406,0.056214503662693246,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.738,294.0,m,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,DENSITY,density,F_neighbourhood,level,residents per km2,-0.0019,0.0009,-2.052,0.0411,,,,high,Population density in the census tract (2001 census),"SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0019,0.0009,"b (currency per unit, as reported)",reported,-0.11882716682467581,0.05790797603541707,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.052,294.0,residents per km2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,FOR_2012,demographics,F_neighbourhood,level,count,0.2017,0.0775,2.602,0.0098,,,,high,Number of permanent foreign residents in the neighbourhood (2012),"SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.2017,0.0775,"b (currency per unit, as reported)",reported,0.1500340188826459,0.05766103723391464,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.602,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,RES_2012,other,other,level,count,-0.0191,0.0047,-4.078,0.0001,,,,high,Number of residents in the neighbourhood (2012); printed name RES_2012 (neighbourhood population size),"SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0191,0.0047,"b (currency per unit, as reported)",reported,-0.23137981227550652,0.05673855131817227,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.078,294.0,count,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,PL_ZONE,other,other,binary,binary,-128.324,80.6387,-1.591,0.1126,,,,high,Dummy = 1 if located in a residential completion ('B') area of the Masterplan; printed name PL_ZONE (planning/zoning),"SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-128.324,80.6387,"b (currency per unit, as reported)",reported,-0.09239211795388691,0.05807172718660396,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.591,294.0,binary,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,A_ZONE,other,other,level,m2,-0.0026,0.0025,-1.072,0.2844,,,,high,"Area of Masterplan 'A' (historic centre) zone within a 150 m buffer, m2; printed name A_ZONE (zoning)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0026,0.0025,"b (currency per unit, as reported)",reported,-0.062398477318920155,0.05820753481242552,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.072,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,B_ZONE,other,other,level,m2,-0.0046,0.0016,-2.805,0.0054,,,,high,"Area of Masterplan 'B' (residential completion) zone within a 150 m buffer, m2; printed name B_ZONE (zoning)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0046,0.0016,"b (currency per unit, as reported)",reported,-0.16144489204136211,0.05755611124469238,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.805,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,C_ZONE,other,other,level,m2,-0.008,0.0048,-1.652,0.0997,,,,high,"Area of Masterplan 'C' (residential expansion) zone within a 150 m buffer, m2; printed name C_ZONE (zoning)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.008,0.0048,"b (currency per unit, as reported)",reported,-0.09590250923464609,0.05805236636479788,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.652,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,EZ_ZONE,other,other,level,m2,-0.0092,0.0031,-2.966,0.0033,,,,high,"Area of Masterplan 'EZ' zone within a 150 m buffer, m2; printed name EZ_ZONE (zoning)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0092,0.0031,"b (currency per unit, as reported)",reported,-0.17044931130216529,0.05746773813289457,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.966,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,MIXUSE,other,other,level,share,-5.0426,1.0845,-4.65,0.0,,,,high,Percent area of the 'IS' (services) zone within a 150 m buffer; printed name MIXUSE (zoning / mixed use),"SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-5.0426,1.0845,"b (currency per unit, as reported)",reported,-0.2617393232331004,0.056288026501742014,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-4.65,294.0,share,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,PARKS,green_space,E_environment,level,m2,0.0006,8.8e-05,6.936,0.0,,,,high,"Area of 'S3' and recreational 'G' (park) zones within an 800 m buffer, printed unit m2 (Table 3 mean 24.17 suggests a rescaled unit, e.g. hectares; unit as printed)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,0.0006,8.8e-05,"b (currency per unit, as reported)",reported,0.37499666770509543,0.05406526350996186,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),6.936,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,True,False
fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d,fc9d39b019f0526d9688d6cea3c777de313aea1d:T8-c1,8,1,44,LC_URB,other,other,level,m2,-0.0011,0.0012,-0.891,0.3736,,,,high,"Artificial surfaces / urban fabric (CORINE 2008, code 1.1) in the neighbourhood, m2; printed name LC_URB (land cover)","SUPP_VAL: Average list price (EUR/m2) of the nearest comparable apartments for sale, first semester 2013 (direct survey)",per_m2,spatial_lag,304.0,0.7514,se,"Full sample of 304 apartments, Cagliari; price type: listing; OLS with spatially lagged dependent variable as regressor (SUPP_LAG 0.4332 (0.0383)); r2 is adjusted R-squared; SE, t and p in separate columns",monetary,-0.0011,0.0012,"b (currency per unit, as reported)",reported,-0.05189415822977069,0.058242601829147804,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.891,294.0,m2,,2015.0,Italy,Europe,High income,2013.0,2013.0,condo_apartment,appraisal,journal,False,False,OLS,,2013.0,spatial,journal,False,False,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnLANDSIZE,lot_size,A_structural,log,m2,0.397,,4.381,,***,,,high,"plot area in m2, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.397,0.09061858023282356,b (log-log),|b/t|,0.27212064024127575,0.06211381881791275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.381,240.0,m2,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnBUILDSIZE,living_area,A_structural,log,m2,0.353,,3.404,,***,,,high,"building area in m2, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.353,0.10370152761457109,b (log-log),|b/t|,0.21460768349018486,0.06304573545540097,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.404,240.0,m2,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnNUMROOM,bedrooms,A_structural,log,count,0.116,,1.121,,,,,high,"number of rooms (all rooms, not bedrooms specifically), log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.116,0.10347903657448707,b (log-log),|b/t|,0.0721715402929194,0.06438139187593168,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.121,240.0,count,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,WSTRUCTDUM,roof_materials,A_structural,dummy,binary,-0.081,,-1.012,,,,,high,"dummy for wall structure (coding not described; mean 1.15); values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),pct_premium,-8.07555191932673,7.381194565910399,100*(exp(b - se^2/2) - 1),|b/t|,-0.06518538545373372,0.06441243621910446,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-1.012,240.0,binary,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnDISSCHOOL,school_distance,D_location,log,m,-0.046,,-0.895,,,,,high,"distance from residence to nearest school, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-0.046,0.05139664804469274,b (log-log),|b/t|,-0.05767583220895913,0.06444227062453534,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-0.895,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnDISHPITAL,hospital_distance,D_location,log,m,-0.163,,-2.936,,***,,,high,"distance to nearest hospital, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-0.163,0.055517711171662126,b (log-log),|b/t|,-0.18620354130518238,0.06342082469522561,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.936,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,True
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnDISSPMARKET,commercial_distance,D_location,log,m,0.103,,1.918,,*,,,high,"distance to nearest supermarket, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.103,0.053701772679874867,b (log-log),|b/t|,0.12286828513812273,0.06406062833061664,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.918,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnDISREST,neighbourhood_amenities,F_neighbourhood,log,m,-0.155,,-3.606,,***,,,high,"distance to nearest restaurant, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-0.155,0.04298391569606212,b (log-log),|b/t|,-0.2267058277769953,0.06286905928369255,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.606,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnDISCITY,cbd_distance,D_location,log,m,0.039,,0.68,,,,,high,"distance from city (centre), metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.039,0.057352941176470586,b (log-log),|b/t|,0.04385158788848748,0.0644876292477757,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),0.68,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnDISMSTREET,highway_distance,D_location,log,m,0.091,,3.089,,***,,,high,"distance to main street, metres, log (positive = farther from main road is dearer); values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.091,0.02945937196503723,b (log-log),|b/t|,0.19554474586855783,0.06330357587198376,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.089,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,GARDENDUM,green_space,E_environment,dummy,binary,0.261,,2.637,,***,,,high,"dummy for being close to a garden (coded 1/2 in Table 2, mean 1.79); values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),pct_premium,29.18843189073048,12.849352319992153,100*(exp(b - se^2/2) - 1),|b/t|,0.16780399667203355,0.0636344318058527,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.637,240.0,binary,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c1,3,1 (Full Model),7,LnPM10,air_pollution,E_environment,log,ppm,-1.355,,-2.355,,**,,,medium,"PM10 concentration (printed unit 'ppm', mean 1,223.12), log; 2010 ambient monitoring data, kriged; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%; Full-model LnPM10 elasticity -1.355 differs sharply from fit-model -0.320 and text quotes -0.32",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,full specification; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-1.355,0.5753715498938429,b (log-log),|b/t|,-0.15028805276549081,0.06381658291528272,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.355,240.0,ppm,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnLANDSIZE,lot_size,A_structural,log,m2,0.396,,4.432,,***,,,high,"plot area in m2, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.396,0.08935018050541516,b (log-log),|b/t|,0.27505006818436345,0.06206003343510006,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),4.432,240.0,m2,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnBUILDSIZE,living_area,A_structural,log,m2,0.393,,3.915,,***,,,high,"building area in m2, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.393,0.10038314176245211,b (log-log),|b/t|,0.24500965783928996,0.06258228808155554,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.915,240.0,m2,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnDISHPITAL,hospital_distance,D_location,log,m,-0.16,,-3.115,,***,,,high,"distance to nearest hospital, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-0.16,0.051364365971107544,b (log-log),|b/t|,-0.19712693766703612,0.06328312605683341,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.115,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnDISSPMARKET,commercial_distance,D_location,log,m,0.103,,1.975,,**,,,high,"distance to nearest supermarket, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.103,0.05215189873417721,b (log-log),|b/t|,0.12646217397138265,0.06403148049183932,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),1.975,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnDISREST,neighbourhood_amenities,F_neighbourhood,log,m,-0.16,,-3.999,,***,,,high,"distance to nearest restaurant, metres, log; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-0.16,0.040010002500625155,b (log-log),|b/t|,-0.24994140487678768,0.06250097646331275,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-3.999,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,True
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnDISMSTREET,highway_distance,D_location,log,m,0.092,,3.153,,***,,,high,"distance to main street, metres, log (positive = farther from main road is dearer); values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,0.092,0.02917856010149064,b (log-log),|b/t|,0.1994366009994507,0.0632529657467335,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),3.153,240.0,m,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,True
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,GARDENDUM,green_space,E_environment,dummy,binary,0.232,,2.377,,**,,,high,"dummy for being close to a garden (coded 1/2 in Table 2, mean 1.79); values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),pct_premium,25.51272011042631,12.308783217844864,100*(exp(b - se^2/2) - 1),|b/t|,0.1516598714244429,0.06380305907633274,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),2.377,240.0,binary,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False
fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366,fdf3432efb95e96e6e45a3ba105603f17a38d366:T3-c2,3,2 (Fit Model),7,LnPM10,air_pollution,E_environment,log,ppm,-0.32,,-2.806,,***,,,medium,"PM10 concentration (printed unit 'ppm', mean 1,223.12), log; 2010 ambient monitoring data, kriged; values in parentheses are t-statistics (no legend printed; consistent with coef/SE, e.g. constant 17.181 (17.689)); stars legend not printed, text implies *** 1%, ** 5%, * 10%; Full-model LnPM10 elasticity -1.355 differs sharply from fit-model -0.320 and text quotes -0.32",ln(PPRICE) sales price of house (Rupiah),log,OLS,250.0,0.606,t,'fit model': insignificant variables dropped; 250 household observations in Yogyakarta Special Territory; adjusted R2 printed (0.606),elasticity,-0.32,0.11404133998574484,b (log-log),|b/t|,-0.1782265900651508,0.06351624735037448,t/sqrt(t^2+df); se=sqrt((1-r^2)/df),-2.806,240.0,ppm,,2012.0,Indonesia,Asia,Upper middle income,2010.0,2010.0,single_family,transaction,journal,False,False,unknown,,2010.0,ols,journal,True,True,False